Lighting system capable of adjusting posture of drilling machine

Abstract

The invention discloses a lighting system capable of adjusting the posture of a drilling machine, and relates to the field of drilling machine equipment, the system comprises the following components: a laser emission and control module, a sensor fusion module, an intelligent algorithm processing module, an environment adaptability structure and a power supply and energy efficiency management module; three-dimensional dynamic compensation is conducted on the drilling track of the drilling machine through an intelligent algorithm processing module and an improved RBF neural network algorithm, the drilling pointing precision is remarkably improved, according to actual measurement, under the drilling depth of 4 meters, the pointing error is reduced from + / -5 mm to + / -2.3 mm, the overexcavation amount is reduced by 62%, and in addition, the drilling accuracy is improved. The drilling track prediction sub-module adopts an improved long-short-term memory network model, can predict and adjust the laser orientation in advance, triggers a real-time compensation mechanism when the prediction deviation exceeds 1.5 mm, enables the track coincidence rate of a drilling machine to be improved from 78% to 96% when the drilling machine works in a curved roadway, and greatly improves the precision and efficiency of drilling operation through the combined action of the improvements.

Description

Technical Field

[0001] The present invention relates to the field of drilling equipment, in particular to a lighting system capable of adjusting the posture of a drilling rig. Background Art

[0002] In the mining and engineering drilling fields, the accuracy and efficiency of drilling operations are directly related to the success and cost-effectiveness of the project. With the continuous advancement of technology, higher requirements are placed on the intelligence, automation and ability of drilling equipment to adapt to complex environments. Especially in deep hole drilling, curved tunnel operations, high dust, strong vibration and other harsh working conditions, how to ensure the stable operation of the drilling rig and drilling accuracy has become a technical challenge in the industry.

[0003] Traditional drilling rig lighting systems mainly rely on fixed light sources or simple mechanical adjustment methods, and are difficult to dynamically adjust according to real-time changes in drilling depth and drilling rig posture. During deep hole drilling, due to the fixed divergence angle of the laser beam, the light spot expands at a long distance and the pointing is blurred, increasing over-excavation and operation errors. At the same time, the traditional system lacks multi-sensor fusion and intelligent algorithm processing, and is unable to perceive and calibrate the drilling rig's posture changes in real time, resulting in the drilling trajectory deviating from the designed path when operating in complex geological conditions and curved tunnels, seriously affecting operation efficiency and project quality. In addition, the traditional system also has deficiencies in environmental adaptability, such as poor dustproof, vibration resistance and heat dissipation performance, making it difficult to operate stably for a long time under harsh working conditions.

[0004] In view of the shortcomings of traditional drilling rig lighting systems in drilling accuracy, environmental adaptability and intelligence, the present invention proposes a lighting system that can adjust the drilling rig posture, which is particularly important. Summary of the Invention

[0005] The purpose of this invention is to make up for the shortcomings of the existing technology and provide a lighting system that can adjust the drilling rig posture. It can realize real-time perception and dynamic adjustment of the drilling rig posture by integrating laser emission and control module, sensor fusion module, intelligent algorithm processing module and environmental adaptability structure, significantly improving the drilling pointing accuracy and operation efficiency. At the same time, the system is designed with comprehensive dust-proof, anti-vibration and heat dissipation components to ensure stable operation in harsh working environments.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a lighting system capable of adjusting the drilling rig posture, the system comprising the following components: a laser emission and control module, a sensor fusion module, an intelligent algorithm processing module, an environmental adaptability structure, and a power supply and energy efficiency management module;

[0007] The laser emission and control module uses the high-precision laser emission and control technology of automotive intelligent laser headlights to achieve a balance between high brightness and low energy consumption. Through dynamic focusing technology, it adjusts the laser beam divergence angle to adapt to different drilling depths, ensuring stable laser pointing in different operating scenarios.

[0008] The sensor fusion module combines the vehicle system's camera and millimeter-wave radar sensing technology with the inclination sensor of the mining laser pointing device. The camera and millimeter-wave radar can perceive the surrounding environment in real time, while the inclination sensor is used to monitor the drill's posture changes. Through sensor fusion technology, dynamic calibration of the drill's posture is achieved.

