An energy-saving drilling device for blasting in mining engineering and its application method

Abstract

This invention provides an energy-saving drilling device for blasting in mining engineering and its usage method, belonging to the technical field of drilling devices. The device includes: a main body, a drilling main module, a power drive module, an intelligent control module, a positioning and navigation module, a hydraulic support module, a dust removal and slag discharge module, and a data acquisition module. The intelligent control module controls the drilling main module, power drive module, positioning and navigation module, hydraulic support module, dust removal and slag discharge module, and data acquisition module to complete device positioning and leveling, drilling path planning, adaptive drilling operations, drilling completion, and status reset. This invention achieves high-precision positioning and leveling, enables efficient drilling in complex rock formations, provides efficient dust removal and slag discharge, ensures a safe working environment, achieves a high degree of automation and intelligence, reduces labor intensity, and thus achieves energy conservation.

Description

Technical Field

[0001] This invention relates to the field of drilling equipment technology, and in particular to an energy-saving drilling device for blasting in mining engineering and its usage method. Background Technology

[0002] In mining blasting operations, drilling is a crucial step, and its quality and efficiency directly impact blasting effectiveness, mining progress, and production costs. Traditional energy-saving drilling equipment for mining blasting has several drawbacks:

[0003] Low positioning and leveling accuracy: In the past, the positioning of drilling equipment mainly relied on manual experience and simple measuring tools. In complex mining terrain, it was difficult to accurately determine the drilling position, resulting in large deviations in the drilling position and affecting the accuracy of blasting results. Moreover, manual leveling operations are not only inefficient, but also difficult to keep the drilling equipment in an ideal horizontal state, which can easily cause the drilling to tilt, reduce the blasting quality, and even cause safety hazards.

[0004] Unable to adapt to complex rock formations: Mine geological conditions are complex and varied, with significant differences in the thickness, hardness, brittleness, and presence of karst caves among different rock strata. Traditional drilling equipment lacks effective means of detecting and analyzing the characteristics of rock strata, and cannot adjust drilling parameters in real time according to changes in rock strata during the drilling process. This often leads to problems such as excessive drill bit wear, low drilling efficiency, and stuck drill bits, which seriously affect the mining progress and increase production costs.

[0005] Poor dust and slag removal efficiency: Drilling operations generate a large amount of dust and rock debris. Traditional dust and slag removal methods are relatively simple and cannot be cleaned up in a timely and efficient manner, resulting in dust filling the work site. This not only deteriorates the working environment and endangers the health of operators, but may also cause safety accidents such as explosions due to dust accumulation.

[0006] Low levels of automation and intelligence: Traditional drilling equipment mostly relies on manual operation, resulting in low levels of automation and intelligence. Operators face high labor intensity and are prone to errors due to human factors. Furthermore, comprehensive monitoring and data analysis of the drilling process are impossible, hindering the scientific management and optimization of drilling operations.

[0007] Due to the aforementioned reasons, the energy consumption in existing drilling operations is relatively high. Summary of the Invention

[0008] This invention provides an energy-saving drilling device for blasting in mining engineering and its usage method, in order to reduce energy consumption and achieve the goal of sustainable development.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] An energy-saving drilling device for blasting in mining engineering includes:

[0011] The main body of the device consists of four L-shaped plate frames, which can be viewed as a cross-shaped structure from above.

[0012] The main drilling module is located at the bottom cross-shaped center of the main body of the device;

[0013] A power drive module is mounted on the main body of the device and connected to the drilling main module;

[0014] An intelligent control module is located on the main body of the device and is connected to the power drive module;

[0015] A positioning and navigation module is mounted on the main body of the device and connected to the intelligent control module;

[0016] The hydraulic support module includes four sets of retractable hydraulic outriggers and rollers. The four sets of retractable hydraulic outriggers are respectively located on the side of the L-shaped plate frame away from the drilling main module, and the rollers are arranged at the bottom of the end. The hydraulic support module is connected to the intelligent control module.

[0017] A dust removal and slag discharge module is installed on the main body of the device and connected to the intelligent control module;

[0018] The data acquisition module includes integrated ground-penetrating radar, temperature sensors, and vibration sensors to collect borehole-related data from all angles.

[0019] The intelligent control module controls the drilling main module, power drive module, positioning and navigation module, hydraulic support module, dust removal and slag removal module, and data acquisition module to complete device positioning and leveling, drilling path planning, adaptive drilling operation, drilling completion and status reset.

[0020] In this specification, the drilling main module is equipped with a drill rod and a carbide composite tooth drill bit. The drill bit integrates a triaxial force sensor to collect drilling axial pressure and radial torque data in real time; it also has a built-in depth encoder to accurately feed back the drilling depth.

[0021] In this specification, the positioning and navigation module includes a UWB positioning system and an inertial navigation unit. The base station of the UWB positioning system is deployed at the mine working face to provide a position reference, and the inertial navigation unit monitors the attitude of the device in real time.

[0022] In this specification, the dust removal and slag discharge module includes a negative pressure dust pump, a spiral slag discharge machine, and a dust filter. The negative pressure dust pump, spiral slag discharge machine, and dust filter are connected in sequence and work together. The negative pressure dust pump is sealed to the borehole opening to extract dust and rock debris in a timely manner.

[0023] A method of using an energy-saving drilling device for blasting in mining engineering, employing any one of the above-mentioned energy-saving drilling devices for blasting in mining engineering, the method of using the energy-saving drilling device for blasting in mining engineering includes:

[0024] S1. Device positioning and leveling:

[0025] By inputting the target coordinates through the intelligent control module, the device obtains position and attitude data using UWB and inertial navigation, and combines the hydraulic outrigger pressure data with the outrigger height adjustment amount calculated through the leveling model to achieve automatic leveling of the device.

[0026] S2. Drilling path planning:

[0027] By using ground-penetrating radar to scan rock strata parameters and combining them with topographic data to correct the borehole dip angle, the optimal borehole parameters and cave bypass path are generated through rock strata drillability model and path optimization model.

