Mountain tunnel leakage water intelligent positioning and processing method and system

Abstract

The application provides a mountainous area tunnel leakage water intelligent positioning and processing method and system, relates to the tunnel leakage water positioning technical field, and comprises the following steps: collecting leakage point data and preprocessing, calculating the corresponding relationship between the leakage point and the geological structure to determine the leakage source position; constructing a leakage point correlation graph; extracting topological features to divide levels, determining the leakage water propagation direction and constructing a layered propagation graph; determining the leakage channel distribution and calculating the influence range to perform risk grading; and determining the construction sequence and repair process according to the risk level. The application realizes accurate positioning of the leakage source and accurate identification of the leakage channel, and improves the repair efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to a leakage water positioning technology, in particular to a mountainous tunnel leakage water intelligent positioning and processing method and system. BACKGROUND

[0002] Due to complex terrain, broken rock strata and abundant underground water, mountainous tunnels often face different degrees of leakage problems. Leakage not only accelerates the aging of the tunnel structure, affects driving safety, but also causes secondary disasters such as lining disengagement and reinforcement corrosion, increasing the cost of later maintenance. The traditional manual inspection method relies on experience for judgment, has large positioning error and low efficiency, and is difficult to achieve accurate identification and dynamic management of the leakage source.

[0003] The prior art uses a single information source for leakage analysis, lacks comprehensive analysis of the spatial distribution of tunnel leakage points, leakage intensity and geological structure, and cannot effectively identify the leakage channel and its propagation path. In addition, in the repair processing link, the leakage position is usually worked in sequence, without fully considering the leakage diffusion law and risk level, which easily leads to resource waste and repeated treatment.

[0004] In view of the above problems, an intelligent positioning and processing method for mountainous tunnel leakage water is needed, which integrates spatial calculation, topological analysis and risk assessment, realizes accurate positioning, risk classification and scientific repair of the leakage source, and improves the efficiency of leakage treatment and the safety of tunnel operation. SUMMARY

[0005] The embodiment of the present application provides a mountainous tunnel leakage water intelligent positioning and processing method and system, which can solve the problems in the prior art.

[0006] In a first aspect, the embodiment of the present application provides a mountainous tunnel leakage water intelligent positioning and processing method, comprising:

[0007] Collecting position information, leakage amount information and geological information of the tunnel leakage point and preprocessing to obtain a feature data set of the leakage point, calculating the spatial correspondence between the leakage point and the geological structure according to the feature data set, combining the spatial correspondence with the distribution law of the leakage point to determine the position coordinates of the leakage source, and generating leakage source positioning data;

[0008] Based on the leakage source positioning data, the spatial distance and the leakage amount change ratio between adjacent leakage points are calculated as connection weights to construct a leakage point correlation graph;

[0009] Extracting the topological distribution features of the leakage point correlation graph, dividing the leakage points into multiple levels according to the mountain elevation and the leakage amount according to the topological distribution features, calculating the connection strength between adjacent levels of leakage points, determining the propagation direction of the leakage water, and constructing a layered leakage propagation graph;

[0010] According to the hierarchical leakage propagation diagram, the spatial distribution of the leakage channel is determined, the leakage channel trend is drawn, the influence range and the damage degree of each leakage point are calculated by using the leakage channel trend and combining the leakage amount data, the risk classification of the leakage point is performed, and risk classification data is generated;

[0011] According to the risk classification data, the repair construction sequence is determined, and the repair process is selected according to the leakage amount and the channel position, and a repair treatment scheme is formed.

[0012] In an optional embodiment,

[0013] The position information, the leakage amount information and the geological information of the tunnel leakage point are collected and preprocessed to obtain a feature data set of the leakage point, including:

[0014] The three-dimensional spatial coordinates of the leakage point are obtained by total station measurement as the position information, the real-time flow data and pressure data of the leakage point are collected by a flowmeter and a water pressure gauge as the leakage amount information, and the surrounding rock structure data are collected by drilling coring, geological radar scanning and acoustic wave detection as the geological information;

[0015] The position information is converted from the total station coordinate system to the tunnel coordinate system, the position information in the tunnel coordinate system is spatially registered with the tunnel axis to generate the position feature of the leakage point, the high-frequency noise in the leakage amount information is removed by using a digital filtering algorithm, the leakage amount information after filtering is time-series decomposed to obtain a steady component and a fluctuation component, and the water amount feature of the leakage point is generated, the fault information and joint information in the geological information are extracted for structure analysis, and the analyzed structure information is reconstructed in three-dimensional space to generate the geological feature of the leakage point;

[0016] The position feature, the water amount feature and the geological feature are spatio-temporally mapped and data fused to generate the feature data set of the leakage point.

[0017] In an optional embodiment,

[0018] According to the feature data set, the spatial correspondence between the leakage point and the geological structure is calculated, the spatial correspondence is combined with the leakage point distribution rule to determine the leakage source position coordinates, and leakage source positioning data is generated, including:

[0019] By using the feature data set, the geological structure surface is divided into a main control fault, a secondary fault and a joint fissure, the spatial positioning parameters of the main control fault, the secondary fault and the joint fissure are calculated, and geological structure quantization data is generated;

[0020] According to the feature data set and the geological structure quantization data, the spatial correlation degree between the leakage point and the main control fault, the secondary fault and the joint fissure is calculated, and the spatial correlation degree is combined with the leakage amount information in the feature data set to obtain the spatial distribution rule of the leakage point;

[0021] According to the spatial distribution law, the leakage points are grouped according to the leakage amount and the spatial position, the spatial center coordinates and the leakage intensity of each group of leakage points are calculated, and the spatial distribution characteristics of the leakage point group are determined;

[0022] According to the distribution characteristics of the leakage point group, the leakage reference surface is determined by using the steady-state leakage component, and the leakage diffusion direction is calculated by using the dynamic leakage component, the leakage reference surface and the leakage diffusion direction are superimposed, the spatial weight field is constructed, the leakage source candidate area is established by using the spatial weight field, the intersection position of the leakage source candidate area and the main controlling fault, the secondary fault and the joint fissure is calculated, and the spatial correlation degree of the leakage source candidate area and the leakage point group is calculated, the leakage source position coordinates are determined, and the leakage source positioning data is generated.

[0023] In an alternative embodiment,

[0024] Based on the leakage source positioning data, the spatial distance and the leakage amount change ratio between adjacent leakage points are calculated as connection weights, and a leakage point correlation graph is constructed including:

[0025] The leakage source positioning data is obtained, and the spatial coordinate information, the leakage amount time sequence information and the geological environment information of the leakage points in the leakage source positioning data are extracted;

[0026] According to the spatial coordinate information, the position components of adjacent leakage points in the axial direction, the ring direction and the elevation direction of the tunnel are calculated, the position components are weighted and corrected in combination with the lithology and fault distribution in the geological environment information, and the corrected spatial distance between adjacent leakage points is obtained;

[0027] The leakage amount time sequence information is standardized to eliminate seasonal fluctuations and environmental noise, and standardized leakage amount data is obtained; the standardized leakage amount data is analyzed by using a sliding time window, and the leakage amount dynamic change characteristics of adjacent leakage points are calculated; the leakage amount dynamic change characteristics are corrected in combination with the hydrological conditions in the geological environment information, and the corrected leakage amount change is obtained;

[0028] The ratio of the corrected spatial distance and the corrected leakage amount change is calculated, and the ratio is dynamically adjusted in combination with the geological environment information to generate the connection weight between adjacent leakage points;

[0029] According to the connection weight, the connection relationship between adjacent leakage points is determined, the leakage points are set as the vertices of the graph, the connection relationship is set as the edges of the graph, and a leakage point correlation graph is constructed.

[0030] In an alternative embodiment,

[0031] extract topological distribution characteristics of the leakage point correlation graph, divide the leakage points into multiple levels according to the topological distribution characteristics and the mountain elevation and the leakage amount, calculate the connection strength between adjacent level leakage points, determine the propagation direction of the leakage water, and construct a layered leakage propagation graph including:

[0032] Calculate the connection number and the shortest path number of the leakage points in the leakage point correlation graph to obtain centrality data, calculate the connection proportion of the leakage points and adjacent leakage points to obtain clustering data, and obtain the topological distribution characteristics of the leakage points according to the centrality data and the clustering data;

[0033] Obtain the mountain elevation data and the leakage amount data of the leakage points, perform initial layering on the leakage points according to the mountain elevation data, calculate the leakage amount distribution characteristics of the leakage points in each layer, adjust the level boundary according to the leakage amount distribution characteristics, and obtain the level division of the leakage points in combination with the topological distribution characteristics;

[0034] Determine the leakage points of adjacent levels according to the level division, calculate the spatial distance decay value, the leakage amount correlation coefficient, the elevation difference value and the medium permeability coefficient between the leakage points of adjacent levels, and obtain the connection strength between the leakage points of adjacent levels;

[0035] Determine the main propagation path between the leakage points of adjacent levels according to the connection strength, and obtain the propagation direction of the main propagation path based on the time sequence change and the level division of the leakage amount data;

[0036] Arrange the leakage points according to the level division, take the connection strength as the connection relationship between the levels, and take the propagation direction as the propagation trend between the levels, and construct a layered leakage propagation graph.

[0037] In an optional embodiment,

[0038] Determine the spatial distribution of the leakage channel according to the layered leakage propagation graph, draw the strike of the leakage channel, calculate the influence range and the harm degree of each leakage point by using the strike of the leakage channel in combination with the leakage amount data, perform risk grading on the leakage points, and generate risk grading data including:

[0039] Obtain the layered leakage propagation graph and the geological structure surface data, extract the leakage point sequence continuously connected between levels from the layered leakage propagation graph to determine the spatial distribution of the leakage channel, calculate the strike angle, inclination and curvature of the leakage channel, combine the geological structure surface data, and draw the strike of the leakage channel by using three-dimensional spline interpolation;

[0040] The radius of the spherical influence domain is determined according to the leakage amount data, the ellipsoidal influence domain is obtained by extending and compressively deforming the spherical influence domain based on the leakage channel direction, the lithology and fracture distribution data of the geological medium are obtained, and the ellipsoidal influence domain is adjusted according to the lithology and fracture distribution data to obtain the influence range of the leakage point;

[0041] The leakage amount characteristics are obtained by calculating the absolute value and change trend of the leakage amount, the spatial coupling characteristics are obtained by calculating the overlap degree between the influence ranges, and the channel extension characteristics are obtained by calculating the extension length of the leakage channel direction; the leakage amount characteristics, the spatial coupling characteristics and the channel extension characteristics are combined by weighting to obtain the hazard degree of the leakage point;

[0042] The hazard degree distribution of the leakage points in the engineering area is counted to determine the risk level division threshold; the risk level division threshold is dynamically adjusted according to the safety requirements of the construction stage and the operation stage, and the risk classification data is generated by classifying the risk of the leakage point based on the hazard degree and the adjusted risk level division threshold.

[0043] In an optional embodiment,

[0044] The repair construction sequence is determined according to the risk classification data, and the repair process is selected according to the leakage amount and the channel position to form a repair treatment scheme, including:

[0045] The risk classification data and the spatial distribution data of the leakage channel are obtained, the leakage amount ratio between adjacent leakage points in the leakage channel is calculated, and the correlation degree between the leakage points is determined according to the leakage amount ratio; the leakage points with a correlation degree greater than a preset correlation threshold are divided into a leakage point group, the repair sequence in the leakage point group is determined based on the risk classification data, and the repair sequence between the leakage point groups is determined according to the spatial position from upstream to downstream;

[0046] The time series data of the water outlet pressure and the water outlet amount of the leakage point are collected, and the water outlet pressure-water outlet amount change curve is established; the corresponding relationship library of the grouting parameters and the grouting effect is established according to the fluctuation period and amplitude change law of the water outlet pressure-water outlet amount change curve, and the optimal grouting parameter combination is selected from the corresponding relationship library;

[0047] The three-dimensional morphological characteristics of the leakage channel are obtained, the extension direction and the bifurcation position of the leakage channel are extracted, the diffusion range of the grout under different grouting point arrangement schemes is simulated, and the spatial arrangement of the grouting hole is optimized based on the diffusion range;

[0048] Real-time acquisition of grouting pressure and grout diffusion radius in the grouting process, construction of grouting pressure field distribution function; when the water outlet state of the downstream leakage point changes, the stress field change characteristics of the leakage channel are calculated according to the pressure field distribution function, and the evolution trend of the leakage water flow field is predicted; the grouting scheme of the downstream leakage point is dynamically optimized based on the evolution trend, and the repair processing scheme is formed according to the repair order, the grouting parameter combination, the grouting hole space arrangement and the optimized grouting scheme.

