Intelligent posture and tunneling parameter cooperative control method of tunneling machine

Abstract

The application relates to the technical field of heading machine control, and discloses an intelligent posture and heading parameter cooperative control method of a heading machine, which acquires parameters such as a body inclination angle and rock hardness in real time through a posture and geological detection module, calculates posture deviation by a central control unit, divides geological working conditions such as hard rock, medium-hard rock and soft rock, and judges cutting head wear. The core lies in that the system cooperatively adjusts a heading speed, a cutting head rotating speed and a body posture according to an analysis result. Actual parameters are collected through a feedback module to form a closed-loop correction, and a prewarning and emergency shutdown operation are triggered when the parameters exceed a safety threshold. The application can also optimize a parameter library through machine learning to realize self-adaptive control. The method realizes dynamic linkage of the posture and the heading parameters of the heading machine, reduces artificial dependence, improves heading precision and efficiency, reduces equipment wear, improves operation safety, and is suitable for various heading scenes.

Description

Technical Field

[0001] This invention relates to the field of tunneling machine control technology, specifically to a method for intelligent attitude and tunneling parameter coordinated control of a tunneling machine. Background Technology

[0002] Tunnel boring machines (TBMs) are core equipment in mining, tunneling, and other fields. Their tunneling efficiency and operational stability directly determine project progress and construction safety. Traditional TBMs mostly employ manual control, with operators adjusting parameters such as head rotation speed, advance speed, and cutting depth based on experience, while simultaneously correcting the machine's posture through visual observation. This control method is highly dependent on individual skill, and the significant differences in experience among operators lead to inconsistent tunneling parameters, easily resulting in over-excavation, under-excavation, or machine tilting. Secondly, when facing complex geological conditions (such as hard rock interlayers and soft soil layers), the speed at which manual parameter adjustments are made is difficult to match geological changes, easily causing accelerated wear on the cutting head or even equipment failure. Furthermore, in the confined spaces of tunnels and mines, operators are vulnerable to threats such as falling rocks and dust when operating the equipment at close range.

[0003] In existing technologies, some tunneling machines have introduced automatic control based on a single parameter (such as closed-loop control of the advance speed), but they have not achieved coordinated linkage between attitude and tunneling parameters, making it difficult to meet the requirements for precise tunneling under complex working conditions. Therefore, an intelligent control method is needed that can adjust tunneling parameters in real time based on geological conditions and machine attitude. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent attitude and tunneling parameter coordinated control method for tunneling machines. This method solves the problems in existing tunneling machine control systems where attitude adjustment and tunneling parameter regulation are independent and lack a coordinated linkage mechanism based on geological conditions. This makes it difficult for the equipment to balance tunneling efficiency and component protection under complex geological conditions, and also results in low control accuracy and insufficient system stability due to sensor noise interference.

[0005] This invention provides an intelligent attitude and tunneling parameter coordinated control method for a tunneling machine, applied to a tunneling machine control system including an attitude detection module, a geological identification module, a central control unit, an execution module, a feedback module, and an anomaly early warning module. This method achieves dynamic coordinated adjustment of tunneling parameters and machine attitude by establishing a real-time correlation between attitude information and geological information.

[0006] A method for intelligent attitude and tunneling parameter coordinated control of a tunneling machine first initializes the reference tunneling parameters and safety threshold range by the central control unit. The reference tunneling parameters include the reference advance speed, the reference cutter head speed and the reference machine body levelness.

[0007] During tunneling operations, the attitude detection module collects the horizontal tilt angle, pitch angle and spatial position coordinates of the cutting head in real time, while the geological identification module collects the rock hardness value and cutting vibration frequency in real time and transmits the data to the central control unit.

[0008] The central control unit analyzes and processes the collected data. First, it calculates the attitude deviation by comparing the actual horizontal tilt angle of the fuselage with the reference fuselage level. Second, based on the relationship between the rock hardness value and the preset first and second hardness thresholds, it classifies the geological conditions as hard rock, medium-hard rock, or soft rock, and combines this with the cutting vibration frequency to determine whether there is a risk of wear on the cutting head.

[0009] The innovation of this invention lies in the fact that the central control unit generates and outputs differentiated collaborative control commands based on the analysis results of geological condition levels and attitude deviation values: When the working condition is determined to be hard rock, the instruction execution module reduces the advance speed and increases the cutting head rotation speed. If the attitude deviation value is detected to exceed the preset levelness threshold at the same time, a control instruction containing timing logic is generated to first drive the leveling cylinder to correct the machine body attitude. After the attitude stabilizes within the allowable error range, the adjustment of the tunneling parameters is then executed.

[0010] When the working condition is determined to be medium-hard rock, the instruction execution module maintains the cutting head rotation speed, adjusts the advance speed, and drives the lifting cylinder to adjust the cutting head height according to whether the pitch angle exceeds the limit in order to correct the pitch attitude.

[0011] When the working condition is determined to be soft rock, the command execution module increases the advance speed and decreases the cutting head rotation speed. At the same time, the actual cutting depth is obtained by calculating the extension and retraction of the cutting arm and the displacement data of the machine body. The actual cutting depth is used as a feedback quantity to perform closed-loop control of the advance speed, so that the cutting depth is stabilized near the reference value.

[0012] To ensure control accuracy, the feedback module processes the collected actual operating parameters using a moving average filtering algorithm and feeds them back to the central control unit. The central control unit compares the deviation between the actual parameters and the target parameters and performs closed-loop correction until the deviation stabilizes within the allowable range for several consecutive correction cycles.

[0013] Furthermore, when the machine's attitude exceeds the physical limit threshold, rock hardness changes abruptly, or the cutting vibration frequency is abnormal, the central control unit immediately triggers the anomaly warning module and sends a shutdown command to the execution module to ensure operational safety. This method also includes a parameter adaptive optimization step, which uses machine learning algorithms to analyze historical operational data and select the optimal parameter combinations for different geological conditions to update the baseline tunneling parameters.

[0014] This invention provides a method for intelligent attitude and tunneling parameter coordinated control of a tunneling machine. It has the following beneficial effects: 1. This invention achieves coordinated control of attitude and tunneling parameters, solving the problem of disconnect between the two in traditional control by prioritizing attitude and linking parameters. When machine tilt is detected, the system prioritizes correcting the attitude before adjusting the tunneling parameters, effectively avoiding tunneling trajectory deviation and over- or under-digging, thus improving operational accuracy.

[0015] 2. This invention can dynamically match the optimal combination of tunneling speed and rotation speed based on real-time rock hardness, improving tunneling efficiency in complex geological conditions. Simultaneously, by monitoring the cutting vibration frequency, it predicts wear conditions, providing a basis for preventative maintenance, thereby reducing abnormal wear of the cutting head and extending equipment lifespan.

[0016] 3. This invention reduces labor intensity by replacing manual experience-based operation with automated control. Its integrated anomaly warning and emergency handling functions can automatically alarm and execute speed reduction or shutdown when abnormal posture or vibration is detected, constituting active safety protection, greatly reducing operational risks and ensuring the safety of personnel and equipment. Attached Figure Description

[0017] Figure 1 This is a structural block diagram of the tunneling machine control system of the present invention; Figure 2 This is a flowchart illustrating the intelligent control method for tunneling machines according to the present invention. Figure 3 This is a simulation comparison diagram of the control response of the present invention and the prior art under sudden changes in operating conditions.

