A control method and system for a core drilling rig power head

Abstract

The present application provides a control method and system for a coring drill power head, which relates to the field of drilling rig technology. The control method for the coring drill power head includes: using the historical data of the coring drill working process as a training sample to construct a neural network model to obtain a mapping relationship; constructing a visual digital twin model based on the historical data and the mapping relationship; monitoring the vibration information of the drill bit in real time; inputting the vibration information, the initial wear information of the drill bit before this coring, and the formation information of the formation where the drill bit has completed coring into the digital twin model; predicting the hardness of the formation currently acted by the drill bit through the digital twin model; dynamically adjusting the output torque of the power head, and dynamically adjusting the feed speed of the power head. This control method can analyze the vibration information of the drill bit in real time, and predict the hardness information of the formation currently acted by the drill bit, thereby realizing the dynamic adjustment of the output torque and feed speed of the power head.

Description

Technical Field

[0001] The present application relates to the technical field of drilling rigs, and in particular to a control method and system for a power head of a coring drill rig. Background Art

[0002] In geological exploration, mineral development, geotechnical engineering, and other fields, core drilling rigs are critical equipment for obtaining underground core samples. Core drilling rigs cut and extract intact formation cores through the rotation and axial feed of the drill bit.

[0003] In actual operations, formation conditions are complex and variable, with significant variations in formation hardness at different depths. Existing drilling rigs rely heavily on manual experience or preset parameters, making it difficult to accurately identify changes in formation hardness. When the drill encounters high-hardness formations, insufficient torque or excessive feed speed can lead to increased drill wear, core breakage, and even stuck drill bits. In soft formations, overly conservative parameter settings can result in low coring efficiency. Real-time identification of formation hardness and dynamic optimization of drilling rig parameters are currently pressing challenges. Summary of the Invention

[0004] The purpose of this application is to overcome the defects of the prior art and provide a control method and system for a core drilling rig power head to solve the problems in the prior art.

[0005] To solve the above problems, a first aspect of an embodiment of the present application provides a method for controlling a core drilling rig power head, comprising:

[0006] Using historical data of the coring drill's working process as training samples, a neural network model is constructed to obtain a mapping relationship between drill bit wear data, drill bit vibration data during the coring process, and formation hardness data;

[0007] Based on the historical data and the mapping relationship, a visual digital twin model is constructed; wherein the digital twin model includes a drilling rig model and a formation model;

[0008] During the coring process of the drill bit, the vibration information of the drill bit is monitored in real time;

[0009] Inputting the vibration information, initial wear information of the drill bit before coring, and formation information of the formation where the drill bit has completed coring into the digital twin model;

[0010] Predicting the hardness of the formation currently acted upon by the drill bit through the digital twin model and obtaining predicted hardness information;

[0011] Based on the vibration information, the initial wear information and the predicted hardness information, the output torque of the power head is dynamically adjusted according to a torque equation, and the feed speed of the power head is dynamically adjusted according to a feed speed equation.

[0012] In a possible implementation, the torque equation is:

[0013] ;

[0014] The feed rate equation is:

[0015] ;

[0016] Where T represents the output torque of the power head; T max represents the maximum rated output torque of the power head; H represents the formation hardness coefficient; A represents the vibration amplitude of the drill bit; W represents the drill bit wear coefficient; n represents the current drilling speed; n max Indicates the maximum allowable speed of the drill bit; k1 indicates the torque adjustment coefficient; k2 indicates the feed speed basic coefficient; α indicates the vibration compensation coefficient; β indicates the wear compensation coefficient; γ indicates the speed compensation coefficient; ν indicates the feed speed; δ indicates the vibration suppression coefficient; ε indicates the wear suppression coefficient; ζ indicates the hardness enhancement coefficient;

[0017] The vibration information includes the vibration amplitude, the vibration compensation coefficient and the vibration suppression coefficient, the initial wear information includes the drill bit wear coefficient, the wear compensation coefficient and the wear suppression coefficient, and the predicted hardness information includes the formation hardness coefficient and the hardness enhancement coefficient.

