Underground engineering unfavorable geological pre-control analysis method and system fusing multi-source data

Abstract

The disclosure provides a tunnel adverse geological pre-control analysis method and system for fusing multi-source data, and relates to the technical fields of tunnel geological analysis and data processing, and comprises the following steps: acquiring multiple types of reliable data of geological exploration, advanced drilling, advanced geophysical prospecting and operation surface exposure, and respectively generating spatial coordinate points; according to an assignment rule, assigning an initial value to each spatial coordinate point and a corresponding parameter, and generating a spatial coordinate matrix; assigning a confidence value to different spatial coordinate points in the spatial coordinate matrix, and generating multiple confidence space domains; according to a confidence domain correlation mapping rule, acquiring data domain parameters and the relationship between data domains, determining the spatial distribution of parameters in the confidence domain, acquiring parameter feedback of individual spatial coordinate points through a posterior updating method of the confidence domain rule, and using a high-dimensional traversal optimization method to take the minimum residual error between the parameter calculation value and the actual value as the target, constantly adjusting the parameters in the confidence domain distribution rule, and obtaining optimal pre-control parameters to guide actual engineering application.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of underground engineering geological analysis and data processing, in particular to a method and system for pre-control analysis of underground engineering adverse geology by fusing multi-source data. BACKGROUND

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

[0003] In the actual underground engineering excavation process, the interactive information obtained presents some characteristics when used: the data information sources are extensive and diverse in form, and the commonly used geological survey data, advanced drilling data, advanced geophysical prospecting data and operation face exposure data can all reflect the underground engineering geological information to some extent. However, for these multi-source data, there is still a lack of a feasible rule to process them efficiently and scientifically, and then guide the design of pre-control parameters. Especially in the case of limited exploration data, integrating multi-source data and determining rock-soil parameters is still a great challenge. Due to these problems, the underground engineering excavation process still highly depends on subjective decision and experience analysis, and misjudges the adverse geological conditions, thereby causing accidents during construction. In the construction of urban underground space, the consequences caused by this risk may further lead to the failure of surface traffic facilities and the damage of adjacent buildings. Therefore, solving the problem of multi-source data fusion analysis in the process of underground engineering excavation has important significance for safe and efficient construction of underground engineering.

[0004] There are two main problems: problem one is how to map multi-source data into a spatial model, the current method cannot well explain how to express the geological information at non-prospecting points, and the rules for different types of data in establishing a spatial model are obviously different. This situation has not been well explained and studied. Problem two is how to objectively and reasonably fuse multi-source data, establish a multi-source data interactive processing rule, and obtain the most credible geological risk data.

[0005] For the above problems, the existing technology introduces Bayesian and machine learning methods into this research. Including developing a Bayesian geostatistical method for modeling coal seam surfaces using multi-source geological data at different stages and different scales, but these methods still have the following limitations:

[0006] Risk analysis relies too much on subjective experience. At the same time, a large number of studies set too broad goals for the final risk assessment. The target value setting in the research process of many Bayesian methods and machine learning methods also relies on experience, and lacks persuasiveness for small data. The results of such methods are often qualitative results in a region, and cannot handle detailed stratum information, cannot show the full picture of the geological risks faced by underground engineering during excavation, and cannot further guide the fine pre-control of risks. There are still many places that need to be improved and further explored. SUMMARY

[0007] In order to solve the above problems, the disclosure provides a multi-source data fusion underground engineering adverse geological pre-control analysis method and system, establishes a correlation rule of multi-data of underground engineering, sets a posterior updating process for a confidence domain distribution rule, adjusts parameters in the confidence domain distribution rule through parameter feedback of individual monitoring points by using a high-dimensional traversal optimization method, calculates a grouting reinforcement circle range of each section of underground engineering, and guides actual engineering application.