[0018] Among them, 10 is the attitude detection module; 20 is the geological identification module; 30 is the central control unit; 40 is the execution module; 50 is the feedback module; and 60 is the anomaly warning module. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] See attached document Figure 1 With appendix Figure 2 This invention provides a method for intelligent attitude and tunneling parameter coordinated control of a tunneling machine, which is implemented based on a control architecture constructed by an attitude detection module 10, a geological identification module 20, a central control unit 30, an execution module 40, a feedback module 50 and an anomaly early warning module 60.

[0021] Each module works in concert according to the preset timing logic, strictly corresponding to each step node in the system control process, and together completes the closed-loop process from environmental perception to execution control.

[0022] In step S100, during the initialization of reference parameters and thresholds, the central control unit 30, as the logical core of the system, first performs system startup and configuration actions. It is responsible for retrieving the reference tunneling parameters and safety threshold range from the internal memory, completing the initialization settings of the control system, and establishing a data reference for subsequent calculations and control.

[0023] In steps S200 and S300, attitude and geological data are acquired in real time, and attitude deviations are analyzed to determine geological conditions. The attitude detection module 10 and the geological identification module 20 act as sensing front-ends, performing data acquisition and transmission. Specifically, the attitude detection module 10 captures the fuselage horizontal tilt angle and spatial pose data in real time, providing raw input for deviation calculation in step S200; the geological identification module 20 simultaneously detects rock hardness values ​​and cutting vibration frequencies, providing a decision-making basis for determining the working condition level in step S300. The central control unit 30 receives the above data and executes algorithm calculations to identify the current fuselage status and geological environment.

[0024] In step S400, the execution module executes control commands. The execution module 40, as the physical execution terminal, responds to the coordinated control commands generated by the central control unit 30. Through various internally integrated hydraulic cylinders and motor components, the execution module 40 synchronously executes physical actions such as machine body leveling, cutting head height adjustment, propulsion speed change, and cutting speed adjustment, transforming the control strategy in step S400 into the actual mechanical motion of the tunneling machine.

[0025] In step S500, the feedback module collects actual parameters, determines deviations, and adjusts control commands. The feedback module 50 performs real-time monitoring of the system output results, collects the actual working parameters after the execution module 40's actions, and sends them back to the central control unit 30. This supports the system in calculating the target deviation and generating secondary correction commands to ensure that the control accuracy meets the design requirements.

[0026] In step S600, during the anomaly warning and emergency handling phase, the anomaly warning module 60 and the central control unit 30 work together to execute safety protection actions. When the system detects data exceeding limits or an abnormal state, the anomaly warning module 60 immediately sends a warning signal and records the fault, cooperating with the emergency stop or deceleration logic of the execution module 40 to ensure operational safety.

[0027] In addition, the system also includes step S700 parameter adaptive optimization process, which is executed in parallel in the background by the central control unit 30. It uses machine learning algorithms to analyze historical operation data and dynamically update the benchmark parameter library in step S100.

[0028] See attached document Figure 2 A method for intelligent attitude and tunneling parameter coordinated control of a tunneling machine achieves automated control of the tunneling machine's operation process through a logical flow of initialization setting, data acquisition, analysis and processing, coordinated adjustment, closed-loop correction, and anomaly handling.

[0029] During the control process initiation phase, the central control unit 30 first performs parameter initialization, which includes reading the baseline tunneling parameters and safety threshold ranges preset in the storage unit. The baseline tunneling parameters define the ideal operating state of the tunneling machine under standard working conditions, and the safety threshold range defines the allowable fluctuation range of various physical quantities in the system.

[0030] During the data acquisition phase, the attitude detection module 10 and the geological identification module 20 work synchronously. The attitude detection module 10 acquires the horizontal tilt angle, pitch angle, and spatial position coordinates of the machine body according to a preset sampling frequency. The geological identification module 20 simultaneously acquires the rock hardness value of the tunneling face and the vibration frequency of the cutting head. The acquired attitude data and geological data are transmitted to the central control unit 30 in real time.

[0031] During the analysis and processing phase, the central control unit 30 performs calculations on the received data. On one hand, the central control unit 30 calculates the deviation between the actual attitude parameters and the reference attitude parameters, and compares the deviation with a preset attitude threshold to determine whether the fuselage attitude is within the normal range. On the other hand, the central control unit 30 classifies the current geological conditions based on the collected rock hardness values ​​and assesses the wear risk of the cutting head in conjunction with vibration frequency data.

[0032] During the coordinated adjustment phase, the central control unit 30 generates corresponding coordinated control commands based on the geological condition level and attitude deviation judgment results, and sends them to the execution module 40. The execution module 40 synchronously adjusts the advance speed, cutting head rotation speed, and machine attitude according to the commands. For different geological hardness levels and machine attitude conditions, it adopts differentiated parameter combinations and action logic to achieve a balance between tunneling efficiency and equipment protection.

[0033] During the closed-loop correction phase, the feedback module 50 collects the actual operating parameters of the execution module 40 in real time after receiving the command, including the actual propulsion speed, the actual cutting head rotation speed, and the adjusted fuselage attitude parameters. The central control unit 30 calculates the deviation between the actual operating parameters and the target control parameters. If the deviation exceeds the allowable range, the central control unit 30 outputs a secondary adjustment command to the execution module 40 until the actual parameters stabilize within the allowable range.

[0034] During the anomaly handling phase, when the attitude detection module 10 detects that the attitude angle exceeds the physical limit, or the geological identification module 20 detects abnormal vibration or sudden changes in hardness, the central control unit 30 immediately triggers the anomaly warning module 60. Simultaneously, the central control unit 30 controls the execution module 40 to execute a speed reduction or shutdown procedure to ensure equipment and operational safety.

[0035] See attached document Figure 1 The attitude detection module 10 is specifically used to accurately acquire spatial pose data of the tunneling machine body and the cutting head. The attitude detection module 10 consists of a gyroscope sensor and a tilt sensor, which work together to construct a three-dimensional spatial state model of the tunneling machine.

[0036] Gyroscope sensors are installed on the main structure of the tunneling machine or the base of the cutting arm to sense the angular velocity and angle changes of the machine during movement, thereby capturing the dynamic attitude characteristics of the machine. Tilt sensors are installed on the chassis or a key horizontal reference surface to sense the static tilt of the machine relative to the direction of gravitational acceleration. In specific implementations, fiber optic gyroscopes or high-precision microelectromechanical systems (MEMS) gyroscopes can be used as gyroscope sensors, and dual-axis electrolyte tilt sensors or capacitive tilt sensors can be used to meet the anti-interference requirements of the complex downhole environment.

[0037] The attitude parameters output by the attitude detection module 10 are defined as a state vector, specifically including the fuselage horizontal tilt angle. Pitch angle and the spatial coordinates of the cutting head Among them, the horizontal tilt angle of the fuselage The pitch angle represents the degree of lateral tilt of the tunnel boring machine body in the horizontal plane, i.e., the rotation angle about the longitudinal axis. Characterizes the pitch of the tunnel boring machine body in the vertical plane, i.e., the rotation angle about the horizontal axis. Spatial position coordinates of the cutting head. The three-dimensional coordinate values ​​are established based on the tunneling machine's body coordinate system or the absolute geodetic coordinate system. They are obtained through geometric calculation by combining the machine's attitude parameters with the real-time extension and retraction length and swing angle of the cutting arm, or directly obtained through a position tracking sensor installed at the front end of the cutting arm. The attitude detection module 10 is used to periodically collect and output the above parameters according to a preset sampling frequency, ensuring that the subsequent control unit can obtain a continuous and real-time attitude data stream.

