Intelligent operation method and system for double-arm drill jumbo of large-section tunnel

Abstract

The invention discloses an intelligent operation method and system for a double-arm drill jumbo of a large-section tunnel, and relates to the field of tunnel construction, the geometry, integrity, dust and humidity data of a section are collected in real time through a laser radar, an IMU and a surrounding rock pressure and environment sensor, and an operation environment database is constructed and updated; by combining the strength grade of the surrounding rock and the distribution of the soft layer, the posture of the arm support, the chiseling frequency and pressure and the rock entering angle of the bucket teeth are adjusted online in a self-adaptive mode; switching among a high-frequency impact mode, a bucket tooth wedging prying mode and a free face gravity automatic rock breaking mode according to working conditions; excessive impact is restrained and protection is triggered through a vibration monitoring closed loop, efficiency is improved, energy consumption is reduced, secondary damage is reduced, and construction safety and quality are guaranteed.

Description

Technical Field

[0001] This specification relates to the field of tunnel construction, and more specifically, this application relates to an intelligent operation method and system for a dual-arm rock drilling rig for large-section tunnels. Background Technology

[0002] In existing tunnel construction, dual-arm rock drilling rigs typically rely on manual operation for rock breaking. Operators set parameters such as boom posture, drilling frequency, drilling pressure, and bucket tooth entry angle based on experience. However, traditional methods suffer from the following technical drawbacks: The complexity of the working environment and surrounding rock conditions makes it difficult for human experience to match rock breaking parameters in a timely and accurate manner. The geometric differences in tunnel cross-sections vary greatly in different sections, and the integrity and strength grade of the surrounding rock fluctuate significantly. Especially in areas with weak surrounding rock, improper parameter settings can easily lead to excessive disturbance or secondary damage.

[0003] Traditional rock breaking methods are singular and mostly rely on high-frequency impact. Although they have an efficiency advantage in hard surrounding rocks, they are prone to causing rock collapse, crack expansion and increased energy consumption when joints, fissures or weak interlayers are present. There is a lack of a targeted rock breaking mode switching mechanism.

[0004] The construction process lacks a systematic understanding of environmental factors and equipment status. Environmental variables such as dust concentration, humidity, and vibration levels are often not monitored and reported in real time, which can easily lead to a deterioration of the construction environment and potential damage to equipment, as well as increase safety hazards.

[0005] There is a lack of effective vibration monitoring and control measures. If the impact vibration generated during rock breaking is not monitored and suppressed in real time, it can easily cause secondary damage or collapse of the weak surrounding rock when the vibration exceeds the critical threshold, which will have an adverse impact on tunnel safety and construction progress.

[0006] Therefore, it is necessary to propose an intelligent operation method for dual-arm drilling rigs suitable for large-section tunnels in weak surrounding rock to solve some of the above-mentioned problems. Summary of the Invention

[0007] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0008] Firstly, this application proposes an intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock, including: By deploying lidar, inertial measurement units, surrounding rock pressure sensors, and environmental sensors on the dual-arm rock drilling rig, the system acquires tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration, and humidity information to form an operational environment database. Based on the aforementioned operational environment database, and combined with the strength grade of the surrounding rock in the tunnel and the distribution characteristics of weak surrounding rock, the intelligent control system dynamically adjusts the attitude parameters, drilling frequency, drilling pressure, and rock entry angle of the double-arm rock drilling rig to achieve adaptive matching of rock breaking parameters. At different stages of operation, high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking mode utilizing the gravity of the free surface are used separately or in combination. By monitoring the rock-breaking impact response using vibration sensors, excessive vibration can be suppressed in real time to prevent secondary damage to weak surrounding rock.

[0009] In one feasible implementation, the aforementioned laser radar, inertial measurement unit, and surrounding rock sensors deployed on the dual-arm rock drilling rig acquire tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration, and humidity information to form an operational environment database, including: The tunnel cross-section point cloud was scanned by lidar and the cross-sectional geometric parameters were fitted. Attitude data is acquired through an inertial measurement unit, and the geometric parameters are corrected for attitude. The integrity of the surrounding rock is estimated by measuring contact pressure and stress fluctuations using a surrounding rock pressure sensor. Dust concentration and humidity levels are obtained through environmental sensors; The aforementioned geometric parameters, surrounding rock integrity indicators, dust concentration, and humidity information are stored in the work environment database.

[0010] In one feasible implementation, based on the aforementioned operational environment database and considering the strength grade of the tunnel surrounding rock and the distribution characteristics of weak surrounding rock, the intelligent control system dynamically adjusts the attitude parameters, drilling frequency, drilling pressure, and rock-entry angle of the boom of the double-arm rock-drilling rig to achieve adaptive matching of rock-breaking parameters, including: Read the above-mentioned tunnel cross-sectional geometric parameters and surrounding rock integrity indicators from the working environment database, and compare them with the surrounding rock strength grading standard to determine the tunnel surrounding rock strength level. Based on the above-mentioned tunnel surrounding rock strength grade parameter table, set the initial values ​​of the boom posture parameters, drilling frequency, drilling pressure and the rock entry angle of the bucket teeth of the double-arm drilling rig. During operation, feedback results from vibration and pressure sensors are collected to monitor rock-breaking impact response, fracture propagation rate, and energy consumption level. When the parameters deviate from the preset range, parameter correction is triggered. Specifically, when the fracture propagation rate is below the threshold, the drilling frequency is increased or the rock entry angle is increased. When the vibration intensity or energy consumption level exceeds the limit, the drilling pressure is reduced or the boom posture is adjusted.

[0011] In one feasible implementation, at different operational stages, high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free face are employed separately or in combination, including: High-frequency impact rock breaking mode is used in the initial rock breaking stage to quickly form fractures; During the fracture propagation stage, a combination of bucket tooth wedge-pry rock-breaking mode and high-frequency impact rock-breaking mode is used to promote fracture propagation. During the rock breaking and unloading stage, the free-face gravity automatic rock breaking mode is mainly used to achieve natural rock spalling. In the final shaping stage, high-frequency impact rock breaking mode was re-adopted and combined with prying action to ensure the quality of cross-section forming.

[0012] In one feasible implementation, within a preset evaluation window, indices including fracture propagation rate, rock breaking energy consumption per unit volume, vibration intensity and frequency band distribution, slag particle size distribution and output, and contact stiffness are continuously calculated. When at least two indicators stably cross the corresponding threshold and continue to reach the preset time or number of times, it is determined that the transition state has entered the adjacent target stage. Under the aforementioned transitional state, the rock breaking parameters of adjacent two stages are linearly or piecewise S-shaped interpolated according to weights, and the shortest smoothing time is set to avoid abrupt changes that could cause secondary damage to the surrounding rock.

[0013] In one feasible implementation, a vibration sensor monitors the rock-breaking impact response, suppresses excessive vibration in real time, and prevents secondary damage to the weak surrounding rock, including: The triaxial acceleration signals from the vibration sensor, the surrounding rock pressure sensor, and the energy consumption monitoring data are fused from multiple sources to establish a vibration-energy consumption-surrounding rock damage coupling model. Based on the above vibration-energy consumption-surrounding rock damage coupling model, the surrounding rock damage risk index is calculated. When the above risk index reaches the warning level, adaptive control is executed. The adaptive control includes reducing the drilling pressure, fine-tuning the boom posture, and maintaining the drilling frequency within a safe range. Vibration trends are predicted using a vibration trend prediction model trained on historical data. When both the prediction results and real-time monitoring indicate an increase in risk, a protection mode is entered to forcibly reduce the intensity of operations or suspend operations.