[0008] According to some embodiments, the disclosure adopts the following technical solutions:

[0009] The multi-source data fusion underground engineering adverse geological pre-control analysis method comprises the following steps:

[0010] For the surrounding rock environment of underground engineering, obtain multi-type credible data of geological exploration, advanced drilling, advanced geophysical prospecting, and operation face exposure, and generate spatial coordinate points and corresponding parameters respectively;

[0011] According to an assignment rule, an initial numerical value is assigned to each spatial coordinate point and corresponding parameter to generate a plurality of spatial coordinate matrices; and a confidence value is assigned to different spatial coordinate points in each spatial coordinate matrix to generate a plurality of confidence space domains;

[0012] According to a confidence domain correlation mapping rule, obtain data domain parameters and the relationship between data domains, determine the spatial distribution of parameters in the confidence domain, obtain parameter feedback of individual spatial coordinate points by using a posterior updating method of the confidence domain rule, and use a high-dimensional traversal optimization method to continuously adjust parameters in the confidence domain distribution rule with the minimum residual error between the parameter calculation value and the actual value as the target, so as to obtain optimal pre-control parameters;

[0013] According to the obtained optimal pre-control parameters, the grouting reinforcement circle range of each section of underground engineering is calculated to guide actual engineering application.

[0014] According to some embodiments, the disclosure adopts the following technical solutions:

[0015] The multi-source data fusion underground engineering adverse geological pre-control analysis system comprises the following steps:

[0016] The data acquisition module is configured to acquire multiple types of reliable data of geological exploration, advanced drilling, advanced geophysical prospecting and operation face exposure for the surrounding rock environment of the underground engineering, and generate spatial coordinate points and corresponding parameters respectively.

[0017] The confidence space domain generation module is configured to assign initial values to each spatial coordinate point and corresponding parameter according to an assignment rule, generate multiple spatial coordinate matrices, and assign confidence values to different spatial coordinate points in each spatial coordinate matrix to generate multiple types of confidence space domains.

[0018] The pre-control analysis module is configured to acquire data domain parameters and relationships between data domains according to a confidence domain correlation mapping rule, determine the spatial distribution of parameters in the confidence domain, acquire parameter feedback of individual spatial coordinate points through a posterior updating method of the confidence domain rule, utilize a high-dimensional traversal optimization method to continuously adjust parameters in the confidence domain distribution rule to obtain optimal pre-control parameters, and calculate the grouting reinforcement circle range of each section of the underground engineering according to the obtained optimal pre-control parameters to guide actual engineering application.

[0019] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0020] A computer program product comprises a computer program, which, when executed by a processor, implements the underground engineering adverse geological pre-control analysis method fusing multiple source data.

[0021] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0022] A non-transitory computer-readable storage medium is configured to store computer instructions, which, when executed by a processor, implement the underground engineering adverse geological pre-control analysis method fusing multiple source data.

[0023] According to some embodiments, the present disclosure adopts the technical scheme as follows:

[0024] An electronic device comprises a processor, a memory and a computer program; the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the underground engineering adverse geological pre-control analysis method fusing multiple source data.

[0025] Compared with the prior art, the present disclosure has the following beneficial effects:

[0026] The underground engineering adverse geological pre-control analysis method of fusing multi-source data of the present disclosure takes mileage as the longitudinal scale of spatial data, which is beneficial to the rapid input of engineering survey data; the surrounding rock parameter selection rule under the most credible risk condition is established to guide the parameter adjustment and design of pre-control measures. Meanwhile, an exhaustive optimization method is designed for the confidence rule, so that the analysis result can be maximally dependent on objective conditions and free from the influence of subjective experience. This method gives an objective, rapid and automatic analysis means for the risk pre-control in the excavation process of underground engineering, and realizes the integrated use of multi-source data. The invention has been applied in practice, and ensures the safety and economic construction of underground engineering.

[0027] The underground engineering adverse geological pre-control analysis method of fusing multi-source data of the present disclosure can continuously adjust the pre-control parameters in the process of underground engineering excavation, and realize fine processing. This can be seen from the adjustment of each calculation unit (10m) in the calculation result. Through the result of the gradually reinforced range, the volume of the grouting reinforced space area is further calculated, and it is concluded that the space volume of the grouting reinforcement after the optimization design is reduced by 20.64% compared with the original design. In addition, the design of the above-mentioned method in the present disclosure significantly reduces the cost in actual engineering construction, and helps to realize more fine pre-control parameter design. BRIEF DESCRIPTION OF DRAWINGS

[0028] The drawings constituting a part of the specification of the present disclosure serve to provide further understanding of the present disclosure, and the illustrative embodiments of the present disclosure and their descriptions serve to explain the present disclosure, and do not constitute improper limitation on the present disclosure.