[0049] The second aspect of the embodiment of the application provides a mountainous tunnel leakage water intelligent positioning and processing system, comprising:

[0050] The first unit is used for collecting position information, leakage amount information and geological information of a tunnel leakage point and preprocessing to obtain a feature data set of the leakage point, calculating a spatial correspondence relationship between the leakage point and a geological structure according to the feature data set, combining the spatial correspondence relationship with a leakage point distribution law to determine leakage source position coordinates and generate leakage source positioning data;

[0051] The second unit is used for calculating spatial distances and leakage amount change ratios between adjacent leakage points as connection weights based on the leakage source positioning data, and constructing a leakage point correlation graph;

[0052] The third unit is used for extracting topological distribution characteristics of the leakage point correlation graph, dividing the leakage points into multiple levels according to mountain elevations and leakage amounts according to the topological distribution characteristics, calculating the connectivity strength between adjacent level leakage points, determining a leakage water propagation direction, and constructing a layered leakage propagation graph;

[0053] The fourth unit is used for determining the spatial distribution of a leakage channel according to the layered leakage propagation graph, drawing a leakage channel strike, calculating the influence range and damage degree of each leakage point by using the leakage channel strike and combining leakage amount data, classifying the leakage points according to risks, and generating risk classification data;

[0054] The fifth unit is used for determining a repair construction order according to the risk classification data, selecting a repair technology according to the leakage amount and the channel position, and forming a repair processing scheme.

[0055] The third aspect of the embodiment of the application provides an electronic device, comprising:

[0056] a processor;

[0057] a memory for storing processor-executable instructions;

[0058] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0059] In a fourth aspect, the present application provides a computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method described above.

[0060] In the embodiment, by collecting and processing the position information, leakage amount information and geological information of the tunnel leakage points, combining the spatial correspondence relationship and the leakage point distribution law, the accurate positioning of the leakage source position is realized, and the problems of resource waste and low efficiency caused by blind processing in the traditional method are avoided. By constructing the leakage point correlation graph and the hierarchical leakage propagation graph, the propagation path and channel trend of the leakage water can be clearly displayed, so that the technical personnel can intuitively understand the leakage mechanism, thereby targetedly selecting the repair process, improving the pertinence and effectiveness of the repair. According to the influence range and damage degree of the leakage point, the risk is classified, and the repair construction sequence is determined accordingly, so that a scientific and reasonable repair treatment scheme is formed, the safety of the tunnel is ensured, the resource allocation is optimized, the repair efficiency is improved, the maintenance cost is reduced, and the service life of the tunnel is prolonged. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 FIG. 1 is a flowchart of a mountain tunnel leakage water intelligent positioning and processing method according to an embodiment of the present application.

[0062] Figure 2 FIG. 2 is a tunnel leakage source positioning technology flowchart.

[0063] Figure 3 FIG. 3 is a hierarchical leakage propagation graph of a mountain tunnel. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0065] The technical scheme of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0066] Figure 1 FIG. 1 is a flowchart of a mountain tunnel leakage water intelligent positioning and processing method according to an embodiment of the present application, as shown in the figure, the method comprises: Figure 1

[0067] ​Collect position information, leakage amount information and geological information of a tunnel leakage point and preprocess to obtain a feature data set of the leakage point, calculate a spatial correspondence between the leakage point and a geological structure according to the feature data set, combine the spatial correspondence with a leakage point distribution rule to determine a leakage source position coordinate and generate leakage source positioning data;

[0068] Based on the leakage source positioning data, calculate a spatial distance between adjacent leakage points and a leakage amount change ratio as connection weights to construct a leakage point correlation graph;

[0069] Extract a topological distribution feature of the leakage point correlation graph, divide the leakage points into multiple levels according to mountain elevation and leakage amount size according to the topological distribution feature, calculate a connection strength between adjacent level leakage points, determine a leakage water propagation direction and construct a layered leakage propagation graph;

[0070] Determine a spatial distribution of a leakage channel according to the layered leakage propagation graph, draw a leakage channel strike, calculate an influence range and a hazard degree of each leakage point using the leakage channel strike in combination with leakage amount data, risk grade the leakage points and generate risk grading data;

[0071] Determine a repair construction sequence according to the risk grading data and select a repair process according to the leakage amount and channel position to form a repair treatment scheme.

[0072] In an alternative embodiment, collecting position information, leakage amount information and geological information of a tunnel leakage point and preprocessing to obtain a feature data set of the leakage point includes:

[0073] Obtain three-dimensional spatial coordinates of the leakage point as position information through total station measurement, obtain real-time flow data and pressure data of the leakage point as leakage amount information through a flowmeter and a water pressure gauge, and obtain surrounding rock structure data as geological information through drilling coring, geological radar scanning and acoustic detection;

[0074] Convert the position information from a total station coordinate system to a tunnel coordinate system, spatially register the position information in the tunnel coordinate system with a tunnel axis to generate position features of the leakage point, eliminate high-frequency noise in the leakage amount information using a digital filtering algorithm, perform time series decomposition on the filtered leakage amount information to obtain steady-state components and fluctuation components, generate water amount features of the leakage point, and extract fault information and joint information in the geological information for structure analysis, reconstruct the analyzed structure information in three-dimensional space to generate geological features of the leakage point;

[0075] Perform space-time mapping and data fusion on the position features, water amount features and geological features to generate a feature data set of the leakage point.

[0076] The embodiment provides a tunnel leakage point feature data set collection and preprocessing method. A total station is used to measure the leakage point in the tunnel to obtain three-dimensional spatial coordinate information of the leakage point. The total station is arranged at a relatively stable position in the tunnel, a laser is aimed at the leakage point, and angle and distance data are recorded to obtain coordinate values of the leakage point in a total station coordinate system.

[0077] Leakage amount information is collected by installing a flow meter and a water pressure meter at the leakage point position. The flow meter adopts a turbine structure and can record the volume flow of the leakage water flow in real time, generally in milliliters per minute; the water pressure meter measures the pressure value of the leakage water, in kilopascals. In actual application, data can be collected every 10 minutes, continuously monitored for more than 24 hours, and time series data is formed. For example, the flow data range of a certain leakage point within 24 hours is 145-178 milliliters per minute, and the pressure data range is 26-32 kilopascals.

[0078] Geological information collection is realized through three ways: drilling and coring are performed near the leakage point, the drilling depth is usually 5-10 meters, and the rock core sample obtained can intuitively reflect the surrounding rock structure; a geological radar scan uses an antenna to emit high-frequency electromagnetic waves and receives signals reflected from different rock layer interfaces, and the detection depth can reach 5 meters; acoustic wave detection uses the influence of different rock properties on the speed of sound wave propagation to analyze the internal structure of the surrounding rock. Through comprehensive analysis of the three methods, information such as rock type, integrity, and fracture development degree within a 5-meter range around the leakage point can be obtained. For example, a sandstone and shale transition zone is detected around a certain leakage point, and the fracture strikes north by east 30° and the dip angle is 45°.

[0079] Position information preprocessing first performs coordinate system conversion. The leakage point coordinates (X1, Y1, Z1) in the total station coordinate system are converted to the tunnel coordinate system (X2, Y2, Z2). The conversion process is realized by using the known position of the total station in the tunnel coordinate system and its rotation matrix. For the leakage point in the aforementioned example, the converted coordinates in the tunnel coordinate system are (K32+564.8, 0.75, 2.34), where K32+564.8 represents the position of the point on the tunnel mileage, 0.75 represents the distance (meters) from the tunnel center line in the transverse direction, and 2.34 represents the height (meters) relative to the tunnel bottom. Spatial registration is the association of the leakage point with the tunnel axis, which calculates the perpendicular distance of the leakage point to the tunnel axis, the angle with the tunnel longitudinal axis, and the position in the tunnel circumference (crown, haunch, or bottom). The leakage point in this example is located at the right haunch position of the tunnel, with a perpendicular distance of 0.75 meters to the axis, corresponding to a tunnel circumferential angle of 78°.

[0080] The leakage information preprocessing first uses a digital filtering algorithm to eliminate high-frequency noise. A Butterworth low-pass filter is used, with a cutoff frequency set to 0.05 Hz, to process the original flow and pressure data, effectively removing noise caused by equipment jitter and electrical interference. The filtered data is then decomposed into time series, and the sliding average method is used to extract the steady-state component, with a window size of 60 data points, about 10 hours of data; the fluctuation component is obtained by subtracting the steady-state component from the original data. For the leakage point in the example, the filtered average flow is 162 milliliters per minute, and the standard deviation is 8.5 milliliters per minute; the steady-state component shows a slow downward trend, from 173 to 157 milliliters per minute in 24 hours; the fluctuation component peaks between 6:00 and 8:00 every day, with an amplitude of about ±5 milliliters per minute.

[0081] The geological information preprocessing includes fault information and joint information extraction. The fault characteristics are identified by observing the drilling core, such as fault gouge, scratches, and fracture zones; in the geological radar data, it is represented as a clear signal interruption or strong reflection; in the acoustic wave detection data, it is represented as an abnormal wave velocity area. Joint information is obtained by measuring the joint surface direction, spacing, and filling characteristics on the core; linear reflection characteristics are identified in the radar image; local low wave velocity areas are found in the acoustic wave data. After integrating these data, the three-dimensional geological structure around the leakage point is reconstructed. For the leakage point in the example, a total of 2 groups of main joints are identified within a 5-meter range, the first group strikes north by east 30°, with a dip angle of 45° and a spacing of 15-25 cm; the second group strikes north by west 60°, with a dip angle of 70° and a spacing of 40-60 cm. These two groups of joints intersect at the leakage point location, forming a dominant seepage channel.

[0082] Finally, feature data fusion is performed to integrate location features, water quantity features, and geological features into a unified spatio-temporal framework. Through time stamp and spatial coordinates, the correlation between different sources and different scales of data is established, and standardized processing is performed. The fused data set contains complete information for each leakage point: location (three-dimensional coordinates in the tunnel coordinate system, relative position characteristics to the tunnel), water quantity (flow average, fluctuation characteristics, pressure change), geology (surrounding rock type, joint characteristics, permeability evaluation). For the leakage point in the example, the final feature description is: a moderate intensity leakage point located at the right side of the haunch (78°) of the K32+564.8 section, with an average flow of 162 milliliters per minute, a daily fluctuation of ±5 milliliters per minute, located in the sandstone-shale transition zone, mainly controlled by two intersecting joints, and the permeability coefficient is estimated to be 5.8 x 10 -5 cm / s.

[0083] Based on the above technical scheme, the multi-source information of the tunnel leakage point can be accurately collected and fused, and the spatial accuracy and temporal stability of the data can be improved. By registering the three-dimensional spatial coordinates with the tunnel axis, the precise positioning of the leakage point in the tunnel system is ensured; by using digital filtering and time series decomposition method, the noise interference in the leakage amount is effectively eliminated, and the key features reflecting the true leakage trend are extracted; combined with drilling, radar and acoustic detection means, the surrounding rock structure condition is comprehensively reflected, and the influence mechanism of geological structure on leakage is displayed through three-dimensional reconstruction. Finally, through the spatio-temporal mapping and fusion of the position, water quantity and geological characteristics, a clear structure and clear correlation leakage point feature data set is formed, which provides high-quality basic data support for subsequent leakage source positioning, leakage channel analysis and risk assessment, thereby improving the intelligent and scientific level of mountain tunnel leakage treatment.

[0084] In an optional implementation, according to the feature data set, the spatial correspondence relationship between the leakage point and the geological structure is calculated, the spatial correspondence relationship is combined with the leakage point distribution rule, the leakage source position coordinates are determined, and the leakage source positioning data is generated, including:

[0085] Using the feature data set, the geological structure surface is divided into a main control fault, a secondary fault and a joint fissure, the spatial positioning parameters of the main control fault, the secondary fault and the joint fissure are calculated, and the geological structure quantization data is generated;

[0086] According to the feature data set and the geological structure quantization data, the spatial correlation degree between the leakage point and the main control fault, the secondary fault and the joint fissure is calculated, the spatial correlation degree is combined with the leakage amount information in the feature data set, and the spatial distribution rule of the leakage point is obtained;

[0087] According to the spatial distribution rule, the leakage points are grouped according to the leakage amount and the spatial position, the spatial center coordinates and the leakage intensity of each group of leakage points are calculated, and the spatial distribution characteristics of the leakage point group are determined;

[0088] According to the distribution characteristics of the leakage point group, the steady-state leakage component is used to determine the leakage reference surface, the dynamic leakage component is used to calculate the leakage diffusion direction, the leakage reference surface and the leakage diffusion direction are superimposed, the spatial weight field is constructed, the leakage source candidate area is established by using the spatial weight field, the intersection position of the leakage source candidate area and the main control fault, the secondary fault and the joint fissure is calculated, and the spatial correlation degree between the leakage source candidate area and the leakage point group is calculated, the leakage source position coordinates are determined, and the leakage source positioning data is generated.

