Surrounding rock grade identification method and device, electronic equipment and storage medium

Abstract

The invention provides a surrounding rock grade identification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining first surrounding rock grade information, first drilling parameters and simulated first rock breaking source data, and the drilling parameters comprise drilling pressure, propulsion pressure and rotation pressure of rock drilling equipment; according to the first rock breaking source data, the first drilling parameter and the first surrounding rock grade information, training to obtain a surrounding rock identification model; second rock breaking source data and second drilling parameters are obtained; and inputting the second rock breaking source data and the second drilling parameter into the surrounding rock identification model to obtain target surrounding rock grade information. According to the technical scheme provided by the embodiment of the invention, the surrounding rock grade is identified according to the rock breaking source data instead of artificial rock core sampling, so that the surrounding rock identification accuracy is improved, and the identification efficiency is improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of recognition technologies, and particularly relates to a method, device, electronic device and storage medium for identifying surrounding rock grades. Background Art

[0002] Tunnel engineering mainly refers to the engineering technology of excavating channels underground or underwater, and is mainly used for infrastructure construction such as transportation, water conveyance, gas transmission, and power. During the construction process, a rock drilling jumbo is mainly used for drilling work, and the construction method of the rock drilling jumbo depends on the surrounding rock grade of the current construction site.

[0003] In related technologies, the methods for identifying surrounding rock grades mainly include core sampling and RQD (Rock Quality Designation) measurement. These traditional surrounding rock identification methods rely too much on on-site sampling, are prone to sampling errors, affect the accuracy of data, and have a very complex operation mode, relying on a large amount of manpower and material resources, with low identification efficiency. Summary of the Invention

[0004] The embodiments of the present disclosure provide a solution to solve the problems in related technologies that traditional surrounding rock identification methods rely too much on on-site sampling, are prone to sampling errors, affect the accuracy of data, have a very complex operation mode, rely on a large amount of manpower and material resources, and have low identification efficiency.

[0005] In a first aspect, the present disclosure provides a method for identifying surrounding rock grades, the method including:

[0006] Obtain first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data, where the drilling parameters include the drilling pressure, propulsion pressure, and rotation pressure of the rock drilling equipment;

[0007] Train a surrounding rock identification model according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information;

[0008] Obtain second rock-breaking seismic source data and second drilling parameters;

[0009] Input the second rock-breaking seismic source data and the second drilling parameters into the surrounding rock identification model to obtain target surrounding rock grade information.

[0010] In a second aspect, the present disclosure provides a device for identifying surrounding rock grades, the device including:

[0011] An obtaining unit, configured to obtain first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data, where the drilling parameters include the drilling pressure, propulsion pressure, and rotation pressure of the rock drilling equipment;

[0012] A training unit, configured to train a surrounding rock identification model according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information;

[0013] The obtaining unit is further configured to obtain second rock-breaking seismic source data and second drilling parameters;

[0014] An input unit, configured to input the second rock-breaking seismic source data and the second drilling parameters into the surrounding rock identification model to obtain target surrounding rock grade information.

[0015] In a third aspect, the present disclosure provides an electronic device, including:

[0016] A processor; and

[0017] A memory, configured to store executable instructions of the processor;

[0018] Wherein, the processor is configured to execute any method in the first aspect or possible implementations of the first aspect by executing the executable instructions.

[0019] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements any method in the first aspect or possible implementations of the first aspect.

[0020] The technical solution provided by the present disclosure obtains first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data, wherein the drilling parameters include the drilling pressure, the propulsion pressure, and the rotary pressure of the rock drilling equipment; according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information, a surrounding rock identification model is trained; second rock-breaking seismic source data and second drilling parameters are obtained; the second rock-breaking seismic source data and the second drilling parameters are input into the surrounding rock identification model to obtain target surrounding rock grade information. The technical solutions provided by the embodiments of the present disclosure train a surrounding rock grade identification model through the simulated first rock-breaking seismic source data, the first surrounding rock grade information, and the first drilling parameters, and then obtain the target surrounding rock grade according to the second rock-breaking seismic source data and the second drilling parameters through the surrounding rock grade identification model. This process mainly uses the rock-breaking seismic source data, which can improve the accuracy of identification and the efficiency of surrounding rock grade identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0022] Figure 1 A schematic flowchart of a surrounding rock grade identification method provided by an embodiment of the present disclosure;

[0023] Figure 2 A schematic structural diagram of a surrounding rock grade identification device provided by an embodiment of the present disclosure;

[0024] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0025] The embodiments of the present disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present disclosure, and should not be construed as a limitation to the present disclosure.