[0029] Figure 1 The method flowchart of the embodiment of the present disclosure is shown in the figure.

[0030] Figure 2 The confidence domain posterior updating rule flowchart of the embodiment of the present disclosure is shown in the figure. DETAILED DESCRIPTION

[0031] The present disclosure will be further described below in combination with the drawings and embodiments.

[0032] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present disclosure belongs.

[0033] 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 disclosure. 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, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0034] Embodiment 1

[0035] In an embodiment of the present disclosure, a method for analyzing adverse geological pre-control of underground engineering by fusing multi-source data is provided. The method for analyzing multi-source data in the tunneling process of underground engineering based on confidence realizes the fusion analysis of multi-source data in the tunneling process, establishes mathematical rules to guide the parameter adjustment of pre-control measures, and further performs safety control. The steps are as follows:

[0036] Step one: For the surrounding rock environment of underground engineering, obtain multi-type trusted data of geological exploration, advanced drilling, advanced geophysical prospecting, and exposed operation surface, and generate spatial coordinate points and corresponding parameters respectively;

[0037] Step two: According to the assignment rule, an initial value is assigned to each spatial coordinate point and corresponding parameter to generate a plurality of spatial coordinate matrices. A confidence value is assigned to different spatial coordinate points in each spatial coordinate matrix to generate a plurality of confidence space domains;

[0038] Step three: According to the confidence domain correlation mapping rule, the data domain parameters and the relationship between the data domains are obtained, the spatial distribution of the parameters in the confidence domain is determined, the parameter feedback of individual spatial coordinate points is obtained through the posterior updating method of the confidence domain rule, and the high-dimensional traversal optimization method is used to continuously adjust the parameters in the confidence domain distribution rule to obtain the optimal pre-control parameters, with the minimum residual error between the parameter calculation value and the actual value as the target;

[0039] Step four: According to the obtained optimal pre-control parameters, the grouting reinforcement circle range of each section of underground engineering is calculated to guide the actual engineering application.

[0040] As an embodiment, the specific implementation process of the method for analyzing adverse geological pre-control of underground engineering by fusing multi-source data is as follows:

[0041] Step 1: For the surrounding rock environment of underground engineering, obtain multi-type trusted data of geological exploration, advanced drilling, advanced geophysical prospecting, and exposed operation surface, and generate spatial coordinate points and corresponding parameters respectively;

[0042] Specifically, for the underground engineering surrounding rock environment, the underground space is established according to a certain interval (0.5m*0.5m*0.5m) to form the underground space coordinate points. The space range is approximately a cuboid space along the longitudinal dimension with mileage distribution, and a rectangle range (20m, 25m) in the transverse cross section (the coordinate origin is the midpoint of the underground engineering excavation starting face).

[0043] Step 2: According to the assignment rule, the initial value is assigned to each space coordinate point and the corresponding parameter to generate multiple space coordinate matrices; a confidence value is assigned to each space coordinate point in each space coordinate matrix to generate multiple confidence space domains;

[0044] Specifically, an initial value (0) is assigned to each space coordinate point for a certain parameter. This parameter can be the permeability coefficient, cohesion, internal friction angle, lateral pressure coefficient, etc. When the four types of reliable data obtained from geological exploration, advanced drilling, advanced geophysical exploration, and operation face exposure are obtained, the table files of space coordinate points and corresponding parameters are generated.

[0045] The process of generating the table file of space coordinate points and corresponding parameters is as follows: based on the geological exploration data, taking the center of the underground engineering construction opening as the zero coordinate point, using python (VTK) as the compilation basis to construct the underground engineering excavation front geological space coordinate system, selecting the coordinate points according to a certain interval (0.5m*0.5m*0.5m) to form the excavation front space point cloud file. The file contains the three-dimensional information of each space coordinate point.