[0014] In one feasible implementation, the specific processing steps of the above-mentioned vibration-energy consumption-surrounding rock damage coupling model include: Input the above triaxial acceleration signal, the above surrounding rock pressure sensor, and the above energy consumption monitoring data; Frequency band energy, surrounding rock damage factor, and energy consumption per unit volume are extracted through signal processing. The above-mentioned frequency band energy, surrounding rock damage factors and unit volume energy consumption characteristics are integrated into a comprehensive damage index using weighted fuzzy logic, and the surrounding rock damage risk index is output.

[0015] In one feasible implementation, the specific processing steps of the above-mentioned vibration trend prediction model include: Historical vibration amplitude, dominant frequency distribution, and energy consumption trends are used as inputs; The temporal characteristics of vibration signals are modeled using a long short-term memory network; Output the vibration trend for future periods and compare it with the real-time monitoring results. When both the prediction and the real-time results indicate an increased risk, trigger the protection mode.

[0016] In one feasible implementation, the above method further includes: The intelligent control system monitors the power output of the diesel power system and hydraulic system, and dynamically adjusts the energy distribution based on optimization algorithms to minimize energy consumption during the rock breaking process.

[0017] Secondly, this invention also proposes an intelligent operation control system for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock, comprising: The database construction unit is used to acquire tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration and humidity information by using lidar, inertial measurement unit, surrounding rock pressure sensor and environmental sensor deployed on the double-arm rock drilling rig, and to form an operational environment database. The adaptive matching unit is used to dynamically adjust the attitude parameters, drilling frequency, drilling pressure and rock entry angle of the boom of the double-arm rock drilling rig according to the above-mentioned working environment database, combined with the strength level of the surrounding rock of the tunnel and the distribution characteristics of the weak surrounding rock, so as to realize the adaptive matching of rock breaking parameters. The rock breaking mode selection module allows for the use of high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free surface, either individually or in combination, at different operational stages. The damage monitoring unit is used to monitor the rock-breaking impact response through vibration sensors, suppress excessive vibration in real time, and prevent secondary damage to weak surrounding rock.

[0018] In summary, this invention, by deploying lidar, inertial measurement units, surrounding rock pressure sensors, and environmental sensors on a dual-arm rock drilling rig, can collect real-time information on tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, and environmental information such as dust concentration and humidity, forming a dynamically updated operational environment database. This multi-source information fusion method overcomes the limitations of traditional reliance on manual experience, ensuring that rock-breaking parameters are set based on objective data, significantly improving the scientific rigor and accuracy of decision-making. The intelligent control system of this invention can dynamically adjust key parameters such as boom posture, drilling frequency, drilling pressure, and bucket tooth entry angle based on the surrounding rock strength level and the distribution characteristics of weak surrounding rock, achieving adaptive matching of the rock-breaking process. This not only improves rock-breaking efficiency but also effectively reduces energy consumption and avoids construction risks caused by parameter mismatch. This invention proposes a multi-mode rock-breaking mechanism that can flexibly switch between high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free face at different construction stages according to geological conditions. Compared to the single high-frequency impact method in related technologies, this solution significantly enhances the system's adaptability to complex working conditions, optimizing energy utilization efficiency while reducing disturbance to the surrounding rock. This invention introduces a real-time monitoring and suppression mechanism based on vibration sensors, enabling immediate action when vibration intensity exceeds a preset threshold. This includes measures such as reducing drilling pressure, fine-tuning the boom posture, or adjusting the drilling frequency, and entering a protection mode when necessary. This proactive safety control effectively prevents secondary damage to weak surrounding rock, improving the safety and reliability of tunnel construction. In summary, this invention not only transforms rock breaking operations from experience-driven to data-driven but also surpasses existing technologies in terms of construction efficiency, energy consumption control, safety protection, and adaptability to working conditions, demonstrating significant practical value and potential for widespread application.

[0019] Other advantages, objectives and features of this application will be partly apparent from the description below, and partly understood by those skilled in the art through study and practice of this application. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This invention provides a flowchart illustrating an intelligent operation method for a dual-arm rock drilling rig suitable for large-section tunnels in weak surrounding rock. Figure 2 This invention provides a flowchart illustrating a method for forming a work environment database. Figure 3A flowchart illustrating an adaptive matching method for rock breaking parameters provided by this invention. Figure 4 This invention provides a flowchart illustrating a method for selecting automatic rock-breaking modes at different operational stages. Figure 5 This is a flowchart illustrating a method for preventing secondary damage to weak surrounding rock provided by the present invention. Figure 6 This invention provides a structural schematic diagram of an intelligent operation control system for a dual-arm rock drilling rig suitable for large-section tunnels in weak surrounding rock. Detailed Implementation

[0021] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0022] Please see Figure 1 This is a schematic flowchart illustrating an intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock, provided by an embodiment of this application. Specifically, it may include: S110. By using lidar, inertial measurement unit, surrounding rock pressure sensor and environmental sensor deployed on the double-arm rock drilling rig, the tunnel cross-section geometric parameters, surrounding rock integrity index, environmental dust concentration and humidity information are obtained to form an operating environment database. S120. Based on the above-mentioned working environment database, combined with the strength grade of the tunnel surrounding rock and the distribution characteristics of weak surrounding rock, the intelligent control system dynamically adjusts the attitude parameters, drilling frequency, drilling pressure and rock entry angle of the double-arm rock drilling rig boom to achieve adaptive matching of rock breaking parameters. S130. At different operation stages, high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking mode using gravity of the free surface are adopted separately or in combination. S140. The rock-breaking impact response is monitored by a vibration sensor to suppress excessive vibration in real time and prevent secondary damage to the weak surrounding rock.

[0023] For example, at the start of the operation, information such as the cross-sectional geometry of the tunnel face, the integrity of the surrounding rock, dust concentration, and humidity are collected in real time using lidar, inertial measurement units, surrounding rock pressure sensors, and environmental sensors deployed on the trolley. This information is uniformly stored in the operation environment database and organized by cross-section and operation section, enabling the control system to access and update it at any time.

[0024] Based on the obtained environmental and surrounding rock conditions, the intelligent control system dynamically matches rock-breaking parameters by combining the surrounding rock strength grade and the distribution of weak surrounding rock from geological exploration. Specifically, this includes: real-time adjustment of the boom posture of the dual-arm drilling rig, control of drilling frequency and pressure, and optimization of the bucket tooth entry angle. In this way, the rock-breaking action closely matches the actual geological conditions, eliminating reliance on operator experience and thus improving operational efficiency and reducing the risk of misoperation.

[0025] This embodiment supports different rock-breaking modes at different construction stages. When the surrounding rock integrity is high, high-frequency impact rock breaking can be used to improve efficiency. When there are obvious joints, fissures, or weak layers, the system can switch to the bucket tooth wedging and prying rock breaking modes to reduce energy consumption and disturbance to the surrounding rock. When the free surface conditions allow for the use of gravity, the system can induce self-weight fracturing of the free surface with lower energy, forming an automated rock-breaking mode. The flexible combination of multiple modes ensures that the system maintains good adaptability under various complex working conditions.

[0026] In terms of safety, this embodiment incorporates a vibration monitoring mechanism. Vibration sensors deployed at key locations on the equipment enable the system to capture vibration responses during the rock-breaking impact process in real time. When the detected vibration intensity exceeds a preset threshold, the control system immediately takes suppression measures, such as reducing drilling pressure, fine-tuning the boom posture, or adjusting the drilling frequency, thereby preventing secondary damage to the weak surrounding rock. If the vibration remains excessive, a protection mode can be triggered, forcibly reducing the operational intensity or suspending operations to ensure the safety of the equipment and the tunnel.

