Coal mine hydraulic anchor rod drill carriage self-adaptive mode control system based on industrial big data

Abstract

The application discloses a coal mine hydraulic anchor rod drill rig self-adaptive mode control system based on industrial big data, relates to the technical field of coal mine hydraulic anchor rod drill rig control, and comprises a self-adaptive main controller and an industrial big data analysis platform, a multidimensional sensing sensor network and a hydraulic drive actuator group communication connection; the self-adaptive main controller is based on the working condition-load characteristic mapping model, analyzes the current load state of the drilling machine in real time, dynamically adjusts the output parameters of the hydraulic drive actuator group, and realizes self-adaptive speed control of the drilling machine under the condition of load change. Through the construction of the load-throttle self-adaptive adjustment model, the application realizes real-time and accurate matching of the engine power and the drilling load, and significantly improves the drilling efficiency. In addition, the application also realizes real-time collection of the feeding pressure and the rotating pressure through the multidimensional sensor, calls the working condition-load characteristic mapping model in the industrial big data platform to automatically identify the geological working condition, and calculates the optimal throttle value according to the matching result.

Description

Technical Field

[0001] This invention relates to the field of control technology for hydraulic anchor bolt drilling rigs in coal mines, specifically to an adaptive mode control system for hydraulic anchor bolt drilling rigs in coal mines based on industrial big data. Background Technology

[0002] The hydraulic anchor bolt drilling rig for coal mines is an industrial piece of equipment mainly used in support engineering of coal mine roadways and tunnels. It uses a hydraulic system to provide power for drilling operations, thereby installing anchor bolts to reinforce the roadways. It offers advantages such as safety, explosion-proof design, ease of operation, and labor-saving features.

[0003] During operation, the load on a coal mine hydraulic anchor bolt drilling rig changes. If the drilling rig's operating parameters are not adjusted according to the real-time changes in load, the drilling speed of the coal mine hydraulic anchor bolt drilling rig will not operate at its optimal efficiency, which will increase the energy consumption of the coal mine hydraulic anchor bolt drilling rig. Summary of the Invention

[0004] To address the aforementioned technical problems, an adaptive mode control system for coal mine hydraulic anchor bolt drilling rigs based on industrial big data is provided. This technical solution solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An adaptive mode control system for coal mine hydraulic anchor bolt drilling rigs based on industrial big data includes:

[0007] A hydraulically driven actuator assembly is used to drive the actions of each actuator of the anchor drilling rig. The hydraulically driven actuator assembly includes a hydraulic solenoid valve assembly, a multi-way valve, and an electro-proportional relief valve assembly.

[0008] A multi-dimensional sensing network includes an encoder and pressure sensor mounted on the feed cylinder, a speed sensor and pressure sensor mounted on the drill box, a pressure sensor mounted on the anchor box, pressure sensors and displacement sensors mounted on each functional cylinder, a pressure sensor mounted on the water valve, and an encoder mounted on the rope feeding motor and the drug delivery swing cylinder. The multi-dimensional sensing network is used to collect multi-source heterogeneous data of drilling rig operation in real time.

[0009] The industrial big data analysis platform is used to receive and store historical operating data collected by the multi-dimensional sensing network and establish a drilling rig operating condition-load characteristic mapping model.

[0010] An adaptive main controller is communicatively connected to the industrial big data analysis platform, the multi-dimensional sensing network, and the hydraulic drive actuator group. Based on the working condition-load characteristic mapping model, the adaptive main controller analyzes the current load status of the drilling rig in real time and dynamically adjusts the output parameters of the hydraulic drive actuator group to realize adaptive speed control of the drilling rig under load change conditions.

[0011] Preferably, the adaptive main controller includes a main controller and a data acquisition system. The main controller receives data from various sensors through the data acquisition system and analyzes the position and status of relevant actuators of the drilling rig's adaptive speed control system in real time.

[0012] When the adaptive main controller receives a drilling operation command, it first determines the position and status of each actuator and displays it on the remote control panel. Based on the accurate determination of the position and status of the drilling rig actuators, it controls the corresponding actuators to perform actions.

[0013] Preferably, the working condition-load characteristic mapping model established by the industrial big data analysis platform includes:

[0014] A drilling pressure-rotation speed coupled model is used to characterize the nonlinear relationship between drilling pressure and rotation speed under different geological conditions.

[0015] The load-throttle adaptive adjustment model is expressed as follows:

[0016] ;

[0017] In the formula, y represents the throttle position of the diesel engine; The feed pressure adjustment proportional coefficient; The feed pressure value of the drill box; The rotational pressure adjustment ratio coefficient; denoted as , where d is the rotational pressure value of the drill box; and d is the throttle adjustment constant.

