Underground engineering rock mass quality correction method, device, equipment and readable storage medium

Abstract

The application relates to an underground engineering rock mass quality correction method, device, equipment and readable storage medium, and relates to the technical field of underground engineering.The application comprises the following steps: obtaining target while-drilling parameters after a drilling machine drills into a rock mass to be measured, the target while-drilling parameters comprising water pressure parameters, flow parameters, structural surface parameters, attitude parameters and drilling parameters in the drilling process; inputting the target while-drilling parameters into a preset underground water correction model, a preset structural surface correction model and a preset ground stress correction model respectively to obtain target underground water correction indexes, target structural surface correction indexes and target ground stress correction indexes; and inputting the target underground water correction indexes, the target structural surface correction indexes and the target ground stress correction indexes into a preset rock mass quality correction model to obtain a target rock mass correction quality index value.The application corrects the influences of underground water, structural surfaces and ground stress, can obtain the engineering rock mass correction quality of each mileage in real time, and improves the accuracy and objectivity of rock mass classification.

Description

Technical Field

[0001] This application relates to the field of underground engineering technology, and in particular to a method, apparatus, equipment and readable storage medium for correcting the quality of rock mass in underground engineering. Background Technology

[0002] Accurate evaluation of rock mass stability is crucial for the design of underground engineering excavation and support systems. Among the many factors influencing rock mass stability, rock hardness and rock mass integrity reflect its fundamental properties, but they are not the only important factors affecting rock mass quality and stability. The basic quality indicators of the rock mass should be revised based on various influencing factors in conjunction with the engineering project, serving as the basis for rock mass classification. With the increasing scale of underground engineering construction, complex geological conditions such as water-rich and soft rock, fault fracture zones, and high ground stress are frequently encountered, making safety accidents such as water inrush, collapse, and rock bursts more likely. Therefore, it is urgent to introduce key corrective factors affecting rock mass stability to quickly and accurately classify the engineering rock mass. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and readable storage medium for correcting the quality of rock mass in underground engineering, in order to solve the problems existing in related technologies.

[0004] Firstly, a method for correcting the quality of rock mass in underground engineering is provided, including the following steps:

[0005] The target drilling parameters are obtained after the drilling rig enters the rock mass to be tested. The target drilling parameters include water pressure parameters, flow rate parameters, structural surface parameters, attitude parameters and drilling parameters during the drilling process.

[0006] The target drilling parameters are input into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, respectively, to obtain the target groundwater correction index, the target structural surface correction index, and the target geostress correction index.

[0007] The target groundwater correction index, the target structural surface correction index, and the target geostress correction index are input into a preset rock mass quality correction model to obtain the target rock mass corrected quality index value.

[0008] In some embodiments, before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, the method further includes:

[0009] The experimental drilling water pressure and experimental drilling flow rate values ​​in the surrounding rock fractures corresponding to the first preset working condition were obtained by in-situ digital drilling tests. The first preset working condition includes different initial values ​​of basic rock mass quality, lithology, surrounding rock water pressure and surrounding rock water output.

[0010] A preset groundwater correction model is generated based on the experimental drilling water pressure value, the experimental drilling flow rate value, and the initial value of the basic rock mass.

[0011] In some embodiments, the calculation methods for the experimental drilling water pressure value and the experimental drilling flow rate value are as follows:

[0012] ΔP(x)=[P r (x)-P r (x-1)]-[P d (x)-P d (x-1)]

[0013] ΔQ(x)=[Q r (x)-Q r (x-1)]-[Q d (x)-Q d (x-1)]

[0014] In the formula, ΔP(x) represents the experimental drilling water pressure value within the surrounding rock fractures at kilometer x, and P r (x) represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x, P r (x-1) represents the experimental drilling water pressure value at the near-bit position corresponding to mileage x-1, P d (x) represents the experimental drilling water pressure value of the flushing fluid pipeline at mileage x, P d (x-1) represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer x-1, and ΔQ(x) represents the experimental drilling flow rate value in the surrounding rock fracture at kilometer x. r (x) represents the experimental flow rate near the drill bit at kilometer x, Q r (x-1) represents the experimental flow rate near the drill bit at kilometer marker x-1, Q. d (x) represents the experimental flow rate of the flushing fluid pipeline at kilometer x, Q d (x-1) represents the experimental flow rate of the flushing fluid pipeline at mileage x-1.

[0015] In some embodiments, before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, the method further includes:

[0016] The experimental structural plane attitude value and experimental borehole attitude value corresponding to the second preset working condition are obtained by in-situ digital drilling test. The second preset working condition is a combination of different structural plane attitudes and borehole attitudes.

[0017] Based on the experimental structural surface attitude values ​​and the experimental borehole attitude values, a preset structural surface correction model is generated.

[0018] In some embodiments, before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, the method further includes:

[0019] The experimental drilling parameters and the maximum principal stress of the experimental surrounding rock corresponding to the third preset working condition are obtained through indoor digital drilling tests. The saturated uniaxial compressive strength of the experimental rock is obtained through digital core drilling and rock mechanics tests. The third preset working condition includes different initial values ​​of basic rock mass, lithology, rock strength and confining pressure conditions.

[0020] The strength-stress ratio of the experimental surrounding rock was determined based on the maximum principal stress of the experimental surrounding rock and the saturated uniaxial compressive strength of the experimental rock.

[0021] Based on the experimental surrounding rock strength-stress ratio and experimental drilling parameters, a preset neural network model is trained to obtain a preset surrounding rock strength-stress ratio prediction model.

[0022] Based on the preset surrounding rock strength-stress ratio prediction model and the initial value of the basic rock mass, a preset geostress correction model is generated.

