Intelligent drilling cutting method monitoring robot and rock burst danger early warning method

Abstract

The invention belongs to the technical field of robots, and discloses an intelligent drilling cutting method monitoring robot and a rock burst danger early warning method. The robot collects motor parameter information in real time in the drilling cutting implementation process, a motor drill jamming and suction prediction model is established, drill jamming and suction risks are predicted through comprehensive decision, misjudgment caused by stratum changes or sensor noise is prevented, meanwhile, the drill carriage performance detection function is achieved, the drill carriage performance is evaluated according to parameters, and the drilling efficiency is improved. The working state and maintenance suggestions of the current drill carriage are given; according to the method, a self-developed intelligent drilling cutting method monitoring robot is adopted. Based on a drilling cutting method principle and a powder drilling rate index, fitting is carried out on the drill rod thrust and the drill rod torque through the pulverized coal amount, and critical indexes of the drill rod thrust and the drill rod torque are determined. Full-automatic drilling and collecting in the drilling cutting method construction process are achieved, rock burst dangerousness is monitored according to multiple parameter indexes, reliability is high, and the response speed is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of robots, and in particular relates to an intelligent drill cuttings monitoring robot and a rock burst hazard early warning method. Background Art

[0002] The drill cuttings monitoring method is a commonly used and highly recognized method for monitoring rock burst hazards. This method involves drilling holes approximately 42 mm in diameter into the coal seam. The method identifies rock burst hazards based on the amount of coal dust discharged per meter, its changing patterns, and related dynamic phenomena during drilling. The drill cuttings monitoring method is applied to four locations: the coal wall of the mining face, the sides of the mining tunnel, the head of the excavation face, and the sides of the excavation tunnel. At least three drill holes are monitored at each location, with monitoring intervals of 1 to 3 days. Due to the limitations of the underground mine working environment, the drill cuttings monitoring method primarily involves workers manually operating a dedicated drill rig to drill 8-15 meter-long holes at designated locations. Coal dust is collected and weighed every meter, and related dynamic phenomena are recorded. Finally, the drill dust rate index is calculated based on the recorded coal dust volume. Rock burst hazards are then determined based on the coal dust rate index and dynamic phenomena.

[0003] Domestically, when using the drill cuttings monitoring method for impact hazard monitoring, the primary method is manual drilling, collection, weighing, and recording of coal dust. This requires repeated drilling, collection, weighing, and recording, resulting in high labor intensity and backward technology. The drilling process is noisy and produces severe coal dust pollution. Furthermore, technicians primarily use handheld devices to collect coal dust, which can cause coal dust to escape and affect monitoring accuracy. The drill cuttings monitoring method requires a high level of technical expertise, and only a small number of staff are qualified. After the drill cuttings monitoring method is completed, manual calculations are required to determine whether there is a risk of impact pressure, resulting in poor real-time performance.

[0004] When using the drill cuttings monitoring method described above, manual construction is required in hazardous areas, which is highly risky. This method presents challenges such as high labor intensity, low efficiency, low precision, inability to display pulverized coal data in real time, and lags in monitoring rock burst risks.

[0005] Low efficiency and high labor intensity: The drill cuttings monitoring method requires one person to drill with a dedicated drill, another to collect and weigh the coal dust, and another to record the data. Weights are recorded every meter drilled. This results in low efficiency and high labor intensity, as well as high noise and severe coal dust pollution during the drilling process. Furthermore, workers work close to the tunnel walls and drill rods, posing a safety hazard.

[0006] Low manual precision: The drill cuttings method requires a person to manually drill, which is unstable. Manually holding a collection device to collect coal dust can result in incomplete collection, impacting weighing accuracy. The loss rate of coal dust during manual collection can reach 15%-30%, and the standard deviation of coal dust collected by different operators can reach ±2.3 kg / m. High coal stress during testing can lead to drill suction and sticking, resulting in poor safety and reliability, and inaccurate monitoring of dynamic phenomena.

[0007] Poor real-time performance: The single-hole detection cycle is about 90 minutes, and the coal powder quantity data is only recorded once per meter. After recording the data, the coal powder rate index needs to be calculated manually. There is a lag in the monitoring of rock burst danger, and the drilling rig data cannot be viewed. It can only be judged manually whether a dynamic phenomenon has occurred, which makes it difficult to reflect the changes in the dynamic stress field in a timely manner. Summary of the Invention

[0008] To overcome the problems existing in the related art, the disclosed embodiments of the present invention provide an intelligent drill cuttings monitoring robot and a rock burst hazard early warning method. Based on GB / T 25217.6-2019, Rock burst measurement, monitoring, and prevention methods, Part 6: Drill cuttings monitoring methods, the present invention designs an intelligent drill cuttings monitoring robot.

