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

Abstract

The invention belongs to the technical field of geological exploration, and provides a tunnel construction water inflow prediction method and system based on tunnel-water-containing body analysis in order to solve the problem that dynamic changes of local hydrological conditions are difficult to reflect accurately at present. The tunnel-water-containing body analysis-based tunnel construction water inflow prediction method comprises the following steps: according to a water-containing body sample prediction set of a current working face, in combination with a tunnel-water-containing body mathematical analysis model of a research area, obtaining a tunnel face water inflow prediction value of each section position of a next working face; and comparing the predicted value of the water inflow of the tunnel face at each section position of the next working face with the corresponding measured value of the water inflow, and judging whether to directly output the water-containing body sample prediction set of the next working face and the predicted value of the water inflow of the tunnel face at each section position according to the convergence condition. And iteratively correcting the water-containing body sample prediction set of the current working face to continue prediction. The reliability of a prediction result can be improved, and a powerful guarantee is provided for tunnel construction safety.

Description

Technical Field

[0001] The present invention belongs to the field of geological exploration technology, and in particular relates to a method and system for predicting water inflow during tunnel construction based on tunnel-water body analysis. Background Art

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

[0003] Water inflow can lead to a range of problems, including foundation pit collapse, equipment damage, material waste, and project suspension. In severe cases, it can also trigger secondary disasters such as surface subsidence or geological collapse, causing immeasurable damage to the surrounding environment and infrastructure. Traditional water inflow prediction methods typically rely on static models or calculations based on simple empirical formulas. These methods may be applicable to certain relatively uniform geological conditions, but are not suitable for areas with complex geological conditions. They struggle to accurately reflect the dynamic changes in local hydrological conditions. This is especially true when aquifers are unevenly distributed, groundwater flow is complex, and geological conditions are variable. Prediction results generally exhibit significant deviations, resulting in a lack of scientific basis for construction plan formulation. Summary of the Invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method and system for predicting water inflow during tunnel construction based on tunnel-water body analysis, which can comprehensively consider local hydrological characteristics, dynamically adjust the prediction model, and quickly predict water inflow. By acquiring hydrological data, constructing a tunnel-water body analysis model and dynamic iterative optimization technology, it can achieve accurate prediction of tunnel water inflow, significantly improve the reliability of the prediction results, and provide strong protection for tunnel construction safety.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a method for predicting water inflow during tunnel construction based on tunnel-water body analysis.

[0006] A method for predicting water inflow during tunnel construction based on tunnel-water body analysis, comprising: Obtain and pre-process the local hydrological characteristics of the current working face, and combine them with the pre-established mathematical analytical model of the tunnel-water body in the study area to predict the possible spatial location and water content of the water body, and construct a water body sample prediction set for the current working face; Divide the next working face into sections, monitor and count the measured values of water inflow at the tunnel face in different sections; Based on the predicted set of water-bearing samples of the current working face and the mathematical analytical model of the tunnel-water-bearing body in the study area, the predicted values of water inflow at the tunnel face in each section of the next working face are obtained; The predicted water inflow values of the tunnel face at each section of the next working face are compared with the corresponding measured water inflow values. Based on the convergence situation, it is determined whether to directly output the water-containing sample prediction set of the next working face and the predicted water inflow values of the tunnel face at each section, or to iteratively correct the water-containing sample prediction set of the current working face to continue the prediction.

[0007] As an implementation method, when the difference between the predicted water inflow value of the tunnel face at each section position of the next working face and the corresponding measured water inflow value converges within a set range, the water-containing sample prediction set of the next working face and the predicted water inflow value of the tunnel face at each section position are directly output.

[0008] As an implementation method, when the difference between the predicted value of the tunnel face water inflow at each section position of the next working face and the corresponding measured value of the water inflow is not within the set range, the predicted set of water-containing samples of the current working face is iteratively corrected until the difference converges within the set range.

[0009] As an implementation method, the pre-established mathematical analytical model of the tunnel-water body in the study area is expressed as follows: ; ; ; Where: is the total head value, Function, that is ; is the seepage area; Boundary condition 1, that is, the hydraulic head boundary condition at the surface ; Boundary condition 2, that is, the boundary head of the tunnel inner surface ; is the coordinate of the study area coordinate system, which takes the horizontal ground surface as the horizontal axis and the axis perpendicular to the ground surface from the center of the tunnel as the vertical axis.