[0038] The geological identification module 20 is specifically used to realize the real-time perception of the physical properties of the rock and the cutting conditions at the tunneling face. It consists of a hardness sensor and a vibration sensor.

[0039] Hardness sensors are positioned at the cutter head, cutter tooth holder, or cutter arm drive shaft of the tunneling machine to detect the rock hardness value at the tunneling face. Hardness sensors can be either contact pressure sensors, which characterize rock hardness by measuring the instantaneous contact stress when the cutting teeth break the rock; or torque feedback sensors, which indirectly infer rock hardness by monitoring the real-time load torque change of the output shaft of the cutting motor or reducer. Hardness sensors possess high-precision measurement capabilities, with the measurement error range limited to [specific range not specified in the original text]. Within this range, to ensure the accuracy of geological condition classification.

[0040] Vibration sensors are fixedly installed at vibration-sensitive locations such as the reduction gearbox, cantilever section, or main bearing housing of the cutting head to collect mechanical vibration signals generated during rock-breaking operations. The parameter output by the vibration sensor is the cutting vibration frequency. The vibration sensor can be a wideband piezoelectric accelerometer or a magnetoelectric velocity sensor, capable of picking up and outputting frequency domain signals containing information about the cutting head's operating status. The acquired cutting vibration frequency... As a key indicator for assessing whether the cutting head has broken teeth, excessive wear, or abnormal operation.

[0041] Both the attitude detection module 10 and the geological identification module 20 establish a data transmission channel with the central control unit 30 via shielded cables or industrial fieldbuses with electromagnetic interference resistance, to collect the horizontal tilt angle of the fuselage. Pitch angle and the spatial coordinates of the cutting head Rock hardness value and cutting vibration frequency The raw data is transmitted in real time to the data processing unit of the central control unit 30. Primary conditioning of the sensor signals, such as signal amplification and anti-aliasing filtering, can be performed by conditioning circuits integrated within the sensor module. The specific configuration of such signal conditioning circuits is well-known in the field and will not be described further here.

[0042] See attached document Figure 1 The central control unit 30 serves as the core for computation and decision-making in the entire control system. It employs a programmable logic controller (PLC) as its hardware platform. The PLC integrates a high-performance data processing unit and instruction generation unit, and includes a non-volatile memory module to store the baseline parameters, threshold ranges, and adaptive parameter library required for system operation. The central control unit 30 establishes electrical connections and data communication with the attitude detection module 10, geological identification module 20, execution module 40, feedback module 50, and anomaly warning module 60 via an industrial fieldbus interface.

[0043] The data processing unit receives real-time sampled data streams from the attitude detection module 10 and the geological identification module 20. The data processing unit possesses high-speed floating-point arithmetic capabilities, used to execute algorithms such as attitude deviation calculation, geological condition level determination, and cutter head wear state analysis. To meet the rapid response requirements of tunneling operations to sudden geological changes and to ensure the real-time performance of the system under complex working conditions, the computational latency of the data processing unit is strictly limited to [specific parameters]. Within. That is, from the moment the raw data from the sensor is received to the moment all logical operations are completed and the processing result is output, the time interval shall not exceed [a certain value]. .

[0044] The instruction generation unit is used to convert logic signals into specific control instructions based on the calculation results output by the data processing unit and according to a preset cooperative control strategy. The instruction generation unit sends control signals to the execution module 40 using the Controller Area Network (CAN) bus protocol. The CAN bus is used in a high-priority transmission mode, and the signal transmission delay time is limited to [specific parameters]. Within this range. The low latency characteristic ensures that control commands can be received and responded to by the actuators almost instantly, thereby guaranteeing the synchronization of tunneling parameter adjustments and attitude correction actions.

[0045] The memory module stores the initial baseline tunneling parameters and safety thresholds. The baseline tunneling parameters include the baseline tunneling speed. Reference cutting head speed , reference fuselage levelness and benchmark cutting depth Safety thresholds include levelness thresholds. and Tunneling speed threshold and Cutting head speed threshold and Furthermore, the central control unit 30 integrates a machine learning algorithm module to record historical operational data under different geological conditions, including tunneling parameter combinations, tunneling efficiency, and equipment wear data. The central control unit 30 uses this historical operational data to adaptively optimize and update the baseline parameters and control coefficients in the memory module. The specific circuit connection methods and underlying communication protocol configurations of the programmable logic controller and CAN bus can be implemented by those skilled in the art according to industrial automation standards, and are well-known technologies in the field; therefore, they will not be elaborated upon here.

[0046] See attached document Figure 2 The collaborative control strategy and algorithm logic provided by this invention are implemented through the specific operation and processing of the central control unit 30, covering the complete logic chain from parameter initialization to closed-loop steady-state confirmation.

[0047] In step S100, during the initialization of reference parameters and thresholds, the central control unit 30 retrieves the set of reference tunneling parameters and thresholds stored in its internal memory. The reference tunneling parameters include the reference tunneling speed. , reference fuselage levelness Horizontal tilt of the fuselage and benchmark cutting depth The threshold set includes levelness thresholds. and Pitch angle threshold and And rock hardness thresholds used to classify geological conditions. and Among them, the rock hardness threshold satisfies The numerical relationship. In addition, the central control unit 30 also sets a safe range for the cutting vibration frequency, based on the cutting head material and the reference rotation speed. Confirmed, specific settings are as follows The corresponding frequency domain range.

[0048] During the data analysis and operational condition determination phases in steps S200 to S300, the central control unit 30 receives real-time data streams from the attitude detection module 10 and the geological identification module 20. The central control unit 30 calculates the actual fuselage horizontal tilt angle. Level with reference fuselage The absolute difference between them yields the attitude deviation value. The logical expression is: ; If the calculated result Exceeding the level threshold or Within a defined range, the central control unit 30 determines that the current fuselage attitude is abnormal. Simultaneously, the central control unit 30 bases its judgment on the real-time collected rock hardness values. Classify and determine geological conditions: when When, it is determined to be a hard rock working condition; when At that time, it was determined to be a medium-hard rock working condition; when At that time, it was determined to be a soft rock working condition. Furthermore, if the cutting vibration frequency is collected in real time... If the cutting head exceeds the safe range, it is determined that there is a risk of wear and tear.

[0049] In step S400, the execution module executes the control command. Based on the above determination result, the central control unit 30 sends a targeted collaborative control command to the execution module 40 and adopts a differentiated parameter correction model for different working conditions.

