Tunnel construction water inflow prediction method and system based on tunnel-aquifer analysis

Abstract

The present application belongs to the geological exploration technical field, in order to solve the problem that it is difficult to accurately reflect the dynamic change of local hydrological condition, provide a kind of tunnel-water body analysis-based tunnel construction gushing quantity prediction method and system. Wherein, the tunnel-water body analysis-based tunnel construction gushing quantity prediction method includes according to the current working face water body sample prediction set, combined with the tunnel-water body mathematical analysis model of research area, the tunnel face gushing quantity prediction value of each section position of next working face is obtained;The tunnel face gushing quantity prediction value of each section position of next working face is compared with the corresponding gushing quantity measured value, according to the convergence, it is judged whether the water body sample prediction set of next working face and the tunnel face gushing quantity prediction value of each section position are directly output or the water body sample prediction set of current working face is iteratively corrected to continue to predict. It can improve the reliability of prediction result, provide strong guarantee for tunnel construction safety.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of geological exploration, and particularly relates to a tunnel construction water inflow prediction method and system based on tunnel-aquifer analysis. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] Water inflow phenomenon may cause a series of problems such as foundation pit collapse, equipment damage, material waste, and project suspension, and may even cause secondary disasters such as ground subsidence or geological collapse, causing immeasurable damage to the surrounding environment and infrastructure. Traditional water inflow prediction methods usually rely on static models or simple empirical formula-based calculations. These methods may have some applicability in certain more uniform geological conditions, but are not suitable for areas with complex geological conditions, and are difficult to accurately reflect the dynamic changes of local hydrological conditions, especially in the case of uneven aquifer distribution, complex groundwater flow, and variable geological conditions. The prediction results generally have large deviations, resulting in a lack of scientific basis for developing construction plans. SUMMARY

[0004] To solve the technical problems existing in the background art, the present application provides a tunnel construction water inflow prediction method and system based on tunnel-aquifer analysis, which can comprehensively consider local hydrological characteristics, dynamically adjust the prediction model, quickly predict water inflow, and achieve accurate prediction of tunnel water inflow by obtaining hydrological data, constructing a tunnel-aquifer analysis model, and using dynamic iterative optimization technology, thereby significantly improving the reliability of the prediction results and providing a strong guarantee for tunnel construction safety.

[0005] To achieve the above-mentioned purposes, the present application adopts the following technical solutions:

[0006] The first aspect of the present application provides a tunnel construction water inflow prediction method based on tunnel-aquifer analysis.

[0007] A tunnel construction water inflow prediction method based on tunnel-aquifer analysis, comprising:

[0008] Obtain the local hydrological characteristics of the current working face and preprocess them, and combine the pre-established tunnel-aquifer mathematical analysis model of the study area to predict the possible spatial position and water content of the aquifer, and construct an aquifer sample prediction set for the current working face;

[0009] Divide the next working face into sections, monitor and count the measured values of the tunnel face water inflow at different section positions;

[0010] According to the current working face water body sample prediction set, combined with the tunnel-water body mathematical analysis model of the research area, the tunnel face water inflow prediction value of each section position of the next working face is obtained;

[0011] The tunnel face water inflow prediction value of each section position of the next working face is compared with the corresponding measured water inflow value, and according to the convergence condition, it is judged whether to directly output the water body sample prediction set of the next working face and the tunnel face water inflow prediction value of each section position, or to iterate and correct the water body sample prediction set of the current working face to continue prediction.

[0012] As an embodiment, when the difference between the tunnel face water inflow prediction value of each section position of the next working face and the corresponding measured water inflow value converges within a set range, the water body sample prediction set of the next working face and the tunnel face water inflow prediction value of each section position are directly output.

[0013] As an embodiment, when the difference between the tunnel face water inflow prediction value of each section position of the next working face and the corresponding measured water inflow value does not converge within a set range, the water body sample prediction set of the current working face is iteratively corrected until the difference converges within a set range.

[0014] As an embodiment, the expression of the tunnel-water body mathematical analysis model of the research area established in advance is:

[0015] ;

[0016] ;

[0017] ;

[0018] In the formula: is the total water head value, and is the function, that is ; is the seepage area; is the boundary condition 1, that is, the water head boundary condition at the ground surface ; is the boundary condition 2, that is, the boundary water head of the tunnel inner surface ; is the coordinate of the research area coordinate system, and the research area coordinate system takes the horizontal ground surface as the horizontal axis and the tunnel center vertical to the ground surface as the vertical axis.

