Tunnel advanced geological forecast risk early warning method and system, terminal and storage medium

Abstract

The invention belongs to the technical field of tunnel early warning, and particularly relates to a tunnel advanced geological forecast risk early warning method and system, a terminal and a storage medium, and the method comprises the steps: carrying out the non-contact scanning of the geological condition in front of tunneling of a tunnel, and obtaining the geological data; preprocessing the collected geological data, and performing three-dimensional modeling on the preprocessed geological data to construct a three-dimensional model of the geological structure in front of the tunnel; identifying a potential unfavorable geologic body based on the constructed three-dimensional geologic model; the geological data is obtained through non-contact scanning, interference of a traditional exploration method under complex geological conditions is avoided, and the data quality and the forecasting precision are effectively improved. Compared with a traditional geological exploration and drilling sampling method, the method has the advantages that risk assessment is carried out through a pre-constructed geological disaster risk assessment model, risk grade division is carried out according to a risk assessment result, and different early warning thresholds are set; lots of manpower and material resource investments are not needed, and the construction cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel early warning, and particularly relates to a method, a system, a terminal and a storage medium for risk early warning of tunnel advanced geological prediction. Background Art

[0002] In the construction of tunnel projects, the geological conditions are complex and changeable, and various adverse geological phenomena are often encountered, such as faults, soft interlayers, karst caves, and groundwater enrichment areas. These adverse geological bodies not only seriously affect the construction safety of tunnels, but also may lead to construction period delays and cost increases. Therefore, accurate prediction and risk assessment of the geological conditions ahead of tunnel excavation are of great significance for ensuring tunnel construction safety and optimizing construction plans.

[0003] Traditional tunnel geological prediction methods mainly rely on means such as geological exploration and drilling sampling, such as seismic wave exploration and electromagnetic wave exploration. These methods are easily interfered with under complex geological conditions (the collected geological data often contains a large amount of noise and redundant information), resulting in inaccurate detection results. In addition, although these methods can provide certain geological information, they often have problems such as limited detection range, high cost, and insufficient accuracy in identifying geological bodies. Especially under complex geological conditions, traditional methods are often difficult to accurately judge the location and scale of adverse geological bodies, bringing great uncertainty and risks to tunnel construction; and most of the existing warning mechanisms rely on manual judgment, lacking automated and intelligent warning means, resulting in untimely and inaccurate warning responses, and it is difficult to effectively ensure the safety of tunnel construction. Summary of the Invention

[0004] In view of the above deficiencies of the prior art, the present invention provides a method, a system, a terminal and a storage medium for risk early warning of tunnel advanced geological prediction to solve the above technical problems.

[0005] In a first aspect, the present invention provides a method for risk early warning of tunnel advanced geological prediction, including: Performing non-contact scanning on the geological conditions ahead of tunnel excavation to obtain geological data; Preprocessing the collected geological data, and performing three-dimensional modeling on the preprocessed geological data to construct a three-dimensional model of the geological structure ahead of the tunnel; Based on the constructed three-dimensional geological model, identifying potential adverse geological bodies, and the adverse geological bodies include faults, soft interlayers, karst caves, and groundwater enrichment areas; Inputting the identified adverse geological bodies into a pre-constructed geological disaster risk assessment model for risk assessment; Dividing the risk assessment results into risk levels, and different warning thresholds are set for each risk level. When the predicted risk reaches or exceeds the set threshold, a corresponding warning mechanism is triggered.

[0006] A further improvement of this technical solution is that the instrument for non-contact scanning includes at least one of a ground penetrating radar or an electromagnetic wave CT scanner.

[0007] A further improvement of this technical solution is that the collected geological data is preprocessed, and the preprocessed geological data is used for three-dimensional modeling to construct a three-dimensional model of the geological structure in front of the tunnel. The specific method includes: Performing data denoising, data enhancement, and data normalization on the collected geological data, and inputting the preprocessed geological data into a three-dimensional modeling tool; Identifying different geological bodies according to the characteristics of the geological data, and classifying the identified geological bodies; Generating a three-dimensional grid according to the distribution and shape of the geological bodies, and setting the corresponding grid density and resolution; the three-dimensional grid includes several grid units, and corresponding geological attributes are assigned to each grid unit; Using a three-dimensional modeling tool to construct a three-dimensional model of the geological structure in front of the tunnel according to the three-dimensional grid and attribute data; Verifying the constructed three-dimensional model using historical data, and optimizing and adjusting the constructed three-dimensional model according to the verification results.