[0028] S3. Adaptive Drilling Operation:

[0029] Drilling is started according to the planned parameters. Sensor data is collected in real time to dynamically adjust the rotation speed, feed speed and inclination angle. When there is a risk of the drill getting stuck, the drill is lifted-rotated-pressed down operation is performed, and the power of the dust removal and slag discharge module is adjusted synchronously.

[0030] S4. Drilling completion and status reset:

[0031] When the designed hole depth or hole bottom condition is reached, stop the machine, clean the rock debris inside the hole, retract the hydraulic outriggers to the transport height, and complete the hardware self-check.

[0032] In this specification, during the device positioning and leveling steps, the ground surface is fitted using a weighted least squares method. Combined with attitude deviation, outrigger height standard deviation, and pressure imbalance, the outrigger adjustment amount is calculated by inputting a random forest leveling model. The leveling is completed in two stages: coarse adjustment and fine adjustment.

[0033] In this specification, the drilling path planning step utilizes a random forest rock strata drillability model to output the rotation speed range, feed rate range, and impact risk index. Then, a neural network path optimization model is used to generate optimal drilling parameters for each rock stratum, with energy consumption, drill bit wear, and inclination deviation as objective functions.

[0034] In this specification, during the adaptive drilling operation, when the axial pressure deviation exceeds a preset threshold, the rotational speed is corrected in real time using a proportional coefficient; when the vibration kurtosis value and acceleration exceed the threshold, an anti-jamming operation is triggered.

[0035] In this specification, the dust removal and slag discharge module dynamically adjusts the power of the negative pressure dust suction pump and the speed of the spiral slag discharge machine according to the drilling depth and rock cutting particle size.

[0036] In this specification, the drilling completion determination conditions include: reaching the designed hole depth and the axial pressure being lower than the threshold for a duration that reaches the threshold, or automatically deepening after the density of the rock strata at the bottom of the hole exceeds the standard. After completion, a drilling log containing operation data is generated and synchronized to the mine data center.

[0037] In summary, the embodiments of the present invention have at least the following beneficial effects:

[0038] High-precision positioning and leveling: The drilling device of this invention, through the coordinated operation of a UWB positioning system, an inertial navigation unit, and an intelligent control module, can accurately acquire the device's real-time coordinates and attitude information. It then uses a weighted least squares method to fit the ground surface equation and combines this with a random forest leveling model to calculate the outrigger height adjustment, achieving automatic positioning and precise leveling of the device. This effectively avoids drilling deviations caused by positioning and leveling errors, significantly improving the accuracy and verticality of the drilling position, laying a solid foundation for the precise implementation of subsequent blasting operations. Furthermore, it reduces drilling time, improves overall operational efficiency, reduces energy consumption, and achieves energy conservation.

[0039] High-efficiency drilling adaptable to complex rock formations: Utilizing integrated ground-penetrating radar and other data acquisition modules, comprehensive information such as rock layer thickness, hardness coefficient, brittleness index, and cave markers can be obtained before drilling. Through a random forest rock layer drillability model and a neural network path optimization model, combined with historical data and design constraints, the drilling path can be intelligently planned, determining optimal drilling parameters for different rock formations, such as rotational speed, feed rate, dip angle compensation value, and cave obstacle avoidance offset. During drilling, drilling parameters can be adaptively adjusted based on real-time sensor data, effectively reducing drill bit wear, lowering the risk of stuck drill, and significantly improving drilling efficiency and quality, adapting to the mining needs under various complex geological conditions. This reduces drilling operation time, improves overall operational efficiency, reduces energy consumption, and achieves energy conservation.

[0040] Highly efficient dust and slag removal ensures a safe working environment: The dust and slag removal module consists of a negative pressure dust pump, a spiral slag remover, and a dust filter working in tandem. The negative pressure dust pump is sealed to the borehole opening, enabling it to promptly extract dust and rock debris generated during drilling. After purification by the dust filter, the debris is discharged, effectively reducing dust pollution at the work site, protecting the health of operators, lowering the probability of dust explosions and other safety accidents, and creating a safe and clean working environment. This, in turn, reduces drilling time, improves overall work efficiency, reduces energy consumption, and achieves energy conservation.

[0041] High levels of automation and intelligence reduce labor intensity: The intelligent control module coordinates and controls the drilling main module, power drive module, positioning and navigation module, and other modules to achieve fully automated operation of the entire process, including device positioning and leveling, drilling path planning, adaptive drilling operations, drilling completion, and status reset. Operators only need to input basic information such as the target borehole's three-dimensional coordinates through the human-machine interface, and the device can automatically complete subsequent complex operations, greatly reducing the labor intensity of operators and minimizing human error. Simultaneously, the data acquisition module collects drilling-related data in real time, facilitating comprehensive monitoring and data analysis of drilling operations. This provides strong support for optimizing work processes and equipment performance, improving the level of intelligent management in mining. Ultimately, this reduces drilling operation time, improves overall operational efficiency, reduces energy consumption, and achieves energy conservation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the composition of the energy-saving drilling device for blasting in mining engineering involved in this invention.

[0044] Figure 2 This is a schematic diagram of the main body of the device involved in the present invention.

[0045] Figure 3 This is a schematic diagram of the cross-shaped structure composed of four L-shaped plates involved in this invention.

[0046] Figure 4 This is a schematic diagram illustrating the method of using the energy-saving drilling device for blasting in mining engineering as described in this invention.

[0047] Reference numerals in the attached drawings: 1. Main body of the device; 2. Plate frame; 3. Drilling main module; 4. Power drive module; 5. Hydraulic support module; 6. Roller; 7. Dust removal and slag discharge module. Detailed Implementation

[0048] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0049] The following disclosure provides many different implementations or examples for carrying out different structures of the embodiments of the present invention. To simplify the disclosure of the embodiments of the present invention, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. Furthermore, reference numerals and / or reference letters may be repeated in different examples of the embodiments of the present invention; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various implementations and / or arrangements discussed.