[0050] When the condition is determined to be hard rock, considering the strong reaction force of the high-hardness rock on the equipment, the central control unit 30 executes a control strategy of low-speed propulsion and high-speed cutting. At this time, the hard rock propulsion speed... Rotation speed of hard rock cutting head The calculation formulas are as follows: ; ; In the formula, The propulsion velocity attenuation coefficient is limited to a range of values. Up to 0.6; The value of the cutting head rotation speed gain coefficient is limited to a range of values. to In hard rock conditions, if attitude deviation values ​​are detected simultaneously... An anomaly occurred, and the central control unit 30 executed attitude priority control logic, that is, it prioritized sending commands to drive the leveling cylinder to adjust the reference fuselage level. Within the range, after the attitude correction is completed, a command is sent to control the propulsion motor and the cutting head drive motor to perform the above-mentioned speed and rotation speed adjustment, so as to prevent the equipment from being damaged by cutting hard rock when the machine body is tilted.

[0051] When the working condition is determined to be medium-hard rock, the central control unit 30 executes a steady-speed cutting control strategy. At this time, the cutting head speed is maintained at the reference speed. The settings remain unchanged, with only minor adjustments to the propulsion speed. Propulsion speed for medium-hard rock. The calculation formula is: ; In the formula, This is the velocity adjustment coefficient under medium-hard rock conditions, and its value range is limited to [value range missing]. to Under medium-hard rock conditions, the central control unit 30 focuses on monitoring the pitch angle. If detected The control system lifts the cutting head by controlling the hydraulic cylinder; if it detects... The lifting cylinder is controlled to lower the cutting head until the pitching posture returns to within the allowable deviation range.

[0052] When the working condition is determined to be soft rock, the central control unit 30 implements a control strategy of high-speed propulsion and low-speed cutting to improve efficiency and suppress dust. At this time, the soft rock propulsion speed... Rotation speed of soft rock cutting head The calculation formulas are as follows: ; ; In the formula, The propulsion speed gain coefficient has a value range that is limited to 1. to ; The cutting head rotation speed attenuation coefficient is limited to a range of values. to .

[0053] Meanwhile, to prevent over-excavation during soft rock tunneling, the central control unit 30 monitors the benchmark cutting depth in real time. And by linking the propulsion motor with the cutting arm, the actual cutting depth is ensured to remain stable at [the specified depth]. Within the accuracy range.

[0054] In the closed-loop correction phase of step S500, where the feedback module acquires actual parameters, the feedback module 50 feeds back the actual parameters after the execution module 40's action to the central control unit 30. The central control unit 30 calculates the actual parameters and the target parameters ( The deviation between () and (). If the deviation exceeds the reference parameter The central control unit 30 generates a secondary adjustment command. The system is set to a closed-loop correction cycle of [value missing]. to To prevent system oscillations under critical conditions, the central control unit 30 employs a continuous stability determination mechanism, meaning that it only determines system stability when continuous... Only when the deviation values ​​of each correction cycle are stable within the allowable range are the parameters considered to have reached a stable state and secondary adjustments are stopped, thus entering the continuous tunneling mode. See attached document Figure 2 The primary aspect of this invention lies in establishing an accurate system operating benchmark through the initialization parameter setting step S100, and using it for the corresponding data processing logic, thereby providing a reliable basis for subsequent collaborative control.

[0055] In step S100, during the initialization of baseline parameters and thresholds, the central control unit 30 retrieves configuration data stored in its internal parameter library before the tunneling operation begins, completing the loading of baseline tunneling parameters and threshold ranges. Based on the geological survey report for this tunneling task and the tunneling machine's own equipment performance parameters, the central control unit 30 sets the baseline tunneling parameter set. The baseline tunneling parameter set specifically includes: baseline tunneling speed. This refers to the ideal advance rate of the tunneling machine under standard design conditions; the reference cutting head rotation speed. That is, the rotational speed of the cutting head under rated load; the levelness of the reference machine body. It is usually set to Alternatively, it can be based on the pre-set slope value of the tunnel; and the benchmark cutting depth. This refers to the depth of cut set for a single cutting cycle.

[0056] At the same time, the central control unit 30 defines the safe threshold range for the operation of each parameter to construct the system's safety boundary. The parameter threshold range includes: levelness threshold. and Used to limit the maximum allowable left and right tilt range of the fuselage; tunneling speed threshold. and This is used to prevent overload or stall of the propulsion motor of the execution module 40; cutting head speed threshold. and This is used to define the safe speed range of the cutting motor. Furthermore, to achieve quantitative classification of geological conditions, the central control unit 30 presets a rock hardness threshold. and And satisfy numerical relationship To monitor the wear condition of the cutting head, the central control unit 30 presets a safe range for the cutting vibration frequency. This range is based on the material properties of the cutting head and the reference cutting head rotation speed. The specific frequency range is set as follows: .

[0057] Regarding the timing and accuracy parameters of the control logic, the central control unit 30 is set with a closed-loop correction period of [value missing]. to and the allowable deviation range is the reference parameter. The above rock hardness thresholds , The system supports dynamic adjustments based on geological survey reports of actual tunneling projects. When the hardness threshold is adjusted, the central control unit 30 is configured to synchronously update the tunneling parameter coefficients for the corresponding working conditions in subsequent collaborative control steps. Furthermore, the central control unit 30 can preset a cutter head wear warning threshold as a criterion for triggering wear replacement prompts.

[0058] To address the complex electromagnetic environment underground and the high-frequency mechanical vibrations generated during tunneling operations, this embodiment incorporates data cleaning logic during the initialization phase. The central control unit 30 receives the actual operating parameters collected and filtered by the feedback module 50. The central control unit 30 sets the filter window size to 5 to 10 sampling points. During data processing, for continuous sampling data of a specified parameter, the moving average filtering algorithm calculates the values ​​within the filter window of the feedback module 50. indivual( The arithmetic mean of 5 to 10 consecutive sampling points is taken as the effective feedback value at the current moment. The data processing logic is a conventional technique in this field, aiming to smooth out the instantaneous high-frequency noise interference caused by rock breaking and impact, ensuring that the data fed back to the central control unit 30 can truly reflect the operating trend of the execution module and avoid false triggering of control commands due to signal glitches.

[0059] See attached document Figure 2 After completing the data acquisition and cleaning, the central control unit 30 executes step S200 to acquire attitude and geological data in real time and step S300 to analyze attitude deviation and determine geological conditions. The aim is to transform physical quantities into control states that the system can recognize through logical comparison of real-time data, and to provide a basis for decision-making for subsequent adjustment of coordinated parameters.

[0060] In step S200, during attitude deviation calculation, the central control unit 30 retrieves the fuselage attitude data transmitted in real time by the attitude detection module 10 and analyzes it in conjunction with preset benchmark parameters. The central control unit 30 reads the current fuselage horizontal tilt angle. and the preset baseline fuselage level To quantify the degree of horizontal tilt of the fuselage, the central control unit 30 executes deviation calculation logic to calculate the absolute value of the difference between the two values ​​to obtain the fuselage horizontal tilt angle deviation. The calculation satisfies the expression: ; Obtain fuselage horizontal tilt deviation Then, the central control unit 30 will adjust the fuselage horizontal tilt angle deviation. Compared with the preset level threshold and Perform numerical comparison. If the calculated values ​​are... Greater than or less The central control unit 30 determines that the current fuselage horizontal attitude is abnormal and generates an attitude correction request signal. Simultaneously, the central control unit 30 reads the real-time acquired fuselage pitch angle. and compare it with the preset pitch angle threshold. and Perform a comparison. If... or The central control unit 30 determines that the fuselage pitch attitude is in an abnormal state. The above attitude determination result will be marked and transmitted to the command generation unit.