[0008] A further improvement of this technical solution is that the method for constructing a geological disaster risk assessment model includes: Obtaining historical geological data, and decomposing the obtained geological data into several geological elements; Analyzing the relationships between the geological elements, and establishing a hierarchical structure for geological disaster risk assessment. The hierarchical structure includes a target layer, a criterion layer, and a factor layer from top to bottom; Making pairwise comparisons of the importance of each element in the same factor layer with respect to a preset criterion in the upper criterion layer to construct a pairwise comparison judgment matrix; Calculating the relative weights of the compared elements with respect to the preset criterion according to the judgment matrix, and performing a consistency test for single-layer elements; Calculating the composite weights of each layer of elements with respect to the target layer, performing sorting, and performing an overall consistency test; After completing the overall consistency test, inputting the pre-prepared training geological data into the established hierarchical structure for training to obtain a geological disaster risk assessment model.

[0009] A further improvement of this technical solution is that the judgment matrix is: ; where A is the judgment matrix, is the relative importance degree between the i-th factor and the j-th factor; is the relative weight for the preset criteria in the previous criterion layer.

[0010] A further improvement of this technical solution is that the method for performing single-layer element consistency test according to weights specifically includes: If the judgment matrix A satisfies: , then the judgment matrix A has perfect consistency; Otherwise, calculate the metric judgment matrix A deviation consistency index CI according to the maximum eigenvalue of the judgment matrix A; Calculate the value of the random consistency ratio CR according to the deviation consistency index CI and the average random consistency index RI of the same order; Judge whether the calculated value of the random consistency ratio CR is less than the preset consistency ratio value; If so, it is determined that the matrix A has consistency; otherwise, adjust the geological element values of the judgment matrix A to make the judgment matrix A meet the preset consistency requirements.

[0011] A further improvement of this technical solution is that the composite weights of each layer of elements with respect to the target layer are calculated, sorted, and the overall consistency test is performed. The method specifically includes: The consistency test of the hierarchical total sorting is carried out layer by layer from the upper layer to the lower layer. The consistency test of the hierarchical total sorting is determined by the random consistency ratio of the hierarchical total sorting. The calculation formula of the random consistency ratio of the hierarchical total sorting is: ; Wherein, is the random consistency ratio of the hierarchical total sorting of the kth layer with respect to the factor layer, is the weight vector of the jth factor of the (k - 1)th layer with respect to the kth layer, is the random consistency index of the jth factor of the (k - 1)th layer with respect to the kth layer, is the average random consistency index of the jth factor of the (k - 1)th layer with respect to the kth layer, is the transpose of the weight vector of each factor of the (k - 1)th layer with respect to the kth layer, is the hierarchical total single sorting consistency index of the (k - 1)th layer with respect to the factor layer.

[0012] In a second aspect, the present invention provides a tunnel advanced geological prediction risk warning system, including: An address data acquisition module for non-contact scanning of the geological conditions in front of the tunnel excavation to obtain geological data; A three-dimensional model construction module for preprocessing the collected geological data and performing three-dimensional modeling on the preprocessed geological data to construct a three-dimensional model of the geological structure in front of the tunnel; The poor geological body identification module is used to identify potential poor geological bodies based on the constructed three-dimensional geological model. The poor geological bodies include faults, weak interlayers, karst caves, and groundwater enrichment areas; The geological risk assessment module is used to input the identified poor geological bodies into a pre-constructed geological disaster risk assessment model for risk assessment; The risk warning module is used to classify the risk levels of the risk assessment results. Different warning thresholds are set for each risk level. When the predicted risk reaches or exceeds the set threshold, the corresponding warning mechanism is triggered.

[0013] Thirdly, the present invention provides a terminal, including: A processor and a memory, wherein, The memory is used to store a computer program, The processor is used to call and run the computer program from the memory, so that the terminal executes the method of the above-mentioned terminal.