[0050] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0051] like Figure 1 , Figure 2 and Figure 3 As shown, this embodiment provides an energy-saving drilling device for blasting in mining engineering, including:

[0052] The main body of the device 1 consists of four L-shaped plate frames 2, which can be viewed from above as a cross-shaped structure;

[0053] The drilling main module 3 is located at the bottom cross-shaped center position of the main body 1 of the device;

[0054] The power drive module 4 is mounted on the main body 1 of the device and is connected to the drilling main body module 3;

[0055] An intelligent control module is located on the main body 1 of the device and is connected to the power drive module 4;

[0056] A positioning and navigation module is mounted on the main body 1 of the device and connected to the intelligent control module;

[0057] The hydraulic support module 5 includes four sets of retractable hydraulic outriggers and rollers 6. The four sets of retractable hydraulic outriggers are respectively located on the side of the L-shaped frame 2 away from the drilling main module 3, and the rollers 6 are arranged at the bottom of the end. The hydraulic support module 5 is connected to the intelligent control module.

[0058] The dust removal and slag discharge module 7 is mounted on the main body 1 of the device and is connected to the intelligent control module.

[0059] The data acquisition module includes integrated ground-penetrating radar, temperature sensors, and vibration sensors to collect borehole-related data from all angles.

[0060] The intelligent control module controls the drilling main module 3, power drive module 4, positioning and navigation module, hydraulic support module 5, dust removal and slag removal module 7, and data acquisition module to complete device positioning and leveling, drilling path planning, adaptive drilling operation, drilling completion and status reset.

[0061] Each L-shaped frame 2 corresponds to a set of retractable hydraulic outriggers (mainly hydraulic telescopic rods). Each set of hydraulic outriggers has 1, 2, 3, or 4 outriggers, which are evenly distributed on the lower side of the frame 2.

[0062] In some embodiments, the drilling body module 3 is equipped with a drill rod and a carbide composite tooth drill bit. The drill bit integrates a triaxial force sensor to collect drilling axial pressure and radial torque data in real time; it also has a built-in depth encoder to accurately feed back the drilling depth.

[0063] In some embodiments, the positioning and navigation module includes a UWB positioning system and an inertial navigation unit. The base station of the UWB positioning system is deployed at the mine working face to provide a position reference, and the inertial navigation unit monitors the attitude of the device in real time.

[0064] In some embodiments, the dust removal and slag discharge module 7 includes a negative pressure dust pump, a spiral slag discharge machine, and a dust filter. The negative pressure dust pump, the spiral slag discharge machine, and the dust filter are connected in sequence and work together. The negative pressure dust pump is sealed to the borehole opening to extract dust and rock debris in a timely manner.

[0065] In some embodiments, the drilling main module 3 is equipped with a drill rod and a carbide composite tooth drill bit. The drill bit integrates a triaxial force sensor (range X / Y axis ±5000N, Z axis ±10000N) to collect drilling axial pressure and radial torque data in real time; and has a built-in depth encoder (model HEIDENHAINEQN1325) to accurately feedback the drilling depth.

[0066] In some embodiments, the power drive module 4 uses a 75kW hydraulic motor to drive the drill rod to rotate (speed range 0-300rpm), a 2000mm stroke feed cylinder to control the drilling feed (accuracy ±0.5mm), and a Mitsubishi HC-KFS73 servo motor to adjust the drill bit tilt angle to achieve precise power output.

[0067] In some embodiments, the intelligent control module is based on a Siemens S7-1500 PLC and is equipped with a self-developed adaptive drilling parameter algorithm (integrating models such as BP neural networks and random forests). It integrates a human-machine interface, supports manual / automatic mode switching, receives and processes data from various modules, and issues control commands.

[0068] In some embodiments, the positioning and navigation module is a combination of a UWB high-precision positioning system (positioning accuracy ±10mm) and an ADIS16480 inertial navigation unit. The UWB base station is deployed at the mine working face to provide a position reference, and the IMU monitors the device attitude (pitch angle, roll angle, yaw angle) in real time.

[0069] In some embodiments, the hydraulic support module 5 includes four sets of retractable hydraulic outriggers (stroke 500-1500mm, leveling accuracy ±0.1°) and a bottom pressure sensor (range 0-50MPa) to monitor the distribution of support force in real time to ensure the stability of the device.

[0070] In some embodiments, the dust removal and slag discharge module 7: 1500m 3 A negative pressure dust pump, a 50rpm spiral slag discharger, and a 5μm precision dust filter work together. The dust pump is sealed to the borehole opening to promptly extract dust and rock debris.

[0071] In some embodiments, the data acquisition module integrates a ground-penetrating radar (detection depth 0-10m, resolution ±5cm), a temperature sensor (range -40℃-120℃, accuracy ±0.5℃), and a vibration sensor (range 0-50g, frequency range 1-1000Hz) to collect borehole-related data from all directions.

[0072] In some embodiments, the device operates as follows:

[0073] Device positioning and leveling (S1)

[0074] The operator inputs the three-dimensional coordinates of the target borehole through the human-machine interface. After the device is started, the UWB positioning system quickly calculates the real-time coordinates, the IMU acquires and filters the original attitude angles, and the hydraulic outrigger pressure sensors acquire the ground pressure. Based on the acquired data, a weighted least squares method is used to fit the equation of the ground surface, with the outrigger pressure as the weight to improve the fitting accuracy. The surface fitting coefficients, attitude deviations, and other data are input into the random forest leveling model, which outputs the coarse and fine adjustment amounts of the outriggers. During the leveling process, adjustments are made in stages according to the initial deviation: when the deviation is >5°, the diagonal outriggers are adjusted first at a speed of 80mm / s; when the deviation is ≤1°, PID control is used for fine adjustment. At the same time, the outrigger pressure and attitude stability are checked in real time. If the pressure of a single leg is abnormal or the pressure imbalance exceeds 15%, the corresponding processing mechanism is triggered until the device attitude meets α≤0.3° and β≤0.3°, and the pressure of each outrigger is within a reasonable range. The leveling is then completed, and the relevant data is output to the next step.