[0023] In some embodiments, the formula for calculating the strength-stress ratio of the experimental surrounding rock is:

[0024]

[0025] The preset surrounding rock strength-stress ratio prediction model is as follows:

[0026] SSR'=f(F',M',N',V',SE',PR',FPI',MPI')

[0027] In the formula, SSR represents the strength-stress ratio of the experimental surrounding rock, and R c σ represents the saturated uniaxial compressive strength of the experimental rock. max denoted by , SSR' represents the maximum principal stress of the experimental surrounding rock, f represents the strength-stress ratio of the target surrounding rock, F' represents the training function, M' represents the target drilling pressure, N' represents the target drill bit torque, V' represents the target drill pipe rotation speed, SE' represents the target drilling specific energy, PR' represents the target penetration, FPI' represents the target drill pressure penetration index, and MPI' represents the target torque penetration index.

[0028] In some embodiments, the calculation formula for the preset rock mass quality correction model is as follows:

[0029]

[0030] In the formula, KQ(x) represents the target rock mass corrected quality index value at kilometer x, KQ(x) represents the initial value of the basic rock mass quality at kilometer x, K1(x) represents the target groundwater correction index at kilometer x, K2(x) represents the target structural surface correction index at kilometer x, and K3(x) represents the target geostress correction index at kilometer x.

[0031] Secondly, a device for correcting the quality of rock mass in underground engineering is provided, comprising:

[0032] The parameter acquisition unit is used to acquire the target drilling parameters after the drilling rig enters the rock mass to be tested. The target drilling parameters include water pressure parameters, flow rate parameters, structural surface parameters, attitude parameters and drilling parameters during the drilling process.

[0033] The index prediction unit is used to input the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model and the preset geostress correction model respectively, to obtain the target groundwater correction index, the target structural surface correction index and the target geostress correction index.

[0034] The quality correction unit is used to input the target groundwater correction index, the target structural surface correction index, and the target geostress correction index into the preset rock mass quality correction model to obtain the target rock mass corrected quality index value.

[0035] Thirdly, an underground engineering rock mass quality correction device is provided, comprising: a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned underground engineering rock mass quality correction method.

[0036] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for correcting the quality of rock mass in underground engineering.

[0037] This application provides a method, apparatus, equipment, and readable storage medium for correcting the quality of rock mass in underground engineering. The technical solution provided by this application brings at least the following beneficial effects:

[0038] This application, through digital drilling in specific underground engineering projects, inputs the collected target drilling parameters into pre-set groundwater correction models, structural plane correction models, and geostress correction models to obtain the groundwater correction index, structural plane correction index, and geostress correction index corresponding to the rock mass under test, respectively. Finally, by substituting these correction indices into a pre-set rock mass quality correction model, the corrected quality index value of the engineering rock mass corresponding to each mileage can be determined, thereby achieving real-time and accurate evaluation of the underground engineering rock mass. Therefore, this application, by inputting the drilling parameters during the underground engineering rock mass drilling process into the corresponding correction models, quantitatively corrects the influence of groundwater, structural planes, and geostress on rock mass quality, thus enabling real-time acquisition of the corrected quality of the engineering rock mass at each mileage location, improving the accuracy and objectivity of rock mass classification. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating a method for correcting the quality of rock mass in underground engineering, provided as an embodiment of this application;

[0041] Figure 2 A schematic flowchart illustrating a method for correcting the quality of rock mass in underground engineering, provided in an embodiment of this application;

[0042] Figure 3 This is a schematic diagram of the structure of an underground engineering rock mass quality correction device provided in an embodiment of this application;

[0043] Figure 4 This is a structural schematic diagram of an underground engineering rock mass quality correction device provided in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] Figure 1 This application provides a method for correcting the quality of rock mass in underground engineering, comprising the following steps:

[0046] Step S10: Obtain the target drilling parameters after the drilling rig has drilled into the rock mass to be tested. The target drilling parameters include water pressure parameters, flow rate parameters, structural surface parameters, attitude parameters and drilling parameters during the drilling process.

[0047] As an example, it is understandable that after digital transformation, drilling rigs can perform real-time monitoring and analysis of drilling parameters, mechanical parameters, and lithological parameters during the drilling process. For example, in the fields of drilling and geological exploration, common digital systems mounted on drilling rigs include Drilling Process Monitoring (DPM), Measurement While Drilling (MWD), and Logging While Drilling (LWD) systems. Therefore, in this embodiment, a digital drilling rig can be used to conduct advanced digital drilling experiments on specific underground engineering rock masses, and the target parameters during the drilling process can be acquired in real time through various sensing instruments and equipment.

[0048] It should be understood that the target drilling parameters include water pressure parameters, flow rate parameters, structural parameters, attitude parameters, and drilling parameters. Among them, water pressure and flow rate parameters are the drilling parameters collected near the drill bit position and flushing fluid pipeline; structural parameters are the collected information on the orientation and dip angle of the structural surface; attitude parameters are the collected borehole azimuth information; and drilling parameters are the collected drilling pressure, drill bit torque, drill pipe rotation speed, and drilling speed, etc.

[0049] Step S20: Input the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model respectively to obtain the target groundwater correction index, the target structural surface correction index, and the target geostress correction index;

[0050] As an example, in this embodiment, after the target drilling parameters are obtained, the target drilling parameters are input into the preset groundwater correction model, the preset structural surface correction model and the preset geostress correction model respectively. After the relevant processing of each model, the target groundwater correction index, the target structural surface correction index and the target geostress correction index can be derived respectively.

[0051] Furthermore, before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, the method further includes:

[0052] The experimental drilling water pressure and experimental drilling flow rate values ​​in the surrounding rock fractures corresponding to the first preset working condition were obtained by in-situ digital drilling tests. The first preset working condition includes different initial values ​​of basic rock mass quality, lithology, surrounding rock water pressure and surrounding rock water output.

[0053] A preset groundwater correction model is generated based on the experimental drilling water pressure value, the experimental drilling flow rate value, and the initial value of the basic rock mass.