[0026] The terms "first" and "second" etc. in the specification, claims and drawings of the embodiments of the present disclosure are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] The surrounding rock grade identification method provided by the embodiments of the present disclosure can run on a terminal device or a server. Among them, the terminal device can be a local terminal device, including wearable devices such as VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality). Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0028] Tunnel engineering mainly refers to the engineering technology of excavating channels underground or underwater, and is mainly used for infrastructure construction such as transportation, water conveyance, gas transmission, and power. During the construction process, a rock drilling jumbo is mainly used for drilling operations, and the construction method of the rock drilling jumbo depends on the surrounding rock grade of the current construction site.

[0029] In the related art, the methods for identifying the surrounding rock grade mainly include core sampling and RQD (Rock Quality Designation) measurement. These traditional surrounding rock identification methods rely too much on on-site sampling, which is prone to sampling errors, affecting the accuracy of data. Moreover, the operation method is very complex, relying on a large amount of manpower and material resources, and the identification efficiency is low.

[0030] The following uses specific embodiments to elaborate in detail on the technical solutions of the present disclosure and how the technical solutions of the present disclosure solve the above technical problems. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present disclosure will be described below in conjunction with the drawings.

[0031] Figure 1 The following is a schematic flowchart of a method for identifying the surrounding rock grade provided by an exemplary embodiment of the present disclosure. This method can be applied to an electronic device with data processing functions. Taking the application of this method to a rock drilling device as an example, this solution at least includes the following steps S101-S104:

[0032] S101, obtain the first surrounding rock grade information, the first drilling parameters, and the simulated first rock-breaking seismic source data.

[0033] In some embodiments, the drilling parameters include the drilling pressure, the propulsion pressure, and the rotation pressure of the rock drilling device.

[0034] Among them, the drilling pressure mainly refers to the pressure borne by the drill bit of the rock drilling device during drilling.

[0035] The propulsion pressure mainly refers to the pressure provided by the rock drilling device to the drill bit for propulsion during drilling.

[0036] The rotation pressure mainly refers to the pressure generated by the drill bit of the rock drilling device during rotation.

[0037] In other embodiments, the drilling parameters may further include: the drill bit rotation speed, the pump pressure, the pump rate, and the pump volume, etc.

[0038] In some embodiments, obtaining the first surrounding rock grade information, the first drilling parameters, and the simulated first rock-breaking seismic source data includes steps S11-S12:

[0039] S11, determine the first surrounding rock grade information, the first drilling parameters, and the preset in-situ stress.

[0040] In some embodiments, the surrounding rock grade is classified according to the stability of the rock. The higher the level, the higher the stability. Specifically, the surrounding rock grade information may include: Grade Ⅰ, Grade Ⅱ, Grade Ⅲ, Grade Ⅳ, and Grade Ⅴ.

[0041] Among them, the first surrounding rock grade information can be at least one of Grade I, Grade II, Grade III, Grade IV, and Grade V.

[0042] The first drilling parameter can be the drilling pressure, the propulsion pressure, and the rotary pressure of the rock drilling equipment set in advance.

[0043] In some embodiments, in order to simulate a better working environment for the rock drilling equipment, when determining the first surrounding rock grade information and the first drilling parameter, it is also necessary to determine the preset ground stress.

[0044] S12. Based on the first surrounding rock grade information, the first drilling parameter, and the preset ground stress, conduct a multi-level orthogonal experiment to obtain the first rock-breaking seismic source data.

[0045] Specifically, conduct a five-factor multi-level orthogonal experiment L 15 (5 1 ×3 4 ) of the preset ground stress, the first surrounding rock grade information, and the first drilling parameter (the first drilling pressure, the first propulsion pressure, and the first rotary pressure), and collect the rock-breaking seismic source data generated by the rock drilling equipment during the simulated drilling process, that is, the first rock-breaking seismic source data.