[0075] Drilling path planning (S2)

[0076] The data acquisition module uses ground-penetrating radar to scan the lithology ahead of the borehole path, acquiring data such as rock layer thickness, hardness coefficient, brittleness index, and cave identification. Combined with the surface curvature information from S1, it calculates the terrain slope and curvature to correct the initial borehole dip angle. This data, along with the rotational speed range, feed rate range, and impact risk index output from the random forest rock layer drillability model, is input into the neural network path optimization model. This model optimizes for multiple objectives, including energy consumption, drill bit wear, and dip angle deviation, while adhering to constraints such as rotational speed-feed coupling. It outputs optimal borehole parameters (rotational speed, feed rate, dip angle compensation value) for each rock layer and a cave avoidance plan. The A* algorithm generates cave avoidance paths, and the parameter table is verified using Monte Carlo simulation and COMSOL Multiphysics simulation to ensure the reliability and safety of the drilling plan. Finally, a feasible borehole parameter table is output to the drilling operation steps.

[0077] Adaptive drilling operation (S3)

[0078] The intelligent control module sends initial drilling parameters to the power drive module 4 based on the parameter table output by S2, initiating the drilling operation. During the operation, data such as triaxial force, depth, vibration, and temperature are collected in real time. When the axial pressure deviation exceeds 15% or abnormal vibration occurs, parameter self-correction and anti-jamming strategies are triggered; if the actual rock hardness is higher than the estimated value, historical data from similar working conditions are retrieved to optimize parameters; if the drill bit temperature is too high, forced cooling is initiated and the feed rate is reduced. The dust removal and slag removal module 7 dynamically adjusts the power of the dust suction pump and the speed of the spiral slag remover according to the drilling depth and rock slag condition to ensure cleanliness inside the hole, prevent blockage, and ensure efficient and safe drilling operations.

[0079] Drilling completed and status reset (S4)

[0080] Drilling is considered complete when the drilling depth meets design requirements and the axial pressure is at the no-load threshold, or when automatic deepening is completed due to abnormal rock density at the bottom of the hole. At this point, the power drive module 4 stops, the dust removal and slag removal module 7 completes the cleaning of residual rock slag, and all sensors cease operation. The system generates a detailed drilling log containing basic information, leveling data, operational data, and model optimization data, which is synchronized to the mine data center to provide data support for subsequent model training. After data processing is completed, the hydraulic support module 5 retracts its outriggers to the transport height, and the intelligent control module performs a comprehensive self-check of the device to ensure that the equipment is in a ready state, awaiting the next work instruction to start a new drilling cycle.

[0081] A method of using an energy-saving drilling device for blasting in mining engineering, employing the energy-saving drilling device for blasting in mining engineering as described above, such as... Figure 4 As shown, the method of using the energy-saving drilling device for blasting in mining engineering includes:

[0082] S1. Device positioning and leveling:

[0083] The operator inputs the three-dimensional coordinates of the target borehole through the human-machine interface of the intelligent control module; the UWB positioning system acquires the real-time coordinates of the device, and the inertial navigation unit collects the original attitude angle α. raw β raw And processed by Kalman filtering;

[0084] The ground pressure F of the four hydraulic outriggers is obtained through pressure sensors. leg,i (i = 1, 2, 3, 4);

[0085] The weighted least squares method is used to fit the equation of the ground surface z=f(x,y)=a0+a1x+a2y+a3x 2 +a4xy+a5y 2 x and y are the fixed coordinates of the hydraulic outrigger in the local coordinate system; z is the terrain height value of the hydraulic outrigger at coordinate (x, y); f(x, y) is the terrain height fitting function with respect to coordinate (x, y); a0 is the reference height, which determines the intercept of the surface on the Z-axis (i.e., the height at the origin); a1 is the linear coefficient in the x-direction, which controls the linear tilt (slope) of the surface along the X-axis; a2 is the linear coefficient in the y-direction, which controls the linear tilt (slope) of the surface along the y-axis; a3 is the linear coefficient in the x-direction. 2 The term coefficients determine the curvature (convex / concave) of the surface along the x-axis; a4 is the xy cross term coefficient, controlling the degree of surface distortion (saddle-shaped feature); a5 is the y... 2 The coefficient determines the curvature (convex / concave) of the surface in the y-axis direction.

[0086] The surface fitting coefficients a0, a1, a2, a3, a4, a5, are obtained by passing α... raw and β raw The calculated attitude deviation and attitude change rate, the standard deviation of outrigger height calculated by measuring outrigger height, and the F leg,i The calculated pressure imbalance is input into the random forest leveling model, and the random forest leveling model outputs the outrigger height adjustment amount.

[0087] The hydraulic outrigger extension and retraction are controlled based on the outrigger height adjustment. When the attitude angles α≤α1 and β≤β1 are collected for three consecutive times, α1 and β1 are preset thresholds, and F leg,i Within the preset range, the leveling is completed and the leveled posture and device coordinates are output;

[0088] Data acquisition and coordinate system transformation

[0089] Input data:

[0090] Target borehole coordinates: (X tar Y tar Z tarThe target position of the borehole (unit: meters, m) is input by the operator through the human-machine interface.

[0091] UWB positioning data: Real-time coordinates of the device (X) dev Y dev Z dev The UWB positioning system calculates the position at a frequency of 100Hz using the Time-of-Flight (TOF) method, with a positioning accuracy of ±10mm, and is used to determine the real-time position (unit: m) of the device in the global coordinate system.

[0092] IMU data: Original attitude angle α raw (Pitch angle), β raw (roll angle), γ raw (Yaw angle), after extended Kalman filtering, the accuracy reaches ±0.1°. The IMU collects data at a frequency of 200Hz (unit: degrees, °).