[0061] In step S300, which analyzes attitude deviations and determines geological conditions, the central control unit 30 performs feature classification of the current tunneling conditions based on the data collected by the geological identification module 20. The central control unit 30 reads the real-time collected rock hardness values. And call the preset rock hardness threshold. and (satisfy The central control unit 30 is based on the rock hardness value. The numerical range in which the geological conditions fall determines the three levels of geological conditions: When the rock hardness value is collected in real time Greater than hour( The central control unit 30 determines that the current working condition is hard rock, which corresponds to high-strength rock formations, meaning that the cutting resistance is relatively large.

[0062] When the rock hardness value is collected in real time Between and Between ( The central control unit 30 determines that the current working condition is a medium-hard rock condition, corresponding to a medium-hardness rock formation.

[0063] When the rock hardness value is collected in real time Less than hour( The central control unit 30 determines that the current working condition is a soft rock condition, which corresponds to a soft rock or soil layer.

[0064] Simultaneously, the central control unit 30 executes the monitoring logic for the wear status of the cutting head. The central control unit 30 reads the real-time acquired cutting vibration frequency. and cut vibration frequency The frequency is compared with the preset safe range of cutting vibration frequency. If the cutting vibration frequency detected in real time is within the safe range... If the cutting head exceeds the preset range, the central control unit 30 determines that there is a risk of wear or abnormal operating status, and then generates an equipment maintenance prompt signal, which is transmitted to the abnormal warning module 60 for recording and alarm. The specific circuit implementation and data storage method of the above logic operation can be implemented by those skilled in the art using existing industrial control computing technology, and will not be elaborated here.

[0065] See attached document Figure 2 After the central control unit 30 completes the level determination of geological conditions and the identification of attitude anomalies, the processing flow enters step S400, where the execution module executes control commands. During the parameter adjustment phase, the central control unit 30 no longer repeats the data acquisition and comparison; instead, it directly calls the condition level identifier and attitude status identifier generated in steps S200 and S300, sending a coordinated control command containing a preset velocity vector and action sequence to the execution module 40. The coordinated control command logic constructs a coupling model between propulsion speed, cutting head rotation speed, and machine attitude adjustment actions for three different working conditions: hard rock, medium-hard rock, and soft rock.

[0066] When the judgment result output in step S300 is hard rock condition, given that the rock is dense and has high compressive strength under hard rock conditions, the central control unit 30 generates a control command for low-speed propulsion and high-speed cutting. This increases the number of cuts per unit time by increasing the linear velocity of the cutting head, while simultaneously reducing the propulsion speed to decrease the single-tooth cutting thickness, thereby reducing the cutting resistance torque. At this time, the central control unit 30 calculates the target hard rock propulsion speed. Rotation speed of hard rock cutting head The numerical relationship is determined by the following formula: ; ; In the formula, and These are the reference advance speed and the reference cutting head rotation speed set in step S100, respectively. The propulsion velocity attenuation coefficient is limited to a range of values. Up to 0.6; The value of the cutting head rotation speed gain coefficient is limited to a range of values. to .

[0067] Simultaneously with generating the aforementioned control commands, the central control unit 30 queries the attitude status indicator output in step S200. If the indicator shows a deviation in the fuselage horizontal tilt angle... Abnormal (i.e., exceeding the level threshold) or The central control unit 30 executes the attitude priority control logic. At this time, the central control unit 30 temporarily suspends sending speed adjustment commands for the propulsion motor and the cutting head drive motor, and instead prioritizes sending drive signals to the leveling cylinder in the execution module 40. The leveling cylinder performs corresponding extension or retraction actions according to the positive or negative direction of the deviation until the machine body levelness is restored. Within a high-precision range. After confirming that the attitude correction is complete, the central control unit 30 releases the command suspension state and controls the propulsion motor and the cutting head drive motor according to the calculated... and run.

[0068] When the judgment result output by step S300 indicates medium-hard rock working condition, the central control unit 30 generates a control command for steady-speed cutting. Under medium-hard rock working condition, in order to balance tunneling efficiency and energy consumption, the system maintains the cutting head speed as the baseline cutting head speed. The parameters remain unchanged, with only the propulsion speed adjusted adaptively. At this point, the central control unit 30 calculates the propulsion speed in the hard rock within the target area. The calculation formula is: ; In the formula, This is the velocity adjustment coefficient under medium-hard rock conditions, and its value range is limited to [value range missing]. to In the collaborative logic under medium-hard rock conditions, the central control unit 30 primarily responds to the fuselage pitch angle. Abnormal state. If step S200 outputs a pitch attitude abnormality signal, the central control unit 30 controls the lifting cylinder in the execution module 40 to operate: when the pitch angle... When the height exceeds the upper threshold, the lifting cylinder is driven to raise the cutting head height; when the pitch angle... When the height is below the lower threshold, the lifting cylinder is driven to lower the cutting head height until the pitch angle returns to the allowable deviation range, thereby ensuring the verticality of the roadway cross section.

[0069] When the judgment result output in step S300 indicates a soft rock working condition, the central control unit 30 generates a control command for high-speed propulsion and low-speed cutting. Utilizing the easily breakable nature of soft rock, the operation cycle is shortened by increasing the propulsion speed, while simultaneously reducing the cutting head speed to prevent dust accumulation and over-cutting caused by excessive speed. At this time, the central control unit 30 calculates the target soft rock propulsion speed. Rotation speed of soft rock cutting head The numerical relationship is determined by the following formula: ; ; In the formula, The propulsion speed gain coefficient has a value range that is limited to 1. to ; The cutting head rotation speed attenuation coefficient is limited to a range of values. to .

[0070] To address the risk of over-excavation in soft rock conditions, the central control unit 30 incorporates cutting depth constraint logic while executing the aforementioned control commands. The central control unit 30 reads the extension and retraction of the cutting arm and the displacement of the machine body in real time to calculate the current actual cutting depth. If the actual cutting depth deviates from the reference cutting depth... Exceed The central control unit 30 will control the propulsion speed of the soft rock. Fine-tuning compensation is performed to ensure that the tunneling profile strictly conforms to the design requirements. All the above control commands are transmitted to the execution module 40 via the CAN bus, and the variable frequency speed control response of the propulsion motor and the hydraulic drive response of the cylinder are executed within the system's set delay range.

[0071] See attached document Figure 2Throughout the entire process of collaborative control and closed-loop correction, the system executes steps S600 in parallel for abnormal early warning and emergency handling, as well as subsequent parameter adaptive optimization processes. This constitutes a safety defense line and evolution mechanism for tunneling operations, ensuring the safety of the equipment under extreme working conditions and the optimal efficiency of long-term operation.

[0072] In step S600, the central control unit 30 continuously performs safety boundary scanning on the real-time data uploaded by the attitude detection module 10 and the geological identification module 20. When the system detects the fuselage horizontal tilt angle... or pitch angle Exceeding the preset safety threshold (i.e.) , or , When, or when the rock hardness value is detected. When the equipment exceeds its rated design range, the central control unit 30 immediately determines that the current operation is in a dangerous state. Furthermore, regarding the mechanical health of the cutting head, if the cutting vibration frequency is monitored in real time... If the cutting head continues to deviate from the safe range, the central control unit 30 determines that there is a risk of severe wear, tooth breakage, or load instability.