[0089] Figure 2For the tunnel leakage source positioning technology flow chart, in one embodiment, first obtain the feature data set, including leakage point coordinates, leakage amount, water temperature, water quality parameters and geological structure surface data. Based on the collected feature data set, the geological structure surface is divided into three levels of main control fault, secondary fault and joint fissure. The main control fault refers to a large tectonic fault that penetrates the entire tunnel area, the secondary fault refers to a secondary fault in a local area, and the joint fissure refers to a small fissure in the rock mass. For a mountainous tunnel with a length of 3500 meters, two main control faults are identified through drilling data and geophysical data, with strikes of north 45 degrees east and north 30 degrees west, and dip angles of 65 degrees and 72 degrees respectively; 7 secondary faults are identified, with strikes ranging from north 25-55 degrees east and north 15-35 degrees west, and dip angles ranging from 55-75 degrees; multiple groups of joint fissures are identified, mainly concentrated in three directions, with strikes of north 40 degrees east ± 10 degrees, north 25 degrees west ± 8 degrees and near east-west ± 15 degrees, and dip angles of 60 degrees ± 10 degrees, 70 degrees ± 8 degrees and 40 degrees ± 12 degrees respectively.

[0090] The spatial positioning parameters of various geological structures are calculated, including strike angle, trend angle, dip angle, length, width and thickness, etc. The three-point method is used to determine the spatial attitude of the main control fault, and the spatial equation of the fault plane is determined by at least three drill holes or geophysical points. For the first main control fault identified, according to the fault outcrop points at drill hole coordinates (X1=1250m, Y1=350m, Z1=-120m), (X2=1380m, Y2=455m, Z2=-145m) and (X3=1520m, Y3=560m, Z3=-175m), the spatial equation of the fault plane is calculated as ax+by+cz+d=0, where a, b, c, d are the coefficients of the fault plane equation, which are solved by substituting the coordinates. Similarly, the spatial equations of other main control faults and secondary faults are calculated. For joint fissures, the directionality and density distribution are statistically analyzed and measured through core orientation analysis and measurement, and a joint fissure network model is established. In the K2+350 to K2+500 section of the tunnel, the joint density is measured to be 5.8 per meter, with a main strike of north 42 degrees east and a dip angle of 58 degrees. Through the calculation of the spatial positioning parameters of different types of geological structures, a geological structure quantization data table is generated, containing information such as the number, type, spatial equation coefficient, strike angle, dip angle, length and width of each fault and fissure.

[0091] The tunnel leakage point observation data is collected, including the spatial coordinates of the leakage point, the leakage amount, the water quality parameters and the time variation. For example, a leakage point is observed at the tunnel stake number K2+385, with the coordinates (X=1320m, Y=420m, Z=-135m), the leakage amount of 25 liters / hour, and the point-like dripping water. A leakage point is observed at the stake number K2+455, with the coordinates (X=1390m, Y=440m, Z=-140m), the leakage amount of 120 liters / hour, and the linear flowing water. According to the leakage point coordinates and the quantitative data of the geological structure, the shortest distance of each leakage point to each geological structure surface is calculated to determine the spatial correlation degree. The calculation method is to use the point-to-surface distance formula, for the leakage point coordinates (x0, y0, z0) and the fault surface equation ax+by+cz+d=0, the distance of the point to the surface is calculated. For the leakage point at the stake number K2+385, the distance to the first main control fault is calculated to be 2.5 meters, the distance to the nearest secondary fault is 8.7 meters, and the distance to the nearest joint fissure is 0.8 meters. Similarly, the distances of other leakage points to each geological structure surface are calculated to obtain the spatial correlation degree matrix.

[0092] The spatial correlation degree of the leakage point is combined with the leakage amount information for analysis, and it is found that the leakage amount is negatively correlated with the distance to the geological structure, that is, the closer to the geological structure surface, the greater the leakage amount. Through statistical analysis, the spatial distribution law of the leakage points is determined: 70% of the leakage points are located within 3 meters of the main control fault, 25% of the leakage points are located within 2 meters of the secondary fault, and 5% of the leakage points are located within 1 meter of the joint fissure. According to the leakage amount and the spatial position, the leakage points are divided into three groups: group A is the large-flow leakage point (>80 liters / hour), with a total of 12; group B is the medium-flow leakage point (20-80 liters / hour), with a total of 35; and group C is the small-flow leakage point (<20 liters / hour), with a total of 57. The spatial center coordinates and the leakage intensity of each group of leakage points are calculated. The spatial center coordinates of group A are (X=1395m, Y=445m, Z=-142m), and the total leakage intensity is 1850 liters / hour; the spatial center coordinates of group B are (X=1325m, Y=425m, Z=-136m), and the total leakage intensity is 1260 liters / hour; and the spatial center coordinates of group C are (X=1420m, Y=460m, Z=-155m), and the total leakage intensity is 685 liters / hour.

[0093] The distribution characteristics of the leakage point groups are analyzed to distinguish the steady-state leakage component and the dynamic leakage component. The steady-state leakage component refers to the long-term stable leakage phenomenon, which is related to the stable state of the groundwater level and geological structure. The dynamic leakage component refers to the leakage phenomenon that changes with external factors such as rainfall. Through 30 consecutive days of leakage monitoring data, the steady-state leakage baseline and the dynamic leakage change of each leakage point are calculated. For example, the steady-state leakage baseline of Group A is 1200 liters / hour, and the dynamic leakage change is 650 liters / hour, with a change rate of 54.2%. The steady-state leakage baseline of Group B is 850 liters / hour, and the dynamic leakage change is 410 liters / hour, with a change rate of 48.2%. The steady-state leakage baseline of Group C is 510 liters / hour, and the dynamic leakage change is 175 liters / hour, with a change rate of 34.3%.

[0094] Based on the steady-state leakage component, the leakage reference surface is determined. The leakage reference surface represents the spatial distribution of the groundwater level and is constructed by a three-dimensional interpolation method. A coordinate system is established with the tunnel longitudinal axis as the X-axis, the transverse axis as the Y-axis, and the vertical downward direction as the Z-axis. The steady-state leakage of each leakage point is used as the weight for spatial interpolation calculation. The generated leakage reference surface equation is Z = f(X, Y), where f is a three-dimensional interpolation function. In the study area, the elevation of the leakage reference surface varies between -120 meters and -180 meters, with a general trend of being higher in the west and lower in the east. There is a local depression in the X = 1350-1450 meter interval.

[0095] The dynamic leakage component is used to calculate the leakage diffusion direction. The dynamic leakage component reflects the trend of groundwater flow, and the water flow propagation direction is determined by analyzing the time series and spatial distribution of the leakage change after rainfall. By comparing the leakage point change data before and after a rainfall event, the leakage response time and intensity change are calculated. After a rainfall event with a rainfall of 85 millimeters, the average response time of Group A is 18 hours, Group B is 26 hours, and Group C is 32 hours. According to the response time difference and spatial position relationship, the water flow diffusion vector is calculated. The main diffusion direction of Group A is north by east 52 degrees, with an inclination angle of 62 degrees. The main diffusion direction of Group B is north by east 38 degrees, with an inclination angle of 56 degrees. The main diffusion direction of Group C is north by east 45 degrees, with an inclination angle of 60 degrees.

[0096] The leakage reference surface and the leakage diffusion direction are superimposed to construct a spatial weight field. The spatial weight field is a three-dimensional field function W(X, Y, Z) that represents the possibility of each point in space being a leakage source. The weight calculation considers the following factors: the vertical distance to the leakage reference surface, the consistency with the leakage diffusion direction, the distance to various types of geological structure surfaces, and the distance to each leakage point group. According to the calculation results, the spatial weight field has a maximum value of 0.92 near the coordinates (X = 1370m, Y = 435m, Z = -115m), indicating that this region is the most likely location of the leakage source.

[0097] The candidate area of the leakage source is established by using the spatial weight field, and the spatial area with a weight value greater than 0.75 is selected as the candidate area. The candidate area of the leakage source is in the shape of an ellipsoid, with a long axis of about 25 meters, a short axis of about 15 meters, a vertical height of about 18 meters, and a center located at (X = 1370m, Y = 435m, Z = -115m). The intersection of the candidate area with the main control fault, the secondary fault and the joint fissure is calculated. The candidate area intersects with the first main control fault, with an intersection line length of about 20 meters; intersects with two secondary faults, with intersection line lengths of 12 meters and 8 meters respectively; and intersects with multiple groups of joint fissures, forming a grid-shaped water seepage channel. The spatial correlation degree of the candidate area with each leakage point group is calculated, with the correlation degree of group A being 0.86, the correlation degree of group B being 0.78, and the correlation degree of group C being 0.65. According to the intersection position and correlation degree analysis, the optimal position coordinates of the leakage source are determined as (X = 1368m, Y = 432m, Z = -112m), which is located at the intersection of the first main control fault and a secondary fault. Underground water seeps downward along the fault intersection zone, forming the phenomenon of multiple point leakage in the tunnel.

[0098] Based on the determined leakage source position coordinates, a leakage source positioning data packet is generated, including information such as leakage source spatial coordinates, influence range, leakage intensity, geological structure correlation and treatment suggestions.

[0099] The leakage source positioning data generated through the above steps includes: coordinate position, related geological structure information, associated leakage point group characteristics and reliability score. These data can be used for subsequent anti-seepage treatment engineering design and implementation to improve the treatment effect and resource utilization efficiency. In the prior art, leakage source identification relies mainly on manual experience or single leakage point information, and simple spatial interpolation or projection methods are often used for position estimation, lacking comprehensive modeling of fault structure and leakage distribution rules, resulting in low leakage source positioning accuracy and large errors, which is difficult to adapt to the leakage treatment needs in complex geological environments of mountain tunnels. In combination with the three-dimensional spatial coordinates of the leakage points, the leakage amount and its dynamic change characteristics, the spatial correspondence between the leakage points and the geological structure is established in the present application, and multi-source data fusion analysis is realized. Further, through leakage point grouping analysis, steady-state and dynamic leakage component decomposition, spatial diffusion trend modeling and other means, a spatial weight field is constructed and superimposed with geological structure information to form a leakage source candidate area, and finally the precise identification of the leakage source is completed based on the spatial correlation degree. This method starts from multi-dimensional information fusion, improves the traditional technical means relying on static spatial relationship and single point judgment, greatly improves the scientificity and accuracy of the leakage source positioning, and provides reliable data support for subsequent risk assessment and treatment construction.

[0100] In an optional implementation, based on the leakage source positioning data, the spatial distance and the leakage amount change ratio between adjacent leakage points are calculated as connection weights to construct a leakage point correlation graph, including:

[0101] obtain leakage source positioning data, extract spatial coordinate information of leakage points, leakage amount time series information and geological environment information in the leakage source positioning data;

[0102] According to the spatial coordinate information, the position components of adjacent leakage points in the axial direction, the ring direction and the elevation direction of the tunnel are calculated, and the position components are weighted and corrected in combination with the lithology and fault distribution in the geological environment information to obtain the corrected spatial distance between adjacent leakage points;

[0103] The leakage amount time series information is standardized to eliminate seasonal fluctuations and environmental noise to obtain standardized leakage amount data; the sliding time window is used to analyze the standardized leakage amount data to calculate the leakage amount dynamic change characteristics of adjacent leakage points; and the leakage amount dynamic change characteristics are corrected in combination with the hydrological conditions in the geological environment information to obtain the corrected leakage amount change;

[0104] The ratio of the corrected spatial distance and the corrected leakage amount change is calculated, and the ratio is dynamically adjusted in combination with the geological environment information to generate the connection weight between adjacent leakage points;

[0105] According to the connection weight, the connection relationship between adjacent leakage points is determined, the leakage points are set as the vertices of the graph, and the connection relationship is set as the edges of the graph to construct a leakage point correlation graph.