[0018] Preferably, the adaptive main controller determines that the hydraulic system pressure exceeds a threshold by using the pressure sensor in the multi-dimensional sensing network before the control signal drives the hydraulic solenoid valve group to operate.

[0019] The adaptive main controller measures the feed stroke of the drill box and anchor box through the encoder, measures the top and bottom pressures of the feed cylinder through the pressure sensor, and determines the current load condition category by combining historical data in the industrial big data analysis platform.

[0020] Preferably, the adaptive main controller divides the drilling rig load status into three performance levels:

[0021] Level 1 performance: When the abnormal state index is less than or equal to the first abnormal threshold, the drilling rig runs at the initial drilling speed without any control intervention;

[0022] Secondary performance: When the abnormal state index is greater than the first abnormal threshold and less than or equal to the second abnormal threshold, the adaptive main controller analyzes the drilling rig performance change trend, obtains the geological coefficient f in combination with geological condition changes, and combines the performance change trend with the geological coefficient to obtain the adjustment factor. The expression is:

[0023] ;

[0024] In the formula, the The abnormal state index; Where g is the geological coefficient; g is the mapping function.

[0025] Based on the aforementioned regulatory factor Dynamically adjust the initial drilling speed of the drilling rig;

[0026] Level 3 performance: When the abnormal state index exceeds the second abnormal threshold, the adaptive main controller controls the drilling rig to stop running and sends a warning signal to the administrator.

[0027] Preferably, the abnormal state index is calculated as follows:

[0028] Data acquisition and processing are performed on the drill arm to obtain its pitch angle. Yaw angle Roll angle The acceleration a and angular velocity of the drill arm Drilling torque T and hydraulic system pressure P;

[0029] pitch angle of the drill arm Yaw angle Roll angle The acceleration a and angular velocity of the drill arm The abnormal state index is obtained by weighting the drilling torque T and the hydraulic system pressure P.

[0030] The specific formula for calculating the abnormal state index is as follows:

[0031] ;

[0032] In the formula, the , , , , These are weighting coefficients; For angular deviation; the For vibration deviation; the This refers to the deviation in rotational angular velocity.

[0033] Preferably, the dynamic adjustment of drilling speed under the secondary performance includes the following steps:

[0034] When the rate of change of the abnormal state index is greater than the rate of change threshold, and the standard deviation of the abnormal state index is less than or equal to the standard deviation threshold, the drilling rig's health condition is determined to be steadily deteriorating and deteriorating rapidly. The expression for adjusting the drilling speed is:

[0035] ;

[0036] When the rate of change of the abnormal state index is greater than the rate of change threshold, and the standard deviation of the abnormal state index is greater than the standard deviation threshold, the drilling rig's health condition is determined to be deteriorating, but the rate of deterioration is slowing down. The expression for adjusting the drilling speed is as follows:

[0037] ;

[0038] In the formula, the The initial drilling velocity; The adjusted drilling speed is denoted by k, which is the mitigation coefficient.

[0039] Preferably, the system includes a diesel engine protection system, which monitors gas concentration and relevant operating parameters of the diesel engine, and provides alarm information and emergency shutdown functions.

[0040] It also includes a remote controller, and the adaptive main controller is connected to the remote controller via a receiver. The remote controller is equipped with one-click drilling and one-click anchoring function buttons.

[0041] Furthermore, an adaptive mode control method for coal mine hydraulic anchor bolt drilling rigs based on industrial big data is proposed and applied to the aforementioned adaptive mode control system for coal mine hydraulic anchor bolt drilling rigs based on industrial big data, including:

[0042] S100. The adaptive main controller reads data from all sensors and determines the action status and position of each hydraulic cylinder based on the sensor data. The initial status indicator light on the host computer display interface or remote control panel illuminates, and the drill box feed height is [indicated]. Anchor box feed height ;

[0043] S200, The industrial big data analysis platform matches the current working conditions with historical load data to generate an initial drilling speed. ;

[0044] S300 During the drilling process of the drill box rising, the adaptive main controller collects data from the pressure sensor and speed sensor in real time, and combines the working condition-load characteristic mapping model to adjust the diesel engine throttle in real time so that the drilling rig maintains the optimal drilling parameters under load changes.

[0045] S400. The adaptive main controller continuously calculates the abnormal state index, evaluates the health status level of the drilling rig, and executes the corresponding adaptive control strategy according to the health status level.