[0054] The calculation methods for the experimental drilling water pressure value and the experimental drilling flow rate value are as follows:

[0055] ΔP(x)=[P r (x)-P r (x-1)]-[P d (x)-P d (x-1)]

[0056] ΔQ(x)=[Q r (x)-Q r (x-1)]-[Q d (x)-Q d (x-1)]

[0057] In the formula, ΔP(x) represents the experimental drilling water pressure value within the surrounding rock fractures at kilometer x, and P r (x) represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x, P r (x-1) represents the experimental drilling water pressure value at the near-bit position corresponding to mileage x-1, P d (x) represents the experimental drilling water pressure value of the flushing fluid pipeline at mileage x, P d (x-1) represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer x-1, and ΔQ(x) represents the experimental drilling flow rate value in the surrounding rock fracture at kilometer x. r (x) represents the experimental flow rate near the drill bit at kilometer x, Q r (x-1) represents the experimental flow rate near the drill bit at kilometer marker x-1, Q. d (x) represents the experimental flow rate of the flushing fluid pipeline at kilometer x.

[0058] Q d (x-1) represents the experimental flow rate of the flushing fluid pipeline at mileage x-1.

[0059] As an example, it is understandable that groundwater is a significant factor affecting rock mass stability. The main effects of water are dissolving easily soluble cementitious materials in rocks and eroding fine particles in infill materials, leading to rock softening and loosening, and infill material mudification. Therefore, correction measures are needed to reflect the impact of groundwater on rock mass quality. In the quantitative description of water seepage in surrounding rock, the seepage volume over a 10m tunnel length and fissure water pressure are typically used as statistical quantities for correction. Therefore, to address this issue, this embodiment will use a pre-set groundwater correction model to obtain a target groundwater correction index, thereby quantitatively correcting the impact of groundwater on rock mass quality. Thus, this embodiment will construct the pre-set groundwater correction model based on the results of in-situ digital drilling tests.

[0060] Specifically, during the drilling process of a drilling rig into rock masses with known initial basic mass values ​​BQ, corresponding sensors can be used to collect experimental drilling water pressure and flow rate values ​​during the drilling process. That is, the experimental drilling parameters corresponding to different initial basic mass values ​​of the rock mass can be obtained near the drill bit and on the flushing fluid pipeline. After relevant calculations and processing, the experimental drilling water pressure and flow rate values ​​in the surrounding rock fissures corresponding to each mileage station can be obtained. Then, the mapping relationship between the obtained experimental drilling water pressure and flow rate values, the preset groundwater correction index interval, and the known initial basic mass value BQ of the rock mass is constructed to generate a preset groundwater correction model. The target groundwater correction index K1(x) corresponding to each mileage station can then be determined through this preset groundwater correction model.

[0061] It should be understood that the impact of water on rock mass quality is related to the state of water occurrence; that is, the more severe the groundwater outflow, the greater the groundwater correction index K1. Therefore, in this embodiment, the impact of water on rock masses of various lithological types under different surrounding rock water pressures P will be considered. f Water output from surrounding rock Q f Digital drilling tests were conducted under the given conditions (first preset working condition) to obtain the experimental drilling water pressure value ΔP(x) and experimental drilling flow rate value ΔQ(x) within the surrounding rock fractures. Among these, the different surrounding rock water pressures P under the first preset working condition... f Water output from surrounding rock Q f The classification of the parameters and their corresponding drilling water pressure values ​​ΔP(x) and drilling flow rates ΔQ(x) are shown in Table 1:

[0062] Table 1 Classification of Groundwater Outflow Status

[0063]

[0064] Where a, b, c, and d are the boundary values ​​of the drilling parameters under the first preset working condition obtained through digital drilling tests. For example, when the water pressure of the surrounding rock in the drilled formation is P... fWhen the value is ≤0.1, the obtained drilling water pressure value is ΔP(x)≤a; when the water output of the surrounding rock of the drilled formation is Q f When the value is ≤25, the obtained drilling flow rate is ΔQ(x)≤c.

[0065] It is understood that this embodiment will collect data on the near-bit position and the drilling water pressure (P) value of the flushing fluid pipeline during the drilling process. r (x), P d (x) and drilling flow rate (Q) r (x), Q d (x)) to obtain the experimental drilling water pressure value ΔP(x) and experimental drilling flow rate value ΔQ(x) in the surrounding rock fissures corresponding to each mileage station; then, construct the mapping relationship between the obtained experimental drilling water pressure value ΔP(x) and experimental drilling flow rate value ΔQ(x), the preset groundwater correction index interval, and the known initial value of basic rock mass BQ, so as to form the preset groundwater correction model.

[0066] Specifically, the calculation methods for the experimental drilling water pressure value ΔP(x) and the experimental drilling flow rate value ΔQ(x) are as follows:

[0067] ΔP(x)=[P r (x)-P r (x-1)]-[P d (x)-P d (x-1)]

[0068] ΔQ(x)=[Q r (x)-Q r (x-1)]-[Q d (x)-Q d (x-1)]

[0069] In the formula, ΔP(x) represents the experimental drilling water pressure value within the surrounding rock fractures at kilometer x, and P r (x) represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x, P r (x-1) represents the experimental drilling water pressure value at the near-bit position corresponding to mileage x-1, P d (x) represents the experimental drilling water pressure value of the flushing fluid pipeline at mileage x, P d (x-1) represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer x-1, and ΔQ(x) represents the experimental drilling flow rate value in the surrounding rock fracture at kilometer x. r (x) represents the experimental flow rate near the drill bit at kilometer x, Q r (x-1) represents the experimental flow rate near the drill bit at kilometer marker x-1, Q. d(x) represents the experimental flow rate of the flushing fluid pipeline at kilometer x.

[0070] Q d (x-1) represents the experimental flow rate of the flushing fluid pipeline at mileage x-1.

[0071] The minimum sampling frequency of variable x can be determined according to the actual situation. Preferably, in this embodiment, the sampling frequency of mileage x is 10 cm.