[0046] Among them, during the simulation, the surrounding rock grade information can be represented by the saturated uniaxial compressive strength R c (MPa) of the specimen. The surrounding rock grade information includes five levels, namely Grade I surrounding rock (R c1 )R c ∈(60, +∞), Grade II surrounding rock (R c2 )R c ∈(30, 60], Grade III surrounding rock (R c3 )R c ∈(15, 30], Grade IV surrounding rock (R c4 )R c ∈(5, 15], Grade V surrounding rock (R c5 )R c ∈(0, 5).

[0047] Among them, the preset ground stress is represented by σ (MPa). σ (MPa) is divided into three levels, and the value range is related to the specimen R c . Preferably, the first preset ground stress (σ1) The second preset ground stress (σ2) The maximum preset ground stress (σ3) Among them, the first preset ground stress is less than the second preset ground stress.

[0048] Among them, the drilling pressure is represented by P Z (Bar), and the propulsion pressure is represented by PT (Bar) indicates that the rotary pressure is P H (Bar).

[0049] Specifically, during the simulation of drilling, different grades of surrounding rock correspond to different ranges of drilling parameters, as shown in Table 1.

[0050] Table 1 Drilling parameters under different surrounding rock grades

[0051] Drilling pressure (bar) Thrust pressure (bar) Rotary pressure (bar) Grade Ⅰ surrounding rock 175-185 85.4-95.4 103.8-113.8 Grade Ⅱ surrounding rock 160.6-170.6 74.2-84.2 93.5-103.5 Grade Ⅲ surrounding rock 145.6-155.6 63.5-73.5 82.3-92.3 Grade Ⅳ surrounding rock 192.2-139.2 50.7-60.7 74.4-84.4 Grade Ⅴ surrounding rock 114.90-124.9 41.1-51.1 62.2-72.2

[0052] In some embodiments, taking Table 1 as an example, during the simulation, when the drill bit of the rock drilling equipment drills into the grade I surrounding rock, the range of the drilling pressure corresponding to the grade I surrounding rock is 175 - 185, the range of the propulsion pressure is 85.4 - 95.4, and the range of the rotary pressure is 103.8 - 113.8.

[0053] In some embodiments, when conducting a multi - level orthogonal experiment, in order to ensure the stability and reliability of the data, multiple multi - level orthogonal experiments need to be carried out. As shown in Table 2, an L15(51×34) orthogonal experiment table is provided.

[0054] Specifically, as shown in Table 2, each surrounding rock grade corresponds to three different in - situ stresses and three different drilling parameters. For example, the surrounding rock grade R c1 corresponds to the in - situ stress σ1, the drilling pressure P Z3 , the propulsion pressure P T2 , and the rotary pressure P H2 ; or, the surrounding rock grade R c1 corresponds to the in - situ stress σ2, the drilling pressure P Z1 , the propulsion pressure P T1 , and the rotary pressure P H1 ; or, the surrounding rock grade R c1 corresponds to the in - situ stress σ3, the drilling pressure P Z2 , the propulsion pressure P T1 , and the rotary pressure P H3 .

[0055] Table 2 Orthogonal experiment table

[0056] Number of test groups Surrounding rock grade In-situ stress Drilling pressure Thrust pressure Rotary pressure 1 <![CDATA[R c1 > <![CDATA[σ1]]> <![CDATA[P Z3 > <![CDATA[P T2 > <![CDATA[P H2 > 2 <![CDATA[R c1 > <![CDATA[σ2]]> <![CDATA[P Z1 > <![CDATA[P T1 > <![CDATA[P H1 > 3 <![CDATA[R c1 > <![CDATA[σ3]]> <![CDATA[P Z2 > <![CDATA[P T3 > <![CDATA[P H3 > 4 <![CDATA[R c2 > <![CDATA[σ1]]> <![CDATA[P Z2 > <![CDATA[P T1 > <![CDATA[P H2 > 5 <![CDATA[R c2 > <![CDATA[σ2]]> <![CDATA[P Z3 > <![CDATA[P T3 > <![CDATA[P H1 > 6 <![CDATA[R c2 > <![CDATA[σ3]]> <![CDATA[P Z1 > <![CDATA[P T2 > <![CDATA[P H3 > 7 <![CDATA[R c3 > <![CDATA[σ1]]> <![CDATA[P Z1 > <![CDATA[P T3 > <![CDATA[P H1 > 8 <![CDATA[R c3 > <![CDATA[σ2]]> <![CDATA[P Z2 > <![CDATA[P T2 > <![CDATA[P H3 > 9 <![CDATA[R c3 > <![CDATA[σ3]]> <![CDATA[P Z3 > <![CDATA[P T1 > <![CDATA[P H2 > 10 <![CDATA[R c4 > <![CDATA[σ1]]> <![CDATA[P Z1 > <![CDATA[P T1 > <![CDATA[P H3 > 11 <![CDATA[R c4 > <![CDATA[σ2]]> <![CDATA[P Z2 > <![CDATA[P T3 > <![CDATA[P 12 > 12 <![CDATA[R c4 > <![CDATA[σ3]]> <![CDATA[P Z3 > <![CDATA[P T2 > <![CDATA[P H1 > 13 <![CDATA[R c5 > <![CDATA[σ1]]> <![CDATA[P Z3 > <![CDATA[P T2 > <![CDATA[P H3 > 14 <![CDATA[R c5 > <![CDATA[σ2]]> <![CDATA[P Z1 > <![CDATA[P T1 > <![CDATA[P H2 <!-- 4 -->]]> 15 <![CDATA[R c5 > <![CDATA[σ3]]> <![CDATA[P Z2 > <![CDATA[P T3 > <![CDATA[P H1 >

