Surrounding rock water abundance risk analysis method, device, equipment, medium and product

Abstract

The present application relates to the technical field of tunnel engineering, and discloses a surrounding rock water abundance risk analysis method, device, equipment, medium and product.The surrounding rock water abundance risk analysis method comprises the following steps: querying a water abundance index adaptation table according to the resistivity of a plurality of surrounding rock samples of a tunnel, to obtain a plurality of first water abundance indexes corresponding to the surrounding rock samples; constructing a water abundance database of the plurality of surrounding rock samples according to the first water abundance indexes corresponding to the plurality of surrounding rock samples; obtaining a surrounding rock point cloud model of the tunnel, predicting second water abundance indexes of a plurality of target surrounding rocks in the surrounding rock point cloud model based on the water abundance database, and obtaining a target point cloud model; and performing risk analysis on target point cloud data in the target point cloud model according to the second water abundance indexes of the plurality of target surrounding rocks, to obtain a risk level corresponding to the target point cloud data.The present application predicts the water abundance indexes of the plurality of target surrounding rocks, to determine the water abundance risks of the plurality of target surrounding rocks, and improves the accuracy of the water abundance risk analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering, and in particular to an analysis method, device, equipment, medium and product for the water-rich risk of surrounding rocks. Background Art

[0002] During tunnel construction, the water content of the surrounding rock (rock) at the construction site is a key factor affecting project safety and efficiency. This refers to the abundance of water in the surrounding rock, primarily describing the distribution of groundwater within the surrounding rock. Particularly in karst geological conditions, water and mud inrush hazards have become one of the most serious hazards associated with tunnel construction, causing not only significant economic losses but also serious casualties.

[0003] In order to prevent disasters caused by the high water content of the surrounding rock, it is necessary to predict the water content of the surrounding rock. Advanced geological prediction methods in related technologies include drilling, geological radar and transient electromagnetic method. Although these prediction methods can obtain water content information to a certain extent, their accuracy is low and it is difficult to meet the construction requirements under complex geological conditions. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, equipment, medium and product for analyzing the water-richness risk of surrounding rocks to solve the problem of low accuracy of water-richness information obtained through traditional forecasting means.

[0005] In a first aspect, the present invention provides a method for analyzing the water-richness risk of surrounding rock, comprising: querying a water-richness index adaptation table according to the resistivity corresponding to multiple surrounding rock samples of a tunnel, and obtaining first water-richness indices corresponding to multiple surrounding rock samples; wherein the water-richness index adaptation table includes a correspondence between multiple resistivity ranges and multiple first water-richness indices; constructing a water-richness database of multiple surrounding rock samples according to the first water-richness indices corresponding to multiple surrounding rock samples; wherein the water-richness database is used to store the three-dimensional spatial coordinates of multiple surrounding rock samples and the first water-richness indices corresponding to multiple surrounding rock samples; obtaining a surrounding rock point cloud model of the tunnel, and predicting the second water-richness indices of multiple target surrounding rocks in the surrounding rock point cloud model based on the water-richness database, and generating a target point cloud model containing the second water-richness indices; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section; performing risk analysis on the target point cloud data in the target point cloud model according to the second water-richness indices of multiple target surrounding rocks, and obtaining the risk level corresponding to the target point cloud data.

[0006] The present invention queries the first water-richness index corresponding to multiple surrounding rock samples through the water-richness index adaptation table, and constructs a water-richness database of multiple surrounding rock samples based on the first water-richness index corresponding to the multiple surrounding rock samples. The present invention stores the first water-richness index corresponding to the multiple surrounding rock samples and their three-dimensional spatial coordinates in the water-richness database, providing a comprehensive and orderly data basis for subsequent prediction and analysis. The present invention uses the constructed water-richness database to predict the second water-richness index of multiple target surrounding rocks to be predicted for water-richness in addition to the surrounding rock samples, which can more accurately reflect the water-richness of the target surrounding rocks and provide a more reliable basis for engineering decision-making. The present invention performs risk analysis on the target point cloud data in the target point cloud model based on the second water-richness index of multiple target surrounding rocks, obtains the risk level corresponding to the target point cloud data, and converts complex geological information into intuitive risk levels, so that engineering personnel can clearly understand the degree of water-richness risk in different parts of the tunnel, thereby taking corresponding prevention and response measures in a targeted manner to reduce engineering risks and costs. Compared with related technologies, the present invention predicts the water-richness index of multiple target surrounding rocks to determine the water-richness risk of multiple target surrounding rocks, improves the accuracy of water-richness risk analysis, is more suitable for construction requirements under complex geological conditions, and improves the safety of tunnel engineering construction.

[0007] In an optional embodiment, a water-richness index adaptation table is queried according to the resistivity corresponding to multiple surrounding rock samples of the tunnel to obtain a first water-richness index corresponding to the multiple surrounding rock samples, including: querying the water-richness index adaptation table according to the resistivity corresponding to multiple surrounding rock samples of the tunnel to obtain a target resistivity range within which the resistivity corresponding to each surrounding rock sample lies; and determining the first water-richness index corresponding to the target resistivity range.

[0008] In an optional embodiment, a water-richness database of multiple surrounding rock samples is constructed based on the first water-richness index corresponding to multiple surrounding rock samples, including: obtaining the three-dimensional spatial coordinates of the multiple surrounding rock samples, associating the first water-richness index of each surrounding rock sample with the three-dimensional spatial coordinates to obtain an association result; and storing the association result to obtain a water-richness database.