[0093] Outrigger pressure: F leg，i (i = 1, 2, 3, 4) represents the ground pressure of the i-th hydraulic outrigger, collected by a pressure sensor at the bottom of the outrigger, with a range of 0-50 MPa and an accuracy of ±0.5% FS (unit: megapascals, MPa). Coordinate system transformation:

[0094] The local coordinate system O-X2Y2Z2 of the device is converted to the global coordinate system GX′Y′Z′ of the mine. The conversion formula is as follows:

[0095]

[0096] Wherein, R(γ) raw ) is the rotation matrix about the Z-axis, given by the initial yaw angle γ. raw The calculation yields: T = (X) base Y base Z base The global coordinates (in meters) of the first UWB base station are given. The rotation matrix R(γ) is... raw The specific form of ) is:

[0097]

[0098] Solve for the fitting coefficients:

[0099] With outrigger pressure F leg，i The coefficients are calculated using the weighted least squares method, and the weighting function is:

[0100]

[0101] The optimization objective is to minimize the weighted sum of squared residuals:

[0102]

[0103] Transform it into matrix form:

[0104] Let a = [a0, a1, a2, a3, a4, a5] T X1 is the design matrix, and the element in the i-th row is... W is a diagonal weight matrix, with diagonal elements being w. i , Let the measured values ​​of the outrigger height be a vector (unit: m). Then the solution for the coefficient vector is:

[0105] a * =(X1) T WX1) -1 X1 T Wz meas ;

[0106] The iteration termination condition is that the change in the fitting coefficient between two consecutive iterations is less than 10. -4 Furthermore, the residual standard deviation (RMS) is less than 1.5 mm. The formula for calculating the residual standard deviation is:

[0107]

[0108] Input features for training the random forest leveling model:

[0109] Surface fitting coefficients: a0, a1, a2, a3, a4, a5, output from the ground surface fitting model.

[0110] Attitude deviation: Δα=α raw -0°、Δβ=β raw -0° represents the difference between the device's current attitude and its horizontal attitude (unit: °).

[0111] Standard deviation of outrigger height: Where h i Let the height of the i-th outrigger be . The average height is used to measure the degree of terrain relief (unit: mm).

[0112] Rate of attitude change: Reflects the dynamic stability of the device (unit: ° / s, Δt is the sampling time interval, unit: s).

[0113] Pressure imbalance: Used to determine the force balance of the outriggers (dimensionless).

[0114] Output variables:

[0115] Group i outrigger height adjustment amount Includes coarse adjustment (Step length 50mm, suitable for σ) h >100mm) and fine adjustment amount (Step length 5mm, suitable for σ) h ≤100mm), unit: mm.

[0116] Training data:

[0117] The samples were collected from 12 typical terrain types in the mine, with 200 sets of leveling data collected for each terrain type, totaling 2400 sets. The model contains 50 decision trees, with a maximum depth of 8 and a minimum leaf node sample size of 10, using the Gini index as the splitting criterion. To address the sample imbalance problem, class weights were set as follows:

[0118]

[0119] Adjust execution logic

[0120] Graded adjustment: When the initial attitude deviation of the device is >5°, the rapid leveling mode is activated, prioritizing the adjustment of diagonal support legs (1+3 or 2+4), with an adjustment speed of 80mm / s; when the deviation is ≤1°, PID fine-tuning is activated, and the control quantity calculation formula is:

[0121]

[0122] Among them, u i (k) represents the adjustment command of the i-th outrigger in the k-th control cycle, K p =1.5 (proportional coefficient), K i =0.2 (integral coefficient), K d =0.8 (differential coefficient), e i (k) represents the current attitude deviation (unit: °). This represents all historical attitude deviations from the initial cycle to the current cycle.

[0123] Stability Verification: If the pressure on a single outrigger is <5MPa or >30MPa, trigger the outrigger suspension / overload alarm and pause leveling; when the pressure imbalance η > 15%, start the pressure equalization algorithm, and wait 2 seconds for the pressure to stabilize after each adjustment. When alpha ≤ 0.3° and β ≤ 0.3° are collected for 3 consecutive times (2 seconds apart), and the pressure F of all outriggers is within the range... leg，i When the pressure is ∈ [5MPa, 25MPa], leveling is complete, and the leveled attitude (α) is output. lev ,β lev γ lev (and device coordinates to step S2)

[0124] S2. Drilling path planning:

[0125] The thickness d of the rock strata was obtained by scanning ahead of the borehole path using ground-penetrating radar.j Hardness coefficient f j brittleness index b j Cave signage j Combined with the ground surface information of S1, the inlet terrain slope i and terrain curvature K are calculated, and the design borehole inclination angle θ is corrected. des ′;

[0126] Lithological parameters (ground-penetrating radar output):

[0127] Rock layer thickness: d j , where represents the thickness of the j-th rock layer (j = 1, 2, ..., m), and m is the total number of rock layers expected to be penetrated by the borehole (in meters).

[0128] Hardness coefficient: f j Protodyakonov hardness is dimensionless and is obtained through longitudinal wave velocity inversion. The formula is as follows: V p The longitudinal wave velocity is expressed in m / s.

[0129] Brittleness index: b j =V p / V s V s The transverse wave velocity (unit: m / s) is used to optimize vibration control strategies.

[0130] Cave sign: c j If a karst cave exists, cj = 1; otherwise, cj = 1. j =0.

[0131] Terrain parameters (based on S1 surface fitting extension):

[0132] Entrance terrain slope: Taking the partial derivative with respect to the fitted surface f(x, y) yields: Substitute the coordinates of the borehole inlet (e.g., the center of the device chassis (0, 0)) into the calculation of the slope (dimensionless).

[0133] Topographic curvature: Where f xx =2a3,f yy =2a5,f xy =a4, Used to determine the unevenness of terrain (unit: m) -1 ).

[0134] The borehole inclination angle is adjusted according to the terrain curvature and slope:

[0135] θ des ′=θ des -(arctani+k1K);

[0136] Where, θ des The original design borehole inclination angle (unit: °), θ des ′ represents the corrected design tilt angle (unit: °), and k1 is the curvature correction coefficient, which is set based on engineering experience.

[0137] d j f j b j c j i, K, and average rotational speed n in the same region avg Input historical data on drill bit wear rate (w) into the random forest rock formation drillability model. The random forest rock formation drillability model will output the rotational speed range. Feed rate range Shock Risk Index r j =f j ×b j When r j When the value is greater than 50, the anti-shock mode (dimensionless) is activated.

[0138] Training details:

[0139] The training samples contain historical borehole data from over 2000 boreholes, covering optimal parameters and drill bit life under different lithologies. The model consists of 30 decision trees with a minimum split size of 20 samples. An incremental learning mechanism is employed, updating the model every 50 new boreholes and discarding data from 180 days prior.