[0072] Furthermore, it is understood that the impact of water on rock mass quality is related not only to the state of water occurrence but also to the rock properties and the integrity of the rock mass. The denser, stronger, and more intact the rock, the smaller the impact of groundwater. Therefore, for the same water-producing state, the larger the basic rock mass quality index value, the smaller the groundwater correction index K1(x). Based on this, in this embodiment, a preset groundwater correction model will be constructed based on the experimental drilling water pressure value, the experimental drilling flow rate value, and the preset initial value of the basic rock mass quality BQ to determine the target groundwater correction index K1(x). Specifically, the preset groundwater correction model is shown in Table 2:

[0073] Table 2 Preset Groundwater Correction Model

[0074]

[0075] It should be noted that when determining the target groundwater correction index K1(x) from Table 2, the upper and lower limits of its range can be determined according to the actual situation and are not limited here. Specifically, after determining the range of the groundwater correction index of the drilled rock mass based on the initial value of the basic rock mass BQ, a specific value can be determined from the corresponding range as the target groundwater correction index K1(x) according to actual needs, and is not limited here. For example, when the initial value of the basic rock mass BQ = 400, and the groundwater discharge state at kilometer x is in the third state, that is, when the digital drilling test satisfies the condition ΔP(x) > b or ΔQ(x) > d, the target groundwater correction index K1 is:

[0076] Furthermore, in this embodiment, when determining the groundwater correction index K1(x), it is necessary to consider the corresponding groundwater correction index K1(x) obtained from n numerical drilling test boreholes drilled at different locations on the tunnel face. 1i (x)(i=1,2,...,n) take the average value This serves as the groundwater correction index for that mileage marker. The number of boreholes, n, used in the digital drilling test can be determined based on actual conditions and is not limited here.

[0077] Furthermore, before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, the method further includes:

[0078] The experimental structural plane attitude value and experimental borehole attitude value corresponding to the second preset working condition are obtained by in-situ digital drilling test. The second preset working condition is a combination of different structural plane attitudes and borehole attitudes.

[0079] Based on the experimental structural surface attitude values ​​and the experimental borehole attitude values, a preset structural surface correction model is generated.

[0080] As an example, it is understandable that different structural plane attitudes and drilling attitudes (the combination of structural planes and borehole axes) have varying degrees of impact on the stability of underground rock masses. For instance, for small faults or layered rock masses with poor properties, when the slope is steep and the angle between the structural plane and the borehole axis is large, the impact on rock mass stability is minimal, and this adverse effect is not reflected in the initial value of the basic rock mass quality, BQ. Therefore, in this embodiment, experimental structural plane attitude values ​​and experimental borehole attitude values ​​corresponding to different combinations of structural plane attitudes and borehole attitudes will be obtained through digital drilling tests. A mapping relationship will be constructed between the experimental structural plane attitude values, the experimental borehole attitude values, and a preset structural plane correction index range to generate a preset structural plane correction model. The target structural plane correction index K2(x) can then be determined through this preset structural plane correction model.

[0081] Specifically, this embodiment will conduct advanced horizontal digital drilling tests on rock masses with different orientations of structural planes. The orientation, dip angle, and other properties of the structural planes will be acquired during drilling using an in-bore imaging device. Information such as borehole dip angle, azimuth angle, and tool face angle will be acquired using sensors such as gyroscopes or accelerometers. After relevant processing, the acquired structural plane dip angle is referred to as the structural plane orientation value, and the combination of the acquired borehole axis angle and the structural plane orientation is referred to as the borehole attitude value. Then, a preset structural plane correction model is constructed based on the experimental structural plane orientation value and the experimental borehole attitude value. The preset structural plane correction model is shown in Table 3.

[0082] Table 3 Preset Structural Surface Correction Model

[0083]

[0084] It should be understood that when determining the target structural surface correction index K2(x) from Table 3, the upper and lower limits of its range can be determined according to the actual situation and are not limited here. Specifically, after determining the range of the structural surface correction index of the drilled rock mass based on the structural surface dip value and the borehole attitude value, a specific value can be determined from the corresponding range as the structural surface correction index K2(x) according to actual needs, and is not limited here. For example, when the structural surface dip angle is 90° and the borehole axis makes an angle of 90° with the structural surface strike, the target structural surface correction index K2(x) is 0 to 0.2, and the average value of this range, 0.1, can be taken as the value of the target structural surface correction index K2(x) corresponding to this working condition.

[0085] It should be noted that the preset structural surface correction model in this embodiment can be used when there is a set of structural surfaces that play a controlling role. If there are two or more sets of structural surfaces that play a controlling role, the preset structural surface correction model can be adaptively modified and adjusted according to actual needs, which is not limited here.

[0086] Furthermore, before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, the method further includes:

[0087] The experimental drilling parameters and the maximum principal stress of the experimental surrounding rock corresponding to the third preset working condition are obtained through indoor digital drilling tests. The saturated uniaxial compressive strength of the experimental rock is obtained through digital core drilling and rock mechanics tests. The third preset working condition includes different initial values ​​of basic rock mass, lithology, rock strength and confining pressure conditions.

[0088] The strength-stress ratio of the experimental surrounding rock was determined based on the maximum principal stress of the experimental surrounding rock and the saturated uniaxial compressive strength of the experimental rock.

[0089] Based on the experimental surrounding rock strength-stress ratio and experimental drilling parameters, a preset neural network model is trained to obtain a preset surrounding rock strength-stress ratio prediction model.

[0090] Based on the preset surrounding rock strength-stress ratio prediction model and the initial value of the basic rock mass, a preset geostress correction model is generated.

[0091] The formula for calculating the strength-stress ratio of the surrounding rock in the experiment is as follows:

[0092]

[0093] The preset surrounding rock strength-stress ratio inversion model is as follows:

[0094] SSR'=f(F',M',N',V',SE',PR',FPI',MPI')

[0095] In the formula, SSR represents the strength-stress ratio of the experimental surrounding rock, and R c σ represents the saturated uniaxial compressive strength of the experimental rock. max denoted by , SSR' represents the maximum principal stress of the experimental surrounding rock, f represents the strength-stress ratio of the target surrounding rock, F' represents the training function, M' represents the target drilling pressure, N' represents the target drill bit torque, V' represents the target drill pipe rotation speed, SE' represents the target drilling specific energy, PR' represents the target penetration, FPI' represents the target drill pressure penetration index, and MPI' represents the target torque penetration index.