[0009] The intelligent algorithm processing module utilizes the dynamic beam control algorithm of automotive laser headlights to optimize a three-dimensional dynamic compensation model for the drilling trajectory of a drilling rig. This model can perform real-time calculation and compensation of the drilling trajectory of the drilling rig based on data collected by sensors, thereby improving pointing accuracy and reducing over-excavation errors.

[0010] The environmental adaptability structure:

[0011] Dust-proof structure: A three-stage dust-proof filter system is set up and equipped with a 0.8MPa compressed air purge device;

[0012] Anti-vibration structure: It adopts the combination of magnetorheological damper and high-precision INS navigation system;

[0013] Heat dissipation structure: Adopt vacuum heat pipe conduction and circulating water cooling system to ensure that the system maintains good heat dissipation performance during operation, with a temperature difference of ≤5℃;

[0014] The power supply and energy efficiency management module adopts a lithium battery pack power supply solution, performs power interference protection design, and selects low-power green laser diodes to reduce energy consumption while ensuring performance.

[0015] Furthermore, the laser emission and control module adopts a dual-optical path collaborative emission structure. The main optical path is a 520nm green laser diode (output power 50mW), and the auxiliary optical path is a 650nm red light calibration beam. The two optical paths are coupled through a polarization beam splitter prism and dynamically focused through the Optogama galvanometer system. The module controls the laser diode junction temperature through a PID temperature control circuit and combines it with an adaptive duty cycle modulation algorithm. When the ambient temperature exceeds 40°C, the pulse width is automatically adjusted from 200μs to 150μs and the duty cycle is reduced from 30% to 22%, while ensuring the stability of the beam energy. The power consumption is reduced by 18%-23%. The dual-optical path structure can realize independent adjustment of the main beam energy distribution and the calibration beam position, solving the problem of light spot drift in traditional single-optical path systems in deep hole operations. After testing, at a drilling depth of 50 meters, the light spot position repeatability error is ≤0.3mm.

[0016] Furthermore, the sensor fusion module adopts a three-layer data acquisition architecture: the bottom layer is a MEMS tilt sensor that collects the three-axis tilt of the drill rig in real time, the middle layer is a 2-megapixel industrial camera with an 8mm focal length lens to obtain images of the drilling area, and the top layer is a 77GHz millimeter-wave radar that senses obstacles ahead. The data fusion algorithm uses an improved Kalman filter model, and the state equation is:

[0017] X k =F k X k-1 +K k (Z k -H k X k-1 )

[0018] The state vector X k =[θ x ,θ y ,θ z ,v x ,v y ,v z ] T Contains three-axis angles and angular velocities, process matrix F k The observation matrix H is determined by offline identification of the drilling rig dynamics model. k Dynamically adjust according to sensor characteristics, Z k The observation vector at the kth moment, K k is the weight coefficient, and the determination method is: when the dust concentration C≤500mg / m 3 When the camera data weight w c =0.6, millimeter wave radar weight w r =0.3, tilt sensor weight w t =0.1, when C>500mg / m 3 When w is c Down to 0.3, w r It is increased to 0.5, realizing the adaptive fusion of multi-sensor data. After actual measurement and fusion, the root mean square error of the attitude data is reduced from 0.25° when using the inclination sensor alone to 0.08°.

[0019] Furthermore, the intelligent algorithm processing module includes a three-dimensional trajectory dynamic compensation submodule, which is based on an improved RBF neural network algorithm with a network structure of 9-15-3. The input layer is the current drilling depth d, the three-axis inclination deviation Δθ x ,Δθ y ,Δθ z, environmental vibration acceleration a, dust concentration C, laser transmission distance L, historical compensation h1, h2, a total of 9 parameters, the hidden layer uses Gaussian radial basis function, the center vector is generated by clustering 300 groups of mine measured data through K-means clustering algorithm, the width parameter The output layer is the three-axis compensation angle δ x ,δ y ,δ z , the core formula of the compensation algorithm is:

[0020]

[0021] Where δ is the output three-axis compensation angle, i is the hidden layer neuron index, X is the input vector, c i is the center vector of the i-th hidden layer neuron, and the weight w is iteratively optimized using an improved particle swarm optimization algorithm. After training with 30 sets of drilling data from a copper mine, the algorithm reduced the pointing error of a 4-meter drilling depth from ±5 mm to ±2.3 mm, reducing over-excavation by 62%.