[0027] In summary, this invention, by deploying lidar, inertial measurement units, surrounding rock pressure sensors, and environmental sensors on a dual-arm rock drilling rig, can collect real-time information on tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, and environmental information such as dust concentration and humidity, forming a dynamically updated operational environment database. This multi-source information fusion method overcomes the limitations of traditional reliance on manual experience, ensuring that rock-breaking parameters are set based on objective data, significantly improving the scientific rigor and accuracy of decision-making. The intelligent control system of this invention can dynamically adjust key parameters such as boom posture, drilling frequency, drilling pressure, and bucket tooth entry angle based on the surrounding rock strength level and the distribution characteristics of weak surrounding rock, achieving adaptive matching of the rock-breaking process. This not only improves rock-breaking efficiency but also effectively reduces energy consumption and avoids construction risks caused by parameter mismatch. This invention proposes a multi-mode rock-breaking mechanism that can flexibly switch between high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free face at different construction stages according to geological conditions. Compared to the single high-frequency impact method in related technologies, this solution significantly enhances the system's adaptability to complex working conditions, optimizing energy utilization efficiency while reducing disturbance to the surrounding rock. This invention introduces a real-time monitoring and suppression mechanism based on vibration sensors, enabling immediate action when vibration intensity exceeds a preset threshold. This includes measures such as reducing drilling pressure, fine-tuning the boom posture, or adjusting the drilling frequency, and entering a protection mode when necessary. This proactive safety control effectively prevents secondary damage to weak surrounding rock, improving the safety and reliability of tunnel construction. In summary, this invention not only transforms rock breaking operations from experience-driven to data-driven but also surpasses existing technologies in terms of construction efficiency, energy consumption control, safety protection, and adaptability to working conditions, demonstrating significant practical value and potential for widespread application.

[0028] In one feasible implementation, such as Figure 2 As shown, step S110 above uses lidar, inertial measurement units, and surrounding rock sensors deployed on the dual-arm rock drilling rig to acquire tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration, and humidity information, forming a working environment database, including: S1101. Scan the tunnel cross-section point cloud using lidar and fit the cross-section geometric parameters; S1102. Attitude data is acquired through the inertial measurement unit and the geometric parameters are corrected for attitude. S1103. Measure contact pressure and stress fluctuations using a surrounding rock pressure sensor to estimate the integrity of the surrounding rock. S1104. Obtain dust concentration and humidity levels through environmental sensors; S1105. Store the above geometric parameters, surrounding rock integrity index, dust concentration and humidity information in the working environment database.

[0029] For example, after the dual-arm rock drilling rig enters the site, it first completes a power-on self-test and calibration. The lidar and inertial measurement unit share a time reference and perform extrinsic parameter verification, while the surrounding rock pressure sensor and environmental sensor confirm zero point and range. Then, it enters the continuous acquisition step. The lidar performs multiple scans of the working face and adjacent shoulder area. The system automatically removes self-points of the rig body and boom and suppresses isolated echoes caused by dust, obtaining a stable and continuous cross-sectional point cloud. To avoid the influence of minor attitude drift of the rig on geometric judgment, the roll, pitch, and heading data output in real time by the IMU are used to perform spatiotemporal distortion correction and attitude adjustment on the point cloud, ensuring that the cross-sectional contour is always unified to the same reference frame. Through contour recognition and template fitting, the system obtains geometric parameters such as the area of ​​the cross-section, the deviation of the sidewalls and the crown, the free surface normal, and the local curvature, and generates a cross-sectional slice sequence bound to the thrust direction.

[0030] The system synchronously records contact pressure and stress fluctuations during the impact process using surrounding rock pressure sensors deployed at the end of the boom and near the bucket teeth. From this data, the system identifies characteristics such as the stable loading phase, impact peak, and recovery stage. Combined with the force-displacement behavior of the trolley's micro-displacement and the prying action, it infers the integrity and structural features of the surrounding rock. If the peak value is low, recovery is slow, and a significant plateau appears during prying, it often corresponds to weak interlayers or areas with well-developed joints. If the peak value is high, recovery is rapid, and the pulse is dense, it is more likely to be intact and dense surrounding rock. This judgment process is spatially aligned with the newly generated cross-sectional geometric slices to form a joint "geometry-mechanics" annotation layer, marking integrity indicators and their confidence levels on the cross-section in a block-by-block manner.

[0031] A front-end dust sensor provides local dust concentration, while a side-rear humidity sensor provides overall humidity levels. The system uses this information to score the point cloud quality and dynamically adjust the processing strategy. When a short-term increase in dust leads to sparse echoes, the system appropriately extends the point cloud accumulation time on that side and increases the IMU constraint weights to ensure stable contour fitting. When high humidity introduces water film reflection, the algorithm suppresses abnormally high reflectivity areas to prevent cross-sectional shape drift. All observations from lidar, IMU, pressure, and environmental sensors are timestamped and written to the operational environment database using a ternary index of "time-cross-section number-spatial block". The main records include cross-sectional geometric parameters, attitude correction results, surrounding rock integrity indicators, dust and humidity levels, and corresponding data quality markers. The original point cloud and time-series signal are stored together via a foreign key for easy traceability and verification.

[0032] In one feasible implementation, such as Figure 3As shown, in step S120 above, based on the aforementioned operational environment database and considering the strength grade of the tunnel surrounding rock and the distribution characteristics of weak surrounding rock, the intelligent control system dynamically adjusts the attitude parameters, drilling frequency, drilling pressure, and rock-entry angle of the double-arm rock-drilling rig's boom to achieve adaptive matching of rock-breaking parameters, including: S1201. Read the above-mentioned tunnel cross-sectional geometric parameters and the above-mentioned surrounding rock integrity index from the working environment database, and compare them with the surrounding rock strength grading standard to determine the tunnel surrounding rock strength grade. S1202. Based on the above-mentioned tunnel surrounding rock strength grade parameter table, set the initial values ​​of the attitude parameters, drilling frequency, drilling pressure and the angle of the bucket teeth entering the rock of the double-arm drilling rig. S1203. During operation, the feedback results of vibration sensors and pressure sensors are collected to monitor rock breaking impact response, fracture propagation rate and energy consumption level. When the parameters deviate from the preset range, parameter correction is triggered. Specifically, when the fracture propagation rate is lower than the threshold, the chiseling frequency is increased or the rock entry angle is increased. When the vibration intensity or energy consumption level exceeds the limit, the chiseling pressure is reduced or the boom posture is adjusted.

[0033] For example, at the start of the operation, the system first reads the geometric parameters and surrounding rock integrity indices of the most recent one or several cross-sections from the operation environment database, and compares them with the preset surrounding rock strength grading standards to obtain the surrounding rock strength level and its confidence level of the current operation section. To avoid misjudgment caused by fluctuations in a single measurement, the system smooths the strength levels of adjacent cross-sections and applies a reduction coefficient to the confidence level under the weak zone label, making the subsequent control strategy more conservative.

[0034] After determining the strength level, the system uses this information to call the parameter table and generate a set of initial control variables for the dual-arm drilling rig, including boom posture, drilling frequency, drilling pressure, and bucket tooth entry angle. The parameter table is obtained through experimental calibration and offline tuning using historical construction data, and is stored in partitions according to strength level, free surface normal, humidity, and dust quality labels. When the environmental label indicates that dust or humidity is unfavorable for sensing and cutting, the system will automatically add safety corrections to the initial values, such as appropriately reducing the upper limit of drilling pressure and frequency, and tightening the entry angle range. To ensure the stability of the mechanical and hydraulic systems, the initial control variables are subjected to amplitude limiting and rate of change constraint processing before being issued to avoid sudden attitude transitions and sudden increases in impact energy.