[0010] As an implementation method, it is assumed that the water body in the tunnel is circular on a two-dimensional plane, and the distance between the center of the water body and the center of the tunnel is S , the water body radius is R , the water body sample prediction set can be mapped to polar coordinates by conformal mapping, and the solution is in the form of: ; Where: are the polar diameters centered on the tunnel and the water body, are the polar angles centered on the tunnel and the water body, is the coefficient determined by the boundary conditions.

[0011] As an implementation method, the predicted value of water inflow at the tunnel face in each section is: ; Where: is the water inflow, is the permeability coefficient; is the polar diameter centered on the tunnel; is boundary condition 1; is the prediction set of water body samples.

[0012] A second aspect of the present invention provides a tunnel construction water inflow prediction system based on tunnel-water body analysis.

[0013] A tunnel construction water inflow prediction system based on tunnel-water body analysis, comprising: The water-bearing sample prediction set construction module is used to obtain the local hydrological characteristics of the current working face and perform preprocessing. It is then combined with the pre-established tunnel-water-bearing mathematical analytical model of the study area to predict the possible spatial location and water content of the water-bearing body and construct the water-bearing sample prediction set for the current working face. The tunnel face water inflow measurement module is used to divide the next working face into sections, monitor and count the measured values of tunnel face water inflow at different sections; The tunnel face water inflow prediction module is used to derive the predicted value of tunnel face water inflow at each section of the next working face based on the predicted set of water-bearing body samples at the current working face and the tunnel-water-bearing body mathematical analytical model of the study area; The water inflow measurement prediction comparison module is used to compare the predicted water inflow value of the tunnel face at each section position of the next working face with the corresponding measured water inflow value. Based on the convergence situation, it is determined whether to directly output the water-containing sample prediction set of the next working face and the predicted water inflow value of the tunnel face at each section position, or to iteratively correct the water-containing sample prediction set of the current working face to continue the prediction.

[0014] A third aspect of the present invention provides a computer-readable storage medium.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described above.

[0016] A fourth aspect of the present invention provides a computer program product.

[0017] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described above.

[0018] A fifth aspect of the present invention provides an electronic device.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described above are implemented.

[0020] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention obtains the local hydrological characteristics of the current working face, establishes a tunnel-water body mathematical analytical model of the study area, predicts the possible spatial position and water content of the water body based on the local hydrological information of the study area, constructs a water body sample prediction set of the working face, divides the working face to be mined into sections, monitors and counts the water inflow of the tunnel face at different sections, and obtains the water inflow prediction value of the next position based on the water body sample prediction set of the current working face in combination with the tunnel-water body mathematical analytical model. The water inflow predicted by the working face model is compared with the measured water inflow, and the water body sample prediction set is iteratively corrected to obtain a converged water inflow prediction result and a water body sample prediction set, which significantly improves the accuracy and reliability of tunnel water inflow prediction under complex geological conditions.

[0021] (2) This invention comprehensively considers multiple sources of hydrological information, including borehole water level, rock mass permeability, and dynamic water inflow, and uses induced polarization to obtain the rock mass permeability and construct a mathematical analytical model of the tunnel-water body. Assuming that the tunnel water body is circular on a two-dimensional plane, a predicted set of water body samples is obtained, which improves the speed and accuracy of tunnel head boundary condition calculations.

[0022] (3) The water inflow is calculated by combining the given formula with the analytical model of the water-bearing sample prediction set. The difference between the predicted water inflow and the measured water inflow is evaluated. Based on the dynamic iteration mechanism, the spatial position and water content parameters of the water-bearing sample prediction set are updated. The iterative correction is repeated until the error converges to the allowable range, thereby improving the accuracy of the tunnel water inflow.

[0023] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0025] Figure 1This is a flow chart of a method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a process for predicting water inflow during tunnel construction based on tunnel-water body analysis according to an embodiment of the present invention; Figure 3 is a tunnel-water body analytical model according to an embodiment of the present invention; Figure 4 The present invention is a schematic diagram of a tunnel construction water inflow prediction system based on tunnel-water body analysis. DETAILED DESCRIPTION

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0027] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0028] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0029] Example 1 Combine Figure 1 and Figure 2 The embodiment of the present invention provides a method for predicting water inflow during tunnel construction based on tunnel-water body analysis, which includes: S101: Obtain the local hydrological characteristics of the current working face and perform preprocessing. Combined with the pre-established tunnel-water body mathematical analytical model of the study area, the possible spatial location and water content of the water body are predicted, and a water body sample prediction set of the current working face is constructed.