[0046] Compared with existing technologies, this invention provides an adaptive mode control system for coal mine hydraulic anchor bolt drilling rigs based on industrial big data, which has the following beneficial effects:

[0047] This invention achieves real-time and precise matching of engine power and drilling load by constructing a load-throttle adaptive adjustment model, significantly improving drilling efficiency. Furthermore, this invention uses multi-dimensional sensors to collect feed pressure and rotational pressure in real time, calls upon the working condition-load feature mapping model in an industrial big data platform to automatically identify geological conditions, and calculates the optimal throttle value based on the matching results. Attached Figure Description

[0048] Figure 1 This is a structural block diagram of the adaptive mode control system for a coal mine hydraulic anchor bolt drilling rig based on industrial big data proposed in this invention.

[0049] Figure 2 This is a flowchart illustrating steps S100-S400 of the adaptive mode control method for coal mine hydraulic anchor bolt drilling rigs based on industrial big data proposed in this invention. Detailed Implementation

[0050] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0051] Reference Figure 1 As shown, the adaptive mode control system for a coal mine hydraulic anchor bolt drilling rig based on industrial big data includes:

[0052] This system is installed on a hydraulic anchor drilling rig in an underground coal mine. It is used to automatically adjust the drilling rig's operating parameters according to real-time changes in load during drilling and anchoring operations, so as to achieve a balance between optimal drilling efficiency and equipment safety protection.

[0053] The system interconnects the various subsystems via a CAN bus, with a communication cycle set to 10 milliseconds to ensure the synchronous acquisition and real-time transmission of data from multiple sensor sources.

[0054] The system comprises four core components: a hydraulic drive actuator group, a multi-dimensional sensing network, an industrial big data analysis platform, and an adaptive main controller.

[0055] A hydraulically driven actuator assembly is used to drive the actions of each actuator of the anchor drilling rig. The hydraulically driven actuator assembly includes a hydraulic solenoid valve assembly, a multi-way valve, and an electro-proportional relief valve assembly.

[0056] A multi-dimensional sensing network includes an encoder and pressure sensor mounted on the feed cylinder, a speed sensor and pressure sensor mounted on the drill box, a pressure sensor mounted on the anchor box, pressure sensors and displacement sensors mounted on each functional cylinder, a pressure sensor mounted on the water valve, and an encoder mounted on the rope feeding motor and the drug delivery swing cylinder. The multi-dimensional sensing network is used to collect multi-source heterogeneous data of drilling rig operation in real time.

[0057] The industrial big data analysis platform is used to receive and store historical operating data collected by the multi-dimensional sensing network and establish a drilling rig operating condition-load characteristic mapping model.

[0058] An adaptive main controller is communicatively connected to the industrial big data analysis platform, the multi-dimensional sensing network, and the hydraulic drive actuator group. Based on the working condition-load characteristic mapping model, the adaptive main controller analyzes the current load status of the drilling rig in real time and dynamically adjusts the output parameters of the hydraulic drive actuator group to realize adaptive speed control of the drilling rig under load change conditions.

[0059] Example 1

[0060] The hydraulic drive actuator assembly is the execution terminal of the system, responsible for converting control commands into hydraulic actions and providing corresponding power output when the load changes.

[0061] This group of organizations includes the following components:

[0062] First, the hydraulic solenoid valve assembly. This assembly includes eight solenoid valves: drill box feed solenoid valve, drill box rotation solenoid valve, anchor box feed solenoid valve, anchor box rotation solenoid valve, rope feed solenoid valve, medic delivery swing solenoid valve, water valve solenoid valve, and rotary solenoid valve. Each solenoid valve is driven by a pulse-width modulation signal sent via the CAN bus by the adaptive main controller. By adjusting the duty cycle, the valve opening is controlled, thereby regulating the flow rate of the corresponding oil circuit and controlling the speed and direction of movement of each actuator.

[0063] Second, the multi-way valve. The multi-way valve is a six-way multi-directional valve that controls the lifting, extension, and lateral swing of the drill arm, the feed and rotation of the drill box, and the feed and rotation of the anchor box, thereby achieving independent control of each degree of freedom of the drill arm.

[0064] Third, the electro-proportional relief valve assembly. This assembly includes one electro-proportional relief valve in the feed circuit and one in the rotary circuit. The adaptive main controller adjusts the set pressure of the relief valves via analog signals. When a sudden increase in load causes the system pressure to exceed the set value, the relief valves automatically overflow to prevent damage to hydraulic components. Simultaneously, the set pressure of the relief valves also serves as a direct feedback signal of the load magnitude, used by the adaptive main controller to determine the load status.