[0096] As an example, it is understandable that the initial stress of the rock mass has a significant impact on the rock mass of underground engineering projects. For instance, when the strength-stress ratio of the surrounding rock is greater than a certain value, it can be considered to have no controlling effect on the stability of the tunnel rock mass. However, when the strength-stress ratio of the surrounding rock is less than a certain value, it has a significant impact on the stability or deformation and failure of the rock mass. Therefore, in this embodiment, the rock mass grade of underground engineering projects will be quantitatively corrected using the geostress correction index K3(x).

[0097] Specifically, this embodiment will use digital drilling tests to obtain experimental drilling parameters DP (including drilling pressure F, drill bit torque M, drill rod rotation speed N, drilling speed V, etc.) for rocks of various types and strengths with known initial basic mass BQ under different confining pressure conditions. Simultaneously, by setting different confining pressure conditions, the maximum principal stress σ of the experimental surrounding rock will be obtained. max Furthermore, the corresponding experimental rock saturated uniaxial compressive strength R was obtained through digital coring drilling and rock mechanics tests under different working conditions. c Secondly, based on the maximum principal stress σ of the experimental surrounding rock... max And the saturated uniaxial compressive strength R of the experimental rock c The experimental surrounding rock strength-stress ratio (SSR) is determined. Then, a preset neural network model is trained based on the experimental surrounding rock strength-stress ratio (SSR) and the experimental drilling parameters (DP) to obtain a preset surrounding rock strength-stress ratio prediction model. Finally, a preset in-situ stress correction model is generated by constructing this preset surrounding rock strength-stress ratio prediction model and the known initial value of the basic rock mass (BQ). That is, the corresponding experimental surrounding rock strength-stress ratio (SSR) can be obtained through the experimental drilling parameters (DP) and the preset surrounding rock strength-stress ratio prediction model. Then, the mapping relationship between the experimental surrounding rock strength-stress ratio (SSR), the preset in-situ stress correction index, and the known initial value of the basic rock mass (BQ) is constructed.

[0098] Ultimately, this embodiment enables the inversion of the target surrounding rock strength stress ratio SSR' through the target drilling parameter DP' and the preset surrounding rock strength stress ratio prediction model, and then quantitatively determines the target geostress correction index K3(x) of the rock mass to be tested through the preset geostress correction model.

[0099] The preset geostress correction model is shown in Table 4:

[0100] Table 4 Preset Geostress Correction Model

[0101]

[0102] It should be understood that when determining the target geostress correction index K3(x) from Table 4, its upper and lower limits can be determined according to the actual situation and are not limited here. Specifically, after determining the target surrounding rock strength stress ratio of the drilled rock mass based on the target drilling parameters and the preset surrounding rock strength stress ratio prediction model, and determining the range of the geostress correction index of the drilled rock mass based on the target surrounding rock strength stress ratio and the initial value of the basic rock mass mass BQ, a specific value can be determined from the corresponding range as the geostress correction index K3(x) according to actual needs, and is not limited here. For example, when the initial value of the basic rock mass mass BQ = 400, and the target surrounding rock strength stress ratio at mileage x is SSR = 2, the target geostress correction index K3(x) corresponding to this working condition is:

[0103] It is understandable that the maximum principal stress σ of the experimental surrounding rock is... max Different levels of geostress are simulated by setting different confining pressure conditions in indoor experiments. In this embodiment, digital drilling tests will be conducted on rocks of different types and strengths under different confining pressure conditions. Experimental drilling parameters DP will be collected during the drilling process, and rock mechanics tests will be carried out on the obtained rock cores to obtain the saturated uniaxial compressive strength R of the experimental rocks. c Then, the strength-stress ratio (SSR) of the surrounding rock in the experiment was calculated. It should be noted that only the maximum initial stress σ perpendicular to the engineering axis was considered. max The borehole direction has the greatest impact on the stability of the engineering rock mass. The borehole direction for indoor digital drilling tests should be perpendicular to the direction of the applied confining pressure. The specific confining pressure conditions can be determined according to actual needs and are not limited here.

[0104] It is understood that in this embodiment, a preset neural network will be trained based on the experimental surrounding rock strength-stress ratio (SSR) and the experimental drilling parameters (DP) to construct a preset surrounding rock strength-stress ratio prediction model. This model can then invert the target surrounding rock strength-stress ratio (SSR) of the tested rock mass using the target drilling parameters (DP) of the tested rock mass. Specifically, the expression for the preset surrounding rock strength-stress ratio prediction model is:

[0105] SSR'=f(F',M',N',V',SE',PR',FPI',MPI')

[0106] In the formula, SSR' represents the target surrounding rock strength-stress ratio, f represents the training function, F' represents the target drilling pressure, M' represents the target drill bit torque, N' represents the target drill pipe rotation speed, V' represents the target drilling speed, SE' represents the target drilling specific energy, PR' represents the target penetration, FPI' represents the target drill pressure penetration index, and MPI' represents the target torque penetration index.

[0107] The target drilling pressure F', target drill bit torque M', target drill pipe rotation speed N', and target drilling speed V' are all collected and recorded by sensors. The calculation formulas for the target drilling specific energy SE', target penetration depth PR', target drill pressure penetration index FPI', and target torque penetration index MPI' are as follows:

[0108]

[0109]

[0110]

[0111]

[0112] In the formula, SE' is the target drilling specific energy, in MPa; F' is the target drilling pressure, in N; N' is the target drill pipe rotation speed, in rev / min; M' is the target drill bit torque, in N·m; V' is the target drilling speed, in m / min; and D' is the target drill bit diameter, in mm.