[0014] Fourthly, the present invention provides a computer storage medium. Instructions are stored in the computer-readable storage medium. When it runs on a computer, the computer is enabled to execute the methods described in the above aspects.

[0015] The beneficial effects of the present invention are as follows: Improve the prediction accuracy and efficiency: Geological data is obtained through non-contact scanning (such as ground penetrating radar or electromagnetic wave CT scanner), avoiding the interference of traditional exploration methods under complex geological conditions, effectively improving the data quality and prediction accuracy. At the same time, the application of three-dimensional modeling can intuitively display the geological structure in front of the tunnel, facilitating the rapid identification of potential poor geological bodies and improving the prediction efficiency.

[0016] Reduce costs and risks: Compared with traditional geological exploration and drilling sampling methods, the technical solution of the present invention does not require a large amount of human and material resources investment, reducing the construction cost. At the same time, through accurate risk assessment and risk level classification, potential geological disasters can be warned in advance, reducing the uncertainty and risks during the construction process and ensuring the safety of tunnel construction.

[0017] Intelligent and automatic warning: The technical solution of the present invention conducts risk assessment through a pre-constructed geological disaster risk assessment model, classifies the risk levels according to the risk assessment results, and sets different warning thresholds. When the predicted risk reaches or exceeds the set threshold, the corresponding warning mechanism is automatically triggered, realizing intelligent and automatic warning response and improving the timeliness and accuracy of warning.

[0018] Three-dimensional model optimization and verification: Through preprocessing the collected geological data and three-dimensional modeling, the constructed three-dimensional model is verified and optimized using historical data to ensure the accuracy and reliability of the model.

[0019] Scientific risk assessment system: By constructing a geological disaster risk assessment model and using methods such as hierarchical structure, judgment matrix, and weight calculation, systematic analysis and evaluation of geological elements are carried out, and a scientific risk assessment system is established.

[0020] Consistency test and model optimization: When constructing the geological disaster risk assessment model and calculating the synthetic weights of each layer of elements for the target layer, methods such as consistency test and random consistency ratio calculation are used to ensure the stability and reliability of the model. At the same time, the model is optimized and adjusted according to the verification results, improving the prediction ability and adaptability of the model.

[0021] In addition, the design principle of the present invention is reliable, the structure is simple, and it has a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a schematic flowchart of the method according to an embodiment of the present invention.

[0024] Figure 2 is a schematic block diagram of the system according to an embodiment of the present invention.

[0025] Figure 3 is a schematic structural diagram of a terminal provided by an embodiment of the present invention.

[0026] 210 is an address data acquisition module, 220 is a three-dimensional model construction module, 230 is a bad geological body identification module, 240 is a geological risk assessment module, and 250 is a risk warning module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the present invention herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention.

[0029] The tunnel advanced geological prediction risk warning method provided by the embodiments of the present invention is executed by a computer device. Correspondingly, the tunnel advanced geological prediction risk warning system runs in the computer device.

[0030] Figure 1 It is a schematic flowchart of the method of an embodiment of the present invention. Among them, Figure 1 The execution subject can be a tunnel advanced geological prediction risk warning system. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0031] As Figure 1 shown, the method includes: Step 110, perform non-contact scanning on the geological conditions in front of the tunnel excavation to obtain geological data; Step 120, preprocess the collected geological data, and perform three-dimensional modeling on the preprocessed geological data to construct a three-dimensional model of the geological structure in front of the tunnel; Step 130, based on the constructed three-dimensional geological model, identify potential poor geological bodies, and the poor geological bodies include faults, weak interlayers, karst caves, and groundwater enrichment areas; Step 140, input the identified poor geological bodies into a pre-constructed geological disaster risk assessment model for risk assessment; Step 150, divide the risk assessment results into risk levels, and different warning thresholds are set for each risk level. When the predicted risk reaches or exceeds the set threshold, trigger the corresponding warning mechanism.

[0032] For the convenience of understanding the present invention, the principle of the tunnel advanced geological prediction risk warning method of the present invention is hereinafter further described in combination with the process of the risk warning method for tunnel advanced geological prediction in the embodiments.