[0057] S102, train a surrounding rock identification model according to the first rock - breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information.

[0058] In some embodiments, according to the first rock - breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information, a surrounding rock identification model is trained using a neural network.

[0059] In some embodiments, a surrounding rock identification model is trained based on the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information, including steps S21 - S24:

[0060] S21. Perform a short-time Fourier transform on the first rock-breaking seismic source data to determine the spectrum corresponding to the first rock-breaking seismic source data, and determine the spectral characteristics corresponding to the spectrum.

[0061] Among them, the spectral characteristics include frequency and instantaneous frequency.

[0062] In some embodiments, performing a short-time Fourier transform on the first rock-breaking seismic source data to determine the spectrum corresponding to the first rock-breaking seismic source data includes steps S211 - S212:

[0063] S211. Perform a filtering process on the first rock-breaking seismic source data to obtain first sub-rock-breaking seismic source data.

[0064] Among them, the first sub-rock-breaking seismic source data is a data section in the first rock-breaking seismic source data.

[0065] Specifically, the first sub-rock-breaking seismic source data refers to the section in the first rock-breaking seismic source data where the waveform becomes stable.

[0066] S212. Perform a short-time Fourier transform on the first sub-rock-breaking seismic source data to determine the spectrum corresponding to the first sub-rock-breaking seismic source data.

[0067] In some embodiments, when performing a short-time Fourier transform on the first sub-rock-breaking seismic source data, the method further includes: performing denoising and normalization processing on the first sub-rock-breaking seismic source data, and performing a short-time Fourier transform on the processed first sub-rock-breaking seismic source data. By performing denoising and normalization processing on the first sub-rock-breaking seismic source data, the first sub-rock-breaking seismic source data can be made more accurate.

[0068] S22. Combine the spectral characteristics and the first drilling parameters to form a multi-dimensional feature vector.

[0069] S23. Establish a mapping relationship between the multi-dimensional feature vector and the first surrounding rock grade information.

[0070] In some embodiments, establishing a mapping relationship between the multi-dimensional feature vector and the first surrounding rock grade information includes: based on the corresponding relationship between the first drilling parameters and the first surrounding rock grade information, establishing a mapping relationship between the multi-dimensional feature vector and the first surrounding rock grade information.

[0071] Specifically, as shown in Table 1 above, for example, the drilling pressure range corresponding to Class I surrounding rock is 175 - 185, the propulsion pressure range is 85.4 - 95.4, and the rotation pressure range is 103.8 - 113.8. If the multi-dimensional feature vector is formed based on the drilling parameters (drilling pressure 185, propulsion pressure 95.4, rotation pressure 113.8), then the multi-dimensional feature vector establishes a mapping relationship with Class I surrounding rock.

[0072] S24. Based on the mapping relationship, construct the surrounding rock identification model through a neural network.

[0073] In some embodiments, the neural network is preferably CNN-LSTM (Convolutional Neural Networks - Long Short-Term Memory).