[0009] The present invention provides an intelligent drilling cuttings monitoring robot, comprising:

[0010] Track assembly, using a combined sealed track chain made of alloy steel, is used for drilling vehicle work;

[0011] The chassis assembly is connected to the crawler assembly and is used to support the force of the drilling vehicle;

[0012] The drive assembly is located on the upper part of the chassis assembly and is used to provide power to the drilling vehicle through a mining explosion-proof solenoid valve;

[0013] The oil tank assembly is installed on the upper part of the chassis assembly and is used to store oil through the oil tank, dissipate heat, separate bubbles in the oil, and precipitate impurities;

[0014] The control assembly is installed on the upper part of the chassis assembly and is used to display the drilling rig torque, drilling rig position, drilling rig speed, slide torque, slide position, slide speed, real-time pulverized coal quantity, actual pulverized coal quantity per meter, and pulverized coal rate index information in real time. It also monitors the risk of rock burst in real time. When a warning of rock burst is given, construction is immediately stopped and an alarm is issued.

[0015] The column assembly is installed on the upper part of the chassis assembly and is used to adjust the mainframe of the drilling vehicle to different working heights during operation, and to tighten the drilling vehicle against the top of the tunnel;

[0016] The drilling assembly is installed on one side of the column assembly and is used for the rated output torque of the drilling vehicle during drilling and for dynamic feedback and control of the drilling output torque;

[0017] The collection and weighing assembly is installed on the upper part of the chassis assembly and is used to collect and weigh coal powder and transmit the data to the control assembly.

[0018] Furthermore, the crawler track assembly is composed of guide wheels, supporting wheels, sprockets, and tensioning devices;

[0019] The chassis assembly is formed by welding steel plates and connecting them with bolts;

[0020] The drive assembly consists of a motor, a gear pump, and a filter;

[0021] An air filter and an oil inlet and return filter are installed on the oil tank of the oil tank assembly.

[0022] Furthermore, the control assembly is composed of a wireless display screen, a servo drive, buttons, a transformer, an isolation fence, a PLC, and an explosion-proof solenoid valve; the buttons and the wireless display screen are used to control the robot to move forward, backward, turn left, turn right, raise the main machine, lower the main machine, raise the main machine, lower the main machine, turn left, turn right, raise the column, lower the column, rotate the drilling rig forward, reverse the drilling rig, stop the drilling rig, and move the slide forward and backward.

[0023] Furthermore, the column assembly consists of a sliding clamping assembly, a column, a lifting cylinder, and a slewing support; the sliding clamping assembly is a mechanism that fixedly connects the main machine, slewing support, lifting cylinder, and column; the two cylinders enable the main machine to adjust to different working heights during operation and tighten the drilling vehicle against the top of the tunnel; the two slewing supports allow the main machine to achieve different orientations and angles during operation;

[0024] The drilling assembly consists of an explosion-proof servo motor, a reducer, a slide, and a drill bit;

[0025] The collecting and weighing assembly consists of a coal powder collector, a conveying pipe, a weighing sensor, and a coal powder bucket.

[0026] Another object of the present invention is to provide a rock burst hazard early warning method, which is implemented by the intelligent drilling cuttings monitoring robot, and comprises the following steps:

[0027] Step 1: Collect data on normal coal powder quantity, drill rod thrust, and drill rod torque at the working face in the area without mining and geological structure influence. The number of holes is n, the drilling speed is set to be the same, and the normal coal powder quantity A is i It is expressed as follows:

[0028] A i ={a i1 ,a i2 …a im}

[0029] Where, subscript i is the drilling sequence number, i = 1, 2…n;

[0030] The drill rod thrust is the average value of the drill rod thrust per meter. The drill rod thrust data B i It is expressed as follows:

[0031] B i ={b i1 ,b i2 …b im}

[0032] The drill rod torque is the average value of the drill rod torque per meter. The drill rod torque data C i It is expressed as follows:

[0033] C i ={c i1 ,c i2 …c im}

[0034] Calculate the average values ​​of normal pulverized coal quantity, drill pipe thrust, and drill pipe torque for each of the n borehole depths j, j = 1, 2…m. The average normal pulverized coal quantity at different borehole depths is expressed as follows:

[0035]

[0036] The average normal coal powder amount a is obtained at different drilling depths j avj Relationship with drilling depth j:

[0037] a avj =f1(j)

[0038] The average drill rod thrust at different drilling depths is expressed as follows:

[0039]

[0040] The average normal coal powder amount b at different drilling depths j is obtained avj Relationship with drilling depth j:

[0041] b avj =f2(j)

[0042] The average drill pipe torque at different drilling depths is expressed as follows:

[0043]

[0044] The average normal coal powder amount c at different drilling depths j is obtained avj Relationship with drilling depth j:

[0045] c avj =f3(j)

[0046] Step 2: During the drilling process, the amount of pulverized coal, drill pipe thrust, and drill pipe torque all reflect the stress changes of the coal body, and the drill pipe thrust, drill pipe torque, and pulverized coal amount prediction results are consistent;

[0047] For the average drill pipe thrust b avj With coal powder amount a avj The relationship is fitted linearly, exponentially, and logarithmically, and R 2 The relationship at the maximum value is expressed as follows:

[0048] b avj =g1(a avj )

[0049] For the average drill pipe torque c avj With coal powder amount c avj The relationship is fitted linearly, exponentially, and logarithmically, and R 2 The relationship at the maximum value is expressed as follows:

[0050] c avj =g2(a avj )