[0009] By acquiring the three-dimensional spatial coordinates of multiple surrounding rock samples and correlating them with a first water-richness index, this method organically integrates the water-richness index with spatial location information. By correlating the water-richness index with the three-dimensional spatial coordinates, the spatial distribution of the water-richness of each surrounding rock sample can be precisely determined. This facilitates a more detailed understanding of the spatial variation of water-richness in tunnel surrounding rock, providing an accurate basis for targeted treatment of different locations within the project.

[0010] In an optional implementation, based on the water enrichment database, the second water enrichment index of the plurality of target surrounding rocks in the surrounding rock point cloud model is predicted, including: calculating the Euclidean distance between the plurality of first sample points of each surrounding rock sample and the plurality of second sample points of each target surrounding rock; selecting the plurality of target first sample points closest to each second sample point as the plurality of neighbor points according to the Euclidean distance; and predicting the second water enrichment index of the plurality of second sample points of the target surrounding rock according to the weighted average of the first water enrichment index of the plurality of neighbor points of each second sample point.

[0011] In an optional implementation, the target point cloud data in the target point cloud model is subjected to risk analysis according to the second water enrichment index of the plurality of target surrounding rocks, to obtain a risk level corresponding to the target point cloud data, including: determining the characteristic parameters of a fuzzy cloud model according to the second water enrichment index of the plurality of target surrounding rocks; wherein the fuzzy cloud model is a model for uncertainty analysis; simulating the cloud droplet generation process according to the characteristic parameters, and counting the number of cloud droplets to obtain the number of cloud droplets corresponding to a plurality of preset risk levels; generating a risk probability corresponding to each preset risk level according to the number of cloud droplets corresponding to the plurality of preset risk levels; and determining the preset risk level with the highest risk probability as the risk level corresponding to the target point cloud data.

[0012] The present application determines the characteristic parameters of the fuzzy cloud model according to the second water enrichment index of the plurality of target surrounding rocks, uses the fuzzy cloud model for uncertainty analysis, fully considers the complexity and uncertainty factors of the geological environment, and can more truly reflect the actual situation of the surrounding rock water enrichment risk, thereby improving the accuracy of subsequent water enrichment risk analysis.

[0013] In an optional implementation, the risk probability corresponding to each preset risk level is generated according to the number of cloud droplets corresponding to the plurality of preset risk levels, including: determining the quotient of the number of cloud droplets corresponding to each preset risk level and the total amount of cloud droplets as the risk probability corresponding to each preset risk level.

[0014] In the second aspect, the present invention provides an analysis device for the water-richness risk of surrounding rock, comprising: a first water-richness index determination module, for querying a water-richness index adaptation table according to the resistivity corresponding to a plurality of surrounding rock samples of the tunnel, and obtaining first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness index adaptation table includes a correspondence between a plurality of resistivity ranges and a plurality of first water-richness indices; a water-richness database construction module, for constructing a water-richness database of a plurality of surrounding rock samples according to the first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness database is used to store a plurality of surrounding rock samples; The three-dimensional spatial coordinates of the sample and the first water-richness index corresponding to multiple surrounding rock samples; a water-richness index prediction module is used to obtain the surrounding rock point cloud model of the tunnel, and based on the water-richness database, predict the second water-richness index of multiple target surrounding rocks in the surrounding rock point cloud model, and generate a target point cloud model containing the second water-richness index; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section; a water-richness risk analysis module is used to perform risk analysis on the target point cloud data in the target point cloud model according to the second water-richness index of multiple target surrounding rocks, and obtain the risk level corresponding to the target point cloud data.

[0015] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the method for analyzing the risk of water richness of surrounding rocks according to the first aspect or any corresponding embodiment thereof.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for analyzing the risk of surrounding rock water richness according to the first aspect or any corresponding embodiment thereof.

[0017] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for analyzing the risk of surrounding rock water richness according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 4 is a flow chart of a method for analyzing the risk of surrounding rock water content according to an embodiment of the present invention.

[0020] Figure 2 4 is a schematic diagram of the first water-richness index distribution according to an embodiment of the present invention.

[0021] Figure 3 2 is a schematic diagram of a surrounding rock point cloud model construction process according to an embodiment of the present invention.

[0022] Figure 4 4 is a flow chart of another method for analyzing the water-richness risk of surrounding rocks according to an embodiment of the present invention.

[0023] Figure 5 Schematic diagram of cloud droplet distribution according to an embodiment of the present invention.

[0024] Figure 6 3. It is a schematic diagram of risk level classification of the fuzzy cloud probability model according to an embodiment of the present invention.

[0025] Figure 7 Schematic diagram of risk levels in the surrounding rock point cloud model of a tunnel according to an embodiment of the present invention.

[0026] Figure 8 4 is a flow chart of another method for analyzing the risk of water abundance of surrounding rocks according to an embodiment of the present invention.

[0027] Figure 9 4 is a structural block diagram of a device for analyzing the risk of surrounding rock water content according to an embodiment of the present invention.

[0028] Figure 10 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0030] During tunnel construction, the water-rich nature of the surrounding rock is a key factor affecting project safety and efficiency. Particularly in karst geological conditions, water and mud inrush hazards have become one of the most serious hazards in tunnel construction, causing not only enormous economic losses but also serious casualties. Numerous engineering projects have demonstrated that many tunnel accidents are caused by sudden water and mud inrush geological disasters during construction, posing a serious threat to the lives of construction workers.