[0046] At the same time, according to a certain assignment rule, the coordinate points of the associated region are also assigned values. In this way, for each underground environment parameter, four space coordinate matrices are formed according to the four types of exploration data.

[0047] The construction process of the space coordinate matrix is as follows: based on the space point cloud file, combined with the information of geological exploration, advanced drilling, advanced geophysical exploration, and operation face exposure, an attribute column is added to each space coordinate point information in the file, and the attributes include permeability coefficient, cohesion, internal friction angle, lateral pressure coefficient, etc. physical parameters, forming a new space coordinate matrix file, i.e. a data set of space coordinates + physical parameters.

[0048] Further, a confidence value η c is assigned to each coordinate point in each coordinate matrix. Its meaning is the reliability of the data corresponding to each coordinate point. According to the characteristics of the four types of exploration methods of geological exploration, advanced drilling, advanced geophysical exploration, and operation face exposure, a confidence value is assigned to each coordinate point to establish four types of confidence space domains. The coordinate matrix and the corresponding confidence domain are multiplied and weighted averaged to obtain the maximum confidence value of a certain coordinate point with respect to a certain parameter.

[0049] The process of assigning a confidence value to each coordinate point and establishing four types of confidence spatial domains is as follows: Based on the spatial coordinate matrix file, new attribute columns, namely confidence values, are added. The number of confidence value attributes corresponds to the number of added physical parameter attributes, so that each spatial coordinate point in the file corresponds to various types of physical information and their confidence values. The spatial coordinate plus the confidence value of a single physical information constitutes the confidence domain of a certain physical parameter.

[0050] The assignment process is as follows: assign a value of 1 to the physical parameter confidence value of deterministic or revealing coordinate points; assign a value of 0 to the physical parameter confidence value of uncertain or non-revealing coordinate points that are spatially related to the former, in accordance with the rules in step 3; and assign a value of 0 to the physical parameter confidence value of the remaining coordinate points.

[0051] Step 3: Based on the confidence region association mapping rule, obtain the data domain parameters and the relationship between data domains, determine the spatial distribution of parameters in the confidence region, obtain parameter feedback of individual spatial coordinate points through the posterior update method of the confidence region rule, and continuously adjust the parameters in the confidence region distribution rule with the goal of minimizing the residual between the calculated and actual parameter values ​​using the high-dimensional traversal optimization method to obtain the optimal pre-control parameters;

[0052] Specifically, the association rules (spatial mapping rules and horizontal and vertical variation rules) for multi-source data in underground engineering are as follows: First, four types of data confidence domains are introduced.

[0053] 1. Geological exploration data:

[0054] Specifically, for vertical spatial variation, there exists a coordinate point S1(l x ,l y ,l z The geological exploration data is ρ g,s1 There are adjacent survey coordinate points S2(l) x ,l y ,l z The geological exploration data of +Δl) is ρ g,s2 Therefore, the coordinates of point S are determined using interpolation. Δ (l x ,l y ,l z Geological data of +Δz), namely:

[0055] ρ g,sΔ ρ' g,sΔ =(ρ g,s1 -ρ g,s2 )·(Δz / Δl)+ρ g,s1

[0056] Where, ρ' 1,sΔ Let S be the coordinate point Δ (lx ,l y ,l z Geological data of +Δz).

[0057] For lateral spatial variation, in the same l z In planar space, any point S' Δ (l x +Δx,l y ,l z +Δz) parameter data ρ' g,sΔ for:

[0058] ρ' g,sΔ =ρ g,sΔ

[0059] 2. Advanced drilling data:

[0060] Specifically, for advanced drilling data, an extension limit is set in both the horizontal and vertical scales. d This spatial range presents an approximate cylindrical shape. If there exists a coordinate point S1(l)... x ,l y ,l z The advanced drilling data is ρ p,s1 So for S Δ (l x +Δx,l y +Δy,l z The advanced drilling data is ρ d,sΔ =ρ d,s1 Meanwhile, for S Δ (l x +Δx,l y +Δy,l z +Δz), then the following situations exist:

[0061]

[0062] 3. Advanced geophysical data:

[0063] For advanced geophysical data, the Kriging interpolation method is used for areas without data.