[0113] It should be noted that the training function f can be determined using deep learning networks such as convolutional neural networks, fully connected neural networks, and recurrent neural networks. The specific function can be determined according to actual needs and is not limited here. For example, taking convolutional neural networks as an example, the method for determining the preset surrounding rock strength stress ratio prediction model is as follows: First, the experimental drilling parameters DP (including experimental drilling pressure F, experimental drill bit torque M, experimental drill rod speed N, experimental drilling speed V, experimental drilling specific energy SE, experimental penetration PR, experimental drilling pressure penetration index FPI, and experimental torque penetration index MPI) are obtained through digital drilling tests. The experimental surrounding rock strength stress ratio SSR is obtained through digital drilling tests and rock mechanics experiments.

[0114] Then, using the experimental drilling parameters DP as the input to the neural network and the experimental surrounding rock strength-stress ratio SSR as the label, the weights of the convolutional neural network are initialized. The experimental drilling parameters DP are input, and high-dimensional features of the data are extracted through four convolutional layers. Next, downsampling is performed using a max-pooling layer, with local maxima representing the data features of that region. Finally, the learned features are mapped to the output of the neural network through a fully connected layer, which is the predicted value of the experimental surrounding rock strength-stress ratio, SSR. p Then calculate the SSR output of the neural network. p The mean squared error between the target rock strength ratio (SSR) and the label SSR is used; the parameters in the neural network are updated using the Adam optimizer based on the mean squared error; the above steps are iterated until the neural network error converges, resulting in a series of values ​​used to predict the strength-stress ratio (SSR) of the experimental surrounding rock. p The parameters of the convolutional neural network are then used to complete the training of the neural network.

[0115] After obtaining the training function f, a preset prediction model for the strength-stress ratio of surrounding rock can be generated:

[0116] SSR'=f(F',M',N',V',SE',PR',FPI',MPI')

[0117] In the formula, SSR' represents the target surrounding rock strength-stress ratio, f represents the training function, F' represents the target drilling pressure, M' represents the target drill bit torque, N' represents the target drill pipe rotation speed, V' represents the target drilling speed, SE' represents the target drilling specific energy, PR' represents the target penetration, FPI' represents the target drill pressure penetration index, and MPI' represents the target torque penetration index.

[0118] Step S30: Input the target groundwater correction index, target structural surface correction index and target geostress correction index into the preset rock mass quality correction model to obtain the target rock mass correction quality index value.

[0119] Furthermore, the calculation formula for the preset rock mass quality correction model is as follows:

[0120]

[0121] In the formula, KQ(x) represents the target rock mass corrected quality index value at kilometer x, KQ(x) represents the initial value of the basic rock mass quality at kilometer x, K1(x) represents the target groundwater correction index at kilometer x, K2(x) represents the target structural surface correction index at kilometer x, and K3(x) represents the target geostress correction index at kilometer x.

[0122] In this exemplary embodiment, after obtaining the target groundwater correction index K1(x), target structural surface correction index K2(x), and target geostress correction index K3(x) corresponding to the rock mass to be tested, the target rock mass correction quality index value corresponding to each mile x can be determined by using a preset rock mass quality correction model. This enables real-time and accurate evaluation of the quality of the engineering rock mass. It should be noted that when the initial value of the underground engineering rock mass quality index is negative, the corrected engineering rock mass quality is directly considered as Class V rock mass.

[0123] Therefore, see Figure 2 As shown, this embodiment addresses engineering factors such as groundwater, structural planes, and in-situ stress in the rock mass classification of underground engineering projects. First, in-situ and laboratory digital drilling tests are conducted to obtain drilling parameters such as water pressure, flow rate, structural plane, attitude, and drilling parameters. Then, preset groundwater correction models, preset structural plane correction models, and preset in-situ stress correction models are established. Next, after digital drilling is performed in a specific underground engineering project, the collected target drilling parameters are substituted into the aforementioned correction models to obtain the target groundwater correction index K1(x), target structural plane correction index K2(x), and target in-situ stress correction index K3(x) corresponding to the rock mass under test. Finally, these correction indices are substituted into the preset rock mass quality correction model to determine the target rock mass corrected quality index value corresponding to each mile x. To achieve real-time and accurate evaluation of rock masses in underground engineering.

[0124] See Figure 3 As shown in the embodiment of this application, an underground engineering rock mass quality correction device is also provided, comprising:

[0125] The parameter acquisition unit is used to acquire the target drilling parameters after the drilling rig enters the rock mass to be tested. The target drilling parameters include water pressure parameters, flow rate parameters, structural surface parameters, attitude parameters and drilling parameters during the drilling process.

[0126] The index prediction unit is used to input the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model and the preset geostress correction model respectively, to obtain the target groundwater correction index, the target structural surface correction index and the target geostress correction index.

[0127] The quality correction unit is used to input the target groundwater correction index, the target structural surface correction index, and the target geostress correction index into the preset rock mass quality correction model to obtain the target rock mass corrected quality index value.

[0128] Furthermore, the device also includes a model building unit, which is used for:

[0129] The experimental drilling water pressure and experimental drilling flow rate values ​​in the surrounding rock fractures corresponding to the first preset working condition were obtained by in-situ digital drilling tests. The first preset working condition includes different initial values ​​of basic rock mass quality, lithology, surrounding rock water pressure and surrounding rock water output.

[0130] A preset groundwater correction model is generated based on the experimental drilling water pressure value, the experimental drilling flow rate value, and the initial value of the basic rock mass.

[0131] Furthermore, the calculation methods for the experimental drilling water pressure value and the experimental drilling flow rate value are as follows:

[0132] ΔP(x)=[P r (x)-P r (x-1)]-[P d (x)-P d (x-1)]

[0133] ΔQ(x)=[Q r (x)-Q r (x-1)]-[Q d (x)-Q d (x-1)]

[0134] In the formula, ΔP(x) represents the experimental drilling water pressure value within the surrounding rock fractures at kilometer x, and P r (x) represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x, P r (x-1) represents the experimental drilling water pressure value at the near-bit position corresponding to mileage x-1, P d (x) represents the experimental drilling water pressure value of the flushing fluid pipeline at mileage x, P d (x-1) represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer x-1, and ΔQ(x) represents the experimental drilling flow rate value in the surrounding rock fracture at kilometer x. r (x) represents the experimental flow rate near the drill bit at kilometer x, Q r (x-1) represents the experimental flow rate near the drill bit at kilometer marker x-1, Q. d (x) represents the experimental flow rate of the flushing fluid pipeline at kilometer x, Q d (x-1) represents the experimental flow rate of the flushing fluid pipeline at mileage x-1.