[0074] LSTM is a special type of recurrent neural network (RNN) used to process and predict time series data. It controls the flow of information through a gate control mechanism (input gate, forget gate, and output gate), thereby better capturing long-term dependencies and avoiding the gradient vanishing problem in traditional RNNs.

[0075] CNN is a feedforward neural network with a deep structure that includes convolutional calculations, mainly used to extract local features from input data. Through multiple convolutional layers and pooling layers, it captures important patterns and features in the data.

[0076] In some embodiments, after training the surrounding rock identification model according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information, the method further includes steps S31 - S33:

[0077] S31. Collect the actual working parameters of the rock drilling jumbo.

[0078] In some embodiments, the actual working parameters include: current drilling parameters, current surrounding rock grade information, and current rock-breaking seismic source data.

[0079] Among them, the actual working parameters refer to the working parameters collected when the rock drilling equipment is actually performing drilling work.

[0080] S32. Input the current drilling parameters and the current rock-breaking seismic source data into the surrounding rock identification model to obtain the second surrounding rock grade information.

[0081] In some embodiments, inputting the current drilling parameters and the current rock-breaking seismic source data into the surrounding rock identification model to obtain the second surrounding rock grade information includes steps S321 - S322:

[0082] S321. Filter the current number of rock-breaking vibration sources to obtain the current sub-rock-breaking vibration source data.

[0083] In some embodiments, the time interval of the current sub-rock-breaking vibration source data is the same as that of the first sub-rock-breaking vibration source data.

[0084] S322. Input the current drilling parameters and the current sub-rock-breaking vibration source data into the surrounding rock identification model to obtain the second surrounding rock grade information.

[0085] To detect the accuracy of the surrounding rock identification model, it is necessary to optimize and detect the surrounding rock identification model. Therefore, only the current drilling parameters and the current sub-rock-breaking vibration source data need to be obtained and input into the surrounding rock identification model to obtain the current surrounding rock grade information, that is, the second surrounding rock grade information.

[0086] S33. If the second surrounding rock type information is different from the current surrounding rock grade information, train the surrounding rock identification model according to the current drilling parameters, the current surrounding rock grade information, and the current rock-breaking vibration source data to obtain a new surrounding rock identification model.

[0087] In some embodiments, the difference between the second surrounding rock type information and the current surrounding rock grade information indicates that the surrounding rock grade information output by the surrounding rock identification model is inaccurate. Therefore, it is necessary to train the surrounding rock identification model according to the current drilling parameters, the current surrounding rock grade information, and the current rock-breaking vibration source data to obtain a new surrounding rock identification model. By optimizing and adjusting the surrounding rock identification model, the surrounding rock identification model can be made more accurate.

[0088] In some other embodiments, the cross-entropy loss function can be used to evaluate the performance of the surrounding rock identification model, and optimizers such as Adam or SGD can be selected to train the surrounding rock identification model, and the hyperparameters (such as learning rate, batch size, number and size of convolutional kernels) can be adjusted through cross-validation to improve the effect of the surrounding rock identification model and improve the recognition accuracy.

[0089] In some other embodiments, indicators such as accuracy, F1-score, and confusion matrix can be used to evaluate the recognition effect of the surrounding rock identification model, and the recognition ability of the surrounding rock identification model can be further analyzed through the ROC curve and AUC value, and the data with inaccurate recognition of the surrounding rock identification model can be analyzed, thereby improving the accuracy of the surrounding rock identification model.

[0090] In this embodiment, the parameters of the surrounding rock identification model can also be adjusted according to the mean square error (MSE), mean absolute error (MAE), coefficient of determination (R2), etc. of the evaluation index, and the surrounding rock identification model can be optimized to improve the performance.

[0091] In some embodiments, the method includes: collecting current rock-breaking seismic source data during the drilling construction process through a vibration sensor installed on the rock drilling equipment.

[0092] Specifically, the vibration sensor is installed by magnetic adsorption. Among them, the vibration sensor is threadedly connected to a magnetic base, and the magnetic base is adsorbed on the equipment.

[0093] Among them, the suction force of the magnetic base is preferably 70KG.

[0094] In some embodiments, the vibration sensor uses Bluetooth 5.0 to achieve real-time data transmission.

[0095] S103, obtain the second rock-breaking seismic source data and the second drilling parameters.

[0096] In some embodiments, the second drilling parameter refers to the drilling parameter during the actual operation of the rock drilling equipment.