[0140] Output the random forest rock strata drillability model, design constraints (dip angle deviation ≤ ±1°), and θ. des Input neural network path optimization model, with E = D1E energy +D2E wear +D3E deviation Optimize the objective function and output the optimal borehole parameters for each rock layer: optimal rotation speed. Optimal feed rate Tilt compensation value Δθ j And the offset of the cave around the obstacle; D1(0.6), D2(0.3), D3(0.1) are weights, E energy For energy consumption, E wear For drill bit wear, E deviation This is the tilt angle deviation term;

[0141] Energy consumption items: Used to quantify the energy consumed during the drilling process (dimensionless).

[0142] Wear and tear items: Assess the degree of wear on the drill bit during the drilling process (dimensionless).

[0143] Deviation items: Measure the degree of deviation between the actual borehole inclination angle and the corrected design inclination angle (dimensionless).

[0144] Constraints:

[0145] Rotational speed-feed coupling: v j ≤0.005n j Ensure sufficient cutting.

[0146] Rate of change of tilt angle: |Δθ j -Δθ j-1 |≤1°, to prevent sudden changes in the device's attitude.

[0147] Training configuration:

[0148] The neural network employs a two-layer hidden layer structure with 128 neurons per layer and ReLU activation function. The Adam optimizer is used with a learning rate of 0.001. Z-score normalization is applied to the input data. Inverse normalization is performed on the output, and engineering constraints (such as rotational speed n) are incorporated. j ≤300rpm, feed rate v j (≤1m / min), ensuring that the output parameters meet the actual engineering requirements. During training, the weights and biases of the neural network are updated through the backpropagation algorithm to minimize the loss function.

[0149] After training and optimization, the neural network outputs the optimal drilling parameters for each rock layer, including the optimal rotation speed, optimal feed rate, dip angle compensation value, and cave obstacle avoidance offset.

[0150] When c is detected j =1 and the volume of the cave is greater than 0.5m³ 3 At that time, the A* algorithm is used to generate the obstacle avoidance path, with an offset δ. j =min(0.5m, 1 / 3 × design hole spacing), with f being the preferred choice. j Rock strata with a depth of ≤8.

[0151] Path verification and output

[0152] For the generated borehole parameter table, Latin hypercube sampling was used to target f j ±15%, b j Perform 1000 Monte Carlo simulations with ±20% accuracy, ensuring the probability of an inclination deviation >1° is <15%. Use COMSOL Multiphysics to simulate stress distribution, heat transfer, and cuttings transport during drilling to verify whether the drill bit temperature exceeds the material tolerance limit (120°C for carbide drill bits) and whether the cuttings accumulation thickness is <50mm (to avoid clogging the borehole). If the requirements are met, output a drilling parameter table. Proceed to step S3; otherwise, trigger secondary programming (adjust step size by 0.5°).

[0153] S3. Adaptive Drilling Operation:

[0154] According to the drilling parameter table for S2, the hydraulic motor speed is controlled as follows: The feed cylinder speed is The servo motor adjusts the drill bit tilt angle to θ des +Δθ1, start drilling; for The optimal rotational speed of the j=1th layer, for The optimal feed rate for the j=1 layer is Δθ1 = Δθ. j The tilt compensation value for the j=1th layer; θ des The original design borehole inclination angle;

[0155] Real-time acquisition of triaxial force sensor data F z Vibration sensor data A, temperature sensor data T, and depth encoder data H;

[0156] When F z When the deviation exceeds the preset threshold by 15%, pass Correct the rotational speed; For the real-time correction rotation speed of the j-th rock layer, k p This is the proportionality coefficient. This is the measured value of axial pressure. This is a predicted value for axial pressure.

[0157] When the kurtosis value K is calculated through A v >When the preset threshold is 3.5 and the effective value of vibration acceleration A is greater than the preset threshold of 30g, it is determined to be a sign of a stuck drill. The anti-stuck drill operation cycle of lifting the drill - rotating - pressing down is executed (lift the drill by 50mm, rotate it 3 times in each direction, and then press down at a speed of 0.5m / min).

[0158] Dynamically adjust drilling parameters based on actual rock conditions and historical data; adjust the power of negative pressure dust collection pump and the speed of spiral slag discharger based on H and rock cutting particle size.

[0159] when (Located in the j-th rock layer, H is the current borehole depth, in m, d) k When the thickness of the k-th rock layer is (in meters), if ( Given the rated axial pressure (unit: N) and A > 25g, the actual hardness is determined to be higher than the estimated value. The intelligent control module automatically retrieves the top 50 sets of data from similar historical operating conditions (deviation > 15%), matches the optimal parameter combination using the K-nearest neighbor algorithm, and limits the adjustment range to ±20%. When the drill bit temperature > 80℃, forced cooling is initiated (injecting high-pressure air into the hole at a flow rate of 200L / min), while simultaneously reducing the feed rate v = v cur ×0.8(v cur The current feed rate (in m / min) continues until the drill bit temperature is <70℃.

[0160] A pressure transmitter (range -20kPa to 0kPa, accuracy ±0.1kPa) is installed in the dust collection pipeline. When the negative pressure fluctuation is >5% and lasts for 10 seconds, it is determined to be rock debris blockage, and the spiral slag discharge machine is automatically started in alternating forward and reverse rotation mode (10 seconds forward, 5 seconds reverse, cycle 3 times). Based on the drilling depth H and the rock debris particle size (identified by camera image; when the proportion of particles >5mm is >30%, it is determined to be coarse debris), the power of the dust collection pump is dynamically adjusted: under coarse debris conditions, the power is increased by 10% at each depth to ensure that the slag discharge speed is ≥0.8m / s. The correlation rule between the dust collection pump power and the drilling depth is as follows:

[0161]

[0162] (P represents the power of the dust extraction pump, in kW). Spiral slag discharger rotation speed n slag =k·v cur (k = 50 rpm·m) -1 v is the slag discharge coefficient. cur (This refers to the current feed rate, in m / min), ensuring that the slag discharge efficiency is ≥ 120% of the slag discharge rate.