[0031] In order to prevent disasters caused by the high water content of the surrounding rock, it is necessary to predict the water content of the surrounding rock. Advanced geological prediction methods in related technologies include drilling, geological radar and transient electromagnetic method. Although these prediction methods can obtain water content information to a certain extent, their accuracy is low and it is difficult to meet the construction requirements under complex geological conditions.

[0032] An embodiment of the present invention provides a method for analyzing the water-richness risk of surrounding rocks, which predicts the water-richness indexes of multiple target surrounding rocks to determine the water-richness risks of multiple target surrounding rocks, thereby improving the accuracy of water-richness risk analysis.

[0033] According to an embodiment of the present invention, an embodiment of a method for analyzing the water-richness risk of surrounding rocks is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] In this embodiment, a method for analyzing the risk of water-rich surrounding rocks is provided, which can be used in computer equipment. Figure 1 FIG. 1 is a flow chart of a method for analyzing the risk of surrounding rock water content according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0035] Step S101: query a water-richness index adaptation table according to the resistivity corresponding to multiple surrounding rock samples of the tunnel to obtain first water-richness indices corresponding to the multiple surrounding rock samples; wherein the water-richness index adaptation table includes correspondences between multiple resistivity ranges and multiple first water-richness indices.

[0036] Among them, multiple surrounding rock samples are multiple representative rock samples selected from the rocks around the tunnel in the tunnel project. The embodiment of the present invention obtains the resistivity of multiple surrounding rock samples according to the transient electromagnetic method, and uses a suitable transient electromagnetic instrument to detect the multiple surrounding rock samples to obtain the resistivity of the multiple surrounding rock samples.

[0037] In some optional embodiments, Table 1 illustrates a water-richness index adaptation table.

[0038] Table 1: Water-richness index adaptation table.

[0039]

[0040]

[0041] For example, the resistivity of a surrounding rock sample is 750 ohm·m, which belongs to the resistivity range of 0-800 ohm·m, and the first water-richness index corresponding to the surrounding rock sample is 1.50.

[0042] Step S102: constructing a water-richness database of the plurality of surrounding rock samples according to the first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness database is used to store the three-dimensional spatial coordinates of the plurality of surrounding rock samples and the first water-richness indices corresponding to the plurality of surrounding rock samples.

[0043] In some optional embodiments, a water-richness database of multiple surrounding rock samples is constructed based on the first water-richness index corresponding to multiple surrounding rock samples, including: obtaining the three-dimensional spatial coordinates of the multiple surrounding rock samples, associating the first water-richness index of each surrounding rock sample with the three-dimensional spatial coordinates to obtain an association result; and storing the association result to obtain a water-richness database.

[0044] Among them, the first water-richness index of each surrounding rock sample is aligned with the three-dimensional spatial coordinates. Specifically, the first water-richness index of each surrounding rock sample is accurately corresponded to its actual three-dimensional spatial position, so as to construct a water-richness database containing water-richness information and its spatial position.

[0045] In some optional embodiments, such as Figure 2 As shown in the figure, it is a schematic diagram of the distribution of the first water-richness index. First, the resistivity corresponding to multiple surrounding rock samples is obtained according to the transient electromagnetic method. The water-richness index adaptation table is queried according to the resistivity to obtain the first water-richness index corresponding to the multiple surrounding rock samples. The darker the color, the larger the first water-richness index. Then, the first water-richness index of each surrounding rock sample is associated with the three-dimensional spatial coordinates to obtain the first water-richness index distribution in the three-dimensional space. Finally, the classification of the first water-richness index can be observed in the three-dimensional space. The darker the color, the higher the risk level corresponding to the first water-richness index.

[0046] Step S103: Obtain a point cloud model of the surrounding rock of the tunnel, predict the second water-richness index of multiple target surrounding rocks in the surrounding rock point cloud model based on the water-richness database, and generate a target point cloud model containing the second water-richness index; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section.

[0047] The multiple target surrounding rocks are multiple surrounding rocks in the tunnel other than the surrounding rock samples whose water-richness is to be predicted.

[0048] In some optional embodiments, the coordinates of the point cloud data of the surrounding rock of the tunnel section are obtained by measurement technology, and a surrounding rock point cloud model is constructed based on the coordinates of the point cloud data of the surrounding rock of the tunnel section, wherein the measurement technology can be laser scanning.

[0049] In some optional embodiments, such as Figure 3 The figure shows the process of building the surrounding rock point cloud model. First, the coordinates of multiple point cloud data of the surrounding rock of the tunnel section are obtained. Figure 3In the figure, H is the tunnel height, and D / 2 is the radius of the arc-shaped part of the tunnel section. Then, multiple point cloud data are classified into low risk, medium risk, and high risk according to the first water-richness index. Finally, a certain step size is assigned to the multiple point cloud data to obtain a surrounding rock point cloud model. In the surrounding rock point cloud model, the risk levels of multiple surrounding rock samples can be distinguished by color.