[0019] As an embodiment, it is assumed that the tunnel water body is circular in a two-dimensional plane, the distance between the water body center and the tunnel center is S , the water body radius is R , and the water body sample prediction set can be mapped into the polar coordinates in the form of solution by conformal mapping:

[0020] ;

[0021] wherein: are the polar radii centered at the tunnel and the aquifer respectively, are the polar angles centered at the tunnel and the aquifer respectively, is a coefficient determined by the boundary condition.

[0022] As an implementation, the tunnel face water inflow prediction value at each section position is:

[0023] ;

[0024] wherein: is the water inflow, is the permeability coefficient; is the polar radius centered at the tunnel; is the boundary condition 1; is the aquifer sample prediction set.

[0025] The second aspect of the present application provides a tunnel construction water inflow prediction system based on tunnel-aquifer analysis.

[0026] A tunnel construction water inflow prediction system based on tunnel-aquifer analysis, comprising:

[0027] An aquifer sample prediction set construction module for obtaining and preprocessing the local hydrological characteristics of the current working face, and combining the tunnel-aquifer mathematical analysis model of the study area established in advance to predict the possible spatial position and water content of the aquifer, and to construct the aquifer sample prediction set of the current working face;

[0028] A tunnel face water inflow measurement module for dividing the next working face into sections, monitoring and counting the measured values of the tunnel face water inflow at different section positions;

[0029] A tunnel face water inflow prediction module for obtaining the tunnel face water inflow prediction value at each section position of the next working face according to the aquifer sample prediction set of the current working face, in combination with the tunnel-aquifer mathematical analysis model of the study area;

[0030] A water inflow measurement and prediction comparison module for comparing the tunnel face water inflow prediction value at each section position of the next working face with the corresponding measured value, and according to the convergence condition, determining whether to directly output the aquifer sample prediction set of the next working face and the tunnel face water inflow prediction value at each section position, or to continue the prediction by iteratively correcting the aquifer sample prediction set of the current working face.

[0031] The third aspect of the present application provides a computer readable storage medium.

[0032] A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the tunnel construction water inflow prediction method based on tunnel-aquifer analysis as described above.

[0033] A fourth aspect of the present application provides a computer program product.

[0034] A computer program product comprising computer programs / instructions which, when executed by a processor, implement the steps of the tunnel construction water inflow prediction method based on tunnel-aquifer analysis as described above.

[0035] A fifth aspect of the present application provides an electronic device.

[0036] An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the tunnel construction water inflow prediction method based on tunnel-aquifer analysis as described above when executing the program.

[0037] Compared with the prior art, the present application has the following beneficial effects:

[0038] (1) The present application acquires the local hydrological characteristics of the current working face, establishes a tunnel-aquifer mathematical analysis model of the research area, predicts the possible spatial position and water content of the aquifer according to the local hydrological information of the research area, constructs an aquifer sample prediction set of the working face, divides the working face to be mined into sections, monitors and counts the tunnel face water inflow of different section positions, obtains the water inflow prediction value of the next position according to the aquifer sample prediction set of the current working face and the tunnel-aquifer mathematical analysis model, compares the model predicted water inflow with the measured water inflow, iteratively corrects the aquifer sample prediction set, and obtains the convergent water inflow prediction result and the aquifer sample prediction set, which significantly improves the accuracy and reliability of the tunnel water inflow prediction under complex geological conditions.

[0039] (2) The present application comprehensively considers multiple sources of hydrological information such as borehole water level, rock mass permeability coefficient and dynamic water inflow, obtains the rock mass permeability coefficient through the excitation polarization method, and constructs a tunnel-aquifer mathematical analysis model. Assuming that the tunnel aquifer is circular in a two-dimensional plane, an aquifer sample prediction set is obtained, which improves the speed and accuracy of the calculation of the tunnel water head boundary condition.

[0040] (3) The water inflow is calculated by the given formula combined with the aquifer sample prediction set analysis model, the difference between the predicted water inflow and the measured water inflow is evaluated, the spatial position and water content parameters of the aquifer sample prediction set are updated based on the dynamic iteration mechanism, and the iteration correction is repeatedly executed until the error converges to the allowable range, which improves the accuracy of the tunnel water inflow.