[0051] Step 3: Use the drilling powder rate index to calculate the critical index of coal powder quantity. The critical index of coal powder quantity a maxj It is expressed as follows:

[0052]

[0053] According to the average drill pipe thrust b avj With coal powder amount a avj The relationship formula is used to obtain the critical index b of the drill pipe thrust. maxj It is expressed as follows:

[0054]

[0055] In actual drilling construction, the same drilling speed as the normal working surface is used. When the actual drill rod thrust per meter is b acj Exceeding the critical index b of drill pipe thrust maxj When the rock burst hazard occurs, it is determined that there is a rock burst hazard;

[0056] The critical index of drill pipe torque c is obtained maxj :

[0057]

[0058] In actual drilling construction, the same drilling speed as the normal working surface is used. When the actual drill rod torque per meter is c acj Exceeding the critical index c of drill pipe torque maxj When the rock burst hazard occurs, it is determined that there is a rock burst hazard;

[0059] Step 4: Drilling in the coal seam, when the amount of coal powder and the thrust of drill rod per meter b acj , torque per meter of drill pipe c acj When any parameter exceeds its critical index, the workplace is judged to have an impact hazard.

[0060] Furthermore, during the drilling cuttings method, motor parameter information is collected and a prediction model for drill rig stuck and drill suction is established. Based on this parameter information, the drill rig performance is evaluated and the current working status of the drill rig and maintenance recommendations are given:

[0061] Step 1: Data preprocessing: filtering and denoising the raw data to eliminate sensor noise and transient interference;

[0062] Step 2, feature extraction, including: extracting dynamic resistance index, extracting feed efficiency, exponentially weighted moving average, and comprehensive decision prediction;

[0063] Step 3: Evaluate the drilling rig performance based on the drilling rig torque and thrust data, and provide the current working status of the drilling rig and maintenance recommendations.

[0064] In step 1, the raw data is filtered and denoised to eliminate sensor noise and instantaneous interference. The formula is:

[0065]

[0066] Where X is N, T, W, and V, N is the drilling rig speed, T is the drilling rig torque, W is the drilling rig feed force, V is the real-time drilling speed of the drilling rig, and k is the sliding window length.

[0067] In step 2, the dynamic resistance index DRI represents the interaction force between the drill bit and the formation, and is calculated as follows:

[0068]

[0069] Where, α, β are both empirical coefficients, and α=1, β=1;

[0070] Feed efficiency FE reflects the drilling speed under unit thrust, and the calculation formula is:

[0071]

[0072] Where V is the real-time drilling speed. FE is abnormally high when the drill is sucking and approaches zero when the drill is stuck.

[0073] In step 2, the formula for calculating the exponentially weighted moving average EWMA is:

[0074] Z t =λ·DRI t +(1+λ)·Z t-1

[0075] M t =λ·FE t +(1+λ)·M t-1

[0076] The threshold is monitored and adjusted dynamically in real time. The weight is dynamically adjusted through the exponential decay coefficient λ. By adjusting λ, the smoothing strength and response speed are balanced. λ is the smoothing factor and the range of λ is [0, 1). When λ is closer to 1, more details are retained, which is suitable for rapidly changing working conditions. When λ is closer to 0, strong noise suppression is achieved, which is suitable for steady-state working conditions.

[0077] In step 2, the comprehensive decision prediction is combined with the multi-index voting mechanism to t 、M t Jointly judge the dynamic phenomenon and reduce the false alarm rate. The calculation formula is:

[0078]

[0079] In the formula, μ is the mean, σ is the standard deviation, when Z t 、M t When both exceed three times the standard deviation, drill sticking / drill suction is predicted and an alarm is output.

[0080] In combination with all the above technical solutions, the beneficial effects of the present invention are as follows:

[0081] First, improved production efficiency: Simulation experiments have demonstrated that single-hole inspection time has been reduced from 90 minutes, a traditional manual process, to 45 minutes, a 50% improvement in efficiency. This significantly reduces production interruptions for coal mining enterprises. Based on an annual production of 3,000 holes, this saves 2,250 hours per year, equivalent to approximately 450,000 yuan in labor costs.

[0082] Significant safety benefits: the coal dust emission rate has been reduced from 15%-30% to ≤5%, reducing occupational health hazards; the accuracy rate of dynamic phenomenon identification has reached 96.2%, an increase of 20% compared to manual judgment, and is expected to reduce the impact ground pressure accident rate by more than 60%.

[0083] Outstanding economic value: The weighing standard deviation has been optimized from ±1.3kg / m to ±0.2kg / m, and the calculation accuracy of the drill powder rate index has been improved by 90%. This effectively avoids ineffective pressure relief operations caused by misjudgment, and saves an average of approximately RMB 1.2 million in anti-bumping costs per mine per year.

[0084] Second, the present invention realizes the automation of the entire process of drill cuttings monitoring: integrating crawler walking, servo drilling, closed-loop collection and weighing, and hazard warning systems, breaking through the traditional manual operation mode. Establishing a prediction model for motor drill sticking and drill suction: real-time monitoring of motor parameters, and establishing a prediction model for motor drill sticking and drill suction, to achieve real-time prediction of drill sticking and drill suction risks during the drilling process. Proposes a new method for early warning of rock burst hazard: fitting the equation of the relationship between drill rod thrust, drill rod torque and coal powder amount, determining the critical indicators of drill rod thrust and drill rod torque based on the drill powder rate index, and realizing multi-parameter indicators to judge rock burst hazard during drilling, which is more efficient.