[0050] In some optional embodiments, a K-nearest neighbor algorithm is used to predict the second water-richness index of multiple target surrounding rocks in the surrounding rock point cloud model. Specifically, the execution process of the K-nearest neighbor algorithm is: calculating the Euclidean distance between multiple first sample points of each surrounding rock sample and multiple second sample points of each target surrounding rock; based on the Euclidean distance, selecting multiple target first sample points closest to each second sample point as multiple neighbor points; and predicting the second water-richness index of multiple second sample points of the target surrounding rock based on the weighted average of the first water-richness index of the multiple neighbor points of each second sample point.

[0051] Among them, the formula for calculating the Euclidean distance is:

[0052]

[0053] Where d(p,x) is the Euclidean distance between the first sample points and the second sample points, p x is the coordinate of the second sample point on the x-axis, x ix is the coordinate of the first sample point i on the x-axis, p y is the coordinate of the second sample point on the y-axis, x iy is the coordinate of the first sample point i on the y-axis, p z is the coordinate of the second sample point on the z axis, x iz is the coordinate of the i-th first sample point on the z-axis.

[0054] In some optional embodiments, the calculation formula of the second water-richness index is:

[0055]

[0056] Where W^ is the second water-richness index, K is the total number of target first sample points, and W i is the first sample point of the i-th target.

[0057] Step S104 : performing risk analysis on the target point cloud data in the target point cloud model according to the second water-richness indexes of the plurality of target surrounding rocks to obtain risk levels corresponding to the target point cloud data.

[0058] In some optional implementations, a fuzzy cloud probability model is used to perform risk analysis on target point cloud data in the target point cloud model to obtain a risk level corresponding to the target point cloud data.

[0059] Specifically, the process of performing risk analysis on the target point cloud data in the target point cloud model using the fuzzy cloud probability model is as follows: determining the characteristic parameters of the fuzzy cloud model based on the second water-richness index of multiple target surrounding rocks; wherein the fuzzy cloud model is a model used for uncertainty analysis; simulating the cloud droplet generation process based on the characteristic parameters, and counting the number of cloud droplets to obtain the number of cloud droplets corresponding to multiple preset risk levels; generating the risk probability corresponding to each preset risk level based on the number of cloud droplets corresponding to multiple preset risk levels; and determining the preset risk level with the highest risk probability as the risk level corresponding to the target point cloud data.

[0060] The characteristic parameters of the fuzzy cloud model include expected Ex, entropy En, and super entropy He. The calculation formula of expected Ex is:

[0061]

[0062] Where Ex is the expectation, q is the total number of the second water-richness index, x i is the i-th second water-richness index.

[0063] The calculation formula of entropy En is:

[0064]

[0065] Where En is entropy, q is the total number of the second water-richness index, and x i is the i-th second water-richness index, and Ex is the expectation.

[0066] The calculation formula of excess entropy He is:

[0067]

[0068] Among them, Ex is the super entropy, S 2 is the variance of the second water-richness index, and En is the entropy.

[0069] Among them, the variance S 2 The calculation formula is:

[0070]

[0071] Among them, S 2 is the variance, q is the total number of the second water-richness index, x i is the i-th second water-richness index, and Ex is the expectation.

[0072] In some optional implementations, the cloud droplet formation process is simulated based on characteristic parameters by relying on a membership function. In an embodiment of the present invention, a deviation function is used instead of a membership function. The formula of the deviation function is:

[0073]

[0074] Among them, Z n is the deviation function, Enn is the entropy En as the expectation and the super entropy Ex 2 Normally distributed random numbers generated for the loadings with variance, x n To expect Ex as the expectation, to Enn 2 A random number generated with normal distribution for variance, x i is the i-th second water-richness index.

[0075] In some optional implementations, Table 2 illustrates the correspondence between multiple preset risk levels and water richness index ranges.

[0076] Table 2: Correspondence between multiple preset risk levels and water richness index ranges.

[0077] Risk Level Risk Statement Water richness index range Risk control measures I Low risk <0.1 Routine monitoring and drainage maintenance II Medium risk 0.1-0.5 Pre-drainage, waterproof wall installation, and enhanced monitoring III High risk >0.5 Stop construction, force drainage, and reinforce rock formations

[0078] In some optional implementations, the membership function is expressed as follows:

[0079]

[0080] Among them, f1 is the membership function of the low-risk area, and x is the second water-richness index.

[0081]

[0082] Among them, f2 is the membership function of the medium-risk area, and x is the second water-richness index.

[0083]

[0084] Among them, f3 is the membership function of the high-risk area, and x is the second water-richness index.

[0085] In some optional implementations, based on the number of cloud droplets corresponding to multiple preset risk levels, a formula for generating the risk probability corresponding to each preset risk level is:

[0086]

[0087] Among them, P i is the probability corresponding to the i-th preset risk level, N i is the number of cloud droplets corresponding to the i-th preset risk level, N1 is the number of cloud droplets corresponding to the low preset risk level, N2 is the number of cloud droplets corresponding to the medium preset risk level, N3 is the number of cloud droplets corresponding to the high preset risk level, and N1+N2+N3 is the total number of cloud droplets.

[0088] For example, the probability corresponding to the low preset risk level is 0.1, the probability corresponding to the medium preset risk level is 0.2, and the probability corresponding to the high preset risk level is 0.5. Then, the risk level corresponding to the target point cloud data is a high risk level.

[0089] In some optional implementations, in addition to using the fuzzy cloud probability model, the risk level corresponding to the target point cloud data can also be determined through a threshold division method, a statistical analysis method, and a neural network model.