[0041] Advantages of additional aspects of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following description and drawings or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which constitute a part of this specification, are included to provide a further understanding of the application, and are incorporated by reference in the description of the application, merely

[0043] Figure 1 is a tunnel construction water inflow prediction method flow chart based on tunnel-aquifer analysis of an embodiment of the application;

[0044] Figure 2 is a tunnel construction water inflow prediction process schematic diagram based on tunnel-aquifer analysis of an embodiment of the application;

[0045] Figure 3 is a tunnel-aquifer analysis model of an embodiment of the application;

[0046] Figure 4 is a tunnel construction water inflow prediction system structure schematic diagram based on tunnel-aquifer analysis of an embodiment of the application. DETAILED DESCRIPTION

[0047] The application will be further described below in conjunction with the drawings and embodiments.

[0048] It should be noted that the following detailed description is illustrative only, and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0049] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0050] Embodiment one

[0051] In combination Figure 1 and Figure 2 An embodiment of the application provides a tunnel construction water inflow prediction method based on tunnel-aquifer analysis, which comprises:

[0052] S101: Obtain the current working face local hydrological characteristics and preprocess, and combine the pre-established tunnel-aqueous body mathematical analysis model of the research area to predict the possible spatial position and water content of the aqueous body, and construct the aqueous body sample prediction set of the current working face.

[0053] In step S101, the current working face local hydrological characteristics include the borehole water level data along the tunnel site area, the tunnel face rock mass permeability coefficient, and the local water inflow data, and the missing data is filled by interpolation.

[0054] In the specific implementation process, the borehole water level data along the tunnel site area is obtained through geological data.

[0055] The tunnel face rock mass permeability coefficient is calculated by using the frequency domain induced polarization method, and the tunnel face rock mass permeability coefficient is calculated by formula (1) (2):

[0056] (1);

[0057] (2);

[0058] In the formula, where is the formation factor, is the resistivity of the rock sample with a water content of 100%, is the pore water resistivity, is a correction factor determined according to the ion concentration of the field groundwater, reflecting the influence of ion concentration on the relaxation time and permeability prediction, when the field groundwater is used to establish the permeability prediction model, , is the relaxation time, is the coefficient, which needs to be determined by examples, is the rock mass permeability coefficient.

[0059] The local water inflow is calculated according to the water flow velocity of the ditch, and the specific method is to introduce the tunnel water inflow into the drainage ditch, select the ditch section with flat bottom and wall, no rapid flow in the ditch, and no change in section, measure the flow velocity in the drainage ditch, and then calculate the local water inflow.

[0060] After obtaining the above data, for example, the borehole water level and permeability coefficient are interpolated by Makima interpolation method to improve the integrity of the basic data.

[0061] It should be noted that those skilled in the art can also use other interpolation methods to interpolate the borehole water level and permeability coefficient, which will not be described in detail here.

[0062] Formula (3), formula (4), formula (5) and formula (6) are non-averaged anisotropic equivalent continuous medium models, and the water head boundary conditions of the research area are obtained, such asFigure 3

[0063] (3);

[0064] (4);

[0065] (5);

[0066] wherein: is the total water head value, is the water head value of the tunnel surface, is a function, i.e. , is the seepage area, is the boundary condition 1, i.e. the water head boundary condition at the ground surface , refers to y =0 level; is the boundary condition 2, i.e. the boundary water head of the tunnel inner surface , which can be obtained by the drilling water level; is the coordinate of the research area coordinate system, the research area coordinate system takes the horizontal ground surface as the horizontal axis and the tunnel center vertical upward to the ground surface as the vertical axis.

[0067] Assuming that the tunnel water body is circular in the two-dimensional plane, the distance between the center of the water body and the center of the tunnel is S , the radius of the water body is R , and the water body sample prediction set can be mapped into the polar coordinates by the conformal mapping as follows:

[0068] (6);

[0069] wherein: are the polar radii with the tunnel and the water body as the center, respectively, are the polar angles with the tunnel and the water body as the center, respectively, is the coefficient determined by the boundary condition. The conformal mapping expression is , , the parameter A is related to the tunnel depth and radius, can be obtained by simple coordinate translation transformation; z is an intermediate parameter. When solving the undetermined coefficients, the boundary conditions can be sequentially brought in.