[0097] The second rock-breaking seismic source data refers to the rock-breaking seismic source data obtained through the vibration sensor during the actual operation of the rock drilling equipment.

[0098] S104, input the second rock-breaking seismic source data and the second drilling parameters into the surrounding rock identification model to obtain the target surrounding rock grade information.

[0099] Specifically, after determining the rock-breaking seismic source data obtained through the vibration sensor during the actual operation of the rock drilling equipment and the drilling parameter during the actual operation of the rock drilling equipment, the second rock-breaking seismic source data and the second drilling parameters can be input into the surrounding rock identification model to output the target surrounding rock grade information, that is, the surrounding rock grade of the hole drilled during the actual operation of the current rock drilling equipment.

[0100] The technical solution provided by the present disclosure obtains the first surrounding rock grade information, the first drilling parameters, and the simulated first rock-breaking seismic source data. Among them, the drilling parameters include the drilling pressure, the propulsion pressure, and the rotary pressure of the rock drilling equipment; according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information, a surrounding rock identification model is trained; the second rock-breaking seismic source data and the second drilling parameters are obtained; the second rock-breaking seismic source data and the second drilling parameters are input into the surrounding rock identification model to obtain the target surrounding rock grade information. The technical solutions provided by the embodiments of the present disclosure train a surrounding rock grade identification model through the simulated first rock-breaking seismic source data, the first surrounding rock grade information, and the first drilling parameters, and then obtain the target surrounding rock grade according to the second rock-breaking seismic source data and the second drilling parameters through the surrounding rock grade identification model. This process mainly uses the rock-breaking seismic source data, which can improve the accuracy of identification and the efficiency of surrounding rock grade identification.

[0101] Figure 2 Schematic diagram of a surrounding rock grade identification device provided by an exemplary embodiment of the present disclosure;

[0102] Among them, the device includes: an acquisition unit 201, a training unit 202, and an input unit 203;

[0103] The acquisition unit 201 is used to acquire first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data, where the drilling parameters include the drilling pressure, propulsion pressure, and rotary pressure of the rock drilling equipment;

[0104] The training unit 202 is used to train a surrounding rock identification model according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information;

[0105] The acquisition unit 201 is further used to acquire second rock-breaking seismic source data and second drilling parameters;

[0106] The input unit 203 is used to input the second rock-breaking seismic source data and the second drilling parameters into the surrounding rock identification model to obtain target surrounding rock grade information.

[0107] In some embodiments, the device is used to acquire first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data. Specifically, the device is used to:

[0108] Determine the first surrounding rock grade information, the first drilling parameters, and the preset in-situ stress;

[0109] Based on the first surrounding rock grade information, the first drilling parameters, and the preset in-situ stress, perform a multi-level orthogonal test to obtain the first rock-breaking seismic source data.

[0110] In some embodiments, the device is used to train a surrounding rock identification model according to the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information. Specifically, the device is used to:

[0111] Perform a short-time Fourier transform on the first rock-breaking seismic source data to determine the spectrum corresponding to the first rock-breaking seismic source data, and determine the spectral characteristics corresponding to the spectrum, where the spectral characteristics include frequency and instantaneous frequency;

[0112] Combine the spectral characteristics and the first drilling parameters to form a multi-dimensional feature vector;

[0113] Establish a mapping relationship between the multi-dimensional feature vector and the first surrounding rock grade information;

[0114] Based on the mapping relationship, construct the surrounding rock identification model through a neural network.

[0115] In some embodiments, the device is used to perform a short-time Fourier transform on the first rock-breaking vibration source data to determine the spectrum corresponding to the first rock-breaking vibration source data. Specifically, the device is configured to:

[0116] Perform filtering processing on the first rock-breaking vibration source data to obtain first sub-rock-breaking vibration source data, where the first sub-rock-breaking vibration source data is a data section in the first rock-breaking vibration source data;

[0117] Perform a short-time Fourier transform on the first sub-rock-breaking vibration source data to determine the spectrum corresponding to the first sub-rock-breaking vibration source data.