[0030] In step S101, the local hydrological characteristics of the current working face, including the borehole water level data along the tunnel site, the rock permeability coefficient of the tunnel face, and the local water inflow data, are interpolated to fill in the missing data.

[0031] During the specific implementation process, geological data are used to obtain borehole water level data along the tunnel site.

[0032] The frequency domain induced polarization method is used to calculate the permeability coefficient of the rock mass at the tunnel face. The permeability coefficient of the rock mass at the tunnel face is calculated using formula (1) (2): (1); (2); In the formula: is the formation factor, is the resistivity of the rock sample with 100% water content, is the pore water resistivity, It represents the correction coefficient determined based on the on-site groundwater ion concentration, reflecting the effect of ion concentration on relaxation time and permeability prediction. When using on-site groundwater to establish a permeability prediction model, , is the relaxation time, is the coefficient, which needs to be determined by specific examples. is the rock mass permeability coefficient.

[0033] The local water inflow is calculated based on the water flow velocity in the ditch. The specific method is to introduce the tunnel water into the drainage ditch, select a ditch section with a relatively straight ditch bottom and ditch wall, no rapids in the ditch, and no changes in the cross-section, measure the flow velocity in the drainage ditch, and then calculate the local water inflow.

[0034] After obtaining the above data, the borehole water level and permeability coefficient can be interpolated and supplemented through the Makima interpolation method, thereby improving the integrity of the basic data.

[0035] It should be noted here that those skilled in the art may also use other interpolation methods to interpolate and complete the borehole water level and permeability coefficient, which will not be described in detail here.

[0036] Formula (3), Formula (4), Formula (5) and Formula (6) are non-mean anisotropic equivalent continuous medium models, which are used to obtain the head boundary conditions of the study area, such as Figure 3 As shown: (3); (4); (5); Where: is the total head value, Function, that is , is the seepage area, Boundary condition 1, that is, the hydraulic head boundary condition at the surface ,refer to y =0 horizontal plane; Boundary condition 2, that is, the boundary head of the tunnel inner surface , which can be obtained from the borehole water level; is the coordinate of the study area coordinate system, which takes the horizontal ground surface as the horizontal axis and the axis perpendicular to the ground surface from the center of the tunnel as the vertical axis.

[0037] Assume that the water body in the tunnel is circular on a two-dimensional plane, and the distance between the center of the water body and the center of the tunnel is S , the water body radius is R , the water sample prediction set can be mapped to polar coordinates by conformal mapping, and the solution is as follows: (6); Where: are the polar diameters centered on the tunnel and the water body, are the polar angles centered on the tunnel and the water body, is the coefficient determined by the boundary conditions. The conformal mapping expression is , , parameter A is related to the tunnel depth and radius, It can be obtained by simple coordinate translation transformation; z is an intermediate parameter. When solving the unknown coefficients, the boundary conditions can be introduced one by one.

[0038] By mapping the surface boundary conditions, we can get , substituting into formula (6) we can get the coefficient C 0. The idea of the configuration point method in the weighted residual method is to satisfy the boundary conditions at specific points on the tunnel circumference. Although the result is a weak form of solution, it can meet the accuracy requirements for engineering applications. If you want a more accurate solution, you can force more boundary points to satisfy the boundary conditions. The two points where the tunnel surface intersects the horizontal axis are used as configuration points, and their polar coordinates and head boundary conditions are obtained by transformation. Substituting them into formula (6) can derive the coefficients C 11 , C 12 , C 21 , C 22 Assuming that the water head at the water body is consistent with the surface, two points on the circumference of the water body that intersect the horizontal axis are selected as configuration points. The coordinates of the two points are conformally mapped and then inserted into formula (6) to obtain the other two sets of relationship equations. By combining the above four sets of equations, the weak solution of the seepage field corresponding to a specific water body can be obtained. By solving multiple different working conditions, a water body sample prediction set can be obtained.

[0039] S102: Divide the next working face into sections, monitor and count the measured values of water inflow at the tunnel face in different sections.

[0040] Among them, the section division is based on the geological profile characteristics and geological exploration data of the construction area, and the zoning settings under complex geological conditions are dynamically adjusted to divide the working face to be mined into several sections.

[0041] For example, for sections with relatively uniform geological conditions, equal-length segmentation can be used; while for areas with complex geological conditions, the section length can be dynamically adjusted according to the local rock mass and aquifer distribution characteristics to improve the accuracy of data collection.