[0085] Third, this invention overcomes the contradiction between explosion protection and precision in automated mine inspection equipment: by combining an explosion-proof servo motor with a closed-loop control system, it achieves an overload torque output of 505 Nm while meeting the GB3836 explosion-proof standard, resolving the industry's challenge of insufficient precision in traditional hydraulic systems. This invention also addresses the technical bottleneck of dynamic coal dust collection: by employing an innovative spiral conveyor pipe + sealed collector structure, it maintains a coal dust emission rate of ≤5% at all operating angles from 0-90°. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure;

[0087] Figure 1 Schematic diagram of an intelligent drilling cuttings monitoring robot provided by an embodiment of the present invention;

[0088] Figure 2 This is a flow chart of a method for predicting drill sticking and drill suction in a robot drill rod using an intelligent drill cuttings method provided by an embodiment of the present invention;

[0089] Figure 3 This is a flow chart of a rock burst hazard warning method for an intelligent drill cuttings monitoring robot provided in an embodiment of the present invention;

[0090] In the figure: 1. Track assembly; 2. Chassis assembly; 3. Drive assembly; 4. Fuel tank assembly; 5. Control assembly; 6. Column assembly; 7. Drilling assembly; 8. Collection and weighing assembly. DETAILED DESCRIPTION

[0091] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0092] The innovation of the present invention is:

[0093] (1) Automating the entire drill cuttings monitoring process: Integrating four major systems: crawler travel, servo drilling, closed-loop collection and weighing, and hazard warning, breaking through the traditional manual operation mode. Through the combined design of an explosion-proof servo motor and a slide closed-loop control, an overload torque output of 505 N.m is achieved while meeting the GB3836 explosion-proof standard, resolving the industry's challenge of insufficient precision in traditional hydraulic systems. The innovative structure of a spiral conveying pipe + sealed collector maintains a coal dust emission rate of ≤5% at all angles of 0-90°.

[0094] (2) Establish a prediction mechanism for motor drill sticking and drill aspiration: During the drilling process, the motor parameters are monitored in real time, and a prediction model for motor drill sticking and drill aspiration is established to predict the risk of drill sticking and drill aspiration in real time during the drilling process. At the same time, it has a drill rig performance detection function, which evaluates the drill rig performance based on data such as the drill rig torque and thrust, and provides the current working status of the drill rig and maintenance recommendations.

[0095] (3) A new method for early warning of rock burst danger is proposed: fitting the relationship equation between drill rod thrust, drill rod torque and coal powder amount, determining the critical indicators of drill rod thrust and drill rod torque based on the drill powder rate index, and realizing multi-parameter index judgment of rock burst danger during drilling, which is more efficient.

[0096] Example 1, in order to adapt to the complex environment of coal mines and the needs of the drilling cuttings method, Figure 1 As shown, the intelligent drilling cuttings monitoring robot provided by the present invention features a crawler-type chassis design, enabling the robot to navigate the complex environment of a mine. Hydraulic power is used to drive the robot's overall movement, tighten the column, and adjust the drilling platform's posture, providing sufficient power for the robot. The drilling platform utilizes an explosion-proof servo motor and a reducer to provide sufficient power for drilling cuttings operations. It also collects data such as motor speed, torque, and slide force to determine whether dynamic phenomena such as drill suction and drill sticking are occurring. The collection and weighing platform collects coal dust through a collector and a conveying pipe, ensuring efficient collection. The amount of coal dust is measured in real time and uploaded to a controller. The robot can be controlled remotely via a display or manually. Collected data is displayed in real time on the display, allowing for real-time determination of rock burst risks. The robot specifically comprises: a crawler assembly 1, a chassis assembly 2, a drive assembly 3, a fuel tank assembly 4, a control assembly 5, a column assembly 6, a drilling assembly 7, and a collection and weighing assembly 8.

[0097] The crawler assembly 1, consisting of idlers, track rollers, sprockets, and a tensioner, is a critical component of the drilling rig. The combined, sealed track chain, made of high-strength alloy steel, ensures the rig's ability to operate properly in harsh environments. The track rollers bear not only the machine's weight but also withstand impacts from the roof.

[0098] Chassis assembly 2: Made of welded steel plates and bolted together, it offers excellent rigidity, strength, and ease of disassembly. The chassis connects the column assembly 6, fuel tank assembly 4, control assembly 5, and drilling assembly 7, and is also the load-bearing component of the drilling vehicle.

[0099] Drive assembly 3: Consists of a motor, gear pump, filter, etc. Power is provided to the working device through a mining explosion-proof solenoid valve.

[0100] Fuel tank assembly 4: The fuel tank is a critical component of the hydraulic system. Besides storing oil, it also dissipates heat, separates bubbles from the oil, and precipitates impurities. The fuel tank is equipped with an air filter, an inlet and return oil filter, and other components.