[0035] Once the operation begins, the system continuously collects feedback from vibration and pressure sensors to construct three types of online observations of the rock-breaking process: first, the rock-breaking impact response, used to identify signs of over-excitation or resonance; second, the fracture propagation rate, inferred from the impact-recovery rhythm, load release characteristics, and effective fragmentation degree of repeated impacts; and third, the energy consumption level, estimated from indicators such as the combined power of hydraulic pressure and flow, energy consumption per unit advance, and number of impacts. The data from these observations are mapped in real-time to a set of preset intervals, and the deviation and duration are calculated based on historical trajectories to determine whether parameter corrections are triggered.

[0036] When the system detects that the crack propagation rate is consistently below the threshold and energy consumption is trending upward, an "efficiency-first" correction strategy is prioritized. This involves slightly increasing the chisel frequency to make the crushing action more concentrated, without exceeding the structural resonance and vibration limits. If the frequency increase is limited or the effect is still insignificant, the rock entry angle is finely adjusted to make the normal component of the bucket teeth more conducive to splitting along the structural surface. To avoid secondary impacts caused by frequent oscillations, the rock entry angle adjustment is set with a minimum step size and a minimum dwell time, and is linked to the boom posture to maintain stable contact between the tool and the free surface.

[0037] When vibration intensity or energy consumption levels approach or exceed the upper limit, the system switches to a "safety-first" correction strategy. This involves first appropriately reducing the impact pressure to mitigate the transmission of single impact energy to the structure. If the vibration still does not return to the safe range, the boom posture is adjusted to improve the force direction, and the frequency is lowered if necessary to avoid potential structural resonance zones. The entire correction process incorporates hysteresis and graded threshold design; parameter adjustment is only implemented when the deviation reaches the trigger value and persists for more than the shortest time window. After returning to the safe range, a dwell period is maintained before considering a correction to avoid repeated fluctuations.

[0038] Considering the coupling relationship of the dual-arm collaborative operation, the system performs a consistency check on the spatial position and force distribution of the two arms before issuing correction commands. Specifically, when one side is marked as a weak zone or has significantly different rebound characteristics, the system adopts a "master-slave" collaborative strategy, allowing the side with more stable rock to undertake more propulsion tasks, while the weak side uses conservative parameters for cutting and directional splitting to prevent local spalling at the working face or equipment instability due to asynchronous operation of the two arms. If both arms simultaneously experience high energy consumption and vibration, the system prioritizes symmetrical convergent correction of pressure and attitude to ensure overall force balance of the frame.

[0039] To balance efficiency and equipment lifespan, the rate of change and accelerometer of all control variables are limited. Vibration monitoring, as a higher-level safety constraint, can apply a real-time "brake" to the parameter solution results. Even if the efficiency module suggests increasing the frequency and pressure, as long as the vibration approaches the upper limit, the safety module can clamp it within the safety envelope. At the same time, the system records a three-dimensional log of "operating condition-command-response" after each correction for rolling evaluation of the correction effectiveness: if two consecutive corrections fail to improve fracture propagation or energy consumption indicators, the system will prompt the operator to check the bucket tooth wear, lubrication, and cooling conditions, or suggest switching to a more suitable rock breaking mode (such as wedge-type pry bar or gravity self-breaking), achieving timely transition at the strategy level.

[0040] In case of abnormalities or sensor degradation, the system possesses an orderly degradation capability. If the vibration or pressure sensor data quality score falls below a threshold, the controller will automatically relax the trigger conditions and converge to a more conservative parameter range, while simultaneously prompting on the interface to enter semi-automatic mode, allowing the operator to fine-tune within a safe envelope. After external interference or data interruption recovery, the system will not immediately return to the previous settings, but will employ a gradual escalation strategy to progressively approach the target value, ensuring a smooth and controllable recovery process.

[0041] Through the above process, this implementation method achieves an adaptive control closed loop based on the surrounding rock strength grade as a priori and online feedback as the correction basis. When fracture propagation is insufficient, the system tends to improve fragmentation efficiency by fine-tuning the frequency and angle; when vibration or energy consumption exceeds limits, the system prioritizes conservative adjustments to pressure and attitude to reduce excitation and power peaks. Combined with the consistency constraint and safety hysteresis mechanism of dual-arm coordination, the overall control can respond quickly to changes in working conditions while avoiding parameter fluctuations and secondary damage, ultimately achieving a stable balance between efficiency, forming quality, and equipment safety.

[0042] In one feasible implementation, such as Figure 4 As shown, step S130 employs, either individually or in combination, high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free face at different operational stages, including: S1301. High-frequency impact rock breaking mode is used in the initial rock breaking stage to quickly form fractures; S1302. During the fracture propagation stage, a combination of bucket tooth wedge-pry rock-breaking mode and high-frequency impact rock-breaking mode is used to promote fracture propagation. S1303. During the rock breaking and unloading stage, the free-face gravity automatic rock breaking mode is mainly adopted to achieve natural rock spalling. S1304. In the final shaping stage, the high-frequency impact rock breaking mode is re-adopted and combined with the prying action to ensure the quality of the cross-section forming.

[0043] For example, the rock-breaking process is divided into four stages: initial rock-breaking, fracture propagation, rock-breaking unloading, and final shaping. Based on the principle of "which action is most effective and safest in advancing the current goal," three modes—high-frequency impact, bucket tooth wedging, and free-face gravity automatic rock-breaking—are dispatched separately or in combination at each stage. Stage determination is not solely based on time, but rather on online identification of indicators such as cross-sectional geometry, stress response, debris particle size, and energy consumption trends. Frequent switching is avoided by using the shortest residence time and hysteresis threshold.

[0044] In the initial rock-breaking stage, the system prioritizes high-frequency impact rock-breaking, aiming to rapidly establish a microfracture network with sufficient density and connectivity using the least possible single-blow energy. The controller aligns the two arms with the free surface normal, setting a relatively high but limited impact frequency and moderate impact pressure, making the impact more inclined towards "fracture initiation rather than brutal splitting." Vibration monitoring continues, and when signs of structural resonance or abnormal accumulation of low-frequency energy appear, it is first resolved by fine-tuning the attitude and slightly reducing the pressure; if the debris particle size distribution shows that the proportion of large pieces is still too high or the energy consumption decreases with the number of blows, the system will appropriately increase the frequency to improve the fracture nucleation density. The signs of entering the next stage are usually manifested as smoother force-displacement recovery after impact, reduced rebound amplitude, a shift towards medium and finer debris particle size, and a more stable energy consumption per unit depth.

[0045] During the fracture propagation stage, the system employs a combined strategy of "high-frequency impact + wedging and prying" to drive the penetration and connection of existing fractures with low incremental energy. On one hand, it maintains high-frequency, low-energy impacts to keep the fracture tip activated and microfractures growing continuously; on the other hand, it fine-tunes the rock entry angle based on the structural plane orientation and weak zone markings, and applies a directional normal component force with prying action to promote crack propagation along favorable planes, connection, and the formation of controllable cleavage. When the two arms work together, the side with more intact lithology bears a slightly higher impact load, while the weak side mainly uses wedging and small-amplitude prying to avoid excessive disturbance to the weak zone. The system continuously observes the effectiveness of fracture propagation. When it is found that the load release after a single impact is more complete, the average size of the debris decreases, and there is obvious surface slippage or slight chipping after prying, the propagation target is considered to have been achieved, and it is ready to move to the unloading stage.