[0118] In some embodiments, after the device is used to train a surrounding rock identification model based on the first rock-breaking vibration source data, the first drilling parameters, and the first surrounding rock grade information, the device is further configured to:

[0119] Collect the actual working parameters of the rock drilling jumbo, where the actual working parameters include: current drilling parameters, current surrounding rock grade information, and current rock-breaking vibration source data;

[0120] Input the current drilling parameters and the current rock-breaking vibration source data into the surrounding rock identification model to obtain second surrounding rock grade information;

[0121] If the second surrounding rock type information is different from the current surrounding rock grade information, then train the surrounding rock identification model based on the current drilling parameters, the current surrounding rock grade information, and the current rock-breaking vibration source data to obtain a new surrounding rock identification model.

[0122] In some embodiments, the device is specifically configured to collect the current rock-breaking vibration source data during the drilling construction process through a vibration sensor installed on the rock drilling equipment.

[0123] In some embodiments, when the device is used to input the current drilling parameters and the current rock-breaking vibration source data into the surrounding rock identification model to obtain second surrounding rock grade information, the device is specifically configured to:

[0124] Perform filtering processing on the current rock-breaking vibration source data to obtain current sub-rock-breaking vibration source data, where the time interval of the current sub-rock-breaking vibration source data is the same as that of the first sub-rock-breaking vibration source data;

[0125] Input the current drilling parameters and the current sub-rock-breaking vibration source data into the surrounding rock identification model to obtain second surrounding rock grade information.

[0126] The technical solution provided by the present disclosure obtains first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data, where the drilling parameters include the drilling pressure, propulsion pressure, and rotary pressure of the rock drilling equipment; a surrounding rock identification model is trained based on the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information; second rock-breaking seismic source data and second drilling parameters are obtained; the second rock-breaking seismic source data and the second drilling parameters are input into the surrounding rock identification model to obtain target surrounding rock grade information. The technical solutions provided by the embodiments of the present disclosure train a surrounding rock grade identification model through the simulated first rock-breaking seismic source data, the first surrounding rock grade information, and the first drilling parameters, and then obtain the target surrounding rock grade according to the second rock-breaking seismic source data and the second drilling parameters through the surrounding rock grade identification model. This process mainly uses the rock-breaking seismic source data, which can improve the accuracy of identification and the efficiency of surrounding rock grade identification.

[0127] It should be understood that the device embodiments and the method embodiments can correspond to each other, and similar descriptions can refer to the method embodiments. To avoid repetition, it will not be elaborated here. Specifically, the device can execute the above method embodiments, and the foregoing and other operations and / or functions of each module in the device respectively correspond to the corresponding processes in each method in the above method embodiments. For the sake of brevity, it will not be elaborated here.

[0128] In the above, the device of the embodiments of the present disclosure has been described from the perspective of functional modules. It should be understood that the functional module can be implemented in the form of hardware, can also be implemented by instructions in the form of software, or can be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiments in the present disclosure can be completed by the integrated logic circuit of the hardware in the processor and / or instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps in the above method embodiments.

[0129] Figure 3 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. The electronic device may include:

[0130] A memory 301 and a processor 302. The memory 301 is used to store a computer program and transmit the program code to the processor 302. In other words, the processor 302 can call and run the computer program from the memory 301 to implement the method in the embodiments of the present disclosure.

[0131] For example, the processor 302 can be used to execute the above method embodiments according to the instructions in the computer program.

[0132] In some embodiments of the present disclosure, the processor 302 may include, but is not limited to:

[0133] a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like.

[0134] In some embodiments of the present disclosure, the memory 301 includes, but is not limited to:

[0135] a volatile memory and / or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synch link dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0136] In some embodiments of the present disclosure, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to complete the method provided by the present disclosure. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0137] As Figure 3 shown, the electronic device may further include:

[0138] a transceiver 303, which may be connected to the processor 302 or the memory 301.

[0139] Among them, the processor 302 may control the transceiver 303 to communicate with other devices. Specifically, it may send information or data to other devices, or receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include an antenna, and the number of antennas may be one or more.

[0140] It should be understood that the various components in the electronic device are connected through a bus system. Among them, the bus system includes not only a data bus, but also a power bus, a control bus, and a status signal bus.

[0141] The present disclosure also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by the computer, the computer can execute the method of the above method embodiment. Or, the embodiment of the present disclosure also provides a computer program product including instructions. When the instructions are executed by the computer, the computer executes the method of the above method embodiment.

[0142] When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present disclosure are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0143] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0144] In several embodiments provided by the present disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or modules can be in an electrical, mechanical, or other form.