[0106] In this embodiment, leakage source positioning data is obtained, which contains spatial coordinate information of leakage points, leakage amount time series information and geological environment information. The spatial coordinate information records the position of each leakage point in three-dimensional space, which uses a tunnel-specific coordinate system, taking the tunnel entrance as the origin, the tunnel axial direction as the X axis, the horizontal transverse direction as the Y axis, and the vertical upward direction as the Z axis. The leakage amount time series information records the leakage water amount of each leakage point at different time points, usually in units of liters / hour, with a collection frequency of every 4 hours and continuous monitoring for more than 30 days. The geological environment information includes the lithological characteristics, fault distribution and hydrogeological conditions around the leakage point. Taking a mountain tunnel as an example, 35 leakage points are identified in the interval of pile number K3+250 to K3+450, numbered as LP001 to LP035. The spatial coordinates of the leakage point LP005 are (3280, 2.5, 1.8), indicating that the point is located 3280 meters forward from the tunnel entrance, 2.5 meters away from the center line of the tunnel on the right side, and 1.8 meters higher than the tunnel bottom plate. The leakage amount record shows that the average leakage amount during the monitoring period is 27.5 liters / hour, the maximum value is 42.8 liters / hour, and the minimum value is 18.2 liters / hour. The geological environment information shows that the point is located in a sandstone and mudstone interbedded area, about 3.2 meters away from a secondary fault with a strike of north by east 65 degrees and a dip angle of 72 degrees.

[0107] According to the spatial coordinate information, the position components of the adjacent leakage points in the axial direction, the ring direction and the elevation direction of the tunnel are calculated. Take leakage points LP005 and LP008 as an example, the spatial coordinates of LP008 are (3295, 1.8, 2.2). The distance component of the two points in the axial direction is 3295-3280=15 meters, the distance component in the ring direction is 1.8-2.5=-0.7 meters, and the distance component in the elevation direction is 2.2-1.8=0.4 meters. Considering that different directions have different effects on the propagation of seepage water, the components in each direction need to be weighted. Based on the lithology and fault distribution in the geological environment information, the position components are weighted and corrected. For the interbedded sandstone and mudstone area, since the bedding surface mainly affects the axial and ring permeability, the axial weight is set to 0.7, the ring weight is set to 0.8, and the elevation direction weight is set to 1.2. When there is a fault near the leakage point, the influence of the fault on the permeability also needs to be considered. For the area between LP005 and LP008, the secondary fault strikes north by east 65 degrees, with an angle of about 25 degrees with the tunnel axial direction, so the axial weight is adjusted to 0.7x1.2=0.84, the ring weight is adjusted to 0.8x0.9=0.72, and the elevation direction weight is maintained at 1.2. The corrected spatial distance is calculated by applying the adjusted weight: the corrected axial distance is 15x0.84=12.6 meters, the corrected ring distance is |-0.7| x 0.72=0.504 meters, and the corrected elevation distance is 0.4x1.2=0.48 meters. By integrating the corrected distances in the three directions, the three-dimensional spatial distance calculation formula is used to obtain the corrected spatial distance between LP005 and LP008, which is 12.61 meters.

[0108] The time series information of the leakage amount is standardized to eliminate seasonal fluctuations and environmental noise, and standardized leakage amount data is obtained. Standardization processing includes three steps: time scale adjustment, baseline correction and noise filtering. Time scale adjustment is to unify the monitoring data of different time intervals to the same time scale, and 4 hours is used as the standard time interval in this embodiment. Baseline correction is to eliminate the influence of long-term trend and seasonal fluctuations, and a 30-day moving average is used as the baseline. Noise filtering is to remove abnormal values caused by monitoring device errors and sudden events, and a median filtering method is used with a window size of 5 sampling points. Take LP005 as an example, the original monitoring data of the leakage amount from 12:00 on February 15 to 12:00 on February 16 is 28.5, 30.2, 32.7, 35.8, 33.4, 30.5, 28.7 liters / hour. After standardization, the standardized leakage amount data is 0.52, 0.58, 0.65, 0.75, 0.67, 0.59, 0.53. The standardized value is between 0 and 1, representing the proportion of the maximum possible leakage of the leakage point.

[0109] The dynamic change characteristics of the seepage rates of adjacent seepage points are calculated by analyzing the standardized seepage rate data with a sliding time window. The dynamic change characteristics include the change rate, the correlation, and the phase difference. The change rate describes the speed of the change of the seepage rate with time, and the maximum change of the standardized seepage rate in 24 hours is calculated. The correlation describes the consistency of the change of the seepage rates of two seepage points, and the correlation coefficient of the standardized seepage rate sequences of the two seepage points is calculated. The phase difference describes the time lag of the change of the seepage rates of two seepage points, and the difference of the occurrence time of the peak values of the seepage rates of the two seepage points is calculated. Taking LP005 and LP008 as examples, after the same rainfall event, the standardized seepage rate of LP005 rises from 0.45 to 0.75, and the change rate is 0.3 / 24 hours. The standardized seepage rate of LP008 rises from 0.38 to 0.62, and the change rate is 0.24 / 24 hours. The correlation coefficient of the standardized seepage rate sequences of the two seepage points is 0.82, indicating that the change trends are highly consistent. The peak value of the seepage rate of LP008 occurs about 5 hours later than that of LP005, and the phase difference is 5 hours.

[0110] The dynamic change characteristics of the seepage rates are corrected in combination with the hydrological conditions in the geological environment information to obtain the corrected seepage rate changes. The hydrological conditions include the groundwater level, the permeability coefficient, and the groundwater flow direction. The groundwater level affects the overall water pressure of seepage, the permeability coefficient affects the difficulty of water flow through the rock mass, and the groundwater flow direction affects the propagation direction of the water flow. In the area where LP005 and LP008 are located, the groundwater level is 25 meters higher than the tunnel top, the permeability coefficient of the rock mass is 3.5 x 10 -6 cm / s, and the groundwater mainly flows along the fault plane, and the flow direction is basically consistent with the fault strike, which is 62 degrees north by east. Based on these hydrological conditions, the dynamic change characteristics of the seepage rates are corrected: the change rate correction coefficient is (25 / 30) x (3.5 / 4.0) = 0.73, the correlation correction coefficient is 0.85, and the phase difference correction coefficient is 0.78. After applying the correction coefficients, the corrected change rate difference is (0.3-0.24) x 0.73 = 0.044, the corrected correlation is 0.82 x 0.85 = 0.697, and the corrected phase difference is 5 x 0.78 = 3.9 hours.

[0111] The ratio of the corrected spatial distance and the corrected leakage rate change is calculated, and the ratio is dynamically adjusted in combination with the geological environmental information to generate the connection weight between adjacent leakage points. The connection weight reflects the hydraulic connection strength between two leakage points and is the core parameter for constructing the leakage point correlation graph. The connection weight calculation considers the spatial distance factor and the leakage dynamic factor, and the importance of the two factors is dynamically adjusted according to the geological conditions. In the fault development area, the weight of the leakage dynamic factor is increased; in the homogeneous rock mass area, the weight of the spatial distance factor is increased. For LP005 and LP008, since they are located in the fault affected area, the weight of the spatial distance factor is set to 0.4, and the weight of the leakage dynamic factor is set to 0.6. The spatial distance index is the reciprocal of the corrected spatial distance, i.e. 1 / 12.61 = 0.079. The leakage dynamic index considers the corrected change rate difference, the corrected correlation and the corrected phase difference, and is calculated as (1-0.044) x 0.697 x (1 / 3.9) = 0.170. The connection weight is 0.079 x 0.4 + 0.170 x 0.6 = 0.134. The connection weight value ranges from 0 to 1, and the larger the value, the tighter the hydraulic connection between the two leakage points. The connection threshold is set to 0.08, and when the connection weight is greater than the threshold, it is considered that there is an effective connection between the two leakage points.

[0112] According to the connection weight, the connection relationship between adjacent leakage points is determined, the leakage points are set as the vertices of the graph, and the connection relationship is set as the edges of the graph to construct the leakage point correlation graph. The leakage point correlation graph is a weighted undirected graph, each leakage point is a vertex in the graph, and the connection weight between two leakage points is the weight of the edge. In the construction process, connections with a weight less than the threshold are ignored, and only the main hydraulic connections are retained. Taking the 35 leakage points in the K3+250 to K3+450 interval as an example, a total of 42 effective connections are formed to constitute the leakage point correlation graph. There are three tightly connected subgraphs in the graph, located in the K3+265 to K3+280, K3+310 to K3+335 and K3+390 to K3+420 intervals, indicating that the leakage points in these three regions may come from the same leakage source. Among them, the subgraph in the K3+310 to K3+335 interval is the most densely connected, containing 12 leakage points and 18 connections, with an average connection weight of 0.156, indicating that the hydraulic connection between the leakage points in this region is the tightest.

[0113] In this embodiment, by introducing a multi-dimensional correction mechanism, dynamic coupling modeling of the spatial relationship between leakage points and the change relationship of leakage quantity is realized. Not only the displacement component calculation in the axial, ring and elevation directions of the tunnel is refined, but also the spatial distance is weighted and corrected in combination with geological factors such as lithology and faults, so that the expression of the spatial relationship is more in line with the actual leakage path characteristics. At the same time, the standardized leakage quantity is dynamically analyzed by using a sliding time window, effectively eliminating the interference of seasonal and occasional fluctuations, and extracting the internal change law of the leakage trend. Further, the leakage quantity change is corrected in combination with the hydrological conditions, improving the accuracy of the leakage intensity comparison. By taking the ratio of the corrected spatial distance and the corrected leakage quantity change as the connection weight, and dynamically adjusting it under the constraint of the geological environment, the scientific weighting of the connection edges in the correlation graph is realized. This method strengthens the expression ability of the topological relationship between leakage points, provides a high-reliability foundation model for subsequent topological structure analysis, hierarchical identification and leakage propagation path inference, and significantly improves the accuracy and engineering adaptability of leakage analysis.

[0114] In an optional implementation, topological distribution characteristics of the leakage point correlation graph are extracted, the leakage points are divided into multiple levels according to the mountain elevation and the leakage quantity size according to the topological distribution characteristics, the connection strength between adjacent level leakage points is calculated, the leakage water propagation direction is determined, and a hierarchical leakage propagation graph is constructed, including:

[0115] The center degree data of the leakage points in the leakage point correlation graph is calculated by calculating the connection number and the shortest path number of the leakage points, the aggregation degree data of the leakage points and adjacent leakage points is calculated by calculating the connection proportion of the leakage points and adjacent leakage points, and the topological distribution characteristics of the leakage points are obtained according to the center degree data and the aggregation degree data.

[0116] The mountain elevation data and the leakage quantity data of the leakage points are obtained, the leakage points are initially layered according to the mountain elevation data, the leakage quantity distribution characteristics of the leakage points in each layer are calculated, the level boundary is adjusted according to the leakage quantity distribution characteristics, and the hierarchical division of the leakage points is obtained in combination with the topological distribution characteristics.

[0117] Adjacent level leakage points are determined according to the hierarchical division, the spatial distance decay value, the leakage quantity correlation coefficient, the elevation difference value and the medium permeability coefficient between the adjacent level leakage points are calculated, and the connection strength between the adjacent level leakage points is obtained.

[0118] The main propagation path between the adjacent level leakage points is determined according to the connection strength, and the propagation direction of the main propagation path is obtained based on the time sequence change of the leakage quantity data and the hierarchical division.

[0119] The leakage points are arranged according to the hierarchical division, the connection strength is taken as the connection relationship between the levels, and the propagation direction is taken as the propagation trend between the levels, and a hierarchical leakage propagation graph is constructed.

[0120] In the present embodiment, first, the centrality data and the clustering data of the leakage points are calculated based on the constructed leakage point correlation graph, and then the topological distribution characteristics of the leakage points are obtained. The centrality data reflects the importance of the leakage points in the correlation network, including two indexes of degree centrality and betweenness centrality. The degree centrality represents the number of other leakage points directly connected to a certain leakage point, and the betweenness centrality represents the number of shortest paths passing through a certain leakage point. Taking the section from K4+200 to K4+450 of the mountain tunnel as an example, a total of 42 leakage points are identified in this section, numbered as LP101 to LP142, forming a correlation graph containing 62 edges. The degree centrality of the leakage point LP115 is calculated to be 5, indicating that this point is directly connected to 5 other leakage points; the betweenness centrality is 28, indicating that there are 28 shortest paths passing through this point in the correlation graph. The clustering data reflects the connection tightness between adjacent nodes of the leakage points, and the connection proportion of the leakage point and its adjacent leakage points is calculated. For the leakage point LP115, the maximum number of connections between its 5 adjacent points is 10, and the actual number of connections is 6, so the clustering coefficient is 0.6. By analyzing the centrality data and the clustering data of all the leakage points, it is found that the leakage points of this tunnel section present a "core-periphery" topological distribution characteristic, and a high centrality and high clustering degree leakage core area is formed near K4+320, surrounded by low centrality leakage points.