[0046] During the rock-breaking and unloading phase, the system's focus shifts from "forceful breaking" to "natural unloading." When the geometry of the free surface and the supporting conditions below meet the requirements for utilizing its own weight, the controller reduces the impact energy. After completing the necessary cutting or weakening actions along a predetermined path, the main impact is briefly stopped, with only low-energy probing and safety monitoring remaining, allowing gravity on the free surface to facilitate natural spalling. To prevent instability in non-target areas, the system strictly controls the attitude and contact state of the two arms. If necessary, one arm maintains the wall-protecting posture while the other arm performs light probing, adjusting the waiting and cleaning rhythm based on dust and humidity information. If a local panel is detected as not being fully released, the system will assist its detachment with a very small torque lever or short pulse impact; if there are signs of unplanned rockfall, the system will immediately retreat and unload, continuing only after the vibration subsides.

[0047] In the final shaping phase, the system reverts to primarily high-frequency impact, supplemented by prying, focusing on contour refining key areas such as sidewalls, arch shoulders, and inverts. The objective shifts from "breaking" to "forming," with more conservative parameter settings prioritizing ensuring the cross-sectional contour closely approximates the design line and surface smoothness, avoiding damage to the established structural stability. During operation, the lidar periodically scans and compares with the design template. If edge deviations or local undulations exceed allowable limits, the controller issues fine-tuning instructions to address high points or burrs. For weak zone boundaries, the system limits the upper limits of the rock penetration angle and pressure, and shortens the duration of continuous impacts to prevent inducing secondary disturbances.

[0048] For example, on a tunnel face with seepage on the right shoulder and consisting of medium-hard rock interspersed with weak layers, the system first establishes a fracture network using high-frequency impact. Then, recognizing the small angle between the strike of the weak layer on the right and the free surface, the system maintains fracture tip activity with high-frequency impact while fine-tuning the rock penetration angle and applying prying to allow the fractures to quickly penetrate along the structural surface. Once a controllable thin plate-like section is formed in the upper part, the system reduces energy, pauses the main impact, and allows gravity to facilitate the plate's detachment. Finally, high-frequency, low-energy reshaping of the boundary is used to achieve the designed cross-sectional shape. Throughout the process, abnormal vibration and energy consumption are constrained by higher-level controls: once approaching the safety limit, the system prioritizes returning to the safe range by reducing drilling pressure, refining prying amplitude, and optimizing posture; if no improvement is seen in a short time, the operation phase will not be forcibly advanced but will maintain the current state or revert to a more conservative combination.

[0049] Through the aforementioned phased and combinatorial arrangement, this implementation method ensures efficiency while also considering forming quality and surrounding rock safety. The initial stage uses high-frequency impact for rapid fracturing; the propagation stage uses wedge-driven prying for directional penetration; the unloading stage utilizes low-energy gravity spalling; and the final stage uses low-energy shaping to control the rock breaking line. Combined with online identification, hysteresis and dwell mechanisms, dual-arm coordination, and safety envelope constraints, the system can adapt to complex and changing geological conditions and environments, reducing energy consumption and the risks associated with excessive vibration, ultimately achieving stable, controllable, and high-quality rock breaking and forming.

[0050] In one feasible implementation, within a preset evaluation window, indices including fracture propagation rate, rock breaking energy consumption per unit volume, vibration intensity and frequency band distribution, slag particle size distribution and output, and contact stiffness are continuously calculated. When at least two indicators stably cross the corresponding threshold and continue to reach the preset time or number of times, it is determined that the transition state has entered the adjacent target stage. Under the aforementioned transitional state, the rock breaking parameters of adjacent two stages are linearly or piecewise S-shaped interpolated according to weights, and the shortest smoothing time is set to avoid abrupt changes that could cause secondary damage to the surrounding rock.

[0051] For example, the control system sets a scrolling preset evaluation window for the rock breaking process. All sensor data within the window are timestamped and spatiotemporally aligned. Within this window, the system continuously extracts and updates five key indicators: fracture propagation rate, rock breaking energy consumption per unit volume, vibration intensity and its frequency band distribution, slag particle size distribution and output, and contact stiffness. Fracture propagation rate is not directly observed but is inferred indirectly from indirect quantities such as impact-recovery rhythm, load release amplitude, and effective fragmentation degree after repeated impacts. Rock breaking energy consumption per unit volume is calculated by combining the power integral of hydraulic pressure and flow rate with the advance volume, reflecting energy conversion efficiency. Vibration intensity and frequency band distribution are derived from spectral analysis of multi-point acceleration sampling at the frame and boom end, used to identify whether structural resonance is approaching or a low-frequency large-amplitude response occurs. Slag particle size distribution and output are estimated using visual or weighing devices to measure particle size distribution and slag discharge per unit time, indirectly reflecting the crushing effect and penetration degree. Contact stiffness is estimated based on the force-displacement slope and rebound characteristics, characterizing the local integrity and bearing capacity of the rock mass. To suppress noise and short-term fluctuations, the system first performs quality scoring and anomaly removal on each indicator, and then uses short-term smoothing and median filtering to form robust window statistics, while saving the rate of change for trend identification.

[0052] The threshold settings incorporate static baselines derived from construction experience and experimental calibration, while also dynamically adjusting for environmental factors such as surrounding rock strength grade, humidity, and dust levels, ensuring the thresholds are adaptive under different working conditions. The system employs a "multiple evidence consistency" principle to initiate stage transitions. Only when at least two key indicators consistently cross their respective target thresholds within the evaluation window, and continuously reach a preset time or number of times, is the current stage considered to be transitioning to the adjacent target stage. This criterion effectively avoids misjudgments caused by abnormalities in a single sensor channel or accidental impacts. For example, in the transition from initial rock breaking to fracture propagation, if the slag gradation becomes finer and the energy consumption per unit volume no longer increases, while the contact stiffness shows a phased decrease, the system can determine that the switching conditions are met. Similarly, in the transition from fracture propagation to unloading, if the proportion of high-frequency vibration intensity decreases, low-frequency energy no longer accumulates, and the slag volume increases dramatically in a short period accompanied by more complete load release, the system will also trigger a transition determination. To avoid back-and-forth fluctuations, the transition conditions use different hysteresis bands for up and down directions, and set a minimum dwell time. Only when the indicator falls below the conservative threshold and the dwell time expires is a reverse return allowed.

[0053] Upon entering the transition state, the system does not immediately switch to the complete set of parameters for the target stage. Instead, it performs weighted interpolation transition based on the parameter templates of the two adjacent stages. The weights are related to the contribution of the indicators that just crossed the threshold, while also considering the rate of change of each indicator and the data quality score, thus giving higher weight to directions with "more substantial evidence." For channels that require rapid response but should not cause shock, the system uses piecewise S-shaped interpolation. In the initial segment, it slowly deviates from the current parameter with a small slope; in the middle segment, it increases the rate of change to shorten the transition time; and in the final segment, it slows down to approach the target value, ensuring the smoothness of the execution link and the continuity of force on the equipment mechanism. For detailed parameters that allow for rapid fine-tuning, linear interpolation is used to obtain a more direct response. Regardless of the interpolation method used, the system sets constraints on the shortest smoothing time and the maximum rate of change, strictly limiting the synchronous change amplitude of pressure, frequency, rock entry angle, and attitude throughout the entire transition period. At the same time, it applies acceleration and deceleration limits to prevent command jumps from causing secondary damage to the surrounding rock or structural resonance.