[0070] After mapping the ground surface boundary condition, we can obtain , which can be substituted into formula (6) to obtain the coefficient C ​0. The configuration point method in the weighted residual method is used, i.e., specific points on the tunnel circumference satisfy the boundary conditions. Although the result is a weak form solution, it can meet the accuracy requirements for engineering applications. If a more accurate solution is desired, more boundary points can be forced to satisfy the boundary conditions. Two points where the tunnel surface intersects the horizontal axis are taken as configuration points, and the polar coordinates and water head boundary conditions are transformed to derive the coefficients C 11 , C 12 , C 21 , C 22 Two groups of relations are derived; assuming that the water head of the water-bearing body is consistent with the ground surface, two points on the circumference of the water-bearing body that intersect the horizontal axis are selected as configuration points, and the coordinates of the two points are mapped conformally and substituted into formula (6) to derive the other two groups of relations; the weak solution of the seepage field corresponding to the specific water-bearing body can be obtained by solving the above four groups of equations, and the sample prediction set of the water-bearing body can be obtained by solving multiple different working conditions.

[0071] S102: The next working face is divided into sections, and the measured values of the tunnel face water inflow at different section positions are monitored and counted.

[0072] The section division is based on the geological profile characteristics and the geological exploration data of the construction area, and the partition setting under complex geological conditions is dynamically adjusted, so as to divide the working face to be mined into a plurality of sections.

[0073] For example, for a section with relatively uniform geological conditions, an equal-length section method can be used; and for a region with complex geological conditions, the section length is dynamically adjusted according to the local rock mass and aquifer distribution characteristics, so as to improve the accuracy of data acquisition.

[0074] Monitoring points are arranged in each section to record the real-time water inflow of the tunnel face. The water inflow can be measured according to the water flow velocity or water flow volume measurement method. Specifically, the water inflow of the tunnel is introduced into the drainage ditch, a section of the ditch with a flat bottom and wall, no rapids in the ditch, and no changes in the cross section is selected, the flow velocity in the drainage ditch is measured, and then the local water inflow is calculated. The water flow volume measurement method is to calculate the water volume flowing into the tunnel drainage facility per unit time to obtain the water inflow.

[0075] S103: According to the water-bearing body sample prediction set of the current working face, combined with the tunnel-water body mathematical analysis model of the research area, the water inflow prediction value of the tunnel face at each section position of the next working face is obtained.

[0076] The water inflow prediction value of the tunnel face at each section position is:

[0077] (7);

[0078] wherein: Q is the water inflow, K is the permeability coefficient; R is the polar radius centered on the tunnel; C1 is the boundary condition 1, referring to y the horizontal plane where Q = 0; is the water-bearing body sample prediction set.

[0079] According to the progress of tunnel construction, the spatial resolution of the prediction model is adjusted step by step to ensure that the prediction results of each working face can adapt to the actual geological characteristics.

[0080] S104: Compare the tunnel face water inflow prediction values of each section position of the next working face with the corresponding measured water inflow values. According to the convergence, determine whether to directly output the water-bearing body sample prediction set of the next working face and the tunnel face water inflow prediction values of each section position, or to continue prediction by iteratively correcting the water-bearing body sample prediction set of the current working face.

[0081] In step S104, when the difference between the tunnel face water inflow prediction values of each section position of the next working face and the corresponding measured water inflow values converges within a set range, the water-bearing body sample prediction set of the next working face and the tunnel face water inflow prediction values of each section position are directly outputted.

[0082] When the difference between the tunnel face water inflow prediction values of each section position of the next working face and the corresponding measured water inflow values is not within the set range, the water-bearing body sample prediction set of the current working face is iteratively corrected until the difference converges within the set range.

[0083] For example, by comparing the model prediction values and the measured values, the prediction error is calculated:

[0084] (8);

[0085] wherein: Qe is the prediction error, Qm is the actual water inflow monitored, Qp is the predicted water inflow.

[0086] This embodiment is based on a dynamic iteration mechanism. By comparing the difference between the working face model prediction water inflow and the measured water inflow in real time, the prediction error is calculated and quantitatively evaluated. If the error exceeds the preset threshold, the spatial position and water content parameters of the water-bearing body sample prediction set are updated. Iterative correction is repeated until the error converges within the allowed range, achieving dynamic optimization of the model and data set with changes in hydrological conditions during tunnel construction, improving the real-time and accuracy of water inflow prediction. This process ensures the dynamic adaptability and high precision of the prediction model.