[0145] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. For example, in each embodiment of the present disclosure, each functional module can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0146] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure can easily think of changes or substitutions, which should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A method for identifying the surrounding rock grade, characterized in that, The method includes: Obtaining first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking seismic source data, where the drilling parameters include the drilling pressure, propulsion pressure, and rotary pressure of the rock drilling equipment; Training a surrounding rock identification model based on the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information; Obtaining second rock-breaking seismic source data and second drilling parameters; Inputting the second rock-breaking seismic source data and the second drilling parameters into the surrounding rock identification model to obtain target surrounding rock grade information.

2. The method according to claim 1, characterized in that, Obtaining the first surrounding rock grade information, the first drilling parameters, and the simulated first rock-breaking seismic source data includes: Determining the first surrounding rock grade information, the first drilling parameters, and a preset ground stress; Conducting multi-level orthogonal experiments based on the first surrounding rock grade information, the first drilling parameters, and the preset ground stress to obtain the first rock-breaking seismic source data.

3. The method according to claim 1, characterized in that, Training a surrounding rock identification model based on the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information includes: Performing a short-time Fourier transform on the first rock-breaking seismic source data to determine the spectrum corresponding to the first rock-breaking seismic source data, and determining the spectral characteristics corresponding to the spectrum, where the spectral characteristics include frequency and instantaneous frequency; Combining the spectral characteristics and the first drilling parameters to form a multi-dimensional feature vector; Establishing a mapping relationship between the multi-dimensional feature vector and the first surrounding rock grade information; Constructing the surrounding rock identification model through a neural network based on the mapping relationship.

4. The method according to claim 3, characterized in that Performing a short-time Fourier transform on the first rock-breaking seismic source data to determine the spectrum corresponding to the first rock-breaking seismic source data includes: Filtering the first rock-breaking seismic source data to obtain first sub-rock-breaking seismic source data, where the first sub-rock-breaking seismic source data is a data section in the first rock-breaking seismic source data; Performing a short-time Fourier transform on the first sub-rock-breaking seismic source data to determine the spectrum corresponding to the first sub-rock-breaking seismic source data.

5. The method according to claim 1, wherein After training a surrounding rock identification model based on the first rock-breaking seismic source data, the first drilling parameters, and the first surrounding rock grade information, the method further includes: Collecting the actual working parameters of the rock drilling jumbo, where the actual working parameters include: current drilling parameters, current surrounding rock grade information, and current rock-breaking seismic source data; Inputting the current drilling parameters and the current rock-breaking seismic source data into the surrounding rock identification model to obtain second surrounding rock grade information; If the second surrounding rock type information is different from the current surrounding rock grade information, training the surrounding rock identification model based on the current drilling parameters, the current surrounding rock grade information, and the current rock-breaking seismic source data to obtain a new surrounding rock identification model.

6. The method according to claim 5, characterized in that, The method includes: collecting current rock-breaking seismic source data during the drilling construction through a vibration sensor installed on the rock drilling equipment.

7. The method according to claim 5, characterized in that, Inputting the current drilling parameters and the current rock-breaking seismic source data into the surrounding rock identification model to obtain second surrounding rock grade information includes: Filter the current rock-breaking vibration source number to obtain current sub-rock-breaking vibration source data, and the time interval of the current sub-rock-breaking vibration source data is the same as that of the first sub-rock-breaking vibration source data; Input the current drilling parameters and the current sub-rock-breaking vibration source data into the surrounding rock identification model to obtain second surrounding rock grade information.

8. A surrounding rock grade identification device, characterized in that, The device includes: An acquisition unit, configured to acquire first surrounding rock grade information, first drilling parameters, and simulated first rock-breaking vibration source data, where the drilling parameters include the drilling pressure, propulsion pressure, and rotation pressure of the rock drilling equipment; A training unit, configured to train a surrounding rock identification model according to the first rock-breaking vibration source data, the first drilling parameters, and the first surrounding rock grade information; The acquisition unit is further configured to acquire second rock-breaking vibration source data and second drilling parameters; An input unit, configured to input the second rock-breaking vibration source data and the second drilling parameters into the surrounding rock identification model to obtain target surrounding rock grade information.

9. An electronic device, characterized in that, Including: A processor; And A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the method according to any one of claims 1-8 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, implements the method according to any one of claims 1-8.

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