[0018] In one possible implementation, establishing the neural network model includes:

[0019] Acquire a sample set, each of the sample sets includes a plurality of the training samples; in the same sample set, the wear degree of the drill bit in each of the training samples is at the same level;

[0020] The training sample includes input data and output data, the input data includes first data, and the output data includes second data; wherein the first data includes vibration data when the drill bit coring a preset formation, and the second data includes measured hardness data of the preset formation;

[0021] The neural network is trained using the input data and the output data; wherein, if the characteristic value of the hardness data of the formation predicted by the neural network model is consistent with the characteristic value of the hardness data of the formation actually measured, the training is completed and the neural network model is obtained.

[0022] In a possible implementation, after the drill bit completes coring each time, the core removed by the drill bit is tested to obtain actual hardness information of the core, and to update the formation model.

[0023] In a possible implementation, if the measured hardness information does not match the predicted hardness information, the mapping relationship is corrected based on the measured hardness information.

[0024] In a possible implementation, the method for determining the initial wear information includes:

[0025] Cleaning the drill bit after the drill bit completes the coring operation each time;

[0026] Performing a three-dimensional scan on the drill bit and establishing a three-dimensional model;

[0027] comparing the three-dimensional model with a standard model of the drill bit to obtain measured wear data of the drill bit;

[0028] The initial wear information is updated based on the measured wear data.

[0029] In a possible implementation, the updated initial wear information is input into the digital twin model, and the wear data of the drill bit model in the drilling rig model is updated.

[0030] In a possible implementation, the wear level of the drill bit is determined based on the measured wear data, and if the wear level of the drill bit reaches a preset wear level, the drill bit is judged to be unqualified.

[0031] In a possible embodiment, before the drill bit coring, the power head is controlled to output a preset torque and drive the drill bit to idle;

[0032] Real-time monitoring of the initial vibration characteristics of the drill bit within a preset time;

[0033] The initial vibration characteristics are analyzed; if the initial vibration characteristics meet the preset vibration characteristics, it is determined that the coring drill meets the working requirements.

[0034] A second aspect of the present application provides a control system for a coring drill power head, comprising:

[0035] a training module for constructing a neural network model using historical data of the coring drill's working process as training samples to obtain a mapping relationship between drill bit wear data, vibration data of the drill bit during the coring process, and formation hardness data;

[0036] A digital twin module is used to construct a visual digital twin model based on the mapping relationship; wherein the digital twin model includes a drilling rig model and a formation model;

[0037] A monitoring module, configured to monitor the vibration information of the drill bit in real time during the coring process of the drill bit;

[0038] an input module, configured to input the vibration information, initial wear information of the drill bit before coring, and formation information of the formation from which the drill bit has completed coring, into the digital twin model;

[0039] A prediction module, configured to predict the hardness of the formation currently acted upon by the drill bit using the digital twin model and obtain predicted hardness information;

[0040] An adjustment module is used to dynamically adjust the output torque of the power head based on the vibration information, the initial wear information and the predicted hardness information according to a torque equation, and to dynamically adjust the feed speed of the power head according to a feed speed equation.

[0041] The beneficial effects of this application include:

[0042] The control method of the coring drill power head proposed in this application establishes a mapping relationship between the wear data of the drill bit, the vibration data of the drill bit during the coring process, and the hardness data of the formation through a neural network model trained with historical data, and constructs a visual digital twin model based on the mapping relationship. During the coring process of the drill bit, the vibration information of the drill bit is monitored in real time. The vibration information, the wear information of the drill bit before this coring, and the formation information of the formation where the drill bit has completed coring are input into the digital twin model. In this way, the digital twin model can predict the hardness of the formation currently acted on by the drill bit based on the mapping relationship and obtain the predicted hardness information, wherein the output torque and feed speed of the power head are dynamically adjusted based on the known and predicted relevant information. In addition, since the digital twin model includes a drilling rig model and a formation model, it can intuitively display the relevant information of the drilling rig and the distribution of the formation hardness, thereby facilitating the operator to perform virtual debugging.