[0087] Example 2

[0088] like Figure 4 As shown, this embodiment of the invention provides a tunnel construction water inflow prediction system based on tunnel-aquifer analysis, which includes:

[0089] The aquifer sample prediction set construction module 401 is used to obtain the local hydrological characteristics of the current working face and perform preprocessing, and combined with the pre-established tunnel-aquifer mathematical analytical model of the study area, predict the possible spatial location and water content of the aquifer, and construct the aquifer sample prediction set of the current working face.

[0090] The tunnel face water inflow measurement module 402 is used to divide the next working face into sections, monitor and statistically analyze the measured values ​​of tunnel face water inflow at different section locations.

[0091] The tunnel face water inflow prediction module 403 is used to derive the predicted values ​​of tunnel face water inflow at each section of the next working face based on the current working face water-bearing body sample prediction set and the tunnel-water-bearing body mathematical analytical model of the study area.

[0092] The measured water inflow prediction comparison module 404 is used to compare the predicted water inflow values ​​of the tunnel face at each section of the next working face with the corresponding measured water inflow values. Based on the convergence status, it determines whether to directly output the water-bearing body sample prediction set and the predicted water inflow values ​​of the tunnel face at each section of the next working face, or to iteratively correct the water-bearing body sample prediction set of the current working face to continue prediction.

[0093] It should be noted that each module in the embodiments of the present invention corresponds one-to-one with each step in the above embodiments, and their specific implementation processes are the same, so they will not be repeated here.

[0094] Example 3

[0095] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the tunnel construction water inflow prediction method based on tunnel-water body analysis as described above.

[0096] Example 4

[0097] A computer program product includes a computer program / instructions that, when executed by a processor, implement the steps in the tunnel construction water inflow prediction method based on tunnel-aquifer analysis as described above.

[0098] Example 5

[0099] The embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps in the tunnel-aquifer analysis-based tunnel construction water inflow prediction method as described above when executing the program.

[0100] The electronic device in the embodiment includes a central processing unit (CPU) which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage section into a random access memory (RAM). In the RAM, various programs and data required for system operation are also stored. The central processing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0101] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, and the like; an output section including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker, and the like; a storage section including a hard disk, and the like; and a communication section including a network interface card such as a local area network (LAN) card, a modem, and the like. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as necessary. A removable medium such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive as necessary, so that a computer program read out therefrom is installed into the storage section as necessary.

[0102] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to the embodiments of the present application. For example, the embodiments of the present application include a computer program product including a computer program carried on a computer-readable medium, the computer program containing program codes for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section, and / or installed from a removable medium. When the computer program is executed by the central processing unit, various functions defined in the apparatus of the present application are executed.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (system) and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce the functions described in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1means for performing the function specified in the block or blocks.

[0104] The above descriptions are only the preferred embodiment of the application, not intended to limit the application. The application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall within the protection scope of the application.

Claims

1. A method for predicting water inflow during tunnel construction based on tunnel-aquifer analysis, characterized in that, include: The local hydrological characteristics of the current working face are acquired and preprocessed. Combined with a pre-established mathematical analytical model of the tunnel-aquifer area, the possible spatial location and water content of the aquifer are predicted, constructing a sample prediction set of the aquifer at the current working face. The predicted water inflow at the tunnel face in each section is as follows: In the formula: Q For the flow rate, k The permeability coefficient of the rock mass is calculated using the frequency domain induced polarization method; ρ 1 represents the extreme diameter centered on the tunnel; Γ1 represents boundary condition 1; H For the prediction set of water-bearing body samples; Assuming the aquifer in the tunnel is circular in a two-dimensional plane, and the distance between the center of the aquifer and the center of the tunnel is... S The radius of the aquifer is R The solution for the water-bearing body sample prediction set, after conformal mapping to polar coordinates, is as follows: ； In the formula: ρ 1, ρ 2 represents the extreme diameter centered on the tunnel and the aquifer, respectively. θ 1, θ 2 represents the polar angle centered on the tunnel and the aquifer, respectively. C 0, C 11 , C 12 , C 21 , C 22 The coefficients are determined by the boundary conditions; The next working face will be divided into sections, and the measured values ​​of water inflow at the tunnel face in different sections will be monitored and statistically analyzed. Based on the current water-bearing body sample prediction set and combined with the tunnel-water-bearing body mathematical analytical model of the study area, the predicted values ​​of water inflow at the tunnel face of each section of the next working face are obtained. The predicted water inflow at each section of the tunnel face in the next working face is compared with the corresponding measured water inflow, and the prediction error is calculated: In the formula: Δ Q For prediction error, Q 1 represents the actual water inflow obtained from monitoring. Q 2. To predict the water inflow, if the error exceeds the preset threshold, update the spatial location and water content parameters of the water-bearing body sample prediction set; repeat the iterative correction until the error converges to the allowable range; based on the convergence, determine whether to directly output the water-bearing body sample prediction set of the next working face and the predicted water inflow value of the tunnel face at each section location, or to iteratively correct the water-bearing body sample prediction set of the current working face to continue prediction.