[0064] 4. Data revealed at the work site:

[0065] Specifically, for the data revealed at the work site, an extension limit is set in both the horizontal and vertical dimensions. s If there exists a coordinate point S1(l) x ,l y ,l z The sampling test data (or data that can be directly observed) is ρ. s,s1 Then for the S closest to this coordinate pointΔ (l x +Δx,l y +Δy,l z +Δz), the following situations exist:

[0066]

[0067] As one example, the confidence region mapping method and the horizontal and vertical variation rules are as follows:

[0068] 1. Geological exploration data:

[0069] Specifically, for geological exploration data, the confidence region distribution exhibits a high confidence level in the linear region where the drilling is located, with the confidence level decreasing as the distance from the drilling area increases. This distribution is characterized by a normal distribution. For the confidence level of a coordinate point, we have:

[0070]

[0071] Among them, μg, represents the parameters of a normal distribution.

[0072] 2. Advanced drilling data:

[0073] Specifically, for advanced drilling data, the confidence region distribution exhibits a high confidence level within the linear region where the drilling is located, with confidence levels decreasing as the distance from the drilling area increases. This distribution is characterized by a normal distribution for coordinate point S. Δ (l x ,l y ,l z The confidence level of +Δz) is:

[0074]

[0075] Where, μ d , represents the parameters of a normal distribution.

[0076] Because advanced drilling exposes geological surfaces over a relatively short distance, it is important to consider the normal distribution parameters. The value is small so that the confidence level in space quickly reaches 0 after leaving the drilling coordinates.

[0077] 3. Advanced geophysical data:

[0078] On the vertical scale, the closer to the work surface, the higher the confidence level. On the horizontal scale, the confidence level is higher for the region P directly in front of the work surface, while the confidence level of other regions decreases as they are further away from the region directly in front.

[0079] For coordinate point S Δ (l x ,ly , z +Δz) of the confidence degree, there is:

[0080]

[0081] 4. The working surface exposure data:

[0082] Specifically, for the working surface exposure data, only the data measured by the test points and the data in a certain range (for both horizontal and vertical dimensions) around the test points are set as reliable, and the setting rules are similar to the early warning rules, and a decay area (Gaussian distribution) is set according to the rule of decreasing confidence degree. The planar area of the working surface is set as P s , the planar area of the working surface affected under the condition of l z is P Δs . The data points in the area P s have high confidence degree, and the data points along the longitudinal extension of the plane rapidly decay to zero confidence degree. Meanwhile, the confidence degree of the area diffused around the working plane is also the same, that is, when the data points are in the area P Δs -P s , the confidence degree decreases accordingly.

[0083] Further, for the confidence degree of the coordinate point S Δ (l x , l y , l z +Δz), there is:

[0084]

[0085] Further, the present disclosure proposes a posterior updating method of the confidence domain rule, as shown in Figure 2 , it can be concluded from the above that the rules of the data domain and the confidence domain have a great influence on the final parameters. The extension rule of the data domain mainly depends on the relationship between the data, but the distribution rule of the confidence domain has a certain subjectivity and regionalism. Therefore, the present disclosure sets a posterior updating process for the distribution rule of the confidence domain, and continuously adjusts the parameters in the distribution rule of the confidence domain by using a high-dimensional traversal optimization method through the parameter feedback of individual monitoring points. For the coordinate point (l x , l y , l z ), the input parameters of the posterior process are the Gaussian parameters of each confidence domain, the optimization target is the residual error between the calculated value and the actual value of the parameter, and the traversal target is to make the residual error minimum.

[0086] As an optional implementation, the present disclosure obtains the parameter calculation formula at any coordinate point (l x , l y , l z ) according to the above analysis:

[0087]

[0088] wherein S * is a parameter at the coordinate point, t is a correction coefficient, when t>1.0, the data of the high-confidence coordinate point can more significantly affect the final result, in the present disclosure, t=2.50.