[0101] Control Assembly 5: Consists of a wireless display, servo drive, buttons, transformer, isolation barrier, PLC, and explosion-proof solenoid valves. The buttons and wireless display allow the robot to move forward, backward, turn left, turn right, raise and lower the mainframe, raise and lower the mainframe, turn left and right, raise and lower the column, rotate the drill rig forward, reverse, stop the drill rig, and advance and retreat the slide. Parameters such as the drill rig and slide speed and torque protection can be set. Information such as drill rig torque, drill rig position, drill speed, slide torque, slide position, slide speed, real-time pulverized coal volume, actual pulverized coal volume per meter, and pulverized coal rate index are displayed in real time. The robot can also monitor the risk of ground burst in real time. When a ground burst warning is issued, construction is immediately stopped and an alarm is issued.

[0102] Column Assembly 6: Consists of a sliding clamping assembly, column, lifting cylinder, and slewing support. The sliding clamping assembly securely connects the mainframe, slewing support, lifting cylinder, and column. Two cylinders, respectively, lift the mainframe and column, enabling the mainframe to adjust to different working heights during operation and tightening the drilling rig against the tunnel ceiling, increasing its flexibility and stability. The two slewing supports allow the mainframe to adjust to different positions and angles during operation.

[0103] Drilling Assembly 7: Consists of an explosion-proof servo motor, reducer, slide, and drill bit. The motor has a rated output torque of 19 Nm and a peak output torque of 51.3 Nm. Combined with a high-efficiency servo reducer, this ensures the drill rig achieves a rated output torque of 185 Nm during drilling, with a short-term overload torque of 505 Nm. This also enables high-speed dynamic feedback and control of the drilling output torque, far exceeding that of currently used drill rigs.

[0104] Collection and weighing assembly 8: It consists of a pulverized coal collector, a conveying pipe, a weighing sensor, a pulverized coal bucket, etc. It is responsible for collecting and weighing the pulverized coal and transmitting the data to the control assembly 5.

[0105] Example 2, as Figure 2As shown, the intelligent drill cuttings monitoring robot provided by the embodiment of the present invention collects motor parameters in real time, establishes a prediction model for motor drill sticking and drill aspiration, and adopts a comprehensive decision-making prediction mechanism to accurately predict whether there is a risk of drill sticking or drill aspiration. Specifically, it includes:

[0106] The system collects the drill rig torque, drill rig rotation speed, real-time drilling speed, and drill rig feed thrust, establishes a motor parameter model, identifies sudden changes in motor parameters such as drill bit sticking or drill suction during drilling in the current hole construction state, and provides comprehensive decision-making and judgment results to prevent misjudgments caused by formation changes or sensor noise;

[0107] Based on precise digital sensing and intelligent control technology for rig drilling, the system collects high-speed data such as drill torque, drill speed, real-time drilling speed, and feed force. By building a motor parameter model, the system determines the current drilling status of the test hole and provides a reasonable drilling plan based on the current status. It also accurately monitors dynamic phenomena such as drill sticking and drill suction during rig drilling and makes appropriate judgments. The system also features rig performance testing, evaluating rig performance based on data such as rig torque and thrust, and providing guidance on the current rig's operating status and maintenance recommendations.

[0108] The specific steps include:

[0109] Data preprocessing, filtering and denoising the raw data to eliminate sensor noise and transient interference:

[0110]

[0111] Where X is N, T, W, and V, N is the drilling rig speed, T is the drilling rig torque, W is the drilling rig feed force, V is the real-time drilling speed of the drilling rig, and k is the sliding window length.

[0112] Feature extraction, including:

[0113] (1) Extract the dynamic resistance index (DRI);

[0114] Expresses the interaction force between the drill bit and the formation:

[0115]

[0116] Where α and β are empirical coefficients, and α = 1, β = 1; DRI increases significantly when the drill is stuck and decreases when the drill is sucked;

[0117] (2) Extraction feed efficiency (FE);

[0118] Reflects the drilling speed under unit thrust:

[0119]

[0120] Where V is the real-time drilling speed (obtained by the displacement sensor). FE is abnormally high when the drill is sucking and approaches zero when the drill is stuck.

[0121] (3) Exponentially weighted moving average (EWMA);

[0122] Z t =λ·DRI t +(1+λ)·Z t-1

[0123] M t =λ·FE t +(1+λ)·M t-1

[0124] Real-time monitoring and dynamic adjustment of thresholds, dynamically adjusting weights via the exponential decay coefficient λ, balances smoothing strength and response speed. λ represents the smoothing factor, and its range is [0, 1]. When λ is closer to 1, more detail is retained, making it suitable for rapidly changing operating conditions. When λ is closer to 0, stronger noise suppression is achieved, making it suitable for steady-state conditions. It can quickly capture sudden changes in the Dynamic Resistance Index (DRI) and Feed Efficiency (FE), and quickly respond to drill sticking and drill suction.

[0125] (4) The present invention innovatively proposes comprehensive decision-making prediction.