[0033] Before performing non-contact scanning on the geological conditions ahead of tunnel excavation, it also includes using an unmanned aerial vehicle (UAV) equipped with a high-resolution camera or lidar to obtain detailed information on the surface geology. This step, as the preliminary stage of the entire prediction method, aims to initially understand the geological structure and potential risks of the surface and shallow layers in a non-contact, efficient, and comprehensive manner, providing basic data and references for subsequent in-depth geological scanning and modeling. Specifically, by using a high-resolution camera or lidar equipped on the UAV, large-scale and high-precision surface geological information of the tunnel excavation area can be collected from the air. The high-resolution camera can capture features such as geological textures, colors, and shapes on the surface, while lidar can obtain geological elevation data of the surface and shallow layers, and these data will provide valuable information for subsequent geological analysis and modeling.

[0034] To avoid interference of traditional exploration methods under complex geological conditions and effectively improve data quality and prediction accuracy, the present invention performs non-contact scanning. The instrument for non-contact scanning includes at least one of a ground-penetrating radar or an electromagnetic wave CT scanner. When using a ground-penetrating radar, a ground-penetrating radar survey line needs to be set in front of the tunnel excavation first to ensure that the survey line covers the entire potential area of bad geological bodies; then start the ground-penetrating radar equipment and perform scanning according to the set parameters (such as frequency, sampling rate, etc.) to obtain geological data. When using an electromagnetic wave CT (Computed Tomography) scanner, an electromagnetic wave CT scanner needs to be arranged in front of the tunnel excavation first to ensure that the scanning area covers the potential bad geological bodies, and perform scanning according to the set scanning parameters (such as electromagnetic wave frequency, scanning angle, etc.) to obtain geological data; and the electromagnetic wave CT scanner can accurately measure the electromagnetic parameters of underground media (such as resistivity, dielectric constant, etc.), thereby reflecting the physical properties of geological bodies.

[0035] Specifically, the collected geological data is preprocessed, and a three-dimensional model of the geological structure ahead of the tunnel is constructed by performing three-dimensional modeling on the preprocessed geological data. The method specifically includes: S121. Perform data denoising, data enhancement, and data normalization processing on the collected geological data, and input the preprocessed geological data into a three-dimensional modeling tool; S122. Identify different geological bodies according to the characteristics of the geological data, and classify the identified geological bodies; S123. Generate a three-dimensional grid according to the distribution and shape of the geological bodies, and set the corresponding grid density and resolution; the three-dimensional grid includes several grid cells, and corresponding geological attributes are assigned to each grid cell; S124. Use the three-dimensional modeling tool to construct a three-dimensional model of the geological structure ahead of the tunnel based on the three-dimensional grid and attribute data; S125. Verify the constructed 3D model using historical data, and optimize and adjust the constructed 3D model according to the verification results.

[0036] Among them, for the collected geological data, data denoising processing can be carried out. Filtering algorithms (such as median filtering, Gaussian filtering, etc.) can be used to remove the noise components in the data. After that, interpolation processing is carried out on the denoised data to fill in the missing parts of the data, and signal enhancement technologies (such as wavelet transform, Fourier transform, etc.) are applied to improve the detail representation ability of the data, improve the signal-to-noise ratio and resolution of the data. Finally, data from different sources and with different dimensions are unified to the same scale for subsequent analysis. In addition, 3D modeling tools such as ArcGIS, Surfer, GoCAD, PLAXIS, gINT, etc. can be used. The preprocessed geological data is imported into the 3D modeling tool to prepare for the subsequent 3D modeling work.

[0037] As can be seen from the above, the construction stage of the 3D model of the geological structure includes key steps such as geological body identification and classification, 3D grid generation, geological attribute assignment, and 3D model construction. First, different geological bodies such as faults, weak interlayers, karst caves, groundwater enrichment areas, etc. are identified and classified according to the characteristics of the geological data (such as reflection intensity, waveform, etc.). Then, using the grid generation function in the 3D modeling tool, 3D grids are generated according to the distribution and shape of the geological bodies, and appropriate density and resolution are set to accurately reflect the detailed characteristics of the geological bodies. Then, corresponding geological attributes such as lithology, density, resistivity, etc. are assigned to each grid unit to ensure the accuracy and consistency of the attributes, providing a reliable basis for subsequent geological hazard risk assessment. Finally, using the model construction function in the 3D modeling tool, a 3D model of the geological structure in front of the tunnel is constructed based on the 3D grid and attribute data, and rendering and visualization processing are carried out for intuitive display and analysis. In the model verification and optimization stage, the model is verified using historical geological data, its accuracy and reliability are evaluated, and the model is optimized and adjusted according to the verification results until it meets the actual application requirements.