[0022] Furthermore, the dustproof component of the environmentally adaptable structure adopts a three-stage gradient filtration design: the primary is a 300-mesh metal mesh, the secondary is a PET non-woven fabric, and the tertiary is a PTFE-coated filter material, combined with a 0.8MPa compressed air pulse purge device. The purge cycle is dynamically adjusted according to the dust concentration sensor data. When C>300mg / m 3 When the purge interval is shortened from 60s to 20s, the anti-vibration structure adopts a magnetorheological damper and a high-precision INS navigation system in series. The vibration suppression algorithm adopts an improved LMS adaptive filter. The reference signal is the angular velocity signal output by the INS, and the error signal is the deviation between the measured value and the theoretical value of the inclination sensor. The weight coefficient update formula is:

[0023] w(k+1)=w(k)+2μe(k)x(k)

[0024] where w(k) is the weight coefficient vector of the kth iteration, w(k+1) is the weight coefficient vector of the k+1th iteration, e(k) is the error signal of the kth iteration, x(k) is the reference signal of the kth iteration, and the step size factor μ = 0.0015 / (1+0.01||x(k)|| 2 ), by adjusting the damper current 0-2A in real time, the 200Hz vibration amplitude is reduced from 0.5q to below 0.1q, meeting the drilling rig's stable operation requirements after F8-level surrounding rock blasting.

[0025] Furthermore, the power supply and energy efficiency management module adopts a dual power supply redundant design. The main power supply is a 12V / 20Ah lithium iron phosphate battery pack, and the backup power supply is a supercapacitor. It automatically switches when the main power supply voltage is lower than 10.5V. The energy efficiency control algorithm is based on fuzzy-PID composite control. The input is the battery SOC and the system load rate. The output is the power adjustment coefficient α of the laser emission module. The fuzzy rule table contains 7×7=49 rules. When SOC≤20% and the load rate>80%, α is adjusted to 0.6, so that the system enters energy-saving mode. The module also integrates a power interference protection circuit, including a common-mode choke, a TVS diode and an RC filter network. It has been tested to withstand a 1.2 / 50μs surge voltage and meet the industrial immunity requirements of GB / T17626.5.

[0026] Furthermore, the laser emission and control module is further provided with a beam divergence angle adaptive adjustment unit, which is based on the drilling depth-divergence angle mathematical model:

[0027] θ(d)=θ0·e -λd +θ min

[0028] Where θ(d) is the laser beam divergence angle when the drilling depth is d, θ0 is the initial divergence angle, λ is the attenuation coefficient, and θ min is the minimum divergence angle, d is the drilling depth. When the depth exceeds 10 meters, the optical prism group is driven by a stepper motor to adjust the divergence angle with an adjustment accuracy of 0.05mrad. The model is obtained by fitting the measured data of the spot diameter of 200 sets of drilling holes at different depths. The spot diameter at a depth of 50 meters is reduced from 150mm with a traditional fixed divergence angle to 85mm, and the energy concentration is increased by 43%, solving the problem of fuzzy pointing caused by excessive spot size in deep hole operations.

[0029] Furthermore, the intelligent algorithm processing module also includes a drilling trajectory prediction submodule, which adopts an improved long short-term memory network model with a network structure of input layer, two LSTM layers, and output layer. The model training uses 800 sets of historical data of mine construction, and the loss function is:

[0030]

[0031] Where L is the loss function value, N is the number of training data samples, i is the sample index, x i 、y i 、z i is the actual coordinate value of the i-th sample, The model predicts the coordinate value of the i-th sample, and the Adam optimizer is used to update the weight. The prediction result is used to adjust the laser pointing in advance. When the prediction deviation exceeds 1.5mm, the real-time compensation mechanism is triggered. The measured mean square error of the coordinate prediction of the next three steps of the prediction model is 0.8mm, which increases the trajectory compliance rate of the drilling rig when operating in a curved tunnel from 78% to 96%.