[0054] During the transition process, the safety monitoring layer continuously monitors vibration intensity, the proportion of low-frequency energy, and the stress imbalance of the gantry. If any safety indicator touches the protection threshold, the system will "brake" the interpolation trajectory, prioritizing freezing or reversing changes in drilling pressure and frequency, adjusting the attitude and rock entry angle to a more conservative combination, and extending the remaining smoothing time to ensure the switch is completed within the safety envelope. If a single sensor degrades or data quality suddenly drops, the system will temporarily reduce the weight of that indicator, and if necessary, use the remaining indicators and historical trajectories to replace the judgment, maintaining the stability of the phase transition. In addition, if the expected combination of "increased efficiency - decreased energy consumption - controlled vibration" does not appear in multiple consecutive windows after the transition begins, the system will pause the transition and issue a check prompt, suggesting switching to conservative mode or manual review to avoid continuing under incorrect judgment.

[0055] For example, on a face consisting entirely of medium-hard rock with a weak interlayer on the right side, the system initially initiates fracturing rapidly with high frequency and low energy. As the operation progresses, the slag gradation changes from coarse to fine, and the energy consumption per unit volume no longer increases. Both indicators stably exceed the threshold and remain above it for several windows, at which point the system determines it has entered the "initial-expansion" transition state. At this point, segmented S-shaped interpolation is used for the frequency and rock penetration angle to smoothly increase the frequency and finely adjust the rock penetration angle to align with the interlayer strike. Simultaneously, the pressure is slightly limited, and the vibration intensity remains in the middle of the safety zone during the transition period. Shortly after entering the expansion phase, a decrease in the proportion of low-frequency energy, more complete load release, and a rapid increase in slag output are detected. Once two or more indicators again exceed the threshold and remain so, the system determines it has entered the "expansion-unloading" transition state. Subsequently, the impact energy is rapidly reduced using S-shaped interpolation, and the main impact is briefly stopped, with only small-scale prying and safety probing remaining, allowing the free face to naturally spall due to its own weight. Finally, before final shaping, the system re-evaluates the cross-sectional deviation and surface undulation. If the contour deviation and particle refinement reach the set target, the high-frequency low energy is restored in a linear small step manner to perform light shaping on the sidewalls and arch shoulders. The entire switching process did not experience any sudden changes or secondary disturbances because the shortest smoothing time and hysteresis threshold were set.

[0056] Through the aforementioned windowed evaluation and controllable smooth transition, this implementation method tightly couples "stage identification - evidence fusion - smooth switching - safety supervision" within the same control framework. This not only significantly reduces the risk of stage misjudgment and parameter fluctuations, but also achieves simultaneous improvement in rock breaking efficiency and forming quality while ensuring the safety of the surrounding rock and the stability of the equipment.

[0057] In one feasible implementation, such as Figure 5 As shown, step S140 monitors the rock-breaking impact response using a vibration sensor, suppresses excessive vibration in real time, and prevents secondary damage to the weak surrounding rock, including: S1401. The triaxial acceleration signal of the vibration sensor, the surrounding rock pressure sensor and the energy consumption monitoring data are fused from multiple sources to establish a vibration-energy consumption-surrounding rock damage coupling model. S1402. Calculate the surrounding rock damage risk index based on the above vibration-energy consumption-surrounding rock damage coupling model. When the above risk index reaches the warning level, execute adaptive control. The adaptive control includes reducing the drilling pressure, fine-tuning the boom posture, and maintaining the drilling frequency within a safe range. S1403. A vibration trend prediction model trained based on historical data is used to predict the vibration trend. When both the prediction result and real-time monitoring indicate an increase in risk, a protection mode is entered to forcibly reduce the intensity of work or suspend work.

[0058] For example, the vibration-energy consumption-surrounding rock damage coupling model operates in a closed-loop process of "unified acquisition - robust enhancement - fuzzy fusion - risk output". First, the system synchronously receives triaxial acceleration signals deployed at key locations on the boom end and frame, surrounding rock pressure sensor signals deployed on the force-bearing links, and energy consumption monitoring data from the hydraulic and electrical sides. All data undergoes denoising, de-drifting, and anti-aliasing processing after entering the same time reference, and is segmented according to a rolling evaluation window (e.g., a fixed window of several seconds with sliding overlap). To eliminate the influence of attitude and installation differences, the acceleration signals undergo coordinate unification and gravity component subtraction, and then energy and spectral proportions are calculated in three typical frequency bands: low frequency, working zone, and high frequency. Pressure-side signals are sliced ​​according to stable loading, impact peak, and recovery zone, extracting key response features during loading and unloading. On the energy consumption side, the instantaneous power is integrated using a combination of pressure, flow rate, and motor power, and correlated with the advance volume within the same window to obtain the rock-breaking energy consumption per unit volume.

[0059] The system extracts three core features from the preprocessing results. The first is frequency band energy characteristics, focusing on low-frequency large-amplitude response (reflecting the overall vibration of the structural component and surrounding rock), working band energy (reflecting tool-rock coupling efficiency), and high-frequency fine-fragmentation energy (reflecting local disintegration and workability). The second is the surrounding rock damage factor, derived from a comprehensive characterization of behavioral quantities such as "loading stiffness attenuation, unloading rebound hysteresis, post-impact residual deformation, and force-displacement hysteresis area" from the stress curve, used to indicate the generation, propagation, and connection trends of microcracks. The third is energy consumption per unit volume, used to assess the conversion efficiency of energy input relative to the effective rock-breaking volume, identifying abnormal "high-energy, low-efficiency" conditions. To suppress misjudgments caused by occasional pulses and single-channel anomalies, all three features undergo quality scoring, anomaly removal, and robust smoothing within a window, while retaining the rate of change for trend assessment.

[0060] Subsequently, the system enters the weighted fuzzy logic fusion stage. Membership functions of "low-medium-high" are defined for frequency band energy, surrounding rock damage factor, and unit volume energy consumption, respectively. Adaptive weights for data quality and environmental labels (such as dust and humidity) are introduced: when the quality score of a certain sensor channel decreases or the environment is unfavorable, the influence of that channel in the fusion process automatically decreases. The rule base reasons around three semantic categories: "damage suspected," "damage development," and "high-risk damage." For example, when low-frequency energy increases and the surrounding rock damage factor rises, it is judged as a "damage development" tendency. When the working band energy decreases while the unit volume energy consumption continues to rise, combined with the exacerbation of unloading lag, it is judged as a "high-risk damage" tendency. If high-frequency energy increases but the damage factor and energy consumption remain low, it is more like tool-side flutter or poor contact, and is judged as "damage suspected." The contributions of each rule are summarized according to their weights, and after defuzzification, a continuous comprehensive damage index is obtained, which is further mapped to a 0-100 surrounding rock damage risk index, while providing interval-based risk levels and confidence levels.

[0061] Finally, the risk index is linked to higher-level safety supervision and control. The system sets two levels of thresholds: early warning and protection, with hysteresis. When the risk index exceeds the early warning threshold, a mild suppression strategy is triggered first, such as slightly reducing the drilling pressure, fine-tuning the rock entry angle, and avoiding the resonance zone. When the risk index continues to exceed the protection threshold or rises too rapidly, protective actions are immediately executed, including pausing high-energy impacts, switching to low-energy modes such as wedge-operated levers or gravity unloading, and recording the full "Working Condition - Command - Response" log in the window for review and offline calibration. If a channel degrades during operation, the risk calculation will automatically downweight that channel and extend the index smoothing time to ensure a robust and conservative safety judgment even with incomplete information.