[0126] Combined with the multi-index voting mechanism, through Z t 、M t Jointly judge dynamic phenomena and reduce false alarm rates:

[0127]

[0128] In the formula, μ is the mean, σ is the standard deviation, when Z t 、M t An alarm is only issued when both the standard deviation and the error exceed three times the standard deviation. The motor parameter model boasts computational efficiency, dynamic adaptability, and noise robustness, enabling real-time monitoring of the drilling process. During drill sticking and drill suction monitoring, it can quickly identify sudden parameter changes while avoiding misjudgments caused by formation changes or sensor noise, significantly improving system reliability and response speed.

[0129] Based on the results of comprehensive decision-making predictions, triggering feedback on drill suction or stuck drill data, the system alerts on-site workers to the robot's drilling cuttings status. If risks arise, construction work is halted immediately to prevent damage to the drill bit and drill rod, which could impact efficiency. The system also features a drill rig performance monitoring function, evaluating the rig's performance based on torque, thrust, and other data, providing current rig operating status and maintenance recommendations.

[0130] Example 3, to monitor rock burst risk, Figure 3 As shown, the rock burst hazard early warning method for the intelligent drill cuttings monitoring robot provided in the embodiment of the present invention can enable the robot to accurately predict the rock burst hazard during the drilling cutting construction process. The independently developed intelligent drill cuttings monitoring robot is used to monitor parameters such as the amount of coal powder, drill rod thrust, and drill rod torque during the drilling process. Based on the principle of the drill cuttings method and the drill powder rate index, the drill rod thrust and drill rod torque are fitted by the amount of coal powder, and the critical indicators of the drill rod thrust and drill rod torque are determined. During the drilling cutting construction process, the rock burst hazard is identified by monitoring whether the parameters of coal powder amount, drill rod thrust, and drill rod torque exceed the critical indicators. Specifically including:

[0131] Step 1: Collect data on normal pulverized coal quantity, drill rod thrust, and drill rod torque at the working face in an area free of mining and geological structure influence. Assuming the number of holes is n and the drilling speed is the same, the normal pulverized coal quantity data can be expressed as follows:

[0132] A i ={a i1 ,a i2 …a im}

[0133] Where, subscript i is the drilling sequence number, i = 1, 2…n;

[0134] The drill rod thrust is the average value of the drill rod thrust per meter. The drill rod thrust data B i It is expressed as follows:

[0135] B i ={b i1 ,b i2 …b im}

[0136] The drill rod torque is the average value of the drill rod torque per meter. The drill rod torque data C i It is expressed as follows:

[0137] C i ={c i1 ,c i2 …c im}

[0138] Calculate the average values ​​of normal pulverized coal quantity, drill pipe thrust, and drill pipe torque for each of the n borehole depths j, j = 1, 2…m. The average normal pulverized coal quantity at different borehole depths is expressed as follows:

[0139]

[0140] The average normal coal powder amount a is obtained at different drilling depths j avj Relationship with drilling depth j:

[0141] a avj=f1(j)

[0142] The average drill rod thrust at different drilling depths is expressed as follows:

[0143]

[0144] The average normal coal powder amount b at different drilling depths j is obtained avj Relationship with drilling depth j:

[0145] b avj =f2(j)

[0146] The average drill pipe torque at different drilling depths is expressed as follows:

[0147]

[0148] The average normal coal powder amount c at different drilling depths j is obtained avj Relationship with drilling depth j:

[0149] c avj =f3(j)

[0150] Step 2: During the drilling process, the coal powder amount, drill rod thrust, and drill rod torque can reflect the stress changes of the coal body, and the drill rod thrust, drill rod torque and coal powder amount prediction results are consistent.

[0151] For the average drill pipe thrust b avj With coal powder amount a avj The relationship is fitted linearly, exponentially, logarithmically, etc., and R 2 The relationship at the maximum value is expressed as follows:

[0152] b avj =g1(a avj )

[0153] Similarly, for the average drill pipe torque c avj With coal powder amount a avj The relationship is fitted linearly, exponentially, and logarithmically, and R 2 The relationship at the maximum value is expressed as follows:

[0154] c avj =g2(a avj )

[0155] Step 3: According to GB / T25217.6-2019 "Methods for determination, monitoring and prevention of rock burst - Part 6: Drill cuttings monitoring method", the drill dust rate index is used to calculate the critical index of coal powder amount. The critical index of coal powder amount a maxj It is expressed as follows:

[0156]

[0157] According to the average drill pipe thrust b avj With coal powder amount a avj The relationship formula is used to obtain the critical index b of the drill pipe thrust. maxj It is expressed as follows:

[0158]

[0159] In actual drilling construction, the same drilling speed as the normal working surface is used. When the actual drill rod thrust per meter is b acj Exceeding the critical index b of drill pipe thrust maxj When the rock burst hazard occurs, it is determined that there is a rock burst hazard;

[0160] Similarly, the critical index c of drill pipe torque is obtained maxj :

[0161]

[0162] In actual drilling construction, the same drilling speed as the normal working surface is used. When the actual drill rod torque per meter is c acj Exceeding the critical index c of drill pipe torque maxj When the rock burst hazard occurs, it is determined that there is a rock burst hazard;

[0163] Step 4: Drilling in the coal seam, when the amount of coal powder and the thrust of drill rod per meter b acj , torque per meter of drill pipe c acj When any parameter exceeds its critical index, the workplace is judged to have an impact hazard. In actual application, the drill dust rate index of the workplace impact hazard is determined through actual measurement and analysis, such as the amount of coal dust, the thrust per meter of drill pipe b acj , torque per meter of drill pipe c acj The critical index can be calculated by the actual drill dust rate index. This patent is calculated and analyzed based on the parameters in Table 1 of the national standard for the drill cuttings method.