[0038] In addition, the methods for constructing a geological hazard risk assessment model include: S141. Obtain historical geological data and decompose the obtained geological data into several geological elements; S142. Analyze the relationships between the geological elements, establish a hierarchical structure for geological hazard risk assessment, and the hierarchical structure includes a target layer, a criterion layer, and a factor layer from top to bottom; S143. Compare the importance of each element in the same factor layer pairwise with respect to the preset criterion in the previous criterion layer, and construct a pairwise comparison judgment matrix; S144. Calculate the relative weights of the compared elements with respect to the preset criterion according to the judgment matrix, and conduct the consistency test for single-layer elements; S145. Calculate the synthetic weights of each layer of elements with respect to the target layer, conduct sorting, and conduct the overall consistency test; S146. After completing the overall consistency test, input the pre-prepared training geological data into the established hierarchical structure for training to obtain a geological disaster risk assessment model.

[0039] Specifically, the judgment matrix is: ; where A is the judgment matrix, is the relative importance degree between the i-th factor and the j-th factor; is the relative weight with respect to the preset criterion in the previous criterion layer.

[0040] Specifically, the method for conducting the consistency test for single-layer elements according to the weights specifically includes: S1441. If the judgment matrix A satisfies: , then the judgment matrix A has perfect consistency; if not, go to S1442; S1442. Calculate the measure of the deviation of the judgment matrix A from the consistency index CI according to the largest eigenvalue of the judgment matrix A; S1443. Calculate the value of the random consistency ratio CR according to the deviation consistency index CI and the average random consistency index RI of the same order; S1444. Judge whether the value of the calculated random consistency ratio CR is less than the preset consistency ratio value; if so, go to S1445; S1445. Determine that the matrix A has consistency; otherwise, adjust the geological element values of the judgment matrix A to make the judgment matrix A meet the preset consistency requirements.

[0041] Specifically, the calculation formula for the largest eigenvalue of matrix A is: ; where n is the number of factors in matrix A, is the i-th factor of the vector , is the i-th element of the weight vector w.

[0042] The calculation formula for the deviation consistency index CI is: .

[0043] In addition, the value of the average random consistency index RI can be obtained from Table 1 obtained through Saaty's experiment, and there are values from 1 to 11 orders.

[0044] Table 1 is the table of average random consistency index RI values

[0045] Among them, the calculation formula for the random consistency ratio CR of each layer is: CR = CI / RI. When CR < 0.1, it is considered that matrix A has satisfactory consistency. Otherwise, the factor values of matrix A need to be adjusted to meet the consistency requirements.

[0046] In addition, calculate the composite weights of each layer of elements with respect to the target layer, perform sorting, and conduct an overall consistency test. The specific methods include: The consistency test of the overall sorting of the hierarchy is carried out layer by layer from the upper layer to the lower layer. The consistency test of the overall sorting of the hierarchy is determined by the random consistency ratio of the overall sorting of the hierarchy. The calculation formula for the random consistency ratio of the overall sorting of the hierarchy is: ; Among them, is the random consistency ratio of the overall sorting of the k-th layer with respect to the factor layer, is the weight vector of the j-th factor of the (k - 1)-th layer with respect to the k-th layer, is the random consistency index of the j-th factor of the (k - 1)-th layer with respect to the k-th layer, is the average random consistency index of the j-th factor of the (k - 1)-th layer with respect to the k-th layer, is the transpose of the weight vector of each factor of the (k - 1)-th layer with respect to the k-th layer, is the consistency index of the overall single sorting of the (k - 1)-th layer with respect to the factor layer. When all (assuming the hierarchical structure of the model has p layers), it is considered that the result of the overall sorting of the hierarchy has satisfactory consistency. Otherwise, the factor values of matrix A need to be readjusted.