[0090] Among them, the steps of the threshold division method are: obtaining the correspondence between the preset interval of the preset water-richness index and the risk level, matching the second water-richness index of multiple target surrounding rocks with the preset interval, and determining the risk level corresponding to the successfully matched target preset interval as the risk level corresponding to the target point cloud data.

[0091] The statistical analysis method involves performing a statistical analysis of the second water-richness index of all target surrounding rocks, calculating statistical quantities such as the mean and standard deviation, and then classifying risk levels based on the statistical results. The mean reflects the overall water-richness level, while the standard deviation reflects the degree of dispersion in the water-richness index. A greater degree of dispersion indicates greater uncertainty in water-richness and a potentially greater risk. Areas where the second water-richness index is lower than the mean minus a certain multiple (e.g., 2) of the standard deviation are classified as low-risk, areas where the second water-richness index is higher than the mean plus a certain multiple of the standard deviation are classified as high-risk, and areas in between are classified as medium-risk.

[0092] The steps of using the neural network model to determine the risk level corresponding to the target point cloud data are as follows: obtaining a large amount of surrounding rock sample data with known risk level labels, using the surrounding rock sample data to train the neural network model, inputting the second water-richness index of multiple target surrounding rocks into the trained neural network model, and obtaining the risk level corresponding to the target point cloud data.

[0093] The analysis method of the surrounding rock water-richness risk provided in this embodiment queries the first water-richness index corresponding to multiple surrounding rock samples through the water-richness index adaptation table, and constructs a water-richness database of multiple surrounding rock samples based on the first water-richness index corresponding to multiple surrounding rock samples. The embodiment of the present invention stores the first water-richness index corresponding to multiple surrounding rock samples and their three-dimensional spatial coordinates in the water-richness database, providing a comprehensive and orderly data basis for subsequent prediction and analysis. The embodiment of the present invention uses the constructed water-richness database to predict the second water-richness index of multiple target surrounding rocks to be predicted for water-richness in addition to the surrounding rock samples, which can more accurately reflect the water-richness of the target surrounding rocks and provide a more reliable basis for engineering decision-making. The embodiment of the present invention performs risk analysis on the target point cloud data in the target point cloud model based on the second water-richness index of multiple target surrounding rocks, obtains the risk level corresponding to the target point cloud data, and converts complex geological information into intuitive risk levels, so that engineering personnel can clearly understand the degree of water-richness risk of different parts of the tunnel, and thus take corresponding prevention and response measures in a targeted manner to reduce engineering risks and costs. Compared with related technologies, the embodiments of the present invention predict the water-richness index of multiple target surrounding rocks to determine the water-richness risks of multiple target surrounding rocks, thereby improving the accuracy of water-richness risk analysis, being more suitable for construction requirements under complex geological conditions, and improving the safety of tunnel engineering construction.

[0094] In this embodiment, a method for analyzing the risk of water-rich surrounding rocks is provided, which can be used in computer equipment. Figure 4 FIG. 1 is a flow chart of another method for analyzing the risk of water abundance of surrounding rocks according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0095] Step S401: query a water-richness index adaptation table according to the resistivity corresponding to multiple surrounding rock samples of the tunnel to obtain first water-richness indices corresponding to the multiple surrounding rock samples; wherein the water-richness index adaptation table includes correspondences between multiple resistivity ranges and multiple first water-richness indices.

[0096] In some optional implementations, the above step S401 includes:

[0097] Step S4011 , querying a water-richness index adaptation table according to the resistivities corresponding to a plurality of surrounding rock samples of the tunnel, and obtaining a target resistivity range within which the resistivity corresponding to each surrounding rock sample lies.

[0098] In the embodiment of the present invention, the resistivity of multiple surrounding rock samples is obtained according to the transient electromagnetic method. The multiple surrounding rock samples are detected using a suitable transient electromagnetic instrument to obtain the resistivity of the multiple surrounding rock samples.

[0099] In some optional implementations, the water richness index adaptation table includes a correspondence between a plurality of resistivity ranges and a plurality of first water richness indices.

[0100] Step S4012: determining a first water-richness index corresponding to a target resistivity range.

[0101] The water-richness index corresponding to the target resistivity range is determined as the first water-richness index.

[0102] Step S402: constructing a water-richness database of the plurality of surrounding rock samples according to the first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness database is used to store the three-dimensional spatial coordinates of the plurality of surrounding rock samples and the first water-richness indices corresponding to the plurality of surrounding rock samples.

[0103] In some optional implementations, the above step S402 includes:

[0104] Step S4021: Acquire the three-dimensional spatial coordinates of a plurality of surrounding rock samples, associate the first water-richness index of each surrounding rock sample with the three-dimensional spatial coordinates, and obtain an association result.

[0105] Among them, the first water-richness index of each surrounding rock sample is aligned with the three-dimensional spatial coordinates. Specifically, the first water-richness index of each surrounding rock sample is accurately corresponded to its actual three-dimensional spatial position, so as to construct a water-richness database containing water-richness information and its spatial position.

[0106] Step S4022: storing the association results to obtain a water-richness database.

[0107] By acquiring the three-dimensional spatial coordinates of multiple surrounding rock samples and correlating them with a first water-richness index, this embodiment of the present invention organically integrates the water-richness index with spatial location information. By correlating the water-richness index with the three-dimensional spatial coordinates, the spatial distribution of the water-richness of each surrounding rock sample can be precisely determined. This facilitates a more detailed understanding of the spatial variation of water-richness in tunnel surrounding rock, providing an accurate basis for targeted treatment at different locations within the project.