[0121] The key step to construct the hierarchical leakage propagation graph is to obtain the elevation data of the mountain and the leakage data of the leakage points, and to divide the mountain into different levels. The elevation data of the mountain is obtained through topographic survey and geological drilling, which records the altitude of different positions in the mountain. The leakage data is obtained through field monitoring, which records the amount of seepage and its time variation of each leakage point. Taking the above tunnel section as an example, the elevation of the tunnel portal is 1320 meters, the highest point of the mountain is 1758 meters, and the elevation range of the leakage points in the tunnel is 1315 to 1330 meters. According to the elevation data of the mountain, the mountain is preliminarily divided into 5 levels: the 1st level is 1650-1758 meters, the 2nd level is 1550-1650 meters, the 3rd level is 1450-1550 meters, the 4th level is 1350-1450 meters, and the 5th level is 1315-1350 meters. Calculate the leakage distribution characteristics of each layer, including average leakage, maximum leakage, minimum leakage and coefficient of variation. The 5th layer contains all 42 leakage points, with an average leakage of 43.6 liters / hour, a maximum leakage of 125.8 liters / hour, a minimum leakage of 5.2 liters / hour, and a coefficient of variation of 0.68. Analyzing the distribution of leakage in the elevation direction, it is found that there is a significant change in the amount of seepage at the elevation of 1335 meters. Above this elevation, the average leakage of the leakage points is 58.7 liters / hour, and below this elevation, the average leakage of the leakage points is 32.4 liters / hour. Therefore, the boundary of the 5th layer is adjusted from 1350 meters to 1335 meters, forming a new 5th layer (1335-1350 meters) and a 6th layer (1315-1335 meters). Combined with the topological distribution characteristics, it is found that the centrality and aggregation of the leakage points in the 6th layer are significantly higher than those in the 5th layer, further confirming the rationality of the adjustment of the level boundary. The final level division is: the 1st level (1650-1758 meters), the 2nd level (1550-1650 meters), the 3rd level (1450-1550 meters), the 4th level (1350-1450 meters), the 5th level (1335-1350 meters) and the 6th level (1315-1335 meters).

[0122] According to the hierarchical division, the leakage points of adjacent layers are determined, and the connectivity strength between the leakage points is calculated, which is an important indicator to understand the interlayer hydraulic connection. The connectivity strength is determined by four factors: spatial distance decay value, leakage amount correlation coefficient, elevation difference value and medium permeability coefficient. The spatial distance decay value represents the degree of weakening of hydraulic connection with the increase of distance. The three-dimensional spatial distance between the leakage points of adjacent layers is calculated and converted into a decay coefficient between 0 and 1. The leakage amount correlation coefficient represents the consistency of the change of leakage amount of two leakage points. The correlation of the time series of the leakage amount of two points is calculated. The elevation difference value represents the distance between two points in the vertical direction, reflecting the influence of gravity on water flow. The medium permeability coefficient represents the degree of difficulty of water flow through rock-soil mass, which is determined according to drilling and hydrogeological test. Taking the leakage point LP125 of the 5th layer and the leakage point LP132 of the 6th layer as an example, the distance between them in the horizontal plane is 18.5 meters, the elevation difference is 16.3 meters, the three-dimensional spatial distance is 24.7 meters, and the distance decay value is converted to 0.74. The correlation coefficient of the 30-day leakage amount time series of the two points is 0.83, indicating that the change of leakage amount is highly consistent. LP125 is located at an elevation of 1342 meters, and LP132 is located at an elevation of 1325.7 meters, with an elevation difference of 16.3 meters, and the corresponding elevation influence coefficient is 0.85. The rock mass between the two points is mainly medium weathered sandstone, and the permeability coefficient is 4.2×10^-6 cm / s, and the corresponding permeability influence coefficient is 0.68. By integrating the four factors, the connectivity strength between LP125 and LP132 is calculated as 0.74×0.83×0.85×0.68=0.36. Similarly, the connectivity strength of other adjacent layer leakage point pairs is calculated, and a connectivity strength matrix is established. The connectivity strength threshold is set to 0.25, and when the connectivity strength is greater than the threshold, it is considered that there is effective hydraulic connection between the two leakage points.

[0123] The main propagation paths between the leakage points in adjacent levels were determined according to the connectivity strength, and the flow law of the leakage water in the mountain was analyzed. The main propagation path is the path formed by the pairs of leakage points with higher connectivity strength, which reflects the preferential channel of water flow in the mountain. Between the 4th and 5th levels, there are 15 pairs of leakage points with connectivity strength greater than the threshold value, forming 3 main propagation paths. Path 1 is composed of 4 pairs of leakage points, starting from LP108 in the 4th level, passing through LP112, LP118, and LP123 in turn, and finally reaching LP127 in the 5th level, with an average connectivity strength of 0.41. Path 2 is composed of 3 pairs of leakage points, starting from LP110 in the 4th level, passing through LP115, and reaching LP129 in the 5th level, with an average connectivity strength of 0.38. Path 3 is composed of 8 pairs of leakage points, starting from LP113 in the 4th level, passing through multiple leakage points, and finally reaching LP131 in the 5th level, with an average connectivity strength of 0.33. Similarly, the main propagation paths between other adjacent levels were analyzed, and a complete propagation path network was constructed. Based on the time series variation of the leakage amount data and the level division, the propagation direction of the main propagation path was determined. The propagation direction was determined by analyzing the time series of the increase in the leakage amount of the leakage points in different levels after a rainfall event. Taking a rainfall event with a rainfall amount of 85 mm as an example, the groundwater level in the 1st layer was detected to rise the earliest, followed by the 2nd, 3rd, 4th, 5th, and 6th layers, and the time when the leakage amount of the leakage points in each layer began to increase significantly was 10 hours, 16 hours, 22 hours, 28 hours, 32 hours, and 36 hours after the rainfall, respectively. This indicates that the water flow mainly propagates from the mountain top to the direction of the tunnel, and the propagation direction is from the high level to the low level. Further analysis found that the water flow in path 1 propagates the fastest, with an average propagation time of 4.8 hours per layer; path 2 is second, with an average propagation time of 5.2 hours per layer; and path 3 is the slowest, with an average propagation time of 5.8 hours per layer. This is negatively correlated with the average connectivity strength of the path, i.e., the higher the connectivity strength, the faster the propagation speed.

[0124] The leakage points are arranged according to the hierarchical distribution, the connection strength is taken as the connection relationship between the hierarchies, and the propagation direction is taken as the propagation trend between the hierarchies, to construct a hierarchical leakage propagation graph. The hierarchical leakage propagation graph is a multi-level directed weighted graph, each layer represents a height hierarchy in the mountain, the nodes in the layer are the leakage points of the hierarchy, the edges between the layers are the connection relationships between the leakage points of the adjacent hierarchies, the weight of the edge is the connection strength, and the direction of the edge is the propagation direction. On the graph representation, six hierarchies are vertically arranged from high to low according to the height, and the leakage points in each layer are arranged according to the horizontal position. Taking the hierarchical leakage propagation graph of the K4+200 to K4+450 tunnel section as an example, the first layer contains a virtual source point, representing a rainfall infiltration point; the second layer contains three key leakage path starting points; the third layer contains five leakage nodes; the fourth layer contains eight leakage nodes; the fifth layer contains twelve leakage nodes; and the sixth layer contains forty-two actually observed tunnel leakage points. There are 78 connections between the layers, of which 28 high-intensity connections with a connection strength greater than 0.3 are mainly distributed on the three main propagation paths. The propagation direction is clearly marked in the graph, all of which are from high hierarchy to low hierarchy, in line with the characteristics of gravity-driven water flow. Between the fifth layer and the sixth layer, there is a connection with a particularly high connection strength (0.52) connecting LP128 of the fifth layer and LP135 of the sixth layer, indicating that this is the main channel for the propagation of leakage water and should be the priority for processing.

[0125] The technical scheme fuses topological analysis and terrain hydrological characteristics to construct a hierarchical leakage propagation graph that can reflect the leakage diffusion process. The prior art usually only performs hierarchical distribution based on a single factor of leakage amount or height, ignores the structural relationship and propagation trend between leakage points, and is difficult to accurately identify the leakage path and the influence range of the source point. The application uses centrality and clustering degree to extract the topological characteristics of the leakage points in the association graph, reflects their core role and local aggregation trend in the leakage propagation network, and on this basis, combines the double factors of mountain height and leakage amount to perform hierarchical distribution division, and dynamically adjusts the boundary to adapt to the real leakage distribution situation. At the same time, by considering the spatial distance attenuation, height difference, hydraulic connection and other factors, the connection strength between hierarchies is calculated, and the physical connection relationship between the leakage points is quantified. Further, the propagation direction is derived by using the time variation trend of the leakage amount, and the hierarchical leakage propagation structure conforming to the geological characteristics and leakage mechanism is constructed. The method improves the traditional hierarchical division and path analysis method, improves the modeling ability of the complex leakage diffusion process in the mountain tunnel, and provides scientific and reliable propagation graph support for subsequent channel identification, risk assessment and repair decision-making.

[0126] Figure 3A hierarchical leakage propagation map is drawn for the mountainous tunnel, in which the mountain is vertically divided into six elevation levels: Level 1 (1650-1758 m) is located at the top, representing the rainfall infiltration point; Levels 2 to 5 are distributed downward in turn, and Level 6 (1315-1335 m) is located at the bottom, corresponding to the actual observed leakage point in the tunnel. Different colored dots in the figure represent the leakage points of each level, and the size of the dot reflects the centrality characteristics of the leakage point.

[0127] Three main propagation paths are shown in the figure: the red solid line represents the high connectivity intensity path (0.41), with the fastest propagation speed (4.8 hours per level); the yellow solid line represents the medium connectivity intensity path (0.39); and the blue dashed line represents the low connectivity intensity path (0.33), which propagates relatively slowly. It is particularly noteworthy that the purple connecting line between LP128 and LP135 has a connectivity intensity as high as 0.52, constituting the main channel for the propagation of leakage water.

[0128] The response time of each level leakage point to the rainfall event is marked on the right side of the figure, from T+10h of Level 1 to T+36h of Level 6, and the increasing trend of time confirms the downward propagation direction of the leakage water. LP115 in Level 4 is circled by a red dashed line, indicating that this point is the core area of leakage with high centrality and aggregation.

[0129] In an alternative embodiment, the spatial distribution of the leakage channel is determined according to the hierarchical leakage propagation map, the leakage channel trend is drawn, the influence range and harm degree of each leakage point are calculated using the leakage channel trend combined with the leakage amount data, the risk classification of the leakage point is performed, and the risk classification data is generated, including:

[0130] The hierarchical leakage propagation map and the geological structure surface data are obtained, the sequence of leakage points continuously connected between levels is extracted from the hierarchical leakage propagation map to determine the spatial distribution of the leakage channel, the trend angle, inclination and curvature of the leakage channel are calculated, and the leakage channel trend is drawn using three-dimensional spline interpolation combined with the geological structure surface data;

[0131] The radius of the spherical influence domain is determined according to the leakage amount data, the spherical influence domain is extended and compressed to obtain an ellipsoidal influence domain based on the leakage channel trend; the lithology and fracture distribution data of the geological medium are obtained, and the ellipsoidal influence domain is adjusted according to the lithology and fracture distribution data to obtain the influence range of the leakage point;

[0132] The absolute value and trend of the leakage amount are calculated to obtain the leakage amount characteristics, the overlap degree between the influence ranges is calculated to obtain the spatial coupling characteristics, and the extension length of the leakage channel trend is calculated to obtain the channel extension characteristics; the leakage amount characteristics, spatial coupling characteristics and channel extension characteristics are combined by weighting to obtain the harm degree of the leakage point;

[0133] The hazard degree distribution of the leakage points in the statistical engineering area is determined, a risk level division threshold is determined, the risk level division threshold is dynamically adjusted according to safety requirements in construction and operation stages, the leakage points are classified based on the hazard degree and the adjusted risk level division threshold, and risk classification data is generated.