[0073] Once any of the above-mentioned abnormal judgment conditions are triggered, the central control unit 30 immediately sends a trigger signal to the abnormal warning module 60. The abnormal warning module 60 executes a dual alarm response: on the one hand, it controls the audible and visual alarms installed on the tunneling machine's control panel and the ground remote monitoring center to issue warnings simultaneously to remind on-site and remote operators; on the other hand, it executes data alarm logic, packaging and uploading the current abnormal parameter type, value, trigger time, and tunneling machine position coordinates to the ground control center database for storage, providing data traceability for subsequent fault diagnosis and equipment maintenance.

[0074] Simultaneously, the central control unit 30 intervenes in the motion control of the execution module 40, implementing a tiered emergency response strategy. In cases of abnormal vibration frequency or slight deviations in attitude, the central control unit 30 forcibly takes over the speed control of the propulsion motor, executing speed reduction protection logic. The system reduces the current propulsion speed to the baseline tunneling speed. The feed rate is reduced by 30% to 50% to mitigate the impact of the cutting load, providing the system with a buffer time to stabilize itself. In extreme cases where the posture is severely out of control or the rock hardness is uncontrollable, the central control unit 30 controls the execution module 40 to execute the emergency stop logic, cutting off the power output of the cutting head drive motor and the propulsion motor, and locking the position of the hydraulic cylinder. In the stopped state, the system needs to wait for manual confirmation that the fault has been eliminated and reset before the lock can be released and the tunneling operation can be restarted.

[0075] Furthermore, to achieve continuous evolution of the control strategy, this embodiment also employs a parameter adaptive optimization mechanism. The central control unit 30 utilizes its internal memory to record historical operational data under different geological conditions (hard rock, medium-hard rock, soft rock), including the actual parameter combinations used ( The data includes tunneling efficiency per unit time and equipment wear (characterized by the rate of change of vibration frequency). The machine learning algorithm module integrated within the central control unit 30 performs correlation analysis on the above data to evaluate the current benchmark tunneling speed. and the reference cutting head speed and coefficients for each working condition The algorithm determines the matching degree. If it finds that a certain parameter combination has a higher energy efficiency and less wear under certain operating conditions, it will fine-tune the corresponding baseline parameters or coefficient values ​​and update the parameter library. This mechanism enables the tunneling machine to form an adaptive parameter library for the geological characteristics of the mining area as operating time accumulates. When encountering similar operating conditions in the future, it can directly call the optimized parameters, thereby achieving performance improvement without manual intervention.

[0076] See attached document Figure 2 In order to achieve continuous evolution of control strategy and long-term optimization of tunneling efficiency, the system performs collaborative control and closed-loop correction while executing the parameter adaptive optimization process of step S700 in parallel. Relying on the data processing capability of the central control unit 30, a parameter evolution mechanism with self-learning characteristics is constructed.

[0077] In step S700, the central control unit 30 first performs full-cycle operation data recording. The central control unit 30 establishes a historical operation database in its internal memory, recording in real time the combinations of tunneling parameters and corresponding operation performance data under different geological conditions. The tunneling parameter combinations include the actual benchmark tunneling speed. Reference cutting head speed And the machine's attitude adjustment actions; operational performance data includes tunneling progress per unit time (characterizing tunneling efficiency) and the rate of change of cutting vibration frequency (characterizing equipment wear). Each set of data is correlated with the currently collected rock hardness value. The data is stored in association to form a labeled sample dataset.

[0078] The central control unit 30 utilizes a built-in machine learning algorithm module to periodically perform correlation analysis and model training on the aforementioned historical operation database. The machine learning algorithm module compares different parameter combinations for the same or similar rock hardness values. Based on the performance of the system, we can find a control strategy with better energy efficiency. If the machine learning algorithm module finds that within a certain hardness range, we can fine-tune the current benchmark parameters (such as the benchmark propulsion speed). Reference cutting head speed ) or correction factor (such as propulsion speed decay factor) , propulsion speed gain coefficient (etc.), which can improve tunneling efficiency and keep the cutting vibration frequency within a safe range. The central control unit 30 will generate parameter optimization instructions.

[0079] Based on the aforementioned optimization instructions, the central control unit 30 updates the baseline parameters and coefficient values ​​in the parameter library, forming an adaptive parameter library tailored to the geological characteristics of the mining area. For example, if the historical operation database shows rock hardness values... near Under operating conditions, slightly reduce the hard rock propulsion speed attenuation coefficient. It can significantly reduce cutter head wear without significantly reducing tunneling speed. The central control unit 30 will automatically adjust the propulsion speed attenuation coefficient. The set value.

[0080] In subsequent tunneling operations, when the geological identification module 20 detects similar geological conditions again, the central control unit 30 no longer uses the initial default parameters, but directly calls the adaptively optimized parameter combination for control. This mechanism allows the tunneling machine control system to continuously correct the control model as operating time accumulates, achieving performance improvements without manual intervention and ensuring that the equipment always operates at the optimal balance between efficiency and lifespan. The specific code implementation of the above machine learning algorithm can be developed by those skilled in the art using existing supervised learning or reinforcement learning frameworks, and will not be elaborated upon here.

[0081] See attached document Figure 2 To enhance the adaptability of tunneling machines to complex and variable geological environments and address the suboptimal issues that arise from fixed control parameters during long-term operation, the central control unit 30 executes collaborative control and closed-loop correction while simultaneously running a parameter adaptive optimization process. This process, based on machine learning algorithms, establishes a mapping relationship between geological conditions, control parameters, and operational performance. Through in-depth mining and analysis of historical data, it achieves dynamic iteration of baseline parameters and coefficients for various operating conditions within the parameter library.

[0082] The central control unit 30 first performs the full-dimensional recording function of the operation data. During each tunneling operation cycle, the system uses its internal memory to build a historical operation database. The recorded data items cover three dimensions: first, environmental characteristic data, mainly rock hardness values ​​collected by the geological identification module 20. Second, control execution data, including the actual combination of tunneling parameters used and the operating condition coefficients applied at the time; third, operational performance data, including tunneling progress per unit time (characterizing tunneling efficiency) and cutting vibration frequency. Fluctuations (characterizing equipment wear and operational stability).

[0083] Based on the accumulated historical operational data, the machine learning algorithm module built into the central control unit 30 performs correlation analysis and optimization calculations. This algorithm module aims to improve tunneling efficiency and reduce equipment wear, filtering from massive amounts of data to identify factors related to rock hardness. The system identifies the optimal parameter combination within a given interval. Specifically, it evaluates the tunneling performance of different parameter combinations under the same geological conditions. If the algorithm analysis reveals that fine-tuning the baseline tunneling speed under a specific condition yields the best results, the system can determine the optimal combination. Reference cutting head speed Or adjustment factor (e.g.) to This can increase the tunneling footage and the cutting vibration frequency. Always maintain within the preset safety range If the value is within the range, it is determined that the fine-tuned parameter is better than the set value in the current parameter library.