[0042] Monitoring points are deployed within each section to record the real-time water inflow from the tunnel face. Water inflow can be measured using either ditch flow velocity or flow volume measurement. Measuring water inflow based on ditch flow velocity involves directing tunnel water into a drainage ditch. A section with a relatively straight bottom and walls, no rapids, and no cross-sectional changes is selected. The flow velocity in the ditch is then measured to calculate the local water inflow. The flow volume measurement method calculates the volume of water flowing into the tunnel drainage system per unit time to determine the water inflow.

[0043] S103: Based on the predicted set of water-bearing body samples of the current working face and in combination with the tunnel-water-bearing body mathematical analytical model of the study area, the predicted values of water inflow at the tunnel face at each section position of the next working face are obtained.

[0044] Among them, the predicted value of water inflow at the tunnel face in each section is: (7); Where: is the water inflow, is the permeability coefficient; is the polar diameter centered on the tunnel; is boundary condition 1, which means y =0 horizontal plane; is the prediction set of water body samples.

[0045] According to the progress of tunnel construction, the spatial resolution of the prediction model is gradually adjusted to ensure that the prediction results of each working face can be adapted to the actual geological characteristics.

[0046] S104: Compare the predicted values of water inflow at the tunnel face at each section position of the next working face with the corresponding measured values of water inflow. Based on the convergence situation, determine whether to directly output the water body sample prediction set of the next working face and the predicted values of water inflow at the tunnel face at each section position, or to iteratively correct the water body sample prediction set of the current working face to continue the prediction.

[0047] In step S104, when the difference between the predicted value of the water inflow at the tunnel face at each section position of the next working face and the corresponding measured value of the water inflow converges within the set range, the water body sample prediction set of the next working face and the predicted value of the water inflow at each section position of the tunnel face are directly output.

[0048] When the difference between the predicted value of the tunnel face water inflow at each section position of the next working face and the corresponding measured value of the water inflow is not within the set range, the predicted set of water-bearing sample of the current working face is iteratively corrected until the difference converges within the set range.

[0049] For example, by comparing the model predictions with the observed values, the prediction error is calculated: (8); Where: is the prediction error, The actual water inflow obtained by monitoring is To predict water inflow.

[0050] This embodiment utilizes a dynamic iteration mechanism. By comparing the water inflow predicted by the working face model with the measured water inflow in real time, the prediction error is calculated and quantitatively evaluated. If the error exceeds a preset threshold, the spatial location and water content parameters of the predicted water sample set are updated. Iterative corrections are repeated until the error converges to an acceptable range. This allows the model and dataset to be dynamically optimized as hydrological conditions change during tunnel construction, improving the real-time and accuracy of water inflow prediction. This process ensures the dynamic adaptability and high accuracy of the prediction model.

[0051] Example 2 like Figure 4 As shown, an embodiment of the present invention provides a tunnel construction water inflow prediction system based on tunnel-water body analysis, which includes: The water-containing sample prediction set construction module 401 is used to obtain the local hydrological characteristics of the current working face and perform preprocessing. In combination with the pre-established tunnel-water-containing mathematical analytical model of the study area, the module predicts the possible spatial location and water content of the water-containing body and constructs the water-containing sample prediction set of the current working face. The tunnel face water inflow measurement module 402 is used to divide the next working face into sections, monitor and count the measured values of tunnel face water inflow at different sections; The tunnel face water inflow prediction module 403 is used to obtain the predicted value of the tunnel face water inflow at each section of the next working face based on the predicted set of water-bearing body samples of the current working face and the tunnel-water-bearing body mathematical analytical model of the study area; The water inflow measurement prediction comparison module 404 is used to compare the water inflow prediction value of the tunnel face at each section position of the next working face with the corresponding measured water inflow value. According to the convergence situation, it is determined whether to directly output the water body sample prediction set of the next working face and the water inflow prediction value of the tunnel face at each section position, or to iteratively correct the water body sample prediction set of the current working face to continue the prediction.

[0052] It should be noted here that the various modules in the embodiment of the present invention correspond one-to-one to the various steps in the above embodiment, and their specific implementation processes are the same, which will not be repeated here.

[0053] Example 3 This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described above are implemented.

[0054] Example 4 A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described above.

[0055] Example 5 This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described above are implemented.