[0043] The method proposed in this application can analyze the vibration information of the drill bit in real time and predict the hardness information of the formation on which the drill bit is currently acting, thereby realizing dynamic adjustment of the output torque and feed speed of the power head: when the drill bit encounters a high-hardness formation, the output torque of the power head is appropriately increased and the feed speed is reduced; in soft formations, the feed speed is appropriately increased to improve the efficiency of coring, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A schematic flow chart of a method for controlling a core drilling rig power head is shown;

[0046] Figure 2 A schematic diagram of a process for establishing a neural network model is shown;

[0047] Figure 3 A schematic flow chart showing a method for determining initial wear information is shown;

[0048] Figure 4 A schematic diagram of a control system for a core drilling rig power head is shown.

[0049] Description of main component symbols:

[0050] 110-training module; 120-digital twin module; 130-monitoring module; 140-input module; 150-prediction module; 160-regulation module. DETAILED DESCRIPTION

[0051] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0052] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0053] Example

[0054] like Figure 1 As shown, in this embodiment, a control method for a core drilling rig power head is proposed, including:

[0055] S11, using historical data of the coring drill working process as training samples, constructing a neural network model to obtain a mapping relationship between drill bit wear data, drill bit vibration data during the coring process, and formation hardness data;

[0056] S12, build a visual digital twin model based on historical data and mapping relationships;

[0057] S13, during the coring process of the drill bit, real-time monitoring of the vibration information of the drill bit;

[0058] S14, inputting vibration information, initial wear information of the drill bit before coring, and stratigraphic information of the stratigraphic layer where the drill bit has completed coring into the digital twin model;

[0059] S15, predicting the hardness of the formation currently acted upon by the drill bit through the digital twin model and obtaining predicted hardness information;

[0060] S16, based on the vibration information, the initial wear information and the predicted hardness information, the output torque of the power head is dynamically adjusted according to the torque equation, and the feed speed of the power head is dynamically adjusted according to the feed speed equation.

[0061] Historical data includes drilling rig parameters, drill bit vibration information during coring, formation information, and the correlation between vibration information and formation hardness. Drill rig parameters include various parameters of the power head and drill bit. Power head parameters include output torque and feed rate, while drill bit parameters include drill bit specifications and drill bit wear data.

[0062] The digital twin model includes a drilling rig model and a formation model. This allows personnel to virtually debug the coring process, for example. For the same borehole (the location remains unchanged, only the depth changes), known formation information (the formation from which coring has already been completed) during the coring process can be intuitively reflected in the formation model.

[0063] The drill rod is fixed on the chuck of the power head, and the drill bit is installed on the end of the drill rod away from the power head, wherein the feed speed of the drill bit is the same as the feed speed of the power head.

[0064] The control method proposed in this embodiment establishes a mapping relationship between the wear data of the drill bit, the vibration data of the drill bit during the coring process, and the hardness data of the formation through a neural network model trained with historical data, and constructs a visual digital twin model based on the mapping relationship.

[0065] During the coring process, the drill bit's vibration is monitored in real time. This vibration information, along with information about the drill bit's wear prior to the coring operation and the formation information from which the drill bit has already cored, is input into the digital twin model. Based on this mapping, the digital twin model predicts the hardness of the formation the drill bit is currently engaging and obtains predicted hardness information. This information is then used to dynamically adjust the output torque and feed speed of the power unit.

[0066] Because the digital twin model includes both a drilling rig model and a formation model, it can intuitively display relevant information about the drilling rig and the distribution of formation hardness, facilitating virtual commissioning. Furthermore, the digital twin model can simulate the coring process and feed the resulting data into a neural network model for training, further optimizing the mapping relationship.

[0067] In this embodiment, the torque equation is:

[0068] ;

[0069] The feed rate equation is:

[0070] .

[0071] Based on the above equation, the influence of multiple factors on output torque and feed speed can be quantified, avoiding the limitation of single parameter adjustment and improving the stability and reliability of drilling rig operation.

[0072] T represents the output torque of the power head.

[0073] T max Indicates the maximum rated output torque of the power head. max Related to the model of the power head, etc.

[0074] H represents the formation hardness coefficient. The formation hardness is graded based on the actual measured hardness data, thereby determining the formation hardness coefficient.