[0047] In some embodiments, the tunnel advanced geological prediction risk warning system 200 may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the tunnel advanced geological prediction risk warning system 200 can be stored in the memory of the computer device and executed by at least one processor to perform (see details in Figure 1 description) the functions of tunnel advanced geological prediction risk warning.

[0048] In this embodiment, according to the functions it performs, the tunnel advanced geological prediction risk warning system 200 can be divided into multiple functional modules, such as Figure 2As shown in the figure. The functional modules may include: an address data acquisition module 210, a three-dimensional model construction module 220, a bad geological body identification module 230, a geological risk assessment module 240, and a risk warning module 250. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and is stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0049] Specifically, the address data acquisition module is used to perform non-contact scanning on the geological conditions in front of the tunnel excavation to obtain geological data; the three-dimensional model construction module is used to preprocess the collected geological data and perform three-dimensional modeling on the preprocessed geological data to construct a three-dimensional model of the geological structure in front of the tunnel; the bad geological body identification module is used to identify potential bad geological bodies based on the constructed three-dimensional geological model, and the bad geological bodies include faults, soft interlayers, karst caves, and groundwater enrichment areas; the geological risk assessment module is used to input the identified bad geological bodies into a pre-constructed geological disaster risk assessment model for risk assessment; the risk warning module is used to divide the risk levels of the risk assessment results, and different warning thresholds are set for each risk level. When the predicted risk reaches or exceeds the set threshold, the corresponding warning mechanism is triggered.

[0050] Figure 3 It is a schematic structural diagram of a terminal 300 provided by an embodiment of the present invention. The terminal 300 can be used to execute the tunnel advanced geological prediction risk warning method provided by the embodiment of the present invention.

[0051] Among them, the terminal 300 may include: a processor 310, a memory 320, and a communication module 330. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation to the present invention. It can be a bus structure, a star structure, and may also include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0052] Among them, the memory 320 can be used to store the execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can execute some or all of the steps in the above method embodiments.

[0053] The processor 310 is the control center of the storage terminal, connecting various parts of the entire electronic terminal through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 320, and by invoking the data stored in the memory, it performs various functions of the electronic terminal and / or processes data. The processor may be composed of an integrated circuit (IC), for example, it may be composed of a single packaged IC, or it may be composed of multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may only include a central processing unit (CPU). In the embodiments of the present invention, the CPU may be a single arithmetic core or may include multiple arithmetic cores.

[0054] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals. It receives user data sent by other terminals or sends user data to other terminals.

[0055] The present invention also provides a computer storage medium. Among them, the computer storage medium can store a program, and when the program is executed, it can include some or all of the steps in the embodiments provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), etc.

[0056] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes, and includes several instructions to enable a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0057] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the descriptions in the method embodiments.

[0058] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the systems or modules can be in electrical, mechanical or other forms.

[0059] The modules described as separate components may or may not be physically separated. The components displayed as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0061] Although the present invention has been described in detail by referring to the drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope of the present invention. / Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, and they should all be covered within the protection scope of the present invention.

Claims

1. A tunnel advanced geological prediction risk warning method, characterized in that: include: Conduct non-contact scanning of the geological conditions ahead of tunnel excavation to obtain geological data; Preprocess the collected geological data, and perform 3D modeling on the preprocessed geological data to construct a 3D model of the geological structure in front of the tunnel; Based on the constructed 3D geological model, potential adverse geological bodies are identified, including faults, weak interlayers, karst caves and groundwater-rich areas; Input the identified adverse geological bodies into the pre-built geological hazard risk assessment model for risk assessment; The risk assessment results are divided into risk levels, and each risk level is set with a different warning threshold. When the predicted risk reaches or exceeds the set threshold, the corresponding warning mechanism is triggered.

2. The tunnel advanced geological prediction risk warning method according to claim 1 is characterized in that: The instrument for performing non-contact scanning includes at least one of a geological radar or an electromagnetic wave CT scanner.