[0065] Example 2

[0066] Multidimensional sensing networks serve as the system's sensing entry point, used to collect physical quantities reflecting load status from multiple dimensions in real time.

[0067] The specific sensor arrangement is as follows:

[0068] First, encoders and pressure sensors are installed on the feed cylinders. A magnetostrictive displacement sensor is installed as an encoder on both the drill box feed cylinder and the anchor box feed cylinder to accurately measure the extension length of the cylinder piston rod, i.e., the feed stroke of the drill box and anchor box, with an accuracy of ±1 mm. Simultaneously, a pressure sensor is installed in both the rodless and rod chambers of the feed cylinder, with a range of 0-40 MPa and an accuracy of ±0.5 MPa, to measure the load force in the feed direction. When the drill box contacts the rock surface, the pressure in the rod chamber of the feed cylinder rises rapidly, while the pressure in the rodless chamber decreases. The pressure difference between the two chambers accurately determines whether the drill box is in contact with the rock and the magnitude of the contact force. When the drill box reaches the bottom, the pressure in the rod chamber reaches the overflow valve set value, and the pressure in the rodless chamber approaches zero. This indicates whether the feed stroke has been completed.

[0069] Second, speed and pressure sensors are installed on the drill box. A Hall effect speed sensor is installed on the drill box rotary motor to measure the drill rod speed, with a range of 0-300 rpm and an accuracy of ±1 rpm. A pressure sensor is installed in the drill box rotary circuit, with a range of 0-35 MPa, to reflect the load torque in the direction of rotation. When drilling hard rock, the rotational pressure increases and the speed decreases; when drilling soft rock or during dry drilling, the rotational pressure decreases and the speed increases.

[0070] Third, a pressure sensor is installed on the anchor box. A pressure sensor with a range of 0-25 MPa is installed in the rotating circuit of the anchor box to reflect the load torque of the resin cartridge being stirred during anchoring operations.

[0071] Fourth, pressure sensors and displacement sensors are installed on each functional cylinder. Each of the drill arm lifting cylinder, telescopic cylinder, left and right swing cylinder, and rotary cylinder is equipped with one pressure sensor and one displacement sensor to monitor the load and position status of each degree of freedom of the drill arm.

[0072] Fifth, a pressure sensor is installed on the water valve. The water system is equipped with a pressure sensor with a range of 0-20 MPa to monitor the flushing water pressure. When a blockage in the water valve causes an abnormal increase in water pressure, it indicates an abnormal load, and the adaptive main controller uses this information to determine whether a shutdown protection mechanism is needed.

[0073] Sixth, encoders are installed on the rope feeding motor and the drug-feeding swing cylinder. A photoelectric encoder is installed on the rope feeding motor to measure the rope feeding speed and length. When the anchor bolt length reaches the set value, the rope feeding resistance suddenly increases, and the encoder detects a decrease in rotational speed, reflecting the load change in the rope feeding direction. A displacement sensor is installed on the drug-feeding swing cylinder to monitor the drug roll pushing stroke.

[0074] All of the above sensors transmit data to the adaptive main controller in real time via the CAN bus, with a sampling frequency of 100 Hz.

[0075] Example 3

[0076] The industrial big data analysis platform is deployed on a ground-based industrial server or a mine explosion-proof computer and communicates with the underground adaptive main controller via Ethernet.

[0077] The core function of this platform is to establish a working condition-load characteristic mapping model, which specifically includes the following two models:

[0078] First, the drilling pressure-rotation speed coupled model.

[0079] This model was built by collecting a large amount of historical borehole data. Specifically, after the drilling rig has accumulated more than 5,000 hours of operation, the collected drilling pressure and rotation speed data are classified according to geological conditions, and a neural network is used to fit the data to establish a nonlinear mapping relationship between pressure and rotation speed.

[0080] For example, in hard rock conditions, the drilling pressure is between 25-35 MPa and the rotation speed is between 30-60 rpm, showing a strong negative correlation; in medium-hard rock conditions, the drilling pressure is between 15-25 MPa and the rotation speed is between 60-120 rpm; and in soft rock conditions, the drilling pressure is between 5-15 MPa and the rotation speed is between 120-200 rpm.

[0081] The model is stored in the big data platform. When the adaptive main controller collects the current drilling pressure and rotation speed in real time, it can determine the current geological conditions by matching them with the model, thereby predicting the current load level.

[0082] Second, the load-throttle adaptive adjustment model.