[0108] Step S403: Obtain a point cloud model of the surrounding rock of the tunnel, predict the second water-richness index of multiple target surrounding rocks in the surrounding rock point cloud model based on the water-richness database, and generate a target point cloud model containing the second water-richness index; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section.

[0109] In some optional implementations, the above step S403 includes:

[0110] Step S4031 : calculating the Euclidean distances between a plurality of first sample points of each surrounding rock sample and a plurality of second sample points of each target surrounding rock.

[0111] In step S4032, a plurality of target first sample points closest to each second sample point in terms of the Euclidean distance are selected as a plurality of neighbor points.

[0112] In step S4033, a second water abundance index of the plurality of second sample points of the target surrounding rock is predicted according to a weighted average of the first water abundance indexes of the plurality of neighbor points of each second sample point.

[0113] In step S404, risk analysis is performed on the target point cloud data in the target point cloud model according to the second water abundance indexes of the plurality of target surrounding rocks, to obtain a risk level corresponding to the target point cloud data.

[0114] In some optional embodiments, the step S204 includes:

[0115] In step S4041, a feature parameter of a fuzzy cloud model is determined according to the second water abundance indexes of the plurality of target surrounding rocks, wherein the fuzzy cloud model is a model for uncertainty analysis.

[0116] In step S4042, a cloud droplet generation process is simulated according to the feature parameter, and a number of cloud droplets is counted to obtain a number of cloud droplets corresponding to each of a plurality of preset risk levels.

[0117] In some optional embodiments, as shown in FIG. 4B, the feature parameter of the fuzzy cloud model is determined according to the second water abundance indexes of the plurality of target surrounding rocks. Figure 5 As shown in FIG. 4C, a risk level division diagram of the fuzzy cloud probability model is shown, in which three preset risk levels, i.e., a high risk, a medium risk and a low risk, are obtained by dividing the risk levels in the fuzzy cloud probability model, and the number of cloud droplets in different preset risk levels can be obtained by observing the cloud droplet distribution in the fuzzy cloud probability model. Figure 6

[0118] In step S4043, a risk probability corresponding to each preset risk level is generated according to the number of cloud droplets corresponding to each preset risk level.

[0119] In step S4044, a preset risk level with the highest risk probability is determined as the risk level corresponding to the target point cloud data.

[0120] In some optional embodiments, the step S4043 includes:

[0121] In step a1, a quotient of the number of cloud droplets corresponding to each preset risk level and the total number of cloud droplets is determined as the risk probability corresponding to each preset risk level. ​

[0122] In some optional embodiments, such as Figure 7 The figure shows a schematic diagram of the risk levels in the surrounding rock point cloud model of the tunnel. After obtaining the risk levels corresponding to different target surrounding rocks, the risk levels are distinguished by color depth in the surrounding rock point cloud model. The darker the color, the higher the risk level. Through the visualization of the risk levels, the water-rich risk level of the surrounding rock of the tunnel section can be directly observed.

[0123] The method for analyzing surrounding rock water-richness risk provided in this embodiment determines characteristic parameters of a fuzzy cloud model based on the second water-richness index of multiple target surrounding rocks. This model is used to perform uncertainty analysis, fully accounting for the complexity and uncertainty of the geological environment. This method more accurately reflects the actual situation of surrounding rock water-richness risk and improves the accuracy of subsequent water-richness risk analysis. This method simulates the cloud droplet generation process based on characteristic parameters and counts the number of droplets, thereby determining the risk probability corresponding to each preset risk level. This avoids the arbitrariness of subjective judgment and makes the determination of risk levels more accurate and reliable.

[0124] In this embodiment, a method for analyzing the risk of water-rich surrounding rocks is provided, which can be used in computer equipment. Figure 8 FIG. 1 is a flow chart of another method for analyzing the risk of water-rich surrounding rocks according to an embodiment of the present invention. Figure 8 As shown, the process includes the following steps:

[0125] The resistivity of a plurality of surrounding rock samples is obtained according to a transient electromagnetic method, and a water-richness index adaptation table is searched according to the resistivity to obtain first water-richness indices corresponding to the plurality of surrounding rock samples.

[0126] The first water-richness index is registered in three-dimensional space to construct a water-richness database.

[0127] Based on the water-richness database, the second water-richness index of multiple target surrounding rocks in the surrounding rock point cloud model is predicted by the K-nearest neighbor algorithm to obtain the target point cloud model.

[0128] Through the fuzzy cloud probability algorithm, the risk probability corresponding to each preset risk level is quantified, and the risk level is determined based on the risk probability.

[0129] Select corresponding risk control measures according to the risk level.

[0130] The embodiment of the present invention obtains the resistivity data of the surrounding rock in front of the tunnel by transient electromagnetic method, and after three-dimensional space alignment, combines the K nearest neighbor algorithm to construct a target point cloud model. The target point cloud model can accurately predict the water-rich distribution of the surrounding rock, help construction personnel identify possible high-risk areas for sudden water inrush in advance, and provide refined water-rich distribution information. The application of the fuzzy cloud probability model of the embodiment of the present invention can quantitatively analyze the uncertainty of the water-richness of the surrounding rock, and make reasonable decisions based on risk areas of different levels, providing a scientific reference basis for the risk control of sudden water inrush during tunnel construction. The embodiment of the present invention is applied to tunnel construction under complex geological conditions such as karst geology, which can effectively improve the safety of construction.