[0163] S4. Drilling completion and status reset:

[0164] Main decision condition: When H = H tar ± error value 20mm and axial pressure F z <Preset threshold 1500N, reaching duration threshold 10s; auxiliary judgment condition is: the density of the rock layer at the bottom of the hole is greater than the preset threshold by 1.2 times the design value, and after automatically deepening by 200mm, the drilling is judged to be complete; H tar To determine the borehole depth, the power drive module 4 is stopped, and the dust removal and slag discharge module 7 is activated to clean the residual rock debris inside the borehole. The hydraulic support module 5 retracts its outriggers to the transport height, performs a hardware self-check on the device, and awaits the next work instruction.

[0165] Once the conditions are met, perform the following operations:

[0166] Equipment shutdown and cleaning: Power drive module 4 stops, hydraulic motor speed = 0, and feed cylinder stops moving; dust removal and slag discharge module 7 continues to run for 5 minutes, and spiral slag discharge machine reverses for 1 minute to remove residual rock slag.

[0167] Data storage and feedback: Detailed borehole logs are generated, recording basic information (borehole number, operation time, etc.), leveling data (surface coefficients, final orientation, etc.), operation data (actual borehole parameters for each rock stratum, abnormal events, etc.), and model optimization data (model prediction bias values, etc.). The borehole logs are synchronized to the mine data center, and incremental training of the random forest model is triggered every 50 completed borehole operations.

[0168] Device Reset and Self-Check: The hydraulic support module 5 retracts the four outriggers to a transport height of 800mm. Before retraction, the overflow valve is opened to reduce the hydraulic system pressure to below 2MPa. The intelligent control module performs self-checks on all hardware components of the device, including sensor zero-point drift, hydraulic oil level and temperature, and the sealing of the dust removal and slag discharge module 7, to ensure that the equipment is in good condition and awaits the next work instruction. At this time, the input data of step S1 is reset to the new design drilling coordinates, and a new round of work process begins.

[0169] In some embodiments, the random forest leveling model employs an ensemble learning framework, using multiple decision trees to perform nonlinear mapping on the input features and outputting the leg height adjustment amount, specifically expressed mathematically as follows:

[0170] For decision tree T k The internal node N of (k is the decision tree number) has the following input feature vector: Δα and Δβ are attitude deviations. Let σ be the rate of change of attitude. h η represents the standard deviation of the outrigger height, and η represents the pressure unevenness.

[0171] The node splitting rule is:

[0172] if X j ≤θ N，j then X→left child of N else X→right child of N;

[0173] Where: if variable X j The value is less than or equal to θ N，j If X is a child node of node N, then X will be directed to the left child node of node N; otherwise, X will be directed to the right child node of node N. This kind of judgment logic determines the flow direction of data X in the tree structure.

[0174] X j Let θ be the j-th component of the eigenvector. N，jLet θ be the optimal splitting threshold for node N with respect to feature j. N，j Determined by minimizing Gini impurity:

[0175]

[0176] Where p L p R G represents the ratio of samples between the left and right child nodes. l G R The Gini index of the left and right child nodes;

[0177] For input features X, decision tree T k The output is:

[0178] Where: M k For decision tree T k The number of leaf nodes, R k，m Let c be the region of the m-th leaf node in the k-th tree. k，m Leaf node R k，m The predicted value of the outrigger adjustment amount, I(·) is the indicator function;

[0179] Random forest adjusts the height Δh of the four legs i for:

[0180]

[0181] in: The prediction of the adjustment amount of the i-th leg by the k-th tree, where n is the number of decision trees (30 / 50 / 100, etc., set according to the actual situation).

[0182] In this manual, the height adjustment amount Δh i Decomposed into coarse adjustment amount With fine-tuning

[0183] in:

[0184]

[0185] Wherein, round(·) is a rounding function used to convert the adjustment amount into an integer multiple of 50mm;

[0186] During the leveling process, coarse adjustment is first performed based on the coarse adjustment amount, and then fine adjustment is performed based on the fine adjustment amount.

[0187] The weights of the training samples are:

[0188]

[0189] Where: N majorityN represents the number of samples in the majority class. minority For the minority class sample size, Let the pressure of the i-th outrigger be the pressure of the t-th sample. This represents the sum of the ground pressure of the four outriggers in the t-th sample. This weighting mechanism can improve the leveling accuracy of the model in complex terrain (such as uneven ground in a mine) and effectively reduce the leveling failure problem caused by sample bias.

[0190] Pruning and Regularization:

[0191] Pre-pruning strategy is used during the growth of decision trees:

[0192] Maximum depth limit: D max =8;

[0193] Minimum number of leaf node samples: N min =10;

[0194] Split gain threshold: ΔI min =0.01;

[0195] Using the above formula, the random forest leveling model maps input features to four sets of leg height adjustment values, achieving fast and accurate leveling in complex terrain. The model reduces variance through ensemble learning, addresses imbalance by using sample weights, and prevents overfitting through pre-pruning, ultimately achieving a leveling accuracy of ±0.3°.

[0196] In some embodiments, the random forest rock strata drillability model integrates multiple decision trees to perform nonlinear mapping on input features, outputting borehole parameter suggestions and risk assessments, specifically expressed mathematically as follows:

[0197] The input feature vector is:

[0198] Y = [d j f j b j c j i, K, n avg ,w];

[0199] Where: average rotational speed n in the same region avg Drill bit wear rate w;

[0200] For input feature Y, the output of the k-th decision tree is:

[0201]

[0202] Among them, M k For decision tree T k The number of leaf nodes, R k，m Let c be the region of the m-th leaf node in the k-th tree. k,m Leaf node Rk，m The predicted value, I(·) is the indicator function;

[0203] The splitting rules for nodes within a decision tree are based on minimizing Gini impurity:

[0204]

[0205] in: The optimal splitting threshold for feature j, p L p R G represents the sample ratio of the left and right child nodes. L G R : Gini index of left and right child nodes;

[0206] Gini index calculation formula:

[0207]

[0208] Where p i c represents the proportion of samples in category i; c represents the total number of categories in the sample data.

[0209] The final output of a random forest for input features Y is:

[0210]

[0211] in, r j These represent the predicted lower limit of rotational speed, upper limit of rotational speed, lower limit of feed rate, upper limit of feed rate, and impact risk index for the j-th rock layer. r k，j These represent the lower limit of rotational speed, upper limit of rotational speed, lower limit of feed rate, upper limit of feed rate, and impact risk index predicted by the k-th decision tree for the j-th rock layer, respectively, where n is the number of decision trees.