3. The tunnel advanced geological prediction risk warning method according to claim 1 is characterized in that: The collected geological data are preprocessed, and three-dimensional modeling is performed on the preprocessed geological data to construct a three-dimensional model of the geological structure in front of the tunnel. The method specifically includes: Perform data de-noising, data enhancement and data normalization on the collected geological data, and input the pre-processed geological data into the 3D modeling tool; According to the characteristics of geological data, different geological bodies are identified and classified; According to the distribution and morphology of the geological body, a three-dimensional grid is generated, and the corresponding grid density and resolution are set; the three-dimensional grid includes a number of grid cells, and each grid cell is assigned a corresponding geological attribute; Use 3D modeling tools to construct a 3D model of the geological structure ahead of the tunnel based on 3D grid and attribute data; The constructed 3D model is verified using historical data, and optimized and adjusted based on the verification results.

4. The tunnel advanced geological prediction risk warning method according to claim 1 is characterized in that: Methods for constructing geological hazard risk assessment models include: Obtain historical geological data and decompose the acquired geological data into several geological elements; Analyze the relationship between various geological elements and establish a hierarchical structure for geological hazard risk assessment, which includes the target layer, criterion layer and factor layer from top to bottom; Compare the importance of each element in the same factor layer with respect to the preset criteria in the previous criterion layer, and construct a judgment matrix for pairwise comparison; The relative weights of the compared elements to the preset criteria are calculated according to the judgment matrix, and the consistency check of the single-layer elements is performed; Calculate the composite weight of each layer element to the target layer, sort them, and perform an overall consistency check; After completing the overall consistency test, the pre-prepared training geological data is input into the established hierarchical structure for training to obtain a geological hazard risk assessment model.

5. The tunnel advanced geological prediction risk warning method according to claim 4 is characterized in that: The judgment matrix is: ； Among them, A is the judgment matrix, is the relative importance of the i-th factor and the j-th factor; is the relative weight of the preset criteria in the previous criterion layer.

6. The tunnel advanced geological prediction risk warning method according to claim 5 is characterized in that: The method for performing consistency check of single-layer elements according to weights specifically includes: If the judgment matrix A satisfies: , then the judgment matrix A has complete consistency; Otherwise, the deviation of the judgment matrix A from the consistency index CI is calculated based on the maximum characteristic root of the judgment matrix A; The value of random consistency ratio CR is calculated based on the deviation consistency index CI and the same-order average random consistency index RI; Determine whether the calculated random consistency ratio CR value is less than a preset consistency ratio value; If so, the judgment matrix A is consistent. Otherwise, the geological element values ​​of the judgment matrix A are adjusted to make the judgment matrix A meet the preset consistency requirements.

7. The tunnel advanced geological prediction risk warning method according to claim 6 is characterized in that: Calculate the synthetic weight of each layer element to the target layer, sort them, and perform an overall consistency check. The specific method includes: The consistency check of the hierarchical total order is performed layer by layer from the upper layer to the lower layer. The consistency check of the hierarchical total order is determined by the random consistency ratio of the hierarchical total order. The calculation formula of the random consistency ratio of the hierarchical total order is: ； in, is the random consistency ratio of the total hierarchical order of the kth layer to the factor layer, is the weight vector of the jth factor of the kth layer to the kth layer, is the random consistency index of the jth factor of the kth layer to the kth layer, is the average random consistency index of the jth factor of the kth layer on the kth layer, is the transposition of the weight vectors of each factor in the k-1th layer. It is the hierarchical total single ranking consistency index of the k-1th layer to the factor layer.

8. A tunnel advanced geological prediction risk warning system, characterized in that: include: The address data acquisition module is used to perform non-contact scanning of the geological conditions ahead of the tunnel excavation to obtain geological data; A three-dimensional model building module is used to pre-process the collected geological data and perform three-dimensional modeling on the pre-processed geological data to build a three-dimensional model of the geological structure in front of the tunnel; The bad geological body identification module is used to identify potential bad geological bodies based on the constructed 3D geological model. Bad geological bodies include faults, weak interlayers, karst caves and groundwater-rich areas. The geological risk assessment module is used to input the identified adverse geological bodies into the pre-built geological disaster risk assessment model for risk assessment; The risk warning module is used to classify risk assessment results into risk levels. Each risk level is set with a different warning threshold. When the predicted risk reaches or exceeds the set threshold, the corresponding warning mechanism is triggered.

9. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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