[0089] Further, when (l x ,l y )∈P, (Δx 2 +Δy 2 +Δz 2 ) 1 / 2 ≤e d , and (Δx 2 +Δy 2 +Δz 2 ) 1 / 2 ≤e s , the following formula is used:

[0090]

[0091] As an embodiment, according to the above pre-control parameter analysis process, a certain tunnel engineering application research is carried out to verify the application effect of the method.

[0092] Specifically, the input information is the spatial distribution of the surrounding rock permeability coefficient, and the output information is the grouting reinforcement circle range of different construction sections. The analysis process includes the following steps:

[0093] Step 1: according to the multi-source data fusion analysis method, the geological section is modeled;

[0094] Step 2: a calculation method suitable for the reasonable grouting reinforcement circle range of underground engineering is established;

[0095] Step 3: the input rules of multi-source data are determined, and the grouting reinforcement circle range of each section of underground engineering is calculated to guide the actual engineering application.

[0096] The present disclosure takes a more simple and direct grouting reinforcement circle as the research, and the purpose of the present disclosure is to introduce the multi-source data fusion analysis method. Therefore, whether it is the grouting reinforcement circle range parameter or other measure parameters, this approach can be used to solve the problem. In addition, when the present disclosure uses this method to calculate, multiple coordinate point planes perpendicular to the tunneling direction are selected as the research object, which can use the table file to perform associated calculation, thereby reducing the overall data calculation amount.

[0097] Specifically, according to relevant research results, it is assumed that the surrounding rock of underground engineering, the grouting reinforcement circle and the sprayed concrete are all isotropic and continuous media; the underwater underground engineering is circular, the water flow is steady flow, and the movement law obeys the Darcy theorem. At the same time, according to the derivation of the groundwater dynamics theory, the tunnel drainage Q, the water pressure P borne by the surface of the primary support and the water pressure Pg outside the grouting reinforcement circle meet the following formula:

[0098]

[0099] Wherein, E is the thickness of the grouting reinforcement circle, E = r g -r1; Q is the tunnel drainage (m3 / s); P is the initial surface water pressure (kPa); P g is the water pressure outside the grouting reinforcement circle (kPa); k r is the permeability coefficient of the surrounding rock (m / s); k1 is the permeability coefficient of the primary support (m / s); k g is the permeability coefficient of the primary support (m / s); H is the permeability coefficient of the grouting body (m / s); r0 is the radius of the inner surface of the primary support (m); r1 is the radius of the outer surface of the primary support (m); r2 is the far field distance, equal to H; r g is the outer radius of the grouting reinforcement circle (m); γ is the specific gravity of water (kN / m3); h1 is the water head outside the lining (m); h g is the water head outside the grouting reinforcement circle (m).

[0100] Wherein, the selection of part of the parameters: H = r2 water level; k1 is the permeability coefficient of the primary support, which is 6.5*10-10 m / s; γ is the specific gravity of water, which is 10 kN / m3.

[0101] Specifically, as an optional implementation manner, the input of multi-source data in the present disclosure follows the principle of conservatism. That is, in the same mileage, the 90th percentile value of the corresponding parameter value of all (l x ,l y ) is taken as the input value at the mileage. And the pre-control parameter at the mileage is calculated under the input of the input value. In the application research of the present disclosure, the input parameter is the permeability coefficient, and the pre-control parameter is the grouting reinforcement circle range. In addition, it needs to be explained in the application research of the present disclosure that the reason why the 90th percentile value is selected as the input value in the present disclosure is to avoid the adverse effects caused by extreme data.

[0102] Specifically, according to the above calculation process, the application tunnel section is analyzed for the pre-control parameter (grouting reinforcement range).