[0056] In this embodiment, the electronic device includes a central processing unit (CPU), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) or programs loaded from a storage unit into a random access memory (RAM). The RAM also stores various programs and data required for system operation. The CPU, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0057] The following components are connected to the I / O interface: an input section including a keyboard and mouse; an output section including cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; a storage section including a hard disk; and a communication section including network interface cards such as local area network (LAN) cards and modems. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media such as magnetic disks, optical disks, magneto-optical disks, and semiconductor memories are installed in the drive as needed, allowing computer programs read from these media to be installed in the storage section as needed.

[0058] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication portion, and / or installed from a removable medium. When the computer program is executed by a central processing unit, the various functions defined in the apparatus of the present application are performed.

[0059] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams and the combination of processes 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for predicting water inflow during tunnel construction based on tunnel-water body analysis, characterized in that: include: Obtain and pre-process the local hydrological characteristics of the current working face, and combine them with the pre-established mathematical analytical model of the tunnel-water body in the study area to predict the possible spatial location and water content of the water body, and construct a water body sample prediction set for the current working face; Divide the next working face into sections, monitor and count the measured values of water inflow at the tunnel face in different sections; Based on the predicted set of water-bearing samples of the current working face and the mathematical analytical model of the tunnel-water-bearing body in the study area, the predicted values of water inflow at the tunnel face in each section of the next working face are obtained; The predicted water inflow values of the tunnel face at each section of the next working face are compared with the corresponding measured water inflow values. Based on the convergence situation, it is determined whether to directly output the water-containing sample prediction set of the next working face and the predicted water inflow values of the tunnel face at each section, or to iteratively correct the water-containing sample prediction set of the current working face to continue the prediction.

2. The method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to claim 1, wherein: When the difference between the predicted value of the water inflow at the tunnel face in each section position of the next working face and the corresponding measured value of the water inflow converges within the set range, the water-containing sample prediction set of the next working face and the predicted value of the water inflow at the tunnel face in each section position are directly output.

3. The method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to claim 1, wherein: When the difference between the predicted value of the tunnel face water inflow at each section position of the next working face and the corresponding measured value of the water inflow is not within the set range, the predicted set of water-bearing sample of the current working face is iteratively corrected until the difference converges within the set range.

4. The method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to claim 1, wherein: The expression of the pre-established mathematical analytical model of the tunnel-water body in the study area is: ； ； ； Where: is the total head value, Function, that is ; is the seepage area; Boundary condition 1, that is, the hydraulic head boundary condition at the surface ; Boundary condition 2, that is, the boundary head of the tunnel inner surface ; is the coordinate of the study area coordinate system, which takes the horizontal ground surface as the horizontal axis and the axis perpendicular to the ground surface from the center of the tunnel as the vertical axis.

5. The method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to claim 1, wherein: Assume that the water body in the tunnel is circular on a two-dimensional plane, and the distance between the center of the water body and the center of the tunnel is S , the water body radius is R , the solution of the water body sample prediction set is mapped to polar coordinates by conformal mapping: ； Where: are the polar diameters centered on the tunnel and the water body, are the polar angles centered on the tunnel and the water body, is the coefficient determined by the boundary conditions.

6. The method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to claim 1, wherein: The predicted values of water inflow at the tunnel face in each section are: ； Where: is the water inflow, is the permeability coefficient; is the polar diameter centered on the tunnel; is boundary condition 1; is the water body sample prediction set.

7. A tunnel construction water inflow prediction system based on tunnel-water body analysis, characterized in that: include: The water-bearing sample prediction set construction module is used to obtain the local hydrological characteristics of the current working face and perform preprocessing. It is then combined with the pre-established tunnel-water-bearing mathematical analytical model of the study area to predict the possible spatial location and water content of the water-bearing body and construct the water-bearing sample prediction set for the current working face. The tunnel face water inflow measurement module is used to divide the next working face into sections, monitor and count the measured values of tunnel face water inflow at different sections; The tunnel face water inflow prediction module is used to derive the predicted value of tunnel face water inflow at each section of the next working face based on the predicted set of water-bearing body samples at the current working face and the tunnel-water-bearing body mathematical analytical model of the study area; The water inflow measurement prediction comparison module is used to compare the predicted water inflow value of the tunnel face at each section position of the next working face with the corresponding measured water inflow value. Based on the convergence situation, it is determined whether to directly output the water-containing sample prediction set of the next working face and the predicted water inflow value of the tunnel face at each section position, or to iteratively correct the water-containing sample prediction set of the current working face to continue the prediction.

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

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis as described in any one of claims 1 to 6 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for predicting water inflow during tunnel construction based on tunnel-water body analysis according to any one of claims 1 to 6 are implemented.

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