[0134] In the present embodiment, first, the leakage point sequence of continuous connection between layers is extracted from the hierarchical leakage propagation graph, and the spatial distribution of the leakage channel is determined. The leakage channel refers to the spatial path formed by the leakage points with high connectivity between different layers, representing the preferential channel of water flow in the mountain. For example, 6 main leakage channels are identified by analyzing the hierarchical leakage propagation graph. Take channel T3 as an example, which is composed of 6 leakage points of different layers, namely LP201 (1st layer), LP208 (2nd layer), LP215 (3rd layer), LP225 (4th layer), LP232 (5th layer) and LP240 (6th layer). These leakage points form a continuous path from the mountain top to the tunnel in space, and the three-dimensional coordinates of each point are (4280, 230, 1720), (4295, 225, 1640), (4310, 215, 1520), (4325, 205, 1420), (4335, 195, 1350) and (4350, 185, 1320) respectively, where the first value represents the longitudinal distance of the tunnel (m), the second value represents the horizontal distance from the tunnel center line (m), and the third value represents the elevation (m).

[0135] The trend angle, inclination and curvature of the leakage channel are calculated to analyze its spatial morphological characteristics. The trend angle refers to the projection direction of the leakage channel on the horizontal plane, with the north direction as the reference, and the angle of clockwise rotation; the inclination refers to the angle between the leakage channel and the horizontal plane; the curvature represents the bending degree of the leakage channel. For channel T3, according to the spatial coordinates of each leakage point, the trend angle is 56 degrees east of north, which is basically consistent with the trend of main fault F1; the inclination is 71 degrees, which is also close to the inclination of F1 which is 72 degrees; the curvature value is 0.12, indicating that the channel is relatively straight. Combined with the analysis of geological structure surface data, it is found that channel T3 is highly coincident with main fault F1 in space, indicating that the leakage channel is controlled by main fault F1. The three-dimensional spline interpolation method is used to draw the spatial trend curve of the leakage channel with the leakage point sequence as the control point. When interpolating, the influence of the geological structure surface is considered, and higher weight is given to the control points near the structure surface to ensure that the interpolation result conforms to the geological structure characteristics. For channel T3, the generated three-dimensional spline curve has a total length of 427 meters, extending along the trend of main fault F1, and slightly deflecting at the intersection with secondary fault F8.

[0136] The radius of the spherical influence domain is determined based on the leakage data to evaluate the influence range of the leakage point on the surrounding area. The spherical influence domain assumes that the influence of the leakage point spreads uniformly in space, and the radius of the influence domain is proportional to the leakage amount. For the leakage point LP240, the average leakage amount within 30 days is 95.3 liters / hour, and the radius of the spherical influence domain is calculated to be 12.5 meters according to the empirical formula. The spherical influence domain is deformed based on the strike of the leakage channel, stretched along the strike direction, and compressed perpendicular to the strike direction, to obtain an ellipsoidal influence domain. For LP240, the semi-axis length along the strike direction (north 56 degrees east) is 18.7 meters, the horizontal semi-axis length perpendicular to the strike direction is 8.3 meters, and the vertical semi-axis length is 10.6 meters. The lithology and fracture distribution data of the geological medium are obtained, and the lithology type, weathering degree, fracture density, and permeability of different positions in the engineering area are recorded. The lithology at the location of LP240 is medium-weathered sandstone, the average fracture density is 4.2 per meter, and the permeability coefficient is 5.8 x 10^-6 cm / s. The ellipsoidal influence domain is adjusted according to the lithology and fracture distribution data, and the higher the permeability and the greater the fracture density, the larger the influence domain. For LP240, due to the higher permeability and fracture density of the rock mass than the average level of the engineering area, the semi-axis lengths of the influence domain in each direction are adjusted to 22.4 meters, 9.9 meters, and 12.7 meters, respectively. The final influence range of the leakage point is represented by the adjusted ellipsoid, with a total volume of about 11760 cubic meters.

[0137] The absolute value and the trend of the leakage rate are calculated to obtain the leakage rate characteristics. The absolute value of the leakage rate refers to the average leakage rate of the leakage point under standard conditions, and the trend refers to the variation law of the leakage rate with time. For LP240, in addition to the average leakage rate of 95.3 L / h, the maximum leakage rate of 152.6 L / h, the minimum leakage rate of 68.4 L / h, the coefficient of variation of 0.32, the rainfall response time of 22 h, and the leakage rate growth rate of 3.2 L / h·mm (indicating the value of the leakage rate increase per hour caused by each millimeter of rainfall), the leakage rate characteristics of LP240 are calculated to be 0.78, with a value range of 0 to 1. The greater the value, the greater the impact of the leakage rate and its variation on the engineering risk. The overlap between the influence ranges is calculated to obtain the spatial coupling characteristics. The spatial coupling characteristics describe the degree of overlap of the influence ranges of adjacent leakage points, reflecting the interaction between the leakage points. The influence range of LP240 overlaps with the influence ranges of adjacent LP235 and LP242 by 2380 m3and 1850 m3, accounting for 20.2% and 15.7% of the total volume of the influence range of LP240. The spatial coupling characteristics value of LP240 is calculated to be 0.65. The extension length of the leakage channel direction is calculated to obtain the channel extension characteristics. The channel extension characteristics represent the spatial continuity and penetration of the leakage channel. The total length of channel T3 is 427 m, spanning 6 elevation levels, with a high degree of coincidence with the main fault F1, and the channel extension characteristics value is calculated to be 0.82. The leakage rate characteristics, spatial coupling characteristics, and channel extension characteristics are combined by weighting to obtain the hazard degree of the leakage point. For LP240, the weights of the three characteristics are set to be 0.4, 0.25, and 0.35 respectively, and the hazard degree is calculated to be 0.78x0.4+0.65x0.25+0.82x0.35=0.765.

[0138] The hazard degree distribution of the leakage points in the statistical engineering area provides a basis for risk level division. For this tunnel, the hazard degrees of 127 leakage points were evaluated, with the highest value being 0.92, the lowest value being 0.21, the average value being 0.56, and the standard deviation being 0.18. According to the statistical distribution of the hazard degree, the risk level division threshold was determined. The leakage point risk was divided into four levels: extremely high risk (level I), high risk (level II), medium risk (level III), and low risk (level IV). The initial threshold was set as follows: hazard degree greater than 0.80 for level I, 0.60 to 0.80 for level II, 0.40 to 0.60 for level III, and less than 0.40 for level IV. According to the safety requirements of the construction and operation stages, the risk level division threshold was dynamically adjusted. The construction stage pays more attention to the impact of leakage on construction safety and progress, while the operation stage pays more attention to the impact of leakage on the long-term stability of the structure and the safety of operation. For the construction stage of this tunnel, considering that frequent blasting activities during construction may exacerbate the risk of leakage, the threshold values were adjusted to 0.75, 0.55, and 0.35, respectively, i.e., the judgment criteria for reducing the risk level were more conservative. Based on the hazard degree of the leakage points and the adjusted risk level division threshold, the risk of the leakage points was classified. The hazard degree of LP240 is 0.765, and according to the adjusted threshold of the construction stage, it is determined to be level I risk (extremely high risk) and needs to be handled first. Among the 127 leakage points of the entire tunnel, there are 18 level I risk points, accounting for 14.2%; 35 level II risk points, accounting for 27.6%; 48 level III risk points, accounting for 37.8%; and 26 level IV risk points, accounting for 20.4%. The risk classification data, including the leakage point number, spatial location, hazard degree, risk level, and treatment suggestions, were generated.

[0139] In this embodiment, by fusing the leakage channel geometric features, geological structure information, and leakage dynamic data, the accurate influence range identification and risk level assessment of the tunnel leakage points are realized. In the prior art, the leakage point hazard assessment relies on experience or static indicators, ignoring the spatial evolution characteristics of the leakage channel and the modulation effect of the geological medium, leading to lagging risk identification or misjudgment. The three-dimensional spline interpolation is combined with the geological structure surface to accurately depict the spatial trend and geometric properties of the leakage channel, further construct an ellipsoidal influence domain considering the leakage propagation trend, and make morphological corrections based on lithology and fracture distribution, making the influence range closer to the actual flow path. By calculating the leakage amount characteristics, the spatial overlap degree between influence domains, and the extension capacity of the channel, the influence degree that the leakage point may cause is comprehensively represented, and a dynamic threshold mechanism is introduced to adjust the risk level boundary according to the safety requirements of the construction or operation stage. This method breaks through the traditional static assessment mode, realizes multi-factor driven and time-space coupled risk classification, and provides scientific and flexible technical support for the dynamic monitoring and hierarchical treatment of mountain tunnel leakage.

[0140] In an alternative embodiment, the repair construction sequence is determined according to the risk classification data, and the repair process is selected according to the leakage amount and the location of the leakage channel to form a repair treatment scheme, which comprises:

[0141] The risk classification data and the spatial distribution data of the leakage channel are obtained, the leakage amount ratio between adjacent leakage points in the leakage channel is calculated, and the correlation degree between the leakage points is determined according to the leakage amount ratio; the leakage points with a correlation degree greater than a preset correlation threshold are divided into a leakage point group, the repair sequence in the leakage point group is determined based on the risk classification data, and the repair sequence between the leakage point groups is determined according to the spatial position from upstream to downstream;

[0142] The time series data of the water outlet pressure and the water outlet amount of the leakage point are collected, and a water outlet pressure-water outlet amount change curve is established; according to the fluctuation period and amplitude change law of the water outlet pressure-water outlet amount change curve, a corresponding relationship library of grouting parameters and grouting effect is established, and the optimal grouting parameter combination is selected from the corresponding relationship library;

[0143] The three-dimensional morphological characteristics of the leakage channel are obtained, the extension direction and bifurcation position of the leakage channel are extracted, the diffusion range of the grout under different grouting point arrangement schemes is simulated, and the spatial arrangement of the grouting hole is optimized based on the diffusion range;

[0144] The grouting pressure and grout diffusion radius during the grouting process are collected in real time, and a grouting pressure field distribution function is constructed; when the water outlet state of the downstream leakage point changes, the change characteristics of the stress field of the leakage channel are calculated according to the pressure field distribution function, and the evolution trend of the leakage water flow field is predicted; the grouting scheme of the downstream leakage point is dynamically optimized based on the evolution trend;

[0145] According to the repair sequence, the grouting parameter combination, the spatial arrangement of the grouting hole, and the optimized grouting scheme, a repair treatment scheme is formed.

[0146] First, the risk classification data and the spatial distribution data of the leakage channel are obtained, for example, for a high-speed rail mountain tunnel K5+100 to K5+650 section, 5 main leakage channels have been identified, numbered T4 to T8, involving a total of 58 leakage points, including 8 extremely high-risk (Class I) leakage points, 17 high-risk (Class II) leakage points, 22 medium-risk (Class III) leakage points, and 11 low-risk (Class IV) leakage points. Channel T6 is composed of 7 leakage points, namely LP301 (1st layer), LP308 (2nd layer), LP315 (3rd layer), LP322 (4th layer), LP329 (5th layer), LP336 (6th layer), and LP343 (inside the tunnel), among which LP301, LP315, and LP336 are Class I risk points, LP308 and LP329 are Class II risk points, and LP322 and LP343 are Class III risk points.

[0147] The ratio of the leakage amount between adjacent leakage points in the leakage channel is calculated to analyze the correlation between the leakage points. The ratio of the leakage amount reflects the change characteristics of the water flow in the leakage channel and can be used to determine the strength of the hydraulic connection between the leakage points. The calculation formula is the leakage amount of the upstream leakage point divided by the leakage amount of the downstream leakage point. In channel T6, the leakage amount of LP301 is 85.3 liters / hour, the leakage amount of LP308 is 92.6 liters / hour, and the ratio of the leakage amount of the two is 0.92; the ratio of the leakage amount of LP308 and LP315 is 0.68; the ratio of the leakage amount of LP315 and LP322 is 1.24; the ratio of the leakage amount of LP322 and LP329 is 0.85; the ratio of the leakage amount of LP329 and LP336 is 0.72; and the ratio of the leakage amount of LP336 and LP343 is 1.58. A ratio close to 1 indicates that the water flow remains relatively stable in the channel, a ratio significantly greater than 1 indicates that there is a decrease in water quantity downstream (possibly due to diversion or infiltration), and a ratio significantly less than 1 indicates that there is an increase in water quantity downstream (possibly due to confluence). The correlation degree between the leakage points is determined according to the ratio of the leakage amount, and the higher the correlation degree, the tighter the hydraulic connection between the leakage points. The calculation reference value of the correlation degree is set to 1, and the higher the proximity of the ratio of the leakage amount to 1, the greater the correlation degree. For channel T6, the correlation degree between LP301 and LP308 is 0.93, the correlation degree between LP308 and LP315 is 0.78, the correlation degree between LP315 and LP322 is 0.84, the correlation degree between LP322 and LP329 is 0.91, the correlation degree between LP329 and LP336 is 0.82, and the correlation degree between LP336 and LP343 is 0.65.