[0131] This embodiment also provides a device for analyzing the water-richness risk of surrounding rocks. This device is used to implement the above-mentioned embodiments and preferred implementations, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0132] This embodiment provides a device for analyzing the risk of water-rich surrounding rocks. Figure 9 Shown, including:

[0133] The first water-richness index determination module 901 is used to query the water-richness index adaptation table based on the resistivity corresponding to multiple surrounding rock samples of the tunnel to obtain the first water-richness index corresponding to the multiple surrounding rock samples; wherein the water-richness index adaptation table includes the correspondence between multiple resistivity ranges and multiple first water-richness indices.

[0134] The water-richness database construction module 902 is used to construct a water-richness database of multiple surrounding rock samples based on the first water-richness indices corresponding to the multiple surrounding rock samples; wherein the water-richness database is used to store the three-dimensional spatial coordinates of the multiple surrounding rock samples and the first water-richness indices corresponding to the multiple surrounding rock samples.

[0135] The water-richness index prediction module 903 is used to obtain the surrounding rock point cloud model of the tunnel, predict the second water-richness index of multiple target surrounding rocks in the surrounding rock point cloud model based on the water-richness database, and generate a target point cloud model containing the second water-richness index; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section.

[0136] The water-richness risk analysis module 904 is configured to perform risk analysis on target point cloud data in the target point cloud model according to the second water-richness indexes of multiple target surrounding rocks, and obtain risk levels corresponding to the target point cloud data.

[0137] In some optional implementations, the first water richness index determination module 901 includes:

[0138] The resistivity query unit is used to query the water-richness index adaptation table according to the resistivities corresponding to multiple surrounding rock samples of the tunnel, and obtain the target resistivity range of the resistivity corresponding to each surrounding rock sample.

[0139] The first water-richness index determining unit is configured to determine a first water-richness index corresponding to a target resistivity range.

[0140] In some optional embodiments, the water-richness database construction module 902 includes:

[0141] The correlation unit is used to obtain the three-dimensional spatial coordinates of multiple surrounding rock samples, and to correlate the first water-richness index of each surrounding rock sample with the three-dimensional spatial coordinates to obtain a correlation result.

[0142] The correlation result storage unit is used to store the correlation result to obtain a water-richness database.

[0143] In some optional implementations, the water richness index prediction module 903 includes:

[0144] The Euclidean distance determination unit is used to calculate the Euclidean distance between the multiple first sample points of each surrounding rock sample and the multiple second sample points of each target surrounding rock.

[0145] The neighbor point selection unit is used to select a plurality of target first sample points closest to each second sample point as a plurality of neighbor points according to the Euclidean distance.

[0146] The second water-richness index prediction unit is configured to predict the second water-richness indexes of multiple second sample points of the target surrounding rock according to a weighted average of the first water-richness indexes of multiple neighboring points of each second sample point.

[0147] In some optional embodiments, the water-richness risk analysis module 904 includes:

[0148] The characteristic parameter determination unit is used to determine the characteristic parameters of the fuzzy cloud model according to the second water-richness index of multiple target surrounding rocks; wherein the fuzzy cloud model is a model used for uncertainty analysis.

[0149] The cloud droplet number counting unit is used to simulate the cloud droplet generation process according to the characteristic parameters, and count the number of cloud droplets to obtain the number of cloud droplets corresponding to multiple preset risk levels.

[0150] The risk probability determination unit is used to generate a risk probability corresponding to each preset risk level according to the number of cloud droplets corresponding to multiple preset risk levels.

[0151] The risk level determination unit is used to determine the preset risk level with the highest risk probability as the risk level corresponding to the target point cloud data.

[0152] In some optional implementations, the risk probability determination unit includes:

[0153] The risk probability determination subunit is used to determine the quotient of the number of cloud droplets corresponding to each preset risk level and the total amount of cloud droplets as the risk probability corresponding to each preset risk level.

[0154] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0155] The analysis device for the water-richness risk of surrounding rocks in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0156] The embodiment of the present invention also provides a computer device having the above Figure 9 The analytical device for the risk of water abundance in surrounding rocks is shown.

[0157] See also Figure 10 , Figure 10 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 10 As shown, the computer device includes: one or more processors 1010, memory 1020, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides the necessary operation of part (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 10 A processor 1010 is taken as an example.

[0158] Processor 1010 may be a central processing unit, a network processor, or a combination thereof. Processor 1010 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CPLD), a field programmable gate array (FPGA), a general purpose array logic (GAL), or any combination thereof.

[0159] The memory 1020 stores instructions that can be executed by at least one processor 1010, so that the at least one processor 1010 executes the method shown in the above embodiment.

[0160] The memory 1020 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 1020 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 1020 may optionally include a memory remotely located relative to the processor 1010, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0161] The memory 1020 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 1020 may also include a combination of the above types of memory.

[0162] The computer device further includes a communication interface 1030 for the computer device to communicate with other devices or a communication network.