[0083] The mathematical expression for this model is:

[0084] ;

[0085] Where y represents the throttle position of the diesel engine, with a value ranging from 0 to 100%; This is the feed pressure value for the drill box, in megapascals (MPa). This represents the rotational pressure of the drill box, in megapascals (MPA). The feed pressure adjustment coefficient; d is the proportional coefficient for adjusting rotational pressure; d is the throttle adjustment constant, which is 15%, i.e., the minimum idle throttle of the engine.

[0086] and The specific value is dynamically determined by the industrial big data analysis platform based on historical load data. The specific calibration method is as follows: historical borehole data are divided into three categories according to geological conditions: hard rock, medium-hard rock, and soft rock. The feed pressure under each category is then statistically analyzed. The relationship between the throttle and the optimal throttle y is obtained by fitting using the least squares method. Similarly, statistical rotational pressure The relationship between the throttle and the optimal throttle y is obtained by fitting. .

[0087] The calibration results are shown below: under hard rock conditions Take 3.5, Take 2.8, d = 15; under medium-hard rock conditions Take 2.2, Take 1.8, d = 15; under soft rock conditions Take 1.0, Take 0.8, and d = 15.

[0088] The big data platform automatically recalibrates after accumulating 100 hours of new operational data. and This allows the model to be continuously optimized.

[0089] Example 4

[0090] The adaptive main controller uses a mining explosion-proof programmable logic controller as its core processor and is installed in the electrical box of the drilling rig.

[0091] The adaptive master controller consists of two parts: a master controller and a data acquisition system. The data acquisition system comprises multi-channel analog input modules and digital input modules, used to receive signals from various sensors. The master controller is the central processing unit that runs the adaptive control algorithm.

[0092] The working logic of the adaptive main controller is as follows:

[0093] When the operator presses the drilling button on the remote control and issues the drilling operation command, the adaptive main controller first performs an initialization judgment.

[0094] The specific implementation method is as follows: The adaptive main controller reads the initial data from all sensors to determine the position and status of each hydraulic cylinder. For example, it determines whether the encoder reading of the drill box feed cylinder is near zero, whether the anchor box is in the retracted state, and whether each hydraulic cylinder of the drill arm is in the neutral position. After confirming that all actuators are in their initial positions, the initial status indicator light on the remote control panel illuminates, and the display screen shows the drill box feed height. and anchor box feed height All values ​​are zero. At this point, the operator presses the start button, and the drill box begins to rise and feed towards the rock surface.

[0095] During the drilling process, the adaptive main controller collects data from the pressure sensor and encoder of the feed cylinder, as well as data from the speed sensor and pressure sensor of the drill box, in real time at a frequency of 100 Hz.

[0096] The specific implementation method is as follows: the instant the drill box contacts the rock surface, the pressure in the rod chamber of the feed cylinder rises rapidly from near zero to 5-10 MPa, and the encoder displays that the feed stroke no longer increases. After recognizing this feature, the adaptive main controller determines that the drill box has made contact with the rock surface, and the current load suddenly changes from no-load to contact load. It immediately calls the working condition-load characteristic mapping model in the big data platform, matches the geological working condition category according to the current feed pressure and rotation pressure, and calculates the current optimal throttle value.

[0097] The throttle calculation process is as follows: Read the current feed pressure. The rotational pressure is 8 MPa. The pressure is 12 MPa. Based on the big data platform, the current working condition is determined to be medium-hard rock; therefore, the working condition under this condition is retrieved. =2.2、 =1.8, d=15. Substitute into the formula to calculate: The adaptive main controller sends a throttle command to the diesel engine electronic control unit via the CAN bus, increasing the engine throttle from 15% of idle speed to 54.2%. The hydraulic system output power increases accordingly, allowing the drilling rig to drill at optimal power.

[0098] During drilling, if the geological conditions abruptly change from soft rock to hard rock, the feed pressure... The rotational pressure suddenly increased from 6 MPa to 20 MPa. The pressure suddenly increased from 8 MPa to 22 MPa. Upon detecting this sudden change, the adaptive main controller re-evaluated the big data model within 50 milliseconds, matching it to a hard rock condition. Updated to 3.5. Updated to 2.8, recalculated throttle: y=146.6%, but since the throttle limit is 100%, we take y=100%, the engine runs at full speed, providing maximum hydraulic power to cope with high load.

[0099] When encountering cavities or fractured rock zones during drilling, the feed pressure The rotational pressure suddenly dropped from 15 MPa to 2 MPa. The load dropped from 18 MPa to 5 MPa. The adaptive main controller detected the sudden load drop, determined it to be a no-load or low-load condition, and activated the soft rock operating mode. =1.0、 =0.8, calculate the throttle: y=21%, the engine throttle is quickly reduced from full speed to 21%, close to idle speed, to avoid high-speed engine idling causing wear and fuel waste.