[0164] Table 1 Drill dust rate index for determining impact hazard at work site

[0165] Hole depth and lane height ratio j / h Diamond powder rate index t <1.5 ≥1.5 1.5～3 ≥2 >3 ≥3

[0166] Note: j is the drilling depth in m; h is the tunnel height in m; t is the drill dust rate index, which refers to the ratio of the actual drill cuttings per meter to the normal drill cuttings per meter; the normal drill cuttings volume is the drill cuttings volume measured in an area without mining and geological structure influence.

[0167] The above description is only a preferred specific implementation method of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. An intelligent drilling cuttings monitoring robot, characterized in that: The robot includes: The crawler assembly (1) is a combined sealed crawler chain made of alloy steel and is used for drilling vehicle operation; A chassis assembly (2) is connected to the crawler assembly (1) and is used to support the force of the drilling vehicle; A drive assembly (3), located on the upper portion of the chassis assembly (2), is used to provide power to the drilling vehicle through a mining explosion-proof solenoid valve; The oil tank assembly (4) is installed on the upper part of the chassis assembly (2) and is used to store oil through the oil tank, dissipate heat, separate bubbles in the oil, and precipitate impurities; The control assembly (5) is installed on the upper part of the chassis assembly (2) and is used to display the drilling rig torque, drilling rig position, drilling rig speed, slide torque, slide position, slide speed, real-time coal powder quantity, actual coal powder quantity per meter, coal powder rate index information in real time, monitor the impact of ground pressure in real time, and immediately stop construction and issue an alarm when the impact of ground pressure is warned; The column assembly (6) is installed on the upper part of the chassis assembly (2) and is used for adjusting the working height of the main body of the drilling vehicle during operation and for tightening the drilling vehicle against the top of the tunnel; A drilling assembly (7) is installed on one side of the column assembly (6) and is used for rated output torque during drilling by the drilling vehicle and for dynamic feedback and control of the drilling output torque; The collecting and weighing assembly (8) is installed on the upper part of the chassis assembly (2) and is used for collecting and weighing the coal powder and transmitting the data to the control assembly (5).

2. The intelligent drilling cuttings monitoring robot according to claim 1, characterized in that: The crawler track assembly (1) is composed of a guide wheel, a supporting wheel, a sprocket, and a tensioning device; The chassis assembly (2) is formed by welding steel plates and connecting them with bolts; The drive assembly (3) is composed of a motor, a gear pump, and a filter; An air filter and an oil inlet and return filter are installed on the oil tank of the oil tank assembly (4).

3. The intelligent drilling cuttings monitoring robot according to claim 1, characterized in that: The control assembly (5) is composed of a wireless display screen, a servo driver, buttons, a transformer, an isolation fence, a PLC, and an explosion-proof solenoid valve; the buttons and the wireless display screen are used to control the robot to move forward, backward, turn left, turn right, raise the main machine, lower the main machine, raise the main machine head, lower the main machine head, turn left, turn right, raise the column, lower the column, rotate the drilling rig forward, reverse the drilling rig, stop the drilling rig, and move the slide forward and backward.

4. The intelligent drilling cuttings monitoring robot according to claim 1, characterized in that: The column assembly (6) is composed of a sliding clamping component, a column, a lifting cylinder, and a slewing support; the sliding clamping component is a mechanism for fixing the main machine, the slewing support, the lifting cylinder, and the column; the two cylinders enable the main machine to adjust different working heights during operation, and the drilling vehicle to tighten the top of the tunnel; the two slewing supports enable the main machine to achieve different orientations and angles during operation; The drilling assembly (7) is composed of an explosion-proof servo motor, a reducer, a slide, and a drill bit; The collecting and weighing assembly (8) is composed of a coal powder collector, a conveying pipe, a weighing sensor, and a coal powder bucket.