[0075] A represents the vibration amplitude of the drill bit. Install an accelerometer or vibration sensor at the corresponding position of the drill bit to measure the vibration amplitude of the drill bit in three mutually perpendicular directions. The three mutually perpendicular directions are X, Y, and Z (X is the axial direction of the drill bit, and Y and Z are the radial directions of the drill bit). The vibration amplitudes corresponding to the three directions are set as x, y, and z, respectively, where The inner wall of the drill bit may be provided with a mounting groove, and the sensor is arranged in the mounting groove, wherein the mounting groove is reinforced and sealed to protect the sensor, and a conductive contact is provided at the connection portion between the drill bit and the drill rod, and a signal line is built into the drill rod. The sensor, the conductive contact and the signal line are electrically connected, and a wireless communication module is provided at one end of the signal line close to the power head, wherein the signal detected by the sensor is transmitted to the wireless communication module through the conductive contact and the signal line, so that the relevant signal can be transmitted to the corresponding host computer.

[0076] n represents the current drilling speed.

[0077] n max Indicates the maximum allowable speed of the drill bit. max Related to the model of drill bit and power head, etc.

[0078] W represents the drill bit wear coefficient; k1 represents the torque adjustment coefficient; k2 represents the feed rate base coefficient; α represents the vibration compensation coefficient; β represents the wear compensation coefficient; γ represents the speed compensation coefficient; ν represents the feed rate; δ represents the vibration suppression coefficient; ε represents the wear suppression coefficient; and ζ represents the hardness enhancement coefficient. The drill bit wear coefficient is related to the wear state of the drill bit; the torque adjustment coefficient is related to the power parameters of the power unit; and the feed rate base coefficient is related to drilling rig performance parameters. The vibration compensation coefficient adjusts the effect of vibration on torque; the wear compensation coefficient adjusts the effect of drill bit wear on torque; the speed compensation coefficient adjusts the effect of speed on torque; the vibration suppression coefficient adjusts the effect of vibration on feed rate; the wear suppression coefficient adjusts the effect of drill bit wear on feed rate; and the hardness enhancement coefficient adjusts the effect of formation hardness on feed rate.

[0079] Vibration information includes vibration amplitude, vibration compensation coefficient and vibration suppression coefficient; initial wear information includes drill bit wear coefficient, wear compensation coefficient and wear suppression coefficient; predicted hardness information includes formation hardness coefficient and hardness enhancement coefficient.

[0080] The harder the formation, the greater the torque required; the larger the vibration amplitude, the more torque needs to be increased to stabilize drilling; when the output torque increases, the feed speed can be increased appropriately; when the vibration amplitude increases, the feed speed needs to be reduced to stabilize drilling; when the drill bit wear increases, the feed speed needs to be reduced to extend the service life of the drill bit; when the formation hardness increases, the feed speed needs to be reduced to avoid situations such as drill sticking.

[0081] During the coring process, the drill pipe vibration is monitored in real time, and the power head's output torque and feed speed are dynamically adjusted based on the monitoring results to adapt to varying formation conditions and drill pipe status. Specifically, this ensures coring efficiency while also protecting the drill bit and preventing drill bit sticking. If abnormal vibration is detected (such as a sudden increase in vibration amplitude or abnormal frequency), a fault warning is promptly issued and appropriate protective measures are implemented (such as reducing output torque and feed speed, shutting down the machine for inspection, etc.) to prevent problems such as drill bit sticking and drill bit cracking.

[0082] like Figure 2 As shown, in this embodiment, the establishment of the neural network model includes:

[0083] S21, obtaining a sample set, each sample set including multiple training samples; in the same sample set, the wear degree of the drill bit in each training sample is at the same level;

[0084] S22, the training sample includes input data and output data, the input data includes first data, and the output data includes second data; wherein the first data includes vibration data when the drill bit coring a predetermined formation, and the second data includes measured hardness data of the predetermined formation; the historical data may include the input data and the output data;

[0085] S23, using the input data and the output data to train the neural network; wherein, if the characteristic value of the hardness data of the formation predicted by the neural network model is consistent with the characteristic value of the hardness data of the formation actually measured, the training is completed and the neural network model is obtained.