[0212] In some embodiments, the formula for calculating the entrance terrain slope i is:

[0213]

[0214] To find the partial derivative of f(x,y).

[0215] In some embodiments, the formula for calculating the terrain curvature K is:

[0216]

[0217] The embodiments described above are for illustrative purposes only and are not intended to limit the invention. Therefore, any changes in numerical values ​​or substitutions of equivalent elements should still fall within the scope of this invention.

[0218] The above detailed description will enable those skilled in the art to understand that the present invention can indeed achieve the aforementioned objectives and has complied with the provisions of the Patent Law.

[0219] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0220] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0221] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0222] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0223] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Therefore, aspects of this application can be implemented entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. All of the above hardware or software can be referred to as a “unit,” “module,” or “system.” Furthermore, aspects of this application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0224] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, and Python; general programming languages ​​such as C; Visual Basic, Fortran2103, Perl, COBOL2102, PHP, and ABAP; dynamic programming languages ​​such as Python, Ruby, and Groovy; or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0225] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although some currently considered useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a purely software solution, such as an installation on an existing server or mobile device.

[0226] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this approach of the present application should not be construed as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A method of using an energy-saving drilling device for mine engineering blasting, characterized in that, Comprise: S1. Device positioning and leveling: Through the input of target coordinates by the intelligent control module, position and attitude data are obtained by UWB and inertial navigation, combined with hydraulic leg pressure data, the leg height adjustment amount is calculated by the leveling model to realize automatic leveling of the device; S2. Drilling path planning: Utilize the geological radar to scan the rock layer parameters, combine the terrain data to correct the drilling inclination, generate the optimal drilling parameters and cave obstacle avoidance path through the rock drillability model and path optimization model; S3. Adaptive drilling operation: According to the planning parameters, start drilling, real-time acquisition of sensor data to dynamically adjust the rotation speed, feed speed and inclination, and perform the drill lifting-rotation-pressing operation when encountering the risk of sticking drill, and synchronously adjust the dust removal and residue removal module power; S4. Drilling completion and state reset: Stop when the designed hole depth or hole bottom condition is reached, clean the hole rock residue, retract the hydraulic support to the transportation height, and complete the hardware self-checking; The energy-saving drilling device for mine engineering blasting comprises: A device main body composed of four L-shaped plate frames to form a cross-shaped structure in plan view; A drilling main module arranged at the bottom cross-shaped center position of the device main body; A power driving module arranged on the device main body and connected with the drilling main module; An intelligent control module arranged on the device main body and connected with the power driving module; A positioning and navigation module arranged on the device main body and connected with the intelligent control module; A hydraulic support module comprising four groups of telescopic hydraulic legs and rollers, the four groups of telescopic hydraulic legs are respectively arranged on the side of the end of the L-shaped plate frame away from the drilling main module, and the bottom of the end is provided with the rollers; the hydraulic support module is connected with the intelligent control module; A dust removal and residue removal module arranged on the device main body and connected with the intelligent control module; A data acquisition module comprising an integrated geological radar, a temperature sensor and a vibration sensor for omnidirectional acquisition of drilling related data; Wherein, the drilling main module, power driving module, positioning and navigation module, hydraulic support module, dust removal and residue removal module and data acquisition module are controlled by the intelligent control module to complete device positioning and leveling, drilling path planning, adaptive drilling operation, drilling completion and state reset.

2. The method of using the energy-saving drilling device for mine engineering blasting according to claim 1, characterized in that, The drilling main module is equipped with a drill rod and a hard alloy composite tooth drill bit, the drill bit is integrated with a three-axis force sensor to real-time collect drilling axial pressure and radial torque data; a depth encoder is built-in to accurately feedback the drilling depth.

3. The method of using the energy-saving drilling device for mine engineering blasting according to claim 1, characterized in that, The positioning and navigation module comprises a UWB positioning system and an inertial navigation unit, the base station of the UWB positioning system is deployed at the mine operation face to provide a position reference, and the inertial navigation unit monitors the device attitude in real time.

4. The method of using the energy-saving drilling device for mine engineering blasting according to claim 1, characterized in that, The dust removal and residue removal module comprises a negative pressure suction pump, a spiral residue removal machine and a dust filter device, the negative pressure suction pump, the spiral residue removal machine and the dust filter device are sequentially connected and work cooperatively, the negative pressure suction pump is sealingly connected with the drilling hole to timely extract dust and rock residue.

5. The method of using the energy-saving drilling device for mine engineering blasting according to claim 1, characterized in that, In the device positioning and leveling step, the ground surface is fitted by a weighted least squares method, combined with attitude deviation, standard deviation of leg height and pressure imbalance, input into a random forest leveling model to calculate the leg adjustment amount, and leveling is completed in two stages of coarse adjustment and fine adjustment.

6. The method of using the energy-saving drilling device for mine engineering blasting according to claim 5, characterized in that, In the drilling path planning step, the random forest rock drillability model is used to output the rotation speed range, feed speed range and impact risk index, and then the neural network path optimization model is used to generate the optimal drilling parameters for each rock layer with energy consumption, drill bit wear and inclination deviation as the objective functions.

7. The method of using the energy-saving drilling device for mine engineering blasting according to claim 6, characterized in that, In the adaptive drilling operation step, when the axial pressure deviation exceeds the preset threshold, the rotation speed is corrected in real time by a proportional coefficient; when the vibration kurtosis value and acceleration exceed the threshold, the anti-sticking operation is triggered.

8. The method of using the energy-saving drilling device for mine engineering blasting according to claim 7, characterized in that, The dust removal and residue discharge module dynamically adjusts the power of the negative pressure dust collection pump and the rotation speed of the screw residue discharge machine according to the drilling depth and the particle size of the rock debris.

9. The method of using the energy-saving drilling device for mine engineering blasting according to claim 8, characterized in that, The drilling completion determination conditions include: reaching the designed hole depth and the axial pressure being lower than the threshold for a duration reaching the threshold, or automatically deepening after the hole bottom rock density exceeds the standard, and after completion, a drilling log containing operation data is generated and synchronized to the mine data center.

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