5. A rock burst hazard warning method, characterized in that: The method is implemented by the intelligent drilling cuttings monitoring robot according to any one of claims 1 to 4, and the method comprises the following steps: Step 1: Collect data on normal coal powder quantity, drill rod thrust, and drill rod torque at the working face in the area without mining and geological structure influence. The number of holes is n, the drilling speed is set to be the same, and the normal coal powder quantity A is i It is expressed as follows: A i ={a i1 ,a i2 …a im } Where, subscript i is the drilling sequence number, i = 1, 2…n; The drill rod thrust is the average value of the drill rod thrust per meter. The drill rod thrust data B i It is expressed as follows: B i ={b i1 ,b i2 …b im } The drill rod torque is the average value of the drill rod torque per meter. The drill rod torque data C i It is expressed as follows: C i ={c i1 ,c i2 …c im } Calculate the average values ​​of normal pulverized coal quantity, drill pipe thrust, and drill pipe torque for each of the n borehole depths j, j = 1, 2…m. The average normal pulverized coal quantity at different borehole depths is expressed as follows: The average normal coal powder amount a is obtained at different drilling depths j avj Relationship with drilling depth j: has avj =f1(j) The average drill rod thrust at different drilling depths is expressed as follows: The average normal coal powder amount b at different drilling depths j is obtained avj Relationship with drilling depth j: b avj =f2(j) The average drill pipe torque at different drilling depths is expressed as follows: The average normal coal powder amount c at different drilling depths j is obtained avj Relationship with drilling depth j: <h2 style=";text-align:left;direction:ltr">c<h2 style=";text-align:left;direction:ltr"> avj <h2 style=";text-align:left;direction:ltr"> =f3(j) Step 2: During the drilling process, the amount of pulverized coal, drill pipe thrust, and drill pipe torque all reflect the stress changes of the coal body, and the drill pipe thrust, drill pipe torque, and pulverized coal amount prediction results are consistent; For the average drill pipe thrust b avj With coal powder amount a avj The relationship is fitted linearly, exponentially, and logarithmically, and R 2 The relationship at the maximum value is expressed as follows: b avj =g1(a avj ) For the average drill pipe torque c avj With coal powder amount c avj The relationship is fitted linearly, exponentially, and logarithmically, and R 2 The relationship at the maximum value is expressed as follows: c avj =g2(a avj ) Step 3: Use the drilling powder rate index to calculate the critical index of coal powder quantity. The critical index of coal powder quantity a maxj It is expressed as follows: According to the average drill pipe thrust b avj With coal powder amount a avj The relationship formula is used to obtain the critical index b of the drill pipe thrust. maxj It is expressed as follows: In actual drilling construction, the same drilling speed as the normal working surface is used. When the actual drill rod thrust per meter is b acj Exceeding the critical index b of drill pipe thrust maxj When the rock burst hazard occurs, it is determined that there is a rock burst hazard; The critical index of drill pipe torque c is obtained maxj : In actual drilling construction, the same drilling speed as the normal working surface is used. When the actual drill rod torque per meter is c acj Exceeding the critical index c of drill pipe torque maxj When the rock burst hazard occurs, it is determined that there is a rock burst hazard; Step 4: Drilling in the coal seam, when the amount of coal powder and the thrust of drill rod per meter b acj , torque per meter of drill pipe c acj When any parameter exceeds its critical index, the workplace is judged to have an impact hazard.

6. The rock burst hazard warning method according to claim 5, characterized in that: During drilling cuttings method construction, motor parameter information is collected and a prediction model for drill rig stuck and drill suction is established. Based on this parameter information, the drill rig performance is evaluated and the current working status of the drill rig and maintenance recommendations are provided: Step 1: Data preprocessing: filtering and denoising the raw data to eliminate sensor noise and transient interference; Step 2, feature extraction, including: extracting dynamic resistance index, extracting feed efficiency, exponentially weighted moving average, and comprehensive decision prediction; Step 3: Evaluate the drilling rig performance based on the drilling rig torque and thrust data, and provide the current working status of the drilling rig and maintenance recommendations.

7. The rock burst hazard warning method according to claim 6, characterized in that: In step 1, the raw data is filtered and denoised to eliminate sensor noise and instantaneous interference. The formula is: Where X is N, T, W, and V, N is the drilling rig speed, T is the drilling rig torque, W is the drilling rig feed force, V is the real-time drilling speed of the drilling rig, and k is the sliding window length.

8. The rock burst hazard warning method according to claim 6, characterized in that: In step 2, the dynamic resistance index DRI represents the interaction force between the drill bit and the formation, and is calculated as follows: Where, α, β are empirical coefficients, and α=1, β=1; Feed efficiency FE reflects the drilling speed under unit thrust, and the calculation formula is: Where V is the real-time drilling speed. FE is abnormally high when the drill is sucking and approaches zero when the drill is stuck.

9. The rock burst hazard warning method according to claim 6, characterized in that: In step 2, the formula for calculating the exponentially weighted moving average EWMA is: WITH t =λ·DRI t +(1+λ)·Z t-1 M t =λ·FE t +(1+λ)·M t-1 Real-time monitoring and dynamic adjustment of thresholds. Dynamic weight adjustment through the exponential decay coefficient λ. By adjusting λ, the smoothing strength and response speed are balanced. λ is the smoothing factor and the range of λ is [0, 1). When λ is closer to 1, more details are retained, which is suitable for rapidly changing working conditions. When λ is closer to 0, the noise suppression is stronger and it is suitable for steady-state conditions.

10. The rock burst hazard warning method according to claim 9, characterized in that: In step 2, the comprehensive decision prediction is combined with the multi-index voting mechanism to t 、M t Jointly judge the dynamic phenomenon and reduce the false alarm rate. The calculation formula is: In the formula, μ is the mean, σ is the standard deviation, when Z t 、M t When both exceed three times the standard deviation, drill sticking / drill suction is predicted and an alarm is output.

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