[0032] Furthermore, the heat dissipation component of the environmentally adaptable structure adopts a composite heat dissipation solution of vacuum heat pipes and microchannel water cooling. The heat pipe is made of oxygen-free copper, the evaporation section is welded to the laser diode heat sink, and the condensation section is embedded in the microchannel copper radiator. The water cooling system is driven by a 2L / min centrifugal pump, and the coolant is a 50% ethylene glycol aqueous solution. The heat dissipation control algorithm dynamically adjusts the water pump speed n and fan power P according to the laser module temperature T: when T<50°, n=1500rpm, P=20W, when 50℃≤T<65°, n=2500rpm, P=40W, when T≥65°, n=3500rpm, P=60W, and at the same time, the backup heat dissipation circuit is activated. This solution controls the junction temperature of the laser diode below 75°C. After 8 hours of continuous operation, the temperature fluctuation is ≤3°C, ensuring the stability of the optical components.

[0033] Compared with the existing technology, this lighting system capable of adjusting the drilling rig posture has the following beneficial effects:

[0034] First, the system uses an intelligent algorithm processing module and an improved RBF neural network algorithm to perform three-dimensional dynamic compensation for the drilling trajectory of the drill rig, significantly improving the drilling pointing accuracy. Field measurements show that at a drilling depth of 4 meters, the pointing error is reduced from ±5mm to ±2.3mm, and overexcavation is reduced by 62%. In addition, the drilling trajectory prediction submodule uses an improved long-short-term memory network model to predict and adjust the laser pointing in advance. When the predicted deviation exceeds 1.5mm, a real-time compensation mechanism is triggered, increasing the trajectory compliance rate of the drill rig when operating in curved tunnels from 78% to 96%. These improvements work together to significantly improve the accuracy and efficiency of drilling operations.

[0035] Second, the system has been designed with a comprehensive environmental adaptability structure, including dustproof, anti-vibration and heat dissipation components. The dustproof structure adopts a three-stage gradient filtration design, combined with a compressed air purge device, to effectively prevent dust intrusion and ensure the normal operation of the system in harsh dust environments. The anti-vibration structure combines magnetorheological dampers with a high-precision INS navigation system. Through an improved LMS adaptive filtering algorithm, the 200Hz vibration amplitude is reduced from 0.5q to below 0.1q, meeting the stable operation requirements after F8-level surrounding rock blasting. The heat dissipation component uses a vacuum heat pipe and microchannel water cooling composite heat dissipation solution to ensure that the laser diode junction temperature is controlled below 75°C. After 8 hours of continuous operation, the temperature fluctuation does not exceed 3°C, ensuring the stability of the optical components. These designs significantly enhance the system's adaptability to different operating environments and improve the system's stability and reliability.

[0036] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0038] Figure 1 The figure is a flow chart of the overall architecture of a lighting system capable of adjusting the drilling rig posture. DETAILED DESCRIPTION

[0039] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0040] Example 1

[0041] At a gas extraction drilling operation site 15 meters underground in a coal mine, the damp tunnel is filled with fine coal dust, and every advance of the drill is accompanied by rock vibration. In this environment, a slight deviation in the drilling trajectory may cause the gas extraction pipeline to be unable to be accurately connected, affecting the ventilation safety of the entire mine. At this time, the lighting system that can adjust the drill rig's posture needs to provide real-time and accurate posture guidance for the drill rig in a confined space with high dust and strong vibration to ensure that the drill hole extends along the preset path.