[0062] For example, in a medium-hard rock section containing weak interlayers, the low-frequency energy and unloading lag rise synchronously within the window, while the energy consumption per unit volume begins to hover at a high level, with all three characteristics reaching the high-risk domain of the rule base. After fusion, the comprehensive damage index rises rapidly and crosses the protection threshold. Based on this, the system reduces the drilling pressure, fine-tunes the rock entry angle to align with the interlayer strike, and temporarily lowers the impact frequency while switching to a combination of lever-gravity unloading. In the subsequent two windows, the low-frequency energy falls back, the unloading lag decreases, and the energy consumption per unit volume decreases. The risk index stabilizes and returns below the warning threshold, and the system then gradually recovers to a more efficient parameter range according to the set smoothing strategy. Through this coupled modeling and fuzzy fusion, the controller can connect "vibration behavior - energy consumption - damage evolution," both detecting and suppressing potential secondary damage early and preserving rock breaking efficiency and forming quality as much as possible.

[0063] In one feasible implementation, the specific processing steps of the above-mentioned vibration-energy consumption-surrounding rock damage coupling model include: Input the above triaxial acceleration signal, the above surrounding rock pressure sensor, and the above energy consumption monitoring data; Frequency band energy, surrounding rock damage factor, and energy consumption per unit volume are extracted through signal processing. The above-mentioned frequency band energy, surrounding rock damage factors and unit volume energy consumption characteristics are integrated into a comprehensive damage index using weighted fuzzy logic, and the surrounding rock damage risk index is output.

[0064] For example, this embodiment constructs a risk assessment closed loop based on the mechanism coupling of "vibration-energy consumption-surrounding rock damage". Before the data enters the model, the triaxial acceleration... Output of surrounding rock pressure sensor Energy consumption monitoring quantity (hydraulic pressure) ,flow or power In the sliding evaluation window Synchronization and noise reduction are performed internally: bandpass filtering is used to suppress low-frequency drift and high-frequency glitches, and short-time Fourier transform is performed on impact events using a piecewise Hanning window to ensure stable frequency domain estimation. Subsequently, three types of characteristics, namely frequency band energy, surrounding rock damage factor and energy consumption per unit volume, are calculated and fused using weighted fuzzy logic to form a comprehensive damage index, outputting the surrounding rock damage risk index (RRI).

[0065] In frequency band energy extraction, synthetic acceleration is used as a key factor. Based on this, short-time spectral analysis is performed to obtain the amplitude. Based on engineering mechanisms, frequency bands are divided into low, medium, and high bands (example: : ), calculate the energy: And normalized to energy percentage .in More sensitive to rapid propagation of brittle microcracks This corresponds to a low-frequency process dominated by contraction / friction.

[0066] Surrounding rock damage factor A three-evidence synthesis method was employed, combining modal stiffness degradation, nonlinear distortion, and stress instability. First, the dominant peak natural frequency was used as the starting point. Reflects changes in equivalent stiffness, with reference to the initial healthy state. Approximated by continuous damage mechanics: This indicates the frequency drop caused by stiffness attenuation; secondly, it indicates harmonic distortion. The nonlinear response caused by crack initiation / closure was characterized and obtained by quantile normalization. Then, using the pressure instability coefficient Reflecting fluctuations in contact conditions, normalized to The three are weighted and combined according to their security priority. in Generally, a larger weight is used. Provides lead time sensitivity to early nonlinearity and load instability.

[0067] Energy consumption per unit volume Estimating instantaneous power using hydraulic power and slag discharge volume: Window energy Volume increment via mass flow rate With the density of the surrounding rock estimate: For the smallest Lower-limit clipping is used to avoid ratio divergence. Subsequently, historical normal zones corresponding to geological grades are used. Linear normalization to .

[0068] The fusion phase introduces a weighted fuzzy logic with three inputs and one output. The input linguistic variable is the proportion of high-frequency energy. Damage factor D and energy consumption per unit volume Define respectively Low, Medium, High Three membership functions (which can be constructed using trigonometric / Gaussian functions around quantiles and thresholds) output linguistic variables. Low, Medium, High, Very High Comprehensive damage index Typical rules include: (1) If If it is high, then It is very high; (2) if and Both are high, then It is very high; (3) if For China If it is high, then (4) If all three are low, then The value is low. To highlight structural evidence, the triggering terms are multiplied by the input weights. (correspond ), forming a weighted trigger strength The output membership degree is obtained by max-min synthesis. Then, the centroid method was used to resolve the fuzziness: and first-order smoothing Suppress jitter. The system in Exceeding the warning threshold Issue risk warnings when the protection threshold is exceeded. The time-linked execution of suppression strategies, including pressure reduction, frequency reduction, fine-tuning of the rock ingress angle, and extension of the S-shaped smoothing time, continues until... It fell back into the hysteresis band and remained stable.

[0069] The method provided in the above embodiments characterizes microcrack activation through frequency domain energy, quantifies structural stiffness degradation and nonlinear enhancement through damage factors, and characterizes energy utilization efficiency and potential ineffective work through unit volume energy consumption. The three are coupled in a complementary manner within the fuzzy rules, so that the output surrounding rock damage risk index is both real-time and robust, and can form a continuous and controllable response to the evolution stages of early damage, accelerated propagation, and critical failure.

[0070] In one feasible implementation, the specific processing steps of the above-mentioned vibration trend prediction model include: Historical vibration amplitude, dominant frequency distribution, and energy consumption trends are used as inputs; The temporal characteristics of vibration signals are modeled using a long short-term memory network; Output the vibration trend for future periods and compare it with the real-time monitoring results. When both the prediction and the real-time results indicate an increased risk, trigger the protection mode.

[0071] For example, the vibration trend prediction model operates within a closed-loop process of "data access - time series modeling - online prediction - coordinated decision-making". First, in the data access phase, the system receives historical vibration amplitude sequences from multiple vibration sensors at the frame and boom end, while simultaneously extracting the dominant frequency distribution and energy consumption trend aligned with the corresponding window. To ensure source consistency, all signals are standardized on a unified time base and undergo noise reduction and drift correction; that is, the vibration amplitude is obtained by combining bandpass filtering and envelope extraction to obtain a stable amplitude trajectory. The dominant frequency distribution is statistically obtained from short-time spectra within a fixed evaluation window, including the peak position, bandwidth, energy proportion of each key frequency band, and its rate of change. The energy consumption trend is formed by the instantaneous power integration of the hydraulic and electrical sides, and the slope of change per unit time or unit advance is calculated as an exogenous quantity reflecting whether "energy input is accelerating". These three types of inputs undergo missing value compensation, outlier suppression, and standardization before entering the model to reduce inconsistencies in dimensions and scales caused by external factors such as operating condition switching, dust, and humidity.

[0072] The system employs a Long Short-Term Memory (LSTM) network to model multivariate time series data. The model input is a time-sequential sliding window, containing vibration amplitude trajectories, dominant frequency distribution feature vectors, and energy consumption change feature vectors, forming a "multi-channel" input frame within the same window. To balance short-term fluctuations and medium-term trends, the network adopts a stacked structure: the bottom layer focuses on capturing rapid fluctuations and short-cycle resonance signs, while the upper layer focuses on the slow climb across windows and the correlation with energy consumption accumulation. During the training phase, supervised learning is performed using historical "operating condition-response" logs. The goal is for the model to output vibration trend trajectories and their confidence intervals within several future evaluation windows, given a multimodal input over a past period. To suppress overfitting and adapt to different surrounding rock types, training data is stratified and sampled by tunnel section and lithology. The model incorporates a dropout strategy and an early stopping mechanism, and cross-validates across different operating condition subsets. For samples where dust or humidity labels indicate a decrease in perceived quality, the model's weight update step size is smaller to avoid overfitting to abnormal samples.