2. The method for predicting tunnel construction water inflow based on tunnel-aquifer analysis as described in claim 1, characterized in that, When the difference between the predicted water inflow at each section of the tunnel face in the next working face and the corresponding measured water inflow converges within the set range, the predicted set of water-bearing body samples for the next working face and the predicted water inflow at each section of the tunnel face are directly output.

3. The method for predicting tunnel construction water inflow based on tunnel-aquifer analysis as described in claim 1, characterized in that, If the difference between the predicted water inflow at each section of the tunnel face in the current working face and the corresponding measured water inflow is outside the set range, the predicted water-bearing body sample set of the current working face is iteratively corrected until the difference converges within the set range.

4. The method for predicting tunnel construction water inflow based on tunnel-aquifer analysis as described in claim 1, characterized in that, The expression for the pre-established mathematical analytical model of the tunnel-aquifer mathematical model for the study area is as follows: ； ； ； In the formula: H The total head value is... x , y Function, i.e. H ( x , y ); Ω represents the seepage region; Γ1 represents boundary condition 1, i.e., the water head boundary condition at the surface. H G ( x , y ); Γ2 is boundary condition 2, that is, the boundary water head at the inner surface of the tunnel. H T ( x , y ); ( x , y The coordinates of the study area are defined by the horizontal axis of the ground surface and the vertical axis of the tunnel center pointing vertically upwards towards the ground surface.

5. A tunnel construction water inflow prediction system based on tunnel-aquifer analysis, characterized in that, The method for predicting water inflow during tunnel construction based on tunnel-aquifer analysis, as described in any one of claims 1-4, includes the following steps: The aquifer sample prediction set construction module is used to acquire and preprocess the local hydrological characteristics of the current working face, and combine them with a pre-established mathematical analytical model of the tunnel-aquifer in the study area to predict the possible spatial location and water content of the aquifer, thus constructing the aquifer sample prediction set for the current working face; the predicted water inflow at the tunnel face in each section is as follows: In the formula: Q For the flow rate, k The permeability coefficient of the rock mass is calculated using the frequency domain induced polarization method; ρ 1 represents the extreme diameter centered on the tunnel; Γ1 represents boundary condition 1; H For the prediction set of water-bearing body samples; The tunnel face water inflow measurement module is used to divide the next working face into sections, monitor and statistically analyze the measured values ​​of tunnel face water inflow at different section locations. The tunnel face water inflow prediction module is used to derive the predicted values ​​of tunnel face water inflow at each section of the next working face based on the current water-bearing body sample prediction set and the tunnel-water-bearing body mathematical analytical model of the study area. The measured and predicted water inflow comparison module is used to compare the predicted water inflow values ​​at various sections of the tunnel face in the next working face with the corresponding measured water inflow values, and calculate the prediction error. In the formula: Δ Q For prediction error, Q 1 represents the actual water inflow obtained from monitoring. Q 2. To predict the water inflow, if the error exceeds the preset threshold, update the spatial location and water content parameters of the water-bearing body sample prediction set; repeat the iterative correction until the error converges to the allowable range; based on the convergence, determine whether to directly output the water-bearing body sample prediction set of the next working face and the predicted water inflow value of the tunnel face at each section location, or to iteratively correct the water-bearing body sample prediction set of the current working face to continue prediction.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for predicting water inflow during tunnel construction based on tunnel-aquifer analysis as described in any one of claims 1-4.

7. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps in the method for predicting water inflow during tunnel construction based on tunnel-aquifer analysis as described in any one of claims 1-4.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for predicting water inflow during tunnel construction based on tunnel-aquifer analysis as described in any one of claims 1-4.

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