[0086] After each coring operation, the core removed by the drill bit is tested to obtain the measured hardness information of the core and update the formation model, thereby ensuring the accuracy of the formation model and realizing closed-loop feedback of data.

[0087] Furthermore, if the measured hardness information does not match the predicted hardness information, the mapping relationship is corrected based on the measured hardness information, thereby further optimizing the neural network model and the digital twin model.

[0088] like Figure 3 As shown, the method for determining the initial wear information includes:

[0089] S31, cleaning the drill bit after the drill bit completes the coring operation each time;

[0090] S32, performing three-dimensional scanning on the drill bit and establishing a three-dimensional model;

[0091] S33, comparing the three-dimensional model with a standard model of the drill bit to obtain measured wear data of the drill bit;

[0092] S34: Update the initial wear information based on the measured wear data.

[0093] The wear data of the drill bit can be obtained efficiently and accurately through the above method.

[0094] The updated initial wear information is input into the digital twin model, and the wear data of the drill bit model in the drilling rig model is updated.

[0095] The wear level of the drill bit is determined based on the measured wear data. If the wear level of the drill bit reaches the preset wear level, the drill bit is judged to be unqualified and needs to be replaced.

[0096] Before coring, the drill head is controlled to output a preset torque and drive the drill bit to idle. The initial vibration characteristics of the drill bit are monitored in real time within a preset time period and analyzed. These initial vibration characteristics include the vibration amplitude in the X, Y, and Z directions, as well as whether the vibration in each direction is linear.

[0097] If the initial vibration characteristics match the preset vibration characteristics, the core drill is determined to meet the working requirements. If the initial vibration characteristics do not meet the preset vibration characteristics, the core drill is determined to not meet the working requirements. In this case, it is necessary to investigate the cause of the failure, such as whether there is a fault in the power head, whether the drill bit is properly installed, etc. For example, if the vibration amplitudes in the X, Y, and Z directions are less than the corresponding preset values, and the vibrations in each direction are linear vibrations, the core assembly is determined to meet the working requirements.

[0098] like Figure 4 As shown, in this embodiment, a control system for a core drilling rig power head is also proposed, including:

[0099] A training module 110 is configured to use historical data of the coring drill's operation process as training samples to construct a neural network model to obtain a mapping relationship between drill bit wear data, drill bit vibration data during the coring process, and formation hardness data;

[0100] The digital twin module 120 is used to construct a visual digital twin model based on the mapping relationship; wherein the digital twin model includes a drilling rig model and a formation model;

[0101] The monitoring module 130 is used to monitor the vibration information of the drill bit in real time during the coring process;

[0102] An input module 140 is used to input vibration information, initial wear information of the drill bit before coring, and stratigraphic information of the stratigraphic formation where the drill bit has completed coring into the digital twin model;

[0103] The prediction module 150 is used to predict the hardness of the formation currently acted upon by the drill bit through the digital twin model and obtain predicted hardness information;

[0104] The adjustment module 160 is used to dynamically adjust the output torque of the power head according to the torque equation based on the vibration information, the initial wear information and the predicted hardness information, and to dynamically adjust the feed speed of the power head according to the feed speed equation.

[0105] Furthermore, the torque equation is:

[0106] ;

[0107] The feed rate equation is:

[0108] ;

[0109] Where T represents the output torque of the power head; T max represents the maximum rated output torque of the power head; H represents the formation hardness coefficient; A represents the vibration amplitude of the drill bit; W represents the drill bit wear coefficient; n represents the current drilling speed; n max Indicates the maximum allowable speed of the drill bit; k1 indicates the torque adjustment coefficient; k2 indicates the feed speed basic coefficient; α indicates the vibration compensation coefficient; β indicates the wear compensation coefficient; γ indicates the speed compensation coefficient; ν indicates the feed speed; δ indicates the vibration suppression coefficient; ε indicates the wear suppression coefficient; ζ indicates the hardness enhancement coefficient;

[0110] The vibration information includes the vibration amplitude, the vibration compensation coefficient and the vibration suppression coefficient, the initial wear information includes the drill bit wear coefficient, the wear compensation coefficient and the wear suppression coefficient, and the predicted hardness information includes the formation hardness coefficient and the hardness enhancement coefficient.