[0103] Further, in the application research of the present disclosure, for the original construction scheme, the grouting reinforcement range mainly relies on the calculation of geological exploration and subjective experience. Compared with the optimized design method, the parameter construction section range in the original construction scheme is larger, and for the section with adverse geology, a more conservative design is often used. The optimized design method of the present application can continuously adjust the pre-control parameters during tunneling, realizing fine processing. This can be seen from the adjustment of each calculation unit (10m) in the calculation result. Through the result of the gradually reinforced range, the volume of the grouting reinforced space area is further calculated, and it is concluded that the space volume of the grouting reinforcement after the optimization design is reduced by 20.64% compared with the original design. In addition, the design of the above-mentioned method in the present application significantly reduces the cost in actual engineering construction, which is helpful to realize more fine pre-control parameter design.

[0104] Specifically, the present disclosure selects the grouting reinforcement ring range with relatively simple calculation process for application research. In addition, it only needs to provide a spatial distribution data condition to verify the fusion analysis method.

[0105] Embodiment 2

[0106] In an embodiment of the present disclosure, a tunnel adverse geological pre-control analysis system fusing multi-source data is provided, comprising:

[0107] A data acquisition module is configured to acquire multiple types of reliable data of geological exploration, advanced drilling, advanced geophysical prospecting and operation face exposure for the tunnel surrounding rock environment, and generate spatial coordinate points and corresponding parameters respectively;

[0108] A confidence space domain generation module is configured to assign initial values to each spatial coordinate point and corresponding parameter according to the assignment rule, generate multiple spatial coordinate matrices, and assign confidence values to different spatial coordinate points in each spatial coordinate matrix to generate multiple confidence space domains;

[0109] A pre-control analysis module is configured to acquire data domain parameters and the relationship between data domains according to the confidence domain correlation mapping rule, determine the spatial distribution of parameters in the confidence domain, acquire parameter feedback of individual spatial coordinate points through the posterior updating method of the confidence domain rule, use a high-dimensional traversal optimization method to continuously adjust the parameters in the confidence domain distribution rule with the minimum residual error of the parameter calculation value and the actual value as the target, and obtain the optimal pre-control parameters; and calculate the grouting reinforcement ring range of each tunnel section according to the obtained optimal pre-control parameters to guide the actual engineering application.

[0110] Embodiment 3

[0111] In an embodiment of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the tunnel adverse geological pre-control analysis method fusing multi-source data.

[0112] Embodiment 4

[0113] In an embodiment of the present disclosure, a non-transitory computer readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the tunnel adverse geological pre-control analysis method of fusing multi-source data.

[0114] Embodiment 5

[0115] In an embodiment of the present disclosure, an electronic device is provided, comprising a processor, a memory, and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the tunnel adverse geological pre-control analysis method of fusing multi-source data.

[0116] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the 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 general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable data processing device to generate a computer-implemented process, so that the instructions executed by the computer or other programmable data processing device provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The steps for implementing the functions specified in one or more flows and / or blocks.

[0118] Although the specific embodiments of the present disclosure are described above with reference to the accompanying drawings, the present disclosure is not limited to the above-described embodiments, and various modifications or changes can be made by those skilled in the art without departing from the technical solutions of the present disclosure.

Claims

1. A method for tunnel unfavorable geological pre-control analysis of fused multi-source data, characterized in that, Comprise: For tunnel surrounding rock environment, obtain geological exploration, advanced drilling, advanced geophysical prospecting and operation surface exposure of multiple types of reliable data, respectively generate spatial coordinate points and corresponding parameters; According to the assignment rule, give each spatial coordinate point and the corresponding parameter an initial value, generate multiple spatial coordinate matrices; For each spatial coordinate point in each spatial coordinate matrix, give a confidence value, generate multiple confidence space domains; According to the confidence domain correlation mapping rule, obtain the data domain parameters and the relationship between the data domains, determine the spatial distribution of the parameters in the confidence domain, obtain the parameter feedback of individual spatial coordinate points through the posterior updating method of the confidence domain rule, use the high-dimensional traversal optimization method to minimize the residual error between the parameter calculation value and the actual value, and continuously adjust the parameters in the confidence domain distribution rule to obtain the optimal pre-control parameters; According to the obtained optimal pre-control parameters, calculate the grouting reinforcement range of each section of the tunnel to guide the actual engineering application.