[0084] After confirming the optimization direction, the central control unit 30 performs a parameter library update operation. The system writes the calculated, more optimized baseline parameters or coefficient values ​​into the adaptive parameter library, overwriting the original default settings. For example, if historical data indicates that under certain hard rock conditions, the hard rock velocity attenuation coefficient should be adjusted accordingly. Adjusting the value within the range of 0.4 to 0.6 can significantly reduce the vibration amplitude of the cutting head without significantly affecting the advance speed, and the system will then update. The set values. This update mechanism means that the control strategy of the tunneling machine is no longer static, but gradually forms a set of special parameters for the geological characteristics of the mining area as the operation time goes by and data is accumulated.

[0085] Furthermore, the adaptive optimization mechanism also incorporates long-term monitoring of the cutting head's wear condition. According to the claims and specification, when the central control unit 30 adjusts based on the cutting vibration frequency... By analyzing the changing trends and identifying when the wear level of the cutting head reaches a preset wear warning threshold, the machine learning algorithm automatically intervenes with a replacement warning. The system automatically adjusts tunneling parameters to reduce the wear rate; for example, it appropriately reduces the baseline tunneling speed while ensuring basic operational requirements are met. Alternatively, the rotational speed coefficient can be adjusted to extend the service life of the cutting head until it needs to be replaced. In subsequent tunneling operations, when the geological identification module 20 detects similar geological conditions again, the central control unit 30 directly calls the adaptively optimized parameter combination for control, achieving performance improvement and equipment protection without manual intervention.

[0086] Specific application examples: To more intuitively illustrate the application process of this invention in actual engineering, this embodiment takes the tunneling operation of a transportation roadway in the west wing of a coal mine as an example. The roadway is designed with a rectangular cross-section that is 5.5m wide and 4.0m high. The geological environment is complex, with the hardness of the rock strata along the way fluctuating between 30MPa and 100MPa, and there are local geological structural zones.

[0087] The specific execution process of the intelligent attitude and tunneling parameter coordinated control method described in this invention is as follows: First, initialization parameters are loaded. Before the operation begins, the central control unit 30 reads preset parameters: setting the baseline tunneling speed. The reference cutting head speed is 2.0 m / min. The benchmark tunneling speed is 45 r / min. Set the rock hardness threshold to 0°. The pressure is 40 MPa (boundary between soft rock and medium-hard rock). 80 MPa (boundary between medium-hard rock and hard rock); set the cooperative control coefficient: propulsion speed attenuation coefficient under hard rock conditions. The cutting head speed gain coefficient is 0.5. The propulsion speed gain coefficient under soft rock conditions is 1.4. The cutting head speed attenuation coefficient is 1.2. It is 0.8.

[0088] After the tunneling operation begins, it first enters the high-efficiency tunneling stage in soft rock (tunneling distance 0-100m). The tunneling machine operates in the sandy mudstone section, and the geological identification module 20 collects rock hardness values. The hardness is 35 MPa, which is less than the set rock hardness threshold. The central control unit 30 determines that the working condition is soft rock and immediately outputs a control command: increase the soft rock propulsion speed. And reduce the rotation speed of the soft rock cutting head to The parameter adjustments effectively reduced dust generated during soft rock cutting while maintaining the cutting depth, and improved tunneling efficiency.

[0089] The process then entered the stage of sudden change in operating conditions and coordinated control (at the 100m mark of the tunneling mileage). At the 100m mark, a basalt intrusion was suddenly encountered, and the hardness sensor reading instantly increased to the rock hardness value. The hardness is 95 MPa, which is greater than the set rock hardness threshold. Due to the uneven reaction force from the rocks, the attitude detection module 10 measures the horizontal tilt angle of the fuselage. The temperature reached 3.2°, exceeding the set safety threshold of 3.0°. At this point, the central control unit 30 immediately initiated the coordinated adjustment logic: The first step is to prioritize attitude control. After determining that the fuselage attitude is abnormal, the system temporarily suspends the instruction to adjust the propulsion parameters and prioritizes the action of the leveling cylinder to tilt the fuselage horizontally within 1.5 seconds. Correct it back to within 0.5° to prevent further tilting of the fuselage.

[0090] The second step involves parameter coordination and matching. After the attitude correction is complete, for hard rock conditions, the system outputs a command to reduce the hard rock propulsion speed to [a certain value]. At the same time, the cutting head speed was increased to Through the coordinated operation of speed reduction and rotation, the cutting resistance of high-hardness rock was effectively overcome, and the vibration frequency of the cutting head quickly returned to a safe range, avoiding tooth breakage and equipment shutdown accidents.

[0091] Experimental verification and effect comparison: Experimental results are as follows Figure 3 As shown, in terms of system response speed, the existing technology relies on manual observation of instruments and manual operation, resulting in a response delay of about 4.2 seconds when a sudden change in working conditions occurs. Furthermore, due to differences in human experience, the cutting load fluctuates repeatedly during the adjustment process. In contrast, the system using the method of this invention can complete the response in just 0.2 seconds after detecting a sudden change in working conditions, which improves the response speed by about 95%. It can quickly and smoothly switch the tunneling parameters to the optimal matching value.

[0092] Further combine with the appendix Figure 3 The above experimental procedure will be explained in detail. Figure 3 The graph consists of four sub-graphs sharing the same time axis (horizontal axis, unit: seconds), corresponding to the real-time changes in geological hardness, propulsion speed, cutting head rotation speed, and machine attitude deviation, respectively. Solid lines in the graph represent the system response curve using the method of this invention, while dashed lines represent the response curve of manual control in the prior art.

[0093] See attached document Figure 3 The first subplot (counting from top to bottom) shows the simulation set at time t=20s, where the rock hardness jumps from 50MPa to 90MPa, simulating the abrupt change in working conditions.

[0094] See attached document Figure 3The second and third sub-figures visually demonstrate the timing differences in parameter adjustments. At the abrupt change point of t=20s, the solid line representing the present invention exhibits a near-vertical step change, with the propulsion speed rapidly decreasing from 2.0 m / min to 1.0 m / min, and the cutting head rotation speed simultaneously increasing from 45 r / min to 63 r / min. The curve transition is smooth and without overshoot, clearly demonstrating the 0.2s ultra-fast response characteristic. In contrast, the dashed line representing the prior art maintains its original parameters after t=20s, forming a horizontal delay plateau of approximately 4.2s (marked with double arrows in the figure), only beginning to slowly decline around t=24.2s. During the decline, the curve exhibits obvious wave-like oscillations, corresponding to the aforementioned lag in manual operation and unstable load phenomena.

[0095] See attached document Figure 3 The fourth sub-figure illustrates the dynamic convergence process of the fuselage attitude deviation. The upper and lower dashed lines in the figure represent the preset safety thresholds of +3.0° and -3.0°, respectively. After being subjected to a sudden disturbance, the solid line exhibits only minor fluctuations, with the peak value strictly limited to within 1.2° (as indicated by the hollow dots in the figure), and quickly returns to the 0° baseline, consistently operating between the two safety threshold lines. In contrast, the dashed line diverges significantly after being disturbed, with the peak value exceeding 4.5° (as indicated by the solid dots in the figure) and clearly breaching the upper safety threshold line. Furthermore, its return to stability takes a longer time, visually demonstrating the advantages of this invention in suppressing attitude instability.

[0096] Regarding equipment protection and operational stability, existing technologies, when encountering hard rock, fail to reduce speed in time, resulting in peak vibration acceleration of the cutting head reaching [a certain value]. This can easily cause damage to the cutting teeth or fatigue fracture of the cutting arm; however, this invention effectively suppresses the peak vibration acceleration through coordinated adjustment. The vibration amplitude was reduced by approximately 57.8%, extending the service life of the equipment's core components.