[0111] Furthermore, the training module 110 is specifically configured to:

[0112] Acquire a sample set, each of the sample sets includes a plurality of the training samples; in the same sample set, the wear degree of the drill bit in each of the training samples is at the same level;

[0113] The training sample includes input data and output data, the input data includes first data, and the output data includes second data; wherein the first data includes vibration data when the drill bit coring a preset formation, and the second data includes measured hardness data of the preset formation;

[0114] The neural network is trained using the input data and the output data; wherein, if the characteristic value of the hardness data of the formation predicted by the neural network model is consistent with the characteristic value of the hardness data of the formation actually measured, the training is completed and the neural network model is obtained.

[0115] Furthermore, the system also includes: a detection module, which is used to detect the core removed by the drill bit after each coring operation to obtain the measured hardness information of the core; and a digital twin module 120, which is also used to update the formation model.

[0116] Furthermore, the training module 110 is further configured to correct the mapping relationship based on the measured hardness information if the measured hardness information does not match the predicted hardness information.

[0117] Furthermore, the method for determining the initial wear information includes:

[0118] Cleaning the drill bit after the drill bit completes the coring operation each time;

[0119] Performing a three-dimensional scan on the drill bit and establishing a three-dimensional model;

[0120] comparing the three-dimensional model with a standard model of the drill bit to obtain measured wear data of the drill bit;

[0121] The initial wear information is updated based on the measured wear data.

[0122] Furthermore, the digital twin module 120 is also used to input the updated initial wear information into the digital twin model and update the wear data of the drill bit model in the drilling rig model.

[0123] Furthermore, the system also includes: a judgment module for determining the wear level of the drill bit based on the measured wear data, and judging that the drill bit is unqualified if the wear level of the drill bit reaches a preset wear level.

[0124] Furthermore, the adjustment module 160 is further configured to:

[0125] Before the drill bit coring, the power head is controlled to output a preset torque and drive the drill bit to idle;

[0126] Real-time monitoring of the initial vibration characteristics of the drill bit within a preset time;

[0127] The initial vibration characteristics are analyzed; if the initial vibration characteristics meet the preset vibration characteristics, it is determined that the coring drill meets the working requirements.

[0128] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0129] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for controlling a core drilling rig power head, characterized in that: include: Using historical data of the coring drill's working process as training samples, a neural network model is constructed to obtain a mapping relationship between drill bit wear data, drill bit vibration data during the coring process, and formation hardness data; Based on the historical data and the mapping relationship, a visual digital twin model is constructed; wherein the digital twin model includes a drilling rig model and a formation model; During the coring process of the drill bit, the vibration information of the drill bit is monitored in real time; Inputting the vibration information, initial wear information of the drill bit before coring, and formation information of the formation where the drill bit has completed coring into the digital twin model; Predicting the hardness of the formation currently acted upon by the drill bit through the digital twin model and obtaining predicted hardness information; Based on the vibration information, the initial wear information and the predicted hardness information, dynamically adjusting the output torque of the power head according to a torque equation, and dynamically adjusting the feed speed of the power head according to a feed speed equation; The torque equation is: ； The feed rate equation is: ； Where T represents the output torque of the power head; T max represents the maximum rated output torque of the power head; H represents the formation hardness coefficient; A represents the vibration amplitude of the drill bit; W represents the drill bit wear coefficient; n represents the current drilling speed; n max Indicates the maximum allowable speed of the drill bit; k1 indicates the torque adjustment coefficient; k2 indicates the feed speed basic coefficient; α indicates the vibration compensation coefficient; β indicates the wear compensation coefficient; γ indicates the speed compensation coefficient; ν indicates the feed speed; δ indicates the vibration suppression coefficient; ε indicates the wear suppression coefficient; ζ indicates the hardness enhancement coefficient; The vibration information includes the vibration amplitude, the vibration compensation coefficient and the vibration suppression coefficient, the initial wear information includes the drill bit wear coefficient, the wear compensation coefficient and the wear suppression coefficient, and the predicted hardness information includes the formation hardness coefficient and the hardness enhancement coefficient.