[0148] The leakage points with a correlation degree greater than a preset correlation threshold are divided into leakage point groups to form treatment units with close hydraulic connection. The correlation threshold is set to 0.80, and when the correlation degree of adjacent leakage points is greater than the threshold, they are divided into the same leakage point group. For the channel T6, three leakage point groups are formed: group G1 includes LP301 and LP308, group G2 includes LP315, LP322 and LP329, and group G3 includes LP336 and LP343. The repair order in the leakage point group is determined based on the risk classification data, and the leakage points with high risk levels are preferentially processed. For group G1, LP301 is a risk level I point, and LP308 is a risk level II point, so the repair order is LP301 first and then LP308; for group G2, LP315 is a risk level I point, LP329 is a risk level II point, and LP322 is a risk level III point, so the repair order is LP315 first, then LP329, and finally LP322; for group G3, LP336 is a risk level I point, and LP343 is a risk level III point, so the repair order is LP336 first and then LP343. The repair order between the leakage point groups is determined according to the spatial position from upstream to downstream to cut off the source of leakage water and prevent the leakage from intensifying downstream. The three leakage point groups of the channel T6 are G1, G2 and G3 from upstream to downstream, so the repair order is G1 first, then G2, and finally G3. Considering the repair order within and between the groups, the final leakage point repair order of the channel T6 is LP301→LP308→LP315→LP329→LP322→LP336→LP343.

[0149] The time series data of the water outlet pressure and the water outlet quantity of the leakage point are collected, and the leakage characteristics are analyzed to provide a basis for selecting the grouting parameters. The water outlet pressure is measured by a micro pressure sensor, and the unit is MPa; the water outlet quantity is measured by a flowmeter, and the unit is liter / hour. Taking LP301 as an example, the monitoring data for 72 consecutive hours shows that the water outlet pressure ranges from 0.15 to 0.28 MPa, and the average value is 0.21 MPa; the water outlet quantity ranges from 75.6 to 95.2 liters / hour, and the average value is 85.3 liters / hour. The water outlet pressure-water outlet quantity change curve is established to analyze the correlation and change law of the two. The water outlet pressure is taken as the abscissa, and the water outlet quantity is taken as the ordinate to draw a scatter plot and fit a trend line. For LP301, the fitted curve shows a positive correlation, and the slope is 125.4 liters / (hour·MPa), indicating that the water outlet quantity increases by about 1.254 liters / hour for every 0.01 MPa increase in the water outlet pressure. Further analysis of the time-varying characteristics of the water outlet pressure and the water outlet quantity shows that they have obvious periodic changes, and the main period is about 24 hours, which is consistent with the daily change law of rainfall and groundwater level in mountainous areas. The change amplitudes of the water outlet pressure and the water outlet quantity also show a significant positive correlation, and the correlation coefficient is 0.87.

[0150] According to the fluctuation period and amplitude variation law of the water outlet pressure-water outlet quantity curve, the corresponding relationship library of grouting parameters and grouting effect is established. The grouting parameters include grouting pressure, grouting flow, grouting time, slurry ratio and grouting times, etc.; the grouting effect includes leakage reduction rate, grouting radius and water stop stability, etc. Through a large number of tests and engineering practice data, the parameter-effect corresponding relationship matrix is constructed. Taking LP301 as an example, when the average value of water outlet pressure is 0.21 MPa, the change amplitude is ±0.065 MPa, the average value of water outlet quantity is 85.3 liters / hour, and the change amplitude is ±9.8 liters / hour, the corresponding optimal grouting parameters are: grouting pressure 2.8 MPa, grouting flow 75 liters / hour, single grouting time 45 minutes, slurry water-cement ratio 0.7:1, water glass addition ratio 8%, and grouting times 3. The corresponding expected grouting effect is: leakage reduction rate more than 95%, grouting radius reaches 3.5 meters, and water stop stability period can reach more than 5 years. From the corresponding relationship library, the optimal grouting parameter combination is selected, which requires that the leakage reduction rate is not less than 90% and the material consumption is the least. For LP301, the selected grouting parameter combination is: grouting pressure 2.8 MPa, grouting flow 75 liters / hour, single grouting time 45 minutes, slurry water-cement ratio 0.7:1, water glass addition ratio 8%, and grouting times 3, and the total slurry consumption is about 10.1 cubic meters.

[0151] The three-dimensional morphological characteristics of the leakage channel are obtained to guide the spatial arrangement of the grouting hole. The three-dimensional morphological characteristics include the trend, dip angle, width, curvature, and bifurcation of the channel. The main trend of channel T6 is 62 degrees north by east, which is basically parallel to a main fault F3 in the region, with a dip angle of 68 degrees and an average width of 2.5 meters. There is a clear bend between LP322 and LP329, with a radius of curvature of about 65 meters. There is a branch channel that bifurcates eastward at LP315. The extension direction and bifurcation position of the leakage channel are extracted to determine the optimal arrangement of the grouting point. The extension direction determines the dip angle and azimuth angle of the grouting hole, and the bifurcation position requires the addition of grouting holes to cover the branch channel. For LP301, considering the channel trend at its location, which is 62 degrees north by east, with a dip angle of 68 degrees, the azimuth angle of the grouting hole is designed to be 242 degrees north by east (opposite to the channel trend), and the dip angle is 68 degrees (consistent with the channel dip angle). The diffusion range of the grout under different grouting point arrangement schemes is simulated, and the diffusion shape and range of the grout in the rock mass are predicted through numerical simulation technology. The simulation considers factors such as rock permeability, fracture distribution, groundwater flow direction, and grouting pressure. For LP301, three grouting point arrangement schemes are simulated: scheme A is single-hole grouting, with the grouting hole located 3 meters directly above the leakage point; scheme B is two-hole symmetric grouting, with the grouting holes located 2 meters on both sides of the leakage point; scheme C is three-hole ring grouting, with the grouting holes arranged in an equilateral triangle, with the leakage point as the center and a radius of 2.5 meters. The simulation results show that the grout diffusion range of scheme C is the most uniform, covering the leakage channel most completely, with a diffusion radius of 4.2 meters, which can effectively cut off the leakage channel. Based on the diffusion range, the spatial arrangement of the grouting hole is optimized, and scheme C is selected as the grouting hole arrangement scheme for LP301.

[0152] Real-time acquisition of grouting pressure and slurry diffusion radius during grouting process, monitoring of grouting effect, and dynamic adjustment. Grouting pressure is monitored in real time by pressure sensor, and slurry diffusion radius is indirectly obtained by resistivity measurement or acoustic wave measurement. In the grouting process of LP301, the grouting pressure is recorded from the initial 2.0 MPa to gradually rise to 2.8 MPa, and then slowly decrease to 2.5 MPa; the slurry diffusion radius gradually expands from the initial 0.5 meters to 3.5 meters, and the diffusion rate is the fastest in the first 20 minutes, reaching 0.1 meters per minute, and then gradually slows down. The grouting pressure field distribution function is constructed to describe the spatial distribution law of pressure in the grouting area. The pressure field distribution function considers the position of the grouting point, the grouting pressure, the rock mass characteristics and the time factor, which can be used to predict the pressure value at any position. For the grouting condition of LP301, a three-dimensional pressure field model with a radius of 5 meters is established with the grouting point as the center, and the pressure decreases exponentially with the distance, with a pressure of 2.8 MPa at the grouting point and a pressure of 0.3 MPa at a distance of 3.5 meters from the grouting point. When the water outflow state of the downstream leakage point changes, it indicates that the grouting effect has been transmitted to the downstream, and the grouting effect needs to be evaluated and the subsequent plan needs to be adjusted. 35 minutes after the grouting of LP301 is completed, it is observed that the water outflow of LP308 downstream is reduced by 15%, and the water outflow pressure is reduced by 12%, indicating that the grouting effect of LP301 has partially affected LP308.

[0153] According to the pressure field distribution function, the stress field variation characteristics of the leakage channel are calculated to evaluate the influence of grouting on the stress state of the rock mass. The stress field variation characteristics include the change of the principal stress direction, the movement of the stress concentration area and the change of the stress amplitude, etc. After the grouting of LP301, the stress field in the leakage channel has changed significantly, the principal stress direction has changed from the original north-east 32 degrees to north-east 48 degrees, the stress concentration area has moved from LP301 to between LP308 and LP315, and the maximum stress has increased by about 25%. The evolution trend of the leakage flow field is predicted, and the change law of the leakage flow direction and flow after grouting is analyzed. According to the changes of pressure field and stress field, combined with the geometric characteristics of the leakage channel, it is predicted that the leakage water will flow downstream along the newly formed fracture in the stress concentration area, and the leakage amount of LP308 may first decrease in the short term, and then rebound, and the leakage amount of LP315 may increase by about 20%. Based on the evolution trend, the grouting scheme of the downstream leakage point is dynamically optimized to improve the treatment efficiency. For LP308, considering that its upstream LP301 has completed grouting, and the leakage flow field prediction shows that the leakage amount may rebound, the original planned grouting pressure is adjusted from 2.5 MPa to 2.7 MPa, and the number of grouting holes is increased from 2 to 3, forming a more dense grouting barrier.

[0154] According to the repair sequence, the grouting parameter combination, the spatial arrangement of the grouting holes and the optimized grouting scheme, a complete repair treatment scheme is formed. The repair treatment scheme includes construction process, material configuration, quality control and effect evaluation and the like. For the repair treatment scheme of the channel T6, it is clearly stipulated that the processing is carried out in the order of LP301→LP308→LP315→LP329→LP322→LP336→LP343; the specific grouting parameters of each leakage point are as follows: for LP301, the grouting pressure is 2.8 MPa, the grouting flow is 75 liters / hour, and the three-hole annular arrangement is adopted; the grouting material is micro-fine cement-silicate composite slurry, the water-cement ratio is 0.7:1, and the addition ratio of water glass is 8%; during the grouting process, the pressure and flow are monitored in real time, and when the grouting pressure suddenly changes or the flow suddenly decreases, the grouting is paused for inspection; the leakage amount is detected every 4 hours within 72 hours after the grouting is completed, and the leakage reduction rate should be not less than 90%. Through the scientific repair treatment scheme, the accurate positioning and efficient treatment of the leakage water in the mountain tunnel are realized, the engineering quality and safety are effectively improved, and the construction cost and environmental risk are reduced.

[0155] By constructing the leakage source positioning data, the leakage point correlation graph and the hierarchical leakage propagation graph, the accurate identification of the leakage water path is realized. The method can accurately calculate the damage degree of the leakage point and perform risk grading, thereby providing a scientific basis for differential treatment. Through the spatial correlation degree analysis and the extraction of the dynamic change characteristics of the leakage amount, the accurate positioning of the leakage source position is realized, which not only improves the accuracy and effectiveness of the anti-seepage treatment of the mountain tunnel, but also significantly reduces the engineering cost.

[0156] In a second aspect, the embodiment of the present application provides a mountain tunnel leakage water intelligent positioning and processing system, which comprises:

[0157] A first unit is configured to collect position information, leakage amount information and geological information of a tunnel leakage point, and pre-process the information to obtain a feature data set of the leakage point. According to the feature data set, the spatial correspondence between the leakage point and the geological structure is calculated. The spatial correspondence is combined with the distribution law of the leakage point to determine the coordinates of the leakage source position, and leakage source positioning data is generated.

[0158] A second unit is configured to calculate the spatial distance and the leakage amount change ratio between adjacent leakage points as connection weights based on the leakage source positioning data, and construct a leakage point correlation graph.

[0159] A third unit is configured to extract the topological distribution characteristics of the leakage point correlation graph. According to the topological distribution characteristics, the leakage points are divided into multiple levels according to the mountain elevation and the leakage amount. The connectivity strength between adjacent levels of leakage points is calculated, the leakage water propagation direction is determined, and a hierarchical leakage propagation graph is constructed.