[0135] Furthermore, the model building unit is also used for:

[0136] The experimental structural plane attitude value and experimental borehole attitude value corresponding to the second preset working condition are obtained by in-situ digital drilling test. The second preset working condition is a combination of different structural plane attitudes and borehole attitudes.

[0137] Based on the experimental structural surface attitude values ​​and the experimental borehole attitude values, a preset structural surface correction model is generated.

[0138] Furthermore, the model building unit is also used for:

[0139] The experimental drilling parameters and the maximum principal stress of the experimental surrounding rock corresponding to the third preset working condition are obtained through indoor digital drilling tests. The saturated uniaxial compressive strength of the experimental rock is obtained through digital core drilling and rock mechanics tests. The third preset working condition includes different initial values ​​of basic rock mass, lithology, rock strength and confining pressure conditions.

[0140] The strength-stress ratio of the experimental surrounding rock was determined based on the maximum principal stress of the experimental surrounding rock and the saturated uniaxial compressive strength of the experimental rock.

[0141] Based on the experimental surrounding rock strength-stress ratio and experimental drilling parameters, a preset neural network model is trained to obtain a preset surrounding rock strength-stress ratio prediction model.

[0142] Based on the preset surrounding rock strength-stress ratio prediction model and the initial value of the basic rock mass, a preset geostress correction model is generated.

[0143] Furthermore, the formula for calculating the strength-stress ratio of the surrounding rock in the experiment is as follows:

[0144]

[0145] The preset surrounding rock strength-stress ratio prediction model is as follows:

[0146] SSR'=f(F',M',N',V',SE',PR',FPI',MPI')

[0147] In the formula, SSR represents the strength-stress ratio of the experimental surrounding rock, and R c σ represents the saturated uniaxial compressive strength of the experimental rock. max denoted by , SSR' represents the maximum principal stress of the experimental surrounding rock, f represents the strength-stress ratio of the target surrounding rock, F' represents the training function, M' represents the target drilling pressure, N' represents the target drill bit torque, V' represents the target drill pipe rotation speed, SE' represents the target drilling specific energy, PR' represents the target penetration, FPI' represents the target drill pressure penetration index, and MPI' represents the target torque penetration index.

[0148] Furthermore, the calculation formula for the preset rock mass quality correction model is as follows:

[0149]

[0150] In the formula, KQ(x) represents the target rock mass corrected quality index value at kilometer x, KQ(x) represents the initial value of the basic rock mass quality at kilometer x, K1(x) represents the target groundwater correction index at kilometer x, K2(x) represents the target structural surface correction index at kilometer x, and K3(x) represents the target geostress correction index at kilometer x.

[0151] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the above-described apparatus and each unit can be referred to the corresponding processes in the aforementioned embodiments of the underground engineering rock mass quality correction method, and will not be repeated here.

[0152] The apparatus provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 4 The underground engineering rock mass quality correction equipment shown is in operation.

[0153] This application also provides an underground engineering rock mass quality correction device, including: a memory, a processor and a network interface connected via a system bus, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement all or part of the steps of the aforementioned underground engineering rock mass quality correction method.

[0154] The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0155] A processor can be a CPU, or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center of a computer device, connecting all parts of the computer device through various interfaces and lines.

[0156] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as video playback, image playback, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as video data, image data, etc.). Furthermore, memory can include high-speed random access memory (RAM), and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital Cards (SD cards), Flash Cards, at least one disk storage device, flash memory devices, or other volatile solid-state storage devices.

[0157] This application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements all or part of the steps of the aforementioned method for correcting the quality of rock mass in underground engineering.

[0158] The embodiments of this application can implement all or part of the aforementioned processes, or they can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various methods described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0159] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, servers, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0160] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0162] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for correcting the quality of rock mass in underground engineering, characterized in that, Includes the following steps: The target drilling parameters are obtained after the drilling rig enters the rock mass to be tested. The target drilling parameters include water pressure parameters, flow rate parameters, structural surface parameters, attitude parameters and drilling parameters during the drilling process. The target drilling parameters are input into the preset groundwater correction model, the preset structural surface correction model, and the preset geostress correction model, respectively, to obtain the target groundwater correction index, the target structural surface correction index, and the target geostress correction index. The target groundwater correction index, the target structural surface correction index, and the target geostress correction index are input into a preset rock mass quality correction model to obtain the target rock mass corrected quality index value. The method further includes, prior to the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset in-situ stress correction model, respectively: The experimental drilling water pressure and experimental drilling flow rate values ​​in the surrounding rock fractures corresponding to the first preset working condition were obtained by in-situ digital drilling tests. The first preset working condition includes different initial values ​​of basic rock mass quality, lithology, surrounding rock water pressure and surrounding rock water output. Based on the experimental drilling water pressure value, the experimental drilling flow rate value, and the initial value of the basic rock mass mass, a preset groundwater correction model is generated. The calculation methods for the experimental drilling water pressure value and the experimental drilling flow rate value are as follows: In the formula, This represents the experimental drilling water pressure value within the surrounding rock fractures at kilometer x. This represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x. This represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x-1. This represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer x. This represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer marker x-1. This represents the experimental drilling flow rate within the surrounding rock fractures at kilometer x. This represents the experimental flow rate during drilling at the near-bit position corresponding to kilometer x. This represents the experimental drilling flow rate value at the near-bit position corresponding to mileage x-1. This represents the experimental drilling flow rate of the flushing fluid pipeline at kilometer x. This represents the experimental flow rate during drilling for the flushing fluid pipeline at kilometer x-1. Before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset in-situ stress correction model, respectively, the method further includes: The experimental structural plane attitude value and experimental borehole attitude value corresponding to the second preset working condition are obtained by in-situ digital drilling test. The second preset working condition is a combination of different structural plane attitudes and borehole attitudes. Based on the experimental structural surface attitude values ​​and the experimental borehole attitude values, a preset structural surface correction model is generated. Before the steps of inputting the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model, and the preset in-situ stress correction model, respectively, the method further includes: The experimental drilling parameters and the maximum principal stress of the experimental surrounding rock corresponding to the third preset working condition are obtained through indoor digital drilling tests. The saturated uniaxial compressive strength of the experimental rock is obtained through digital core drilling and rock mechanics tests. The third preset working condition includes different initial values ​​of basic rock mass, lithology, rock strength and confining pressure conditions. The strength-stress ratio of the experimental surrounding rock was determined based on the maximum principal stress of the experimental surrounding rock and the saturated uniaxial compressive strength of the experimental rock. Based on the experimental surrounding rock strength-stress ratio and experimental drilling parameters, a preset neural network model is trained to obtain a preset surrounding rock strength-stress ratio prediction model. Based on the preset surrounding rock strength-stress ratio prediction model and the initial value of the basic rock mass, a preset geostress correction model is generated.