[0100] The adaptive main controller is equipped with a pressure safety interlock mechanism.

[0101] The specific implementation method is as follows: the solenoid valve drive signal issued by the adaptive main controller is only effective when the system pressure detected by the hydraulic system pressure sensor is within the safe range (3-35 MPa). When the system pressure is lower than 3 MPa, it is determined to be a hydraulic leak or pump failure, and any action is prohibited; when the system pressure exceeds 35 MPa, it is determined to be an overload or valve jamming, and the solenoid valve drive signal is immediately cut off and an alarm is triggered.

[0102] Simultaneously, the adaptive main controller determines the current load condition category using combined data from the encoder and pressure sensor. The specific determination rules are as follows:

[0103] The first category is the free drilling condition. The encoder shows that the feed stroke is continuously increasing, the feed pressure is below 5 MPa, and the rotational pressure is below 8 MPa. This indicates that the drill pipe has not yet contacted the rock surface or is in soft rock, and the load is low.

[0104] The second type is contact drilling. The encoder shows that the feed stroke remains unchanged, the feed pressure is between 5-25 MPa, and the rotational pressure is between 8-25 MPa. This indicates that the drill pipe has contacted the rock surface and is drilling normally, with a medium load.

[0105] The third category is stuck drill pipe or stalled operation. The encoder shows that the feed stroke remains unchanged for more than 3 seconds, the feed pressure exceeds 30 MPa, the rotation pressure exceeds 28 MPa, and the speed sensor shows that the speed is zero or close to zero. This indicates that the drill pipe is stuck or stalled, and the load is an abnormal high load.

[0106] The fourth type is the bottoming-out condition. The encoder shows that the feed stroke has reached the maximum value, the pressure in the rod chamber of the feed cylinder has reached the relief valve setting value of 35 MPa, and the pressure in the rodless chamber is close to zero. This is determined to be the bottoming-out condition of the drill box, with zero load.

[0107] The adaptive main controller performs similarity matching between the real-time collected pressure-stroke data and the historical feature data of various working conditions stored in the big data platform. When the matching degree exceeds 80%, the current working condition category is confirmed. When the matching degree is less than 80%, it is judged as an abnormal working condition and an alarm is triggered.

[0108] The adaptive main controller divides the drilling rig load status into three performance levels and executes different control strategies based on the magnitude of the abnormal state index β.

[0109] The specific formula for calculating the abnormal state index is as follows:

[0110] ;

[0111] in, To measure the drill arm angle deviation, the pitch angle, yaw angle, and roll angle of the drill arm are measured in real time by an inclination sensor installed on the drill arm, and the deviation value is obtained by comparing it with the set ideal angle. To measure the vibration deviation, the vibration acceleration of the drill arm is measured by an acceleration sensor installed on the drill arm, and the deviation value is obtained by comparing it with the normal value. T represents the rotational angular velocity deviation, measured by a speed sensor to deviate between the actual and set speeds; T represents the drilling torque, calculated using a rotary circuit pressure sensor; and P represents the hydraulic system pressure, measured by the system's main pressure sensor.

[0112] Weighting coefficient , , , , The data is reverse-calibrated by the big data platform based on historical fault data, and the specific values ​​are: , , , , .

[0113] First abnormal threshold Set to 30, the second abnormal threshold. Set to 60.

[0114] The specific implementation method for Level 1 performance is as follows: When When the value is less than or equal to 30, the drilling rig is considered to be in normal operating condition, with the load fluctuating within the allowable range. The adaptive main controller does not actively intervene, and the drilling rig operates at the initial drilling speed generated by the big data platform. Operation. The operator observes the status of each actuator through the remote control panel or the host computer display. If everything is normal, drilling operations continue.

[0115] The specific implementation method for secondary performance is as follows: when When the value is greater than 30 and less than or equal to 60, the drilling rig load is determined to be abnormal but has not yet reached a dangerous level. The adaptive main controller first analyzes the changing trend of the abnormal state index and calculates... The rate of change over the past 10 seconds, calculated simultaneously. Standard deviation over the past 30 seconds.

[0116] Then, the adaptive main controller obtains the current geological coefficient f. The geological coefficient f is calculated by the big data platform based on the mine's geological data and recent borehole data. For hard rock, f=1.2; for medium-hard rock, f=1.0; and for soft rock, f=0.8.

[0117] according to The rate of change, standard deviation, and f are obtained through a mapping function. Calculate the adjustment factor The mapping function g was obtained by fitting a neural network using a big data platform.