[0160] The fourth unit is configured to determine the spatial distribution of the leakage channel according to the hierarchical leakage propagation graph, draw the leakage channel trend, calculate the influence range and damage degree of each leakage point by using the leakage channel trend and combining the leakage amount data, perform risk classification on the leakage points, and generate risk classification data;

[0161] The fifth unit is configured to determine the repair construction sequence according to the risk classification data, select a repair process according to the leakage amount and the channel position, and form a repair treatment scheme.

[0162] In a third aspect, the present application provides an electronic device, comprising:

[0163] a processor;

[0164] a memory for storing processor-executable instructions;

[0165] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0166] In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the method described above.

[0167] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer-readable storage medium having stored thereon computer-readable program instructions that, when executed by a processor, implement various aspects of the present application.

[0168] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent positioning and processing of leakage water in a mountain tunnel, characterized in that, The method comprises the following steps: Collecting position information, leakage amount information and geological information of tunnel leakage points and preprocessing to obtain a feature data set of the leakage points, calculating the spatial correspondence between the leakage points and the geological structure according to the feature data set, combining the spatial correspondence with the distribution law of the leakage points to determine the position coordinates of the leakage source and generate leakage source positioning data; Based on the leakage source positioning data, the spatial distance and the leakage amount change ratio between adjacent leakage points are calculated as connection weights to construct a leakage point correlation graph; Extracting the topological distribution features of the leakage point correlation graph, dividing the leakage points into multiple levels according to the topological distribution features and the elevation and leakage amount of the mountain, calculating the connection strength between adjacent level leakage points, determining the leakage water propagation direction, and constructing a layered leakage propagation graph; According to the layered leakage propagation graph, the spatial distribution of the leakage channel is determined, the leakage channel strike is drawn, and the influence range and damage degree of each leakage point are calculated by using the leakage channel strike and combining the leakage amount data, the leakage points are risk classified to generate risk classification data; According to the risk classification data, the repair construction sequence is determined, and the repair process is selected according to the leakage amount and the channel position to form a repair treatment scheme; According to the feature data set, the spatial correspondence between the leakage points and the geological structure is calculated, the spatial correspondence is combined with the distribution law of the leakage points, the position coordinates of the leakage source are determined, and the leakage source positioning data is generated, which comprises: Using the feature data set, the geological structure surface is divided into a master fault, a secondary fault and a joint fissure, the spatial positioning parameters of the master fault, the secondary fault and the joint fissure are calculated, and geological structure quantitative data is generated; According to the feature data set and the geological structure quantitative data, the spatial correlation degree between the leakage points and the master fault, the secondary fault and the joint fissure is calculated, and the spatial correlation degree is combined with the leakage amount information in the feature data set to obtain the spatial distribution law of the leakage points; According to the spatial distribution law, the leakage points are grouped according to the leakage amount and the spatial position, the spatial center coordinates and the leakage intensity of each group of leakage points are calculated, and the spatial distribution features of the leakage point group are determined; According to the distribution features of the leakage point group, the leakage reference surface is determined by using the steady-state leakage component, the leakage diffusion direction is calculated by using the dynamic leakage component, the leakage reference surface and the leakage diffusion direction are superimposed to construct a spatial weight field, the spatial weight field is used to establish a leakage source candidate area, the intersection position of the leakage source candidate area and the master fault, the secondary fault and the joint fissure is calculated, and the spatial correlation degree between the leakage source candidate area and the leakage point group is calculated, the position coordinates of the leakage source are determined, and the leakage source positioning data is generated; Based on the leakage source positioning data, the spatial distance and the leakage amount change ratio between adjacent leakage points are calculated as connection weights to construct a leakage point correlation graph, which comprises: Obtaining the leakage source positioning data, extracting the spatial coordinate information, the leakage amount time sequence information and the geological environment information of the leakage points in the leakage source positioning data; According to the spatial coordinate information, the position components of adjacent leakage points in the axial direction, the ring direction and the elevation direction of the tunnel are calculated, the position components are weighted and corrected in combination with the lithology and fault distribution in the geological environment information to obtain the corrected spatial distance between adjacent leakage points; The time series information of the leakage amount is standardized to eliminate seasonal fluctuations and environmental noise, and standardized leakage amount data is obtained; the standardized leakage amount data is analyzed by using a sliding time window to calculate the dynamic change characteristics of the leakage amount of adjacent leakage points; the dynamic change characteristics of the leakage amount are corrected in combination with the hydrological conditions in the geological environmental information to obtain corrected leakage amount changes; The ratio of the corrected spatial distance and the corrected leakage amount change is calculated, and the ratio is dynamically adjusted in combination with the geological environmental information to generate connection weights between adjacent leakage points; According to the connection weights, the connection relationship between adjacent leakage points is determined, the leakage points are set as vertices of a graph, and the connection relationship is set as edges of the graph, so as to construct a leakage point correlation graph.

2. The method of claim 1, wherein, The position information, leakage amount information and geological information of the tunnel leakage points are collected and preprocessed to obtain a feature data set of the leakage points, including: The three-dimensional spatial coordinates of the leakage points are obtained by total station measurement as the position information, the real-time flow data and pressure data of the leakage points are collected by flow meters and water pressure gauges as the leakage amount information, and the surrounding rock structure data is collected by drilling coring, geological radar scanning and acoustic detection as the geological information; The position information is converted from the total station coordinate system to the tunnel coordinate system, the position information in the tunnel coordinate system is spatially registered with the tunnel axis to generate position features of the leakage points; high-frequency noise in the leakage amount information is eliminated by using a digital filtering algorithm, and the filtered leakage amount information is time series decomposed to obtain steady-state components and fluctuation components to generate water quantity features of the leakage points; fault information and joint information in the geological information are extracted for structure analysis, and the analyzed structure information is reconstructed in three-dimensional space to generate geological features of the leakage points; The position features, water quantity features and geological features are spatio-temporally mapped and data fused to generate a feature data set of the leakage points.

3. The method of claim 1, wherein, The topological distribution features of the leakage point correlation graph are extracted, the leakage points are divided into multiple levels according to the mountain elevation and the leakage amount size according to the topological distribution features, the connectivity strength between adjacent level leakage points is calculated, the leakage water propagation direction is determined, and a layered leakage propagation graph is constructed, including: The number of connections and the number of shortest paths of the leakage points in the leakage point correlation graph are calculated to obtain centrality data, the connection proportion of the leakage points and adjacent leakage points is calculated to obtain clustering data, and the topological distribution features of the leakage points are obtained according to the centrality data and the clustering data; The mountain elevation data and the leakage amount data of the leakage points are obtained, the leakage points are initially layered according to the mountain elevation data, the leakage amount distribution features of the leakage points in each layer are calculated, the level boundaries are adjusted according to the leakage amount distribution features, and the level division of the leakage points is obtained in combination with the topological distribution features; According to the level division, the leakage points of adjacent levels are determined, the spatial distance decay value, the leakage amount correlation coefficient, the elevation difference and the medium permeability coefficient between adjacent level leakage points are calculated to obtain the connectivity strength between the adjacent level leakage points; According to the connectivity strength, the main propagation path between adjacent level leakage points is determined, and the propagation direction of the main propagation path is obtained based on the time series change of the leakage amount data and the level division. The leakage points are arranged according to the hierarchical distribution, the connectivity strength is taken as the connection relationship between the hierarchies, and the propagation direction is taken as the propagation trend between the hierarchies, so as to construct a hierarchical leakage propagation graph.

4. The method of claim 1, wherein, According to the hierarchical leakage propagation graph, the spatial distribution of the leakage channel is determined, the leakage channel trend is drawn, the influence range and the damage degree of each leakage point are calculated by using the leakage channel trend and combining the leakage amount data, the risk classification of the leakage point is performed, and risk classification data are generated, including: The hierarchical leakage propagation graph and the geological structure surface data are acquired, the spatial distribution of the leakage channel is determined by extracting the leakage point sequence continuously connected between the hierarchies from the hierarchical leakage propagation graph, the trend angle, the inclination and the curvature of the leakage channel are calculated, and the leakage channel trend is drawn by combining the geological structure surface data and using three-dimensional spline interpolation; According to the leakage amount data, the radius of the spherical influence domain is determined, the spherical influence domain is extended and compressed to obtain an ellipsoidal influence domain based on the leakage channel trend, the lithology and the fracture distribution data of the geological medium are acquired, the ellipsoidal influence domain is adjusted according to the lithology and the fracture distribution data, and the influence range of the leakage point is obtained; The absolute value and the change trend of the leakage amount are calculated to obtain the leakage amount feature, the overlap degree between the influence ranges is calculated to obtain the spatial coupling feature, and the extension length of the leakage channel trend is calculated to obtain the channel extension feature; the leakage amount feature, the spatial coupling feature and the channel extension feature are combined by weighting to obtain the damage degree of the leakage point; The damage degree distribution of the leakage point in the engineering area is counted, and the risk level division threshold is determined; the risk level division threshold is dynamically adjusted according to the safety requirements of the construction stage and the operation stage, and the risk classification of the leakage point is performed based on the damage degree and the adjusted risk level division threshold, and risk classification data are generated.

5. The method of claim 1, wherein, According to the risk classification data, the repair construction sequence is determined, the repair process is selected according to the leakage amount and the channel position, and a repair treatment scheme is formed, including: The risk classification data and the spatial distribution data of the leakage channel are acquired, the leakage amount ratio between adjacent leakage points in the leakage channel is calculated, and the correlation degree between the leakage points is determined according to the leakage amount ratio; the leakage points with a correlation degree greater than a preset correlation threshold are divided into a leakage point group, the repair sequence in the leakage point group is determined based on the risk classification data, and the repair sequence between the leakage point groups is determined according to the spatial position from upstream to downstream; The time series data of the water outlet pressure and the water outlet amount of the leakage point are collected, and a water outlet pressure-water outlet amount change curve is established; according to the fluctuation period and the amplitude change law of the water outlet pressure-water outlet amount change curve, a corresponding relationship library of grouting parameters and grouting effects is established, and the optimal grouting parameter combination is selected from the corresponding relationship library; The three-dimensional morphological features of the leakage channel are acquired, the extension direction and the bifurcation position of the leakage channel are extracted, the diffusion range of the grout under different grouting point arrangement schemes is simulated, and the spatial arrangement of the grouting hole is optimized based on the diffusion range. Real-time acquisition of grouting pressure and slurry diffusion radius in the grouting process, construction of grouting pressure field distribution function; when the water outlet state of the downstream leakage point changes, the change characteristics of the stress field of the leakage channel are calculated according to the pressure field distribution function, and the evolution trend of the leakage flow field is predicted; based on the evolution trend, the grouting scheme of the downstream leakage point is dynamically optimized; according to the repair sequence, the grouting parameter combination, the spatial arrangement of the grouting hole and the optimized grouting scheme, a repair treatment scheme is formed.

6. A mountainous tunnel water leakage intelligent positioning and processing system for implementing the method of any one of the preceding claims 1-5, characterized in that, Comprise: The first unit is used for collecting and preprocessing the position information, leakage amount information and geological information of the tunnel leakage point to obtain the feature data set of the leakage point, calculating the spatial correspondence between the leakage point and the geological structure according to the feature data set, combining the spatial correspondence with the leakage point distribution law to determine the leakage source position coordinates, and generating the leakage source positioning data; The second unit is used for calculating the spatial distance and leakage amount change ratio between adjacent leakage points as connection weight based on the leakage source positioning data, and constructing a leakage point correlation graph; The third unit is used for extracting the topological distribution characteristics of the leakage point correlation graph, dividing the leakage points into multiple levels according to the topological distribution characteristics, calculating the connectivity strength between adjacent levels of leakage points, determining the leakage water propagation direction, and constructing a layered leakage propagation graph; The fourth unit is used for determining the spatial distribution of the leakage channel according to the layered leakage propagation graph, drawing the strike of the leakage channel, calculating the influence range and damage degree of each leakage point by using the strike of the leakage channel combined with the leakage amount data, classifying the risk of the leakage point, and generating risk classification data; The fifth unit is used for determining the repair construction sequence according to the risk classification data, and selecting the repair technology according to the leakage amount and the channel position to form a repair treatment scheme.

7. An electronic device, comprising: Comprise: A processor; A memory for storing processor-executable instructions; Wherein, the processor is configured to call the instructions stored in the memory to execute the method of any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions are executed by the processor to implement the method of any one of claims 1 to 5. The computer program instructions are executed by the processor to implement the method of any one of claims 1 to 5.

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