2. The control method of the core drilling rig power head according to claim 1, characterized in that: The establishment of the neural network model includes: Acquire a sample set, each of the sample sets includes a plurality of the training samples; in the same sample set, the wear degree of the drill bit in each of the training samples is at the same level; The training sample includes input data and output data, the input data includes first data, and the output data includes second data; wherein the first data includes vibration data when the drill bit coring a preset formation, and the second data includes measured hardness data of the preset formation; The neural network is trained using the input data and the output data; wherein, if the characteristic value of the hardness data of the formation predicted by the neural network model is consistent with the characteristic value of the hardness data of the formation actually measured, the training is completed and the neural network model is obtained.

3. The control method of the core drilling rig power head according to claim 1, characterized in that: After the drill bit completes coring each time, the core body taken out by the drill bit is tested to obtain the measured hardness information of the core body and update the formation model.

4. The control method of the core drilling rig power head according to claim 3, characterized in that: If the measured hardness information does not match the predicted hardness information, the mapping relationship is corrected based on the measured hardness information.

5. The control method of the core drilling rig power head according to claim 1, characterized in that: The method for determining the initial wear information includes: Cleaning the drill bit after the drill bit completes the coring operation each time; Performing a three-dimensional scan on the drill bit and establishing a three-dimensional model; comparing the three-dimensional model with a standard model of the drill bit to obtain measured wear data of the drill bit; The initial wear information is updated based on the measured wear data.

6. The control method of the core drilling rig power head according to claim 5, characterized in that: The updated initial wear information is input into the digital twin model, and the wear data of the drill bit model in the drilling rig model is updated.

7. The control method of the core drilling rig power head according to claim 5, characterized in that: The wear level of the drill bit is determined based on the measured wear data, and if the wear level of the drill bit reaches a preset wear level, the drill bit is judged to be unqualified.

8. The control method of the core drilling rig power head according to claim 1, characterized in that: Before the drill bit coring, the power head is controlled to output a preset torque and drive the drill bit to idle; Real-time monitoring of the initial vibration characteristics of the drill bit within a preset time; The initial vibration characteristics are analyzed; if the initial vibration characteristics meet the preset vibration characteristics, it is determined that the coring drill meets the working requirements.

9. A control system for a core drilling rig power head, characterized in that: include: a training module for constructing a neural network model using historical data of the coring drill's working process as training samples to obtain a mapping relationship between drill bit wear data, vibration data of the drill bit during the coring process, and formation hardness data; A digital twin module is used to construct a visual digital twin model based on the mapping relationship; wherein the digital twin model includes a drilling rig model and a formation model; A monitoring module, configured to monitor the vibration information of the drill bit in real time during the coring process of the drill bit; an input module, configured to input the vibration information, initial wear information of the drill bit before coring, and formation information of the formation from which the drill bit has completed coring, into the digital twin model; A prediction module, configured to predict the hardness of the formation currently acted upon by the drill bit using the digital twin model and obtain predicted hardness information; an adjustment module, configured to dynamically adjust the output torque of the power head according to a torque equation based on the vibration information, the initial wear information, and the predicted hardness information, and to dynamically adjust the feed speed of the power head according to a feed speed equation; The torque equation is: ； The feed rate equation is: ； Where T represents the output torque of the power head; T max represents the maximum rated output torque of the power head; H represents the formation hardness coefficient; A represents the vibration amplitude of the drill bit; W represents the drill bit wear coefficient; n represents the current drilling speed; n max Indicates the maximum allowable speed of the drill bit; k1 indicates the torque adjustment coefficient; k2 indicates the feed speed basic coefficient; α indicates the vibration compensation coefficient; β indicates the wear compensation coefficient; γ indicates the speed compensation coefficient; ν indicates the feed speed; δ indicates the vibration suppression coefficient; ε indicates the wear suppression coefficient; ζ indicates the hardness enhancement coefficient; The vibration information includes the vibration amplitude, the vibration compensation coefficient and the vibration suppression coefficient, the initial wear information includes the drill bit wear coefficient, the wear compensation coefficient and the wear suppression coefficient, and the predicted hardness information includes the formation hardness coefficient and the hardness enhancement coefficient.

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