2. The method for correcting the quality of rock mass in underground engineering as described in claim 1, characterized in that, The formula for calculating the strength-stress ratio of the surrounding rock in the experiment is as follows: The preset surrounding rock strength-stress ratio prediction model is as follows: In the formula, SSR represents the strength-stress ratio of the experimental surrounding rock, and R c σ represents the saturated uniaxial compressive strength of the experimental rock. max denoted by , SSR' represents the maximum principal stress of the experimental surrounding rock, f represents the strength-stress ratio of the target surrounding rock, F' represents the training function, M' represents the target drilling pressure, N' represents the target drill bit torque, V' represents the target drill pipe rotation speed, SE' represents the target drilling specific energy, PR' represents the target penetration, FPI' represents the target drill pressure penetration index, and MPI' represents the target torque penetration index.

3. The method for correcting the quality of rock mass in underground engineering as described in claim 1, characterized in that, The calculation formula for the preset rock mass quality correction model is as follows: In the formula, This represents the corrected quality index value of the target rock mass at kilometer x. This represents the initial value of the basic mass of the rock mass at kilometer x. This represents the target groundwater correction index at kilometer x. This represents the target structural surface correction index at mileage x. This represents the target ground stress correction index at kilometer x.

4. A rock mass quality correction device for underground engineering, characterized in that, include: The parameter acquisition unit is used to acquire the target drilling parameters after the drilling rig enters the rock mass to be tested. The target drilling parameters include water pressure parameters, flow rate parameters, structural surface parameters, attitude parameters and drilling parameters during the drilling process. The index prediction unit is used to input the target drilling parameters into the preset groundwater correction model, the preset structural surface correction model and the preset geostress correction model respectively, to obtain the target groundwater correction index, the target structural surface correction index and the target geostress correction index. The quality correction unit is used to input the target groundwater correction index, the target structural surface correction index and the target geostress correction index into the preset rock mass quality correction model to obtain the target rock mass correction quality index value. The model building unit is used to obtain the experimental drilling water pressure and experimental drilling flow rate values ​​in the surrounding rock fractures corresponding to the first preset working condition through in-situ digital drilling tests. The first preset working condition includes different initial values ​​of basic rock mass quality, lithology category, surrounding rock water pressure and surrounding rock water output. Based on the experimental drilling water pressure value, the experimental drilling flow rate value, and the initial value of the basic rock mass mass, a preset groundwater correction model is generated. The calculation methods for the experimental drilling water pressure value and the experimental drilling flow rate value are as follows: In the formula, This represents the experimental drilling water pressure value within the surrounding rock fractures at kilometer x. This represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x. This represents the experimental drilling water pressure value at the near-bit position corresponding to kilometer x-1. This represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer x. This represents the experimental drilling water pressure value of the flushing fluid pipeline at kilometer marker x-1. This represents the experimental drilling flow rate within the surrounding rock fractures at kilometer x. This represents the experimental flow rate during drilling at the near-bit position corresponding to kilometer x. This represents the experimental drilling flow rate value at the near-bit position corresponding to mileage x-1. This represents the experimental drilling flow rate of the flushing fluid pipeline at kilometer x. This represents the experimental flow rate during drilling for the flushing fluid pipeline at kilometer x-1. The experimental structural plane attitude value and experimental borehole attitude value corresponding to the second preset working condition are obtained by in-situ digital drilling test. The second preset working condition is a combination of different structural plane attitudes and borehole attitudes. Based on the experimental structural surface attitude values ​​and the experimental borehole attitude values, a preset structural surface correction model is generated. The experimental drilling parameters and the maximum principal stress of the experimental surrounding rock corresponding to the third preset working condition are obtained through indoor digital drilling tests. The saturated uniaxial compressive strength of the experimental rock is obtained through digital core drilling and rock mechanics tests. The third preset working condition includes different initial values ​​of basic rock mass, lithology, rock strength and confining pressure conditions. The strength-stress ratio of the experimental surrounding rock was determined based on the maximum principal stress of the experimental surrounding rock and the saturated uniaxial compressive strength of the experimental rock. Based on the experimental surrounding rock strength-stress ratio and experimental drilling parameters, a preset neural network model is trained to obtain a preset surrounding rock strength-stress ratio prediction model. Based on the preset surrounding rock strength-stress ratio prediction model and the initial value of the basic rock mass, a preset geostress correction model is generated.

5. A rock mass quality correction device for underground engineering, characterized in that, include: A memory and a processor, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the underground engineering rock mass quality correction method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the underground engineering rock mass quality correction method according to any one of claims 1 to 3.

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