[0042] The operator presses the start button on the control panel, immediately activating the dual-power redundancy system of the power supply and energy efficiency management module. The main lithium iron phosphate battery pack begins to power the system, and the backup supercapacitor monitors voltage fluctuations in real time like a guard on standby. Inside the laser emission and control module, the dual-optical path collaborative emission structure starts like a precision clock. The two laser beams are perfectly coupled through the polarization beam splitter prism, and the Optogama galvanometer system slowly rotates to complete the initial focusing adjustment. At the same time, the PID temperature control circuit, like a sensitive thermometer, begins to monitor the junction temperature changes of the laser diode in real time, laying the foundation for subsequent temperature control.

[0043] The sensor fusion module has a three-layer data acquisition architecture. The bottom-layer MEMS tilt sensor captures subtle changes in three-axis tilt in real time. The middle-layer 2-megapixel industrial camera with an 8mm focal length lens. The top-layer 77GHz millimeter-wave radar continuously scans the 10-meter range ahead to identify rock protrusions that may hinder drilling. The data from these three types of sensors is fused and processed using an improved Kalman filter model to eliminate noise interference and generate accurate environmental and posture data. The formula is:

[0044] X k =F k X k-1 +K k (Z k -H k X k-1 )

[0045] The state vector X k =[θ x ,θ y ,θ z ,v x ,v y ,v z ] T Contains three-axis angles and angular velocities, process matrix F k The observation matrix H is determined by offline identification of the drilling rig dynamics model. k Dynamically adjust according to sensor characteristics, Z k The observation vector at the kth moment, K k is the weight coefficient.

[0046] When the drill bit penetrates to 5 meters, the stress of the rock formation gradually appears, and the drill rig begins to deviate slightly. The three-dimensional trajectory dynamic compensation submodule of the intelligent algorithm processing module responds immediately. Based on the improved RBF neural network algorithm, it comprehensively analyzes the current drilling depth d, the three-axis inclination deviation Δθ, and the three-axis inclination deviation Δθ. x ,Δθ y ,Δθ z, the acceleration a caused by rock formation vibration, the dust concentration C in the air, the laser transmission distance L, and the historical compensation amount h1, and quickly calculate the three-axis compensation angle that needs to be adjusted. The core formula of the compensation algorithm is: Where δ is the output three-axis compensation angle, i is the hidden layer neuron index, X is the input vector, c i is the center vector of the i-th hidden layer neuron, and the weight w i Through iterative optimization using an improved particle swarm optimization algorithm, the direction of the laser beam is fine-tuned accordingly. When the drilling depth exceeds 10 meters, the beam divergence angle adaptive adjustment unit of the laser emission and control module is activated. Based on the drilling depth-divergence angle mathematical model, the stepper motor gently drives the optical prism group to accurately change the divergence of the laser beam to ensure that the light spot at the bottom of the deep hole remains clearly visible. At the same time, the drilling trajectory prediction submodule uses an improved long-short-term memory network model to predict the future drilling path based on 800 sets of historical data. Once it is found that the deviation may exceed 1.5mm, the real-time compensation mechanism is immediately triggered to allow the drill to "turn" in advance to avoid error accumulation.

[0047] Coal dust generated by drilling fills the air like smoke, and the three-stage dust filter system is fully prepared for this. A primary 300-mesh wire mesh removes larger particles, a secondary PET non-woven fabric captures fine dust, and a tertiary PTFE-coated filter media acts like a high-efficiency air purifier, intercepting nano-sized particles. A 0.8MPa compressed air purge device periodically purges the filter to prevent dust clogging. Regarding vibration, a magnetorheological damper works in tandem with the high-precision INS navigation system. An improved LMS adaptive filtering algorithm calculates the optimal damping force based on the INS's angular velocity signal. By adjusting the damper current, the vibration amplitude of the drilling rig is controlled within a safe range. When the laser module heats up due to prolonged operation, the vacuum heat pipe and microchannel water cooling composite cooling system comes into play. The oxygen-free copper heat pipe quickly transfers heat to the microchannel radiator. A 50% ethylene glycol aqueous solution, driven by a centrifugal pump, circulates, and the fan automatically adjusts its power based on the temperature, ensuring that the laser diode operates at a comfortable "body temperature."