[0097] Regarding attitude control accuracy, under existing technologies, the fuselage attitude correction time for lateral disturbance torque is as long as 16.5 seconds, and the correction process is accompanied by significant overshoot. However, this invention, thanks to its attitude-priority cooperative logic, reduces the attitude correction time to 4.0 seconds, improving correction efficiency by approximately 75%, and controlling the maximum attitude deviation angle within... Within this range, the forming quality and construction precision of the tunnel cross-section are effectively guaranteed.

[0098] Furthermore, in terms of energy consumption control, this invention avoids ineffective high-energy-consumption cutting by optimizing parameter matching, thereby reducing the energy consumption per unit volume of rock cut compared to existing technologies. Reduce to This achieved an energy saving effect of approximately 28.8%. In summary, this invention solves the tunneling problem under complex geological conditions through real-time coordination and closed-loop correction of attitude and tunneling parameters, thereby improving the adaptability, operational accuracy, and overall economic benefits of the tunneling machine.

Claims

1. A method for intelligent attitude and tunneling parameter coordinated control of a tunneling machine, characterized in that, Applied to the control system of a tunneling machine, the following steps are included: The attitude detection module (10) and the geological identification module (20) collect fuselage attitude data and geological data and transmit them to the central control unit (30). The central control unit (30) initializes the benchmark tunneling parameters and safety threshold range, including the benchmark propulsion speed, benchmark cutting head speed and benchmark body level. The central control unit (30) calculates the attitude deviation value based on the fuselage attitude data and the reference fuselage level, and determines the geological working condition level and the wear status of the cutting head based on the geological data. The central control unit (30) generates a collaborative control command containing target parameters based on the geological condition level and the attitude deviation value, and outputs the collaborative control command to the execution module (40) to drive the execution module (40) to synchronously adjust the propulsion speed, cutting head speed and body attitude. The central control unit (30) performs closed-loop correction based on the deviation between the actual working parameters and the target parameters fed back by the feedback module (50); When the fuselage attitude data or the geological data exceeds the safety threshold range or the cutting head wear condition is abnormal, the abnormal warning module (60) is triggered to issue a warning signal.

2. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 1, characterized in that, The attitude detection module (10) collects the fuselage attitude data including the fuselage horizontal tilt angle, pitch angle and the spatial position coordinates of the cutting head; the geological identification module (20) collects the geological data including the rock hardness value and the cutting vibration frequency.

3. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 2, characterized in that, The specific method for determining the geological condition level is as follows: The central control unit (30) presets a first hardness threshold and a second hardness threshold, and the first hardness threshold is less than the second hardness threshold; When the rock hardness value is greater than the second hardness threshold, it is determined to be a hard rock working condition; When the rock hardness value is between the first hardness threshold and the second hardness threshold, it is determined to be a medium-hard rock working condition; When the rock hardness value is less than the first hardness threshold, it is determined to be a soft rock working condition.

4. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 3, characterized in that, When the condition is determined to be hard rock, the specific control strategy of the coordinated control command is as follows: The central control unit (30) calculates the hard rock target advance speed and the hard rock target cutting head rotation speed, wherein the hard rock target advance speed is the product of the reference advance speed and the preset advance speed attenuation coefficient, and the hard rock target cutting head rotation speed is the product of the reference cutting head rotation speed and the preset cutting head rotation speed gain coefficient. The central control unit (30) detects the attitude deviation value. If the attitude deviation value exceeds the preset level threshold, it generates the cooperative control command containing timing logic. The cooperative control command instructs the execution module (40) to perform attitude correction first, and drives the leveling cylinder in the execution module (40) to perform leveling action to correct the horizontal tilt angle of the fuselage to the allowable error range of the reference fuselage level. After the attitude correction is completed, the adjustment of the hard rock target propulsion speed and the hard rock target cutting head rotation speed is performed.

5. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 3, characterized in that, When the working condition is determined to be medium-hard rock, the specific control strategy of the coordinated control command is as follows: The central control unit (30) maintains the cutting head rotation speed at the reference cutting head rotation speed and sets the medium hard rock target propulsion speed to be the product of the reference propulsion speed and the preset medium hard rock speed adjustment coefficient; Meanwhile, the central control unit (30) monitors the pitch angle. If the pitch angle exceeds the preset pitch angle threshold, the lifting cylinder in the execution module (40) is driven by the coordinated control command to perform a height adjustment action to adjust the height of the cutting head.

6. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 3, characterized in that, When the condition is determined to be soft rock, the specific control strategy of the coordinated control command is as follows: The central control unit (30) sets the soft rock target propulsion speed to the product of the reference propulsion speed and the preset propulsion speed gain coefficient, and sets the soft rock target cutting head rotation speed to the product of the reference cutting head rotation speed and the preset cutting head rotation speed attenuation coefficient. Meanwhile, the central control unit (30) acquires the cutting arm extension and body displacement data monitored in real time by the attitude detection module (10), calculates the actual cutting depth, and controls the actual cutting depth within the allowable error range of the preset benchmark cutting depth by adjusting the soft rock target advance speed.

7. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 1, characterized in that, The specific method for performing the closed-loop correction is as follows: The central control unit (30) sets the closed-loop correction cycle and the preset allowable deviation range; If the deviation between the actual working parameters and the target parameters exceeds the allowable deviation range, a correction instruction is generated and sent to the execution module (40) for parameter compensation. When the deviation of multiple consecutive closed-loop correction cycles is within the allowable deviation range, it is determined that the actual working parameters have reached a stable state and the output of the correction command is stopped.

8. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 2, characterized in that, The specific strategy for determining whether to trigger the abnormal warning module (60) is as follows: The central control unit (30) compares the horizontal tilt angle of the fuselage, the pitch angle, the rock hardness value and the cutting vibration frequency with their respective safety thresholds in real time. When the horizontal tilt angle or pitch angle of the fuselage exceeds the preset physical limit threshold, or the rock hardness value changes abruptly and exceeds the preset equipment rated range, or the cutting vibration frequency exceeds the preset safe frequency range, it is determined to be an abnormal state. While triggering the warning signal, the central control unit (30) sends a shutdown control command to the execution module (40), driving the execution module (40) to stop power output and lock the current equipment status.

9. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 1, characterized in that, The specific steps of the central control unit (30) in performing adaptive parameter optimization are as follows: Establish a historical operation database, and classify and store the parameter combinations, tunneling efficiency and equipment wear of each operation according to the corresponding geological condition level; The influence weights of different parameter combinations on tunneling efficiency and equipment wear within the same geological condition level are analyzed using machine learning algorithms. The optimal parameter combination for the geological condition level is selected, and the benchmark tunneling parameters corresponding to the geological condition level are updated using the optimal parameter combination.

10. The intelligent attitude and tunneling parameter coordinated control method for a tunneling machine according to claim 1, characterized in that, The specific method by which the feedback module (50) performs data cleaning is as follows: Set the preset filter window size and collect sampling point data at multiple consecutive time points; The arithmetic mean of the sampled data within the filtering window is calculated using a moving average filtering algorithm. The arithmetic mean is transmitted as the actual working parameter to the central control unit (30) to perform the closed-loop correction.

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