[0163] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0164] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0165] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for analyzing the water-richness risk of surrounding rocks, characterized in that: The method comprises: querying a water-richness index adaptation table according to the resistivity corresponding to a plurality of surrounding rock samples of the tunnel to obtain first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness index adaptation table includes correspondences between a plurality of resistivity ranges and a plurality of first water-richness indices; Constructing a water-richness database of the plurality of surrounding rock samples according to the first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness database is used to store the three-dimensional spatial coordinates of the plurality of surrounding rock samples and the first water-richness indices corresponding to the plurality of surrounding rock samples; Obtaining a point cloud model of the surrounding rock of the tunnel, predicting a second water-richness index of a plurality of target surrounding rock masses in the surrounding rock point cloud model based on the water-richness database, and generating a target point cloud model containing the second water-richness index; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section; performing risk analysis on target point cloud data in the target point cloud model according to the second water-richness indexes of the plurality of target surrounding rocks to obtain risk levels corresponding to the target point cloud data; The risk analysis of the target point cloud data in the target point cloud model according to the second water-richness index of the multiple target surrounding rocks to obtain the risk level corresponding to the target point cloud data includes: determining the characteristic parameters of the fuzzy cloud model according to the second water-richness index of the multiple target surrounding rocks; wherein the fuzzy cloud model is a model for performing uncertainty analysis; simulating the cloud droplet generation process according to the characteristic parameters, and counting the number of cloud droplets to obtain the number of cloud droplets corresponding to multiple preset risk levels; generating the risk probability corresponding to each preset risk level according to the number of cloud droplets corresponding to the multiple preset risk levels; and determining the preset risk level with the highest risk probability as the risk level corresponding to the target point cloud data.

2. The method according to claim 1, characterized in that The step of querying a water-richness index adaptation table according to the resistivities corresponding to the plurality of surrounding rock samples of the tunnel to obtain first water-richness indices corresponding to the plurality of surrounding rock samples includes: querying a water-richness index adaptation table according to the resistivities corresponding to the plurality of surrounding rock samples of the tunnel, and obtaining a target resistivity range within which the resistivity corresponding to each surrounding rock sample falls; The first water-richness index corresponding to the target resistivity range is determined.

3. The method according to claim 1 or 2, characterized in that The step of constructing a water-richness database of the plurality of surrounding rock samples according to the first water-richness indexes corresponding to the plurality of surrounding rock samples includes: Obtaining the three-dimensional spatial coordinates of the plurality of surrounding rock samples, and correlating the first water-richness index of each surrounding rock sample with the three-dimensional spatial coordinates to obtain a correlation result; The association results are stored to obtain the water-richness database.

4. The method according to claim 1 or 2, characterized in that The step of predicting the second water-richness index of a plurality of target surrounding rocks in the surrounding rock point cloud model based on the water-richness database includes: calculating the Euclidean distances between a plurality of first sample points of each surrounding rock sample and a plurality of second sample points of each target surrounding rock; According to the Euclidean distance, selecting a plurality of target first sample points closest to each second sample point as a plurality of neighbor points; The second water richness indexes of the plurality of second sample points of the target surrounding rock are predicted according to a weighted average of the first water richness indexes of a plurality of neighboring points of each second sample point.

5. The method according to claim 1, wherein Generating a risk probability corresponding to each preset risk level according to the number of cloud droplets corresponding to the multiple preset risk levels includes: The quotient of the number of cloud droplets corresponding to each preset risk level and the total amount of cloud droplets is determined as the risk probability corresponding to each preset risk level.

6. An analysis device for surrounding rock water-rich risk, characterized in that: The device comprises: a first water-richness index determination module, configured to query a water-richness index adaptation table based on the resistivity corresponding to a plurality of surrounding rock samples of the tunnel, and obtain first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness index adaptation table includes correspondences between a plurality of resistivity ranges and a plurality of first water-richness indices; a water-richness database construction module, configured to construct a water-richness database for the plurality of surrounding rock samples based on the first water-richness indices corresponding to the plurality of surrounding rock samples; wherein the water-richness database is configured to store the three-dimensional spatial coordinates of the plurality of surrounding rock samples and the first water-richness indices corresponding to the plurality of surrounding rock samples; a water-richness index prediction module, configured to obtain a point cloud model of the surrounding rock of the tunnel, predict a second water-richness index of a plurality of target surrounding rock masses in the surrounding rock point cloud model based on the water-richness database, and generate a target point cloud model containing the second water-richness index; wherein the surrounding rock point cloud model is used to characterize the geometric structure of the tunnel section; a water-richness risk analysis module, configured to perform risk analysis on target point cloud data in the target point cloud model according to the second water-richness indexes of the plurality of target surrounding rocks, and obtain risk levels corresponding to the target point cloud data; The water-richness risk analysis module includes: a characteristic parameter determination unit, which is used to determine the characteristic parameters of the fuzzy cloud model based on the second water-richness index of multiple target surrounding rocks; wherein the fuzzy cloud model is a model used for uncertainty analysis; a cloud droplet number statistics unit, which is used to simulate the cloud droplet generation process according to the characteristic parameters, and count the number of cloud droplets to obtain the number of cloud droplets corresponding to multiple preset risk levels; a risk probability determination unit, which is used to generate the risk probability corresponding to each preset risk level based on the number of cloud droplets corresponding to multiple preset risk levels; and a risk level determination unit, which is used to determine the preset risk level with the highest risk probability as the risk level corresponding to the target point cloud data.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for analyzing the water-richness risk of surrounding rocks as described in any one of claims 1 to 5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for analyzing the water-richness risk of surrounding rocks according to any one of claims 1 to 5.

9. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for analyzing the risk of water richness of surrounding rocks according to any one of claims 1 to 5.

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