[0048] As the operation continues, the battery power gradually decreases. The fuzzy-PID composite control algorithm of the power supply and energy efficiency management module weighs the battery SOC and system load rate in real time. When the SOC drops to 30% and the load rate is 70%, the power regulation coefficient of the laser emission module is adjusted to reduce energy consumption while ensuring accuracy. If the main power supply voltage is lower than the critical value, the system will instantly switch to supercapacitor power supply like an emergency light switching to a backup power supply, ensuring that key components such as the inclination sensor and radar are not "powered off" until the drilling operation is successfully completed.

[0049] Example 2

[0050] At the tunnel excavation site of a metal mine, the hard granite layer acts like a steel barrier, and the drill rig needs to drill a 20-meter-deep directional blasting hole in it. Every impact of the rock drill causes violent vibrations, and the dust concentration generated by the rock crushing is extremely high. The humid air is also mixed with corrosive gases. At this time, the lighting system needs to provide stable posture guidance for the drill rig in such a harsh environment to ensure the accurate positioning of the blasting hole and lay the foundation for subsequent tunnel excavation.

[0051] The technician inputs the operating parameters at the operating table, and the power supply and energy efficiency management module starts up. The dual power supply system consisting of a lithium iron phosphate battery pack and a supercapacitor completes self-test. The common-mode choke, TVS diode and RC filter network in the power interference protection circuit act as an electromagnetic barrier, isolating the high-frequency interference of the mine power grid. The laser emission and control module presets the initial divergence angle through an internal algorithm based on the drilling depth of 20 meters. The Optogama galvanometer system completes the focus calibration in advance. The PID temperature control circuit and the adaptive duty cycle modulation algorithm are on standby, ready to cope with the high-temperature environment caused by rock friction.

[0052] The MEMS inclination sensor in the sensor fusion module is attached closely to the main shaft of the drill rig, sensing in real time the three-axis inclination fluctuations caused by each rock drilling impact. The industrial camera is aimed at the hole mouth to capture the moment of rock breaking, providing a visual basis for the system to judge the hardness of the rock formation. The 77GHz millimeter-wave radar scans the rock formation contour 15 meters ahead to identify hard rock protrusions that may cause the drill hole to deviate. When the three types of sensor data are fused through the improved Kalman filter model, the system will automatically enhance the data stability algorithm according to the humid environment to ensure that posture monitoring is not affected by humidity.

[0053] After the drill bit penetrates 5 meters into the granite layer, the reaction force of the hard rock causes the drill rig to deviate significantly. The three-dimensional trajectory dynamic compensation submodule of the intelligent algorithm processing module is based on an improved RBF neural network algorithm. It comprehensively analyzes nine parameters, including the current depth, three-axis inclination deviation, rock vibration acceleration, high-concentration dust, laser transmission distance and historical compensation data, and quickly calculates the three-axis compensation angle. The laser beam adjusts its direction like a precise pointer to guide the drill rig to correct its trajectory. The drilling trajectory prediction submodule uses an improved long-short-term memory network model to predict the subsequent drilling path based on 800 sets of historical mining data. When it is found that a sudden change in the hardness of a certain section of rock formation may cause deviation, a "turn" command is issued in advance through laser pointing, allowing the drill rig to make adjustment preparations before reaching the area.

[0054] The dust produced by rock crushing is as dense as volcanic ash. The dust-proof component's three-stage gradient filtration system intercepts it layer by layer. The wire mesh first filters out stone debris, the PET non-woven fabric absorbs fine dust, and the PTFE-coated filter material intercepts micron-sized particles. The 0.8MPa compressed air purge device automatically increases the purge frequency based on the "alarm" of the dust concentration sensor to ensure a clear laser path. For vibration resistance, the magnetorheological damper and the INS navigation system are like a tacit partner. The INS angular velocity signal is used as a reference. The improved LMS adaptive filtering algorithm calculates the optimal damping force. By adjusting the damper current, the amplitude of the severe 200Hz vibration is significantly reduced. When the laser module temperature rises to 65°C, the vacuum heat pipe and microchannel water cooling system activate the backup circuit, the centrifugal pump operates at high speed, the ethylene glycol coolant circulates faster, and the fan runs at full power to quickly cool the laser diode and prevent the loss of pointing accuracy caused by high temperature.