2. The method of claim 1, wherein, For tunnel surrounding rock environment, underground space coordinate points are established according to the set interval, the space range is a cuboid space distributed along the mileage on a longitudinal scale, and the coordinate origin is the midpoint of the tunnel inverted arch. After obtaining multiple types of reliable data from geological exploration, advanced drilling, advanced geophysical prospecting and operation surface exposure, table files of spatial coordinate points and corresponding parameters are generated. According to the assignment rule, each spatial coordinate point and the corresponding parameter is given an initial value. For each underground environment parameter, four spatial coordinate matrices are formed according to the four types of exploration data. The corresponding parameters are permeability coefficient, cohesion, internal friction angle or lateral pressure coefficient.

3. The method of claim 1, wherein, For each spatial coordinate point in each spatial coordinate matrix, give a confidence value, which is the reliability of the data corresponding to each coordinate point. According to the characteristics of the four types of exploration data, a confidence value is given to each coordinate point to establish four types of confidence space domains. Multiply the coordinate matrix and the corresponding confidence domain, and then obtain the maximum confidence value of a coordinate point with respect to a parameter after weighted averaging.

4. The method of claim 1, wherein, According to the confidence domain correlation mapping rule, obtain the data domain parameters and the relationship between the data domains, determine the spatial distribution of the parameters in the confidence domain, including: for geological exploration data and advanced drilling data, the distribution form of the confidence domain is the linear region where the drilling is located with high confidence. The farther the confidence distance from the drilling area, the lower the confidence. Their forms are characterized by normal distribution.

5. The method of claim 4, wherein, For advanced geophysical prospecting data, the closer to the operation surface in the longitudinal scale, the higher the confidence. In the horizontal scale, the confidence in the area directly in front of the operation surface is high, and the confidence in other areas decreases with the distance from the directly in front of the operation surface. For operation surface exposure data, only the data measured by the test point and the region within the set range around it are set as reliable, and the attenuation region is set according to the confidence decreasing rule.

6. The method of claim 1, wherein, A posterior updating process is set for the confidence domain distribution rule, and by parameter feedback of individual monitoring points, parameters in the confidence domain distribution rule are constantly adjusted by using a high-dimensional traversal optimization method, for a coordinate point, input parameters of the posterior process are Gaussian parameters of each confidence domain, an optimization target is a residual error of a parameter calculation value and an actual value, a traversal target is to make the residual error minimum, and finally, pre-control parameters are obtained, and a grouting reinforcement ring range of each tunnel is calculated by using the pre-control parameters.

7. A tunnel adverse geological pre-control analysis system fusing multi-source data, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire multiple types of reliable data of geological exploration, advanced drilling, advanced geophysical prospecting, and operation face exposure for a tunnel surrounding rock environment, and generate spatial coordinate points and corresponding parameters respectively; a confidence space domain generation module is configured to assign initial values to each spatial coordinate point and corresponding parameters according to an assignment rule, and generate multiple spatial coordinate matrices; and assign confidence values to different spatial coordinate points in each spatial coordinate matrix, and generate multiple types of confidence space domains; a pre-control analysis module is configured to acquire data domain parameters and relationships between data domains according to a confidence domain correlation mapping rule, determine spatial distribution of parameters in the confidence domain, acquire parameter feedback of individual spatial coordinate points by using a posterior updating method of the confidence domain rule, and constantly adjust parameters in the confidence domain distribution rule by using a high-dimensional traversal optimization method, so as to obtain optimal pre-control parameters, and calculate grouting reinforcement ring ranges of each tunnel according to the optimal pre-control parameters, thereby guiding actual engineering application.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the tunnel adverse geological pre-control analysis method of fusing multiple source data according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium is configured to store computer instructions, and the computer instructions are executed by the processor to realize the tunnel adverse geological pre-control analysis method of fusing multiple source data according to any one of claims 1-6.

10. An electronic device, comprising: The method comprises the following steps: a processor, a memory, and a computer program; the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the tunnel adverse geological pre-control analysis method of fusing multiple source data according to any one of claims 1-6.

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