[0118] Subsequently, the rate adjustment formula is selected based on the relationship between the rate of change and the standard deviation:

[0119] When the rate of change is greater than 5 per second and the standard deviation is less than or equal to 8, it is judged as a rapidly deteriorating type, and the formula is used. Adjustments are made. For example, 100 millimeters per minute If the value is 2, then the adjusted speed At 50 millimeters per minute, the speed is reduced by half, achieving rapid protection.

[0120] When the rate of change is greater than 5 per second and the standard deviation is greater than 8, it is judged as a slowly deteriorating type, and the formula is used. Adjustments are made. Here, k is the mitigation coefficient, taken as 0.3. For example, 100 millimeters per minute If it is 2, then =40 mm / min, but because it is linearly adjusted, when When the speed is low, the deceleration is also small, avoiding excessive deceleration.

[0121] The specific implementation method for Level 3 performance is as follows: When When the load exceeds 60, the drilling rig is deemed to be under severe abnormal load, which may indicate stuck drill, stalled rotor, or hydraulic system failure. The adaptive main controller immediately cuts off all solenoid valve drive signals, the drilling rig stops all operations, and simultaneously sends an emergency stop alarm message to the remote controller and host computer. It also activates the diesel engine protection system to perform an emergency stop to prevent equipment damage and safety accidents.

[0122] Reference Figure 2 As shown, the adaptive mode control method for coal mine hydraulic anchor bolt drilling rigs based on industrial big data is applied to the aforementioned adaptive mode control system for coal mine hydraulic anchor bolt drilling rigs based on industrial big data, including:

[0123] S100. The adaptive main controller reads data from all sensors and determines the action status and position of each hydraulic cylinder based on the sensor data. The initial status indicator light on the host computer display interface or remote control panel illuminates, and the drill box feed height is [indicated]. Anchor box feed height ;

[0124] S200, The industrial big data analysis platform matches the current working conditions with historical load data to generate an initial drilling speed. ;

[0125] S300 During the drilling process of the drill box rising, the adaptive main controller collects data from the pressure sensor and speed sensor in real time, and combines the working condition-load characteristic mapping model to adjust the diesel engine throttle in real time so that the drilling rig maintains the optimal drilling parameters under load changes.

[0126] S400. The adaptive main controller continuously calculates the abnormal state index, evaluates the health status level of the drilling rig, and executes the corresponding adaptive control strategy according to the health status level.

[0127] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive mode control system for a coal mine hydraulic anchor bolt drilling rig based on industrial big data, characterized in that, include: A hydraulically driven actuator assembly is used to drive the actions of each actuator of the anchor drilling rig. The hydraulically driven actuator assembly includes a hydraulic solenoid valve assembly, a multi-way valve, and an electro-proportional relief valve assembly. A multi-dimensional sensing network includes an encoder and pressure sensor mounted on the feed cylinder, a speed sensor and pressure sensor mounted on the drill box, a pressure sensor mounted on the anchor box, pressure sensors and displacement sensors mounted on each functional cylinder, a pressure sensor mounted on the water valve, and an encoder mounted on the rope feeding motor and the drug delivery swing cylinder. The multi-dimensional sensing network is used to collect multi-source heterogeneous data of drilling rig operation in real time. The industrial big data analysis platform is used to receive and store historical operating data collected by the multi-dimensional sensing network and establish a drilling rig operating condition-load characteristic mapping model. An adaptive main controller is communicatively connected to the industrial big data analysis platform, the multi-dimensional sensing network, and the hydraulic drive actuator group. Based on the working condition-load characteristic mapping model, the adaptive main controller analyzes the current load status of the drilling rig in real time and dynamically adjusts the output parameters of the hydraulic drive actuator group to realize adaptive speed control of the drilling rig under load change conditions.

2. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data as described in claim 1, characterized in that, The adaptive main controller includes a main controller and a data acquisition system. The main controller receives data from various sensors through the data acquisition system and analyzes the position and status of the relevant actuators of the drilling rig's adaptive speed control system in real time. When the adaptive main controller receives a drilling operation command, it first determines the position and status of each actuator and displays it on the remote control panel. Based on the accurate determination of the position and status of the drilling rig actuators, it controls the corresponding actuators to perform actions.

3. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data as described in claim 1, characterized in that, The operating condition-load characteristic mapping model established by the industrial big data analysis platform includes: A drilling pressure-rotation speed coupled model is used to characterize the nonlinear relationship between drilling pressure and rotation speed under different geological conditions. The load-throttle adaptive adjustment model is expressed as follows: ; In the formula, y represents the throttle position of the diesel engine; This is the feed pressure adjustment proportional coefficient; The feed pressure value of the drill box; The rotational pressure adjustment ratio coefficient; denoted as , where d is the rotational pressure value of the drill box; and d is the throttle adjustment constant.

4. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data as described in claim 1, characterized in that, The adaptive main controller determines that the hydraulic system pressure exceeds a threshold by using the pressure sensor in the multi-dimensional sensing network, and only then can the control signal drive the hydraulic solenoid valve group to act. The adaptive main controller measures the feed stroke of the drill box and anchor box through the encoder, measures the top and bottom pressures of the feed cylinder through the pressure sensor, and determines the current load condition category by combining historical data in the industrial big data analysis platform.

5. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data according to claim 1, characterized in that, The adaptive main controller classifies the drilling rig load status into three performance levels: Level 1 performance: When the abnormal state index is less than or equal to the first abnormal threshold, the drilling rig runs at the initial drilling speed without any control intervention; Secondary performance: When the abnormal state index is greater than the first abnormal threshold and less than or equal to the second abnormal threshold, the adaptive main controller analyzes the drilling rig performance change trend, obtains the geological coefficient f in combination with geological condition changes, and combines the performance change trend with the geological coefficient to obtain the adjustment factor. The expression is: ； In the formula, the The abnormal state index; Where g is the geological coefficient; g is the mapping function. Based on the aforementioned regulatory factor Dynamically adjust the initial drilling speed of the drilling rig; Level 3 performance: When the abnormal state index exceeds the second abnormal threshold, the adaptive main controller controls the drilling rig to stop running and sends a warning signal to the administrator.

6. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data according to claim 5, characterized in that, The abnormal state index is calculated as follows: Data acquisition and processing are performed on the drill arm to obtain its pitch angle. Yaw angle Roll angle The acceleration a and angular velocity of the drill arm Drilling torque T and hydraulic system pressure P; pitch angle of the drill arm Yaw angle Roll angle The acceleration a and angular velocity of the drill arm The abnormal state index is obtained by weighting the drilling torque T and the hydraulic system pressure P. The specific formula for calculating the abnormal state index is as follows: ; In the formula, the , , , , These are weighting coefficients; For angular deviation; the For vibration deviation; the This refers to the deviation in rotational angular velocity.

7. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data as described in claim 5, characterized in that, The dynamic adjustment of drilling speed under the secondary performance includes the following steps: When the rate of change of the abnormal state index is greater than the rate of change threshold, and the standard deviation of the abnormal state index is less than or equal to the standard deviation threshold, the drilling rig's health condition is determined to be steadily deteriorating and deteriorating rapidly. The expression for adjusting the drilling speed is: ; When the rate of change of the abnormal state index is greater than the rate of change threshold, and the standard deviation of the abnormal state index is greater than the standard deviation threshold, the drilling rig's health condition is determined to be deteriorating, but the rate of deterioration is slowing down. The expression for adjusting the drilling speed is: ; In the formula, the The initial drilling velocity; The adjusted drilling speed is denoted by k, which is the mitigation coefficient.

8. The adaptive mode control system for coal mine hydraulic anchor bolt drilling rig based on industrial big data according to claim 1, characterized in that, It also includes a diesel engine protection system, which monitors gas concentration and relevant operating parameters of the diesel engine, and provides alarm information and emergency shutdown function; It also includes a remote controller, and the adaptive main controller is connected to the remote controller via a receiver. The remote controller is equipped with one-click drilling and one-click anchoring function buttons.

9. An adaptive mode control method for a coal mine hydraulic anchor bolt drilling rig based on industrial big data, applied to the adaptive mode control system for a coal mine hydraulic anchor bolt drilling rig based on industrial big data as described in any one of claims 1-8, comprising: S100. The adaptive main controller reads data from all sensors and determines the action status and position of each hydraulic cylinder based on the sensor data. The initial status indicator light on the host computer display interface or remote control panel illuminates, and the drill box feed height is [indicated]. Anchor box feed height ; S200, The industrial big data analysis platform matches the current working conditions with historical load data to generate an initial drilling speed. ; S300 During the drilling process of the drill box rising, the adaptive main controller collects data from the pressure sensor and speed sensor in real time, and combines the working condition-load characteristic mapping model to adjust the diesel engine throttle size in real time so that the drilling rig maintains the optimal drilling parameters under load change conditions. S400. The adaptive main controller continuously calculates the abnormal state index, evaluates the health status level of the drilling rig, and executes the corresponding adaptive control strategy according to the health status level.

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