[0055] When the blasting operation instantly increases the system load, the fuzzy-PID composite control algorithm of the power supply and energy efficiency management module responds quickly. When the SOC drops to 20% and the load rate soars to 85%, it decisively adjusts the power adjustment coefficient of the laser emission module to 0.6, and the system enters energy-saving mode, giving priority to the posture monitoring function. After drilling is completed, the system automatically switches to supercapacitor power supply, allowing the laser module to cool down slowly to avoid thermal stress damage caused by sudden power outages. At the same time, the drilling depth, inclination deviation, compensation data, etc. of this operation are fully recorded to provide data support for subsequent blasting hole design.

[0056] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A lighting system capable of adjusting the drilling rig posture, characterized in that: The system includes the following components: laser emission and control module, sensor fusion module, intelligent algorithm processing module and environmental adaptability structure as well as power supply and energy efficiency management module; The laser emission and control module adopts the high-precision laser emission and control technology of automotive intelligent laser headlights and adjusts the laser beam divergence angle through dynamic focusing technology to adapt to different drilling depths. The sensor fusion module combines the vehicle system's camera and millimeter-wave radar sensing technology with the inclination sensor of the mining laser pointing device. The camera and millimeter-wave radar can perceive the surrounding environment in real time, while the inclination sensor is used to monitor changes in the drill rig's posture. The intelligent algorithm processing module utilizes the dynamic beam control algorithm of automotive laser headlights to optimize a three-dimensional dynamic compensation model for the drilling trajectory of a drilling rig. This model can perform real-time calculation and compensation of the drilling trajectory of the drilling rig based on data collected by sensors. The environmental adaptability structure: Dust-proof structure: A three-stage dust-proof filter system is set up and equipped with a 0.8MPa compressed air purge device; Anti-vibration structure: It adopts the combination of magnetorheological damper and high-precision INS navigation system; Heat dissipation structure: adopts vacuum heat pipe conduction and circulating water cooling system; The power supply and energy efficiency management module adopts a lithium battery pack power supply solution, performs power interference protection design, and selects a low-power green laser diode.

2. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The laser emission and control module adopts a dual-optical path collaborative emission structure. The two optical paths are coupled through a polarization beam splitter prism and dynamically focused through an Optogama galvanometer system. The module controls the laser diode junction temperature through a PID temperature control circuit and combines it with an adaptive duty cycle modulation algorithm. When the ambient temperature exceeds 40°C, the pulse width is automatically adjusted from 200μs to 150μs and the duty cycle is reduced from 30% to 22%.

3. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The sensor fusion module adopts a three-layer data acquisition architecture: the bottom layer is a MEMS tilt sensor that collects the three-axis tilt of the drill rig in real time, the middle layer is a 2-megapixel industrial camera with an 8mm focal length lens to obtain images of the drilling area, and the top layer is a 77GHz millimeter-wave radar that senses obstacles ahead. The data fusion algorithm uses an improved Kalman filter model, and the state equation is: X k =F k X k-1 +K k (Z k -H k X k-1 ) The state vector X k =[θ x ,θ y ,θ z ,v x ,v y ,v z ] T Contains three-axis angles and angular velocities, process matrix F k The observation matrix H is determined by offline identification of the drilling rig dynamics model. k Dynamically adjust according to sensor characteristics, Z k The observation vector at the kth moment, K k is the weight coefficient.

4. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The intelligent algorithm processing module includes a three-dimensional trajectory dynamic compensation submodule, which is based on an improved RBF neural network algorithm with a network structure of 9-15-3. The input layer is the current drilling depth d, the three-axis inclination deviation Δθ x ,Δθ y ,Δθ z , environmental vibration acceleration a, dust concentration C, laser transmission distance L, historical compensation h1, h2, a total of 9 parameters, the hidden layer uses Gaussian radial basis function, the center vector is generated by clustering 300 groups of mine measured data through K-means clustering algorithm, the width parameter The output layer is the three-axis compensation angle δ x ,δ y ,δ z , the core formula of the compensation algorithm is: Where δ is the output three-axis compensation angle, i is the hidden layer neuron index, X is the input vector, c i is the center vector of the i-th hidden layer neuron, and the weight w i Iterative optimization is performed using an improved particle swarm optimization algorithm.

5. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The dustproof component of the environmentally adaptable structure adopts a three-stage gradient filtration design: the primary stage is a 300-mesh metal wire mesh, the secondary stage is a PET non-woven fabric, and the tertiary stage is a PTFE-coated filter material, combined with a 0.8MPa compressed air pulse purge device. The anti-vibration structure adopts a magnetorheological damper and a high-precision INS navigation system in series. The vibration suppression algorithm adopts an improved LMS adaptive filter. The reference signal is the angular velocity signal output by the INS, and the error signal is the deviation between the actual value and the theoretical value of the inclination sensor. The weight coefficient update formula is: w(k+1)=w(k)+2μe(k)x(k) where w(k) is the weight coefficient vector of the kth iteration, w(k+1) is the weight coefficient vector of the k+1th iteration, e(k) is the error signal of the kth iteration, x(k) is the reference signal of the kth iteration, and the step size factor μ = 0.0015 / (1+0.01||x(k)|| 2 ), the 200Hz vibration amplitude was reduced from 0.5q to below 0.1q by adjusting the damper current 0-2A in real time.

6. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The power supply and energy efficiency management module adopts a dual power supply redundancy design. The main power supply is a 12V / 20Ah lithium iron phosphate battery pack, and the backup power supply is a supercapacitor. It automatically switches when the main power supply voltage is lower than 10.5V. The energy efficiency control algorithm is based on fuzzy-PID composite control. The input is the battery SOC and system load rate, and the output is the power adjustment coefficient α of the laser emission module. The fuzzy rule table contains 7×7=49 rules. When the SOC ≤ 20% and the load rate > 80%, α is adjusted to 0.6, so that the system enters energy-saving mode. The module also integrates a power interference protection circuit, including a common-mode choke, a TVS diode and an RC filter network.

7. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The laser emission and control module is also provided with a beam divergence angle adaptive adjustment unit, which is based on the drilling depth-divergence angle mathematical model: θ(d)=θ0·e -λd +θ min Where θ(d) is the laser beam divergence angle when the drilling depth is d, θ0 is the initial divergence angle, λ is the attenuation coefficient, and θ min is the minimum divergence angle, d is the drilling depth. When the depth exceeds 10 meters, the optical prism group is driven by a stepper motor to adjust the divergence angle with an adjustment accuracy of 0.05mrad.

8. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The intelligent algorithm processing module also includes a drilling trajectory prediction submodule, which uses an improved long short-term memory network model with a network structure of an input layer, two LSTM layers, and an output layer. The model training uses 800 sets of historical data from mine construction, and the loss function is: Where L is the loss function value, N is the number of training data samples, i is the sample index, x i 、y i 、z i is the actual coordinate value of the i-th sample, The model predicts the coordinate value of the i-th sample. The Adam optimizer is used to update the weight. The prediction result is used to adjust the laser pointing in advance. When the prediction deviation exceeds 1.5mm, the real-time compensation mechanism is triggered.

9. The lighting system capable of adjusting the drilling rig posture according to claim 1, characterized in that: The heat dissipation component of the environmentally adaptable structure adopts a composite heat dissipation solution of vacuum heat pipes and microchannel water cooling. The heat pipe is made of oxygen-free copper, the evaporation section is welded to the laser diode heat sink, and the condensation section is embedded in the microchannel copper radiator. The water cooling system is driven by a 2L / min centrifugal pump, and the coolant is a 50% ethylene glycol aqueous solution. The heat dissipation control algorithm dynamically adjusts the water pump speed n and fan power P according to the laser module temperature T: when t<50°, n=1500rpm, P=20W, when 50℃≤T<65°, n=2500rpm, P=40W, when T≥65°, n=3500rpm, P=60W, and at the same time starts the backup heat dissipation circuit.

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