[0073] During the online prediction phase, the controller continuously inputs the latest window data in fixed step increments. The model instantly outputs the vibration trend for the next short period, including the overall amplitude's rise and fall, the direction of energy proportion in key frequency bands, and risk warnings regarding whether it is approaching historical resonance zones. The prediction results are then compared with the real-time monitoring sequence for consistency. The system not only compares whether the current threshold is exceeded but also focuses on whether the future upward movement will occur and whether the upward rate is significant. When both "predicted upward movement" and "real-time upward movement has already occurred" are met simultaneously and continue to reach preset time or frequency thresholds, the risk trend is deemed credible, triggering the protection mode. If only one is met, the system enters an early warning state, appropriately converges parameters, and shortens the monitoring and reassessment interval. To avoid frequent switching, different hysteresis bands and minimum dwell times are set for upward and downward movements, and suppression rules are applied to single abnormal spikes. The system state is only changed when the trend is continuously stable.

[0074] During the coordinated decision-making phase, the protection mode employs a "soft first, hard later" strategy. In the early warning state, the system prioritizes lightweight suppression measures that do not alter the operational phase, such as slightly reducing drilling pressure, fine-tuning the rock entry angle to avoid unfavorable force directions, or slightly shifting the frequency out of the suspected resonance zone. When entering the protection state, the system further limits impact energy and continuous impact duration, switching from high-frequency impact to low-energy modes such as wedge-operated levers or gravity unloading when necessary, and increasing the sampling and evaluation frequency of vibration and energy consumption monitoring. Throughout the process, the output confidence level of the prediction module dynamically affects the magnitude of the control actions: higher confidence levels result in more decisive parameter adjustments; when confidence levels decrease, the system tends to use smaller steps and multiple gradual adjustments.

[0075] When the data quality score of some vibration or energy consumption channels declines, the model automatically reduces the weight of the corresponding input in feature fusion and expands the prediction confidence band. If the data quality deteriorates further below a threshold, it switches to a simplified trend discriminator (such as rule-based judgment based on envelope slope and frequency band energy ratio), while forcibly converging the operating parameters to a safe envelope. After external conditions recover, the system gradually relaxes parameter restrictions using a gradual easing strategy, and only fully exits the protection state after the predicted and real-time monitoring risks remain consistent and do not increase over a certain period.

[0076] In one feasible implementation, the above method further includes: The intelligent control system monitors the power output of the diesel power system and hydraulic system, and dynamically adjusts the energy distribution based on optimization algorithms to minimize energy consumption during the rock breaking process.

[0077] For example, energy management, as a higher-level module of the intelligent control system, operates in parallel with rock-breaking parameter adaptation and safety monitoring. The system collects key quantities from the diesel power system and hydraulic system in real time via the engine / hydraulic controller bus, including engine speed and load, fuel injection quantity and turbocharger pressure, instantaneous fuel consumption, displacement and outlet pressure of the main / auxiliary hydraulic pumps, pressure-flow requirements of each execution branch (drilling, boom, lever, travel / slewing, cooling fan, and spray), accumulator pressure and effective volume, hydraulic oil temperature, and cooling system power. All data is written to the energy management cache according to a unified time base and aligned with the "current operating condition labels" (surrounding rock strength, weak zone, dust / humidity, vibration risk level) in the operating environment database to form a "power-operating condition" view that can be used for decision-making.

[0078] Strategically, the system treats the entire machine as a "virtual power bus," dynamically allocating power among various energy-consuming branches through optimization algorithms. The goal is to minimize energy consumption per unit advance while meeting efficiency and safety constraints. Specifically: On one hand, the system constrains the diesel engine's operating point near the high-efficiency economy island, avoiding "stalling" at low speeds with high torque and "waste" at high speeds. It maintains a 10%~20% power margin to cope with instantaneous impacts by adjusting the throttle and fuel injection in small increments. On the other hand, the hydraulic side adopts a "pump control priority, valve port throttling reduction" approach. By adjusting the swashplate angle of the main / auxiliary pumps and the load-sensitive (LS) pressure boundary, the pump pressure is made slightly higher than that required by the highest load branch, minimizing overflow valve opening and throttling losses. At the same time, the accumulator is used to release energy briefly during peak impact periods and replenish energy during low-load periods, smoothing out power pulsations. Auxiliary loads such as cooling fans and spray pumps adopt a variable speed strategy, adjusting as needed according to oil temperature and dust index, avoiding background energy consumption caused by "constant high speed."

[0079] In the scheduling process, energy management executes three steps in each short cycle (e.g., 20-50ms). The first step is prediction: based on the drilling rhythm, boom movement, energy consumption, and vibration trends of the past few seconds, the power demand and possible peak times of each branch in the next time window are estimated to identify whether a high-demand condition is about to begin (such as high-strength rock, simultaneous dual-boom impact, or a short skid before unloading). The second step is allocation: according to the principles of "safety first, main operation first, and efficiency weighting," a power limit is reserved for drilling and its coupled skid / attitude fine-tuning; without affecting the safety threshold, non-critical loads are reduced (such as temporarily reducing fan speed or delaying the movement of non-critical boom segments), and accumulators are activated to release energy when necessary to avoid the engine crossing the economy island. On dual-pump dual-circuit equipment, the high-pressure, high-current circuit is prioritized for the current main boom, while the other circuit handles attitude and auxiliary movements, reducing flow regulation across valve ports. The third step is correction: the real-time feedback of engine speed deviation, instantaneous fuel surge, pump outlet pressure difference and overflow valve opening time are used as "energy consumption penalty points". If abnormal energy consumption or speed drop occurs, the upper edge of the chiseling pressure and frequency is immediately slightly reduced, or some power is withdrawn from non-critical branches until the engine returns to the target working area.

[0080] The safety layer sets hard boundaries for engine speed, oil temperature, main pump differential pressure, and structural vibration. Once these boundaries are exceeded, power is immediately frozen and prioritized for cooling and primary operations. The energy consumption layer monitors energy consumption curves per unit time and per unit depth. If a combination of "increasing energy consumption without improving crushing efficiency" occurs, the parameter adaptive module is triggered to switch from "pressure-for-efficiency" to "frequency and angle fine-tuning for-efficiency," while simultaneously reducing the hydraulic pump displacement to avoid heat loss. If the sensing layer detects sensor degradation (e.g., decreased flow estimation reliability), weight allocation automatically favors a conservative strategy and extends the scheduling smoothing time. All scheduling commands are subject to rate of change and jerk limits to prevent power surges from causing secondary vibrations or local instability.

[0081] The second aspect, such as Figure 6 As shown, Figure 6 This invention provides a structural schematic diagram of an intelligent operation control system for a dual-arm rock drilling rig suitable for large-section tunnels in weak surrounding rock. The invention also proposes an intelligent operation control system for a dual-arm rock drilling rig suitable for large-section tunnels in weak surrounding rock, comprising: Database construction unit 21 is used to acquire tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration and humidity information through lidar, inertial measurement unit, surrounding rock pressure sensor and environmental sensor deployed on the double-arm rock drilling rig, and form an operating environment database. The adaptive matching unit 22 is used to dynamically adjust the attitude parameters, drilling frequency, drilling pressure and rock entry angle of the double-arm rock drilling rig boom according to the above-mentioned working environment database, combined with the strength level of the tunnel surrounding rock and the distribution characteristics of the weak surrounding rock, so as to realize the adaptive matching of rock breaking parameters. The rock breaking mode selection module 23 can use high-frequency impact rock breaking, bucket tooth wedge pry rock breaking, and automatic rock breaking mode using gravity of the free surface, either separately or in combination, at different operation stages. The damage monitoring unit 24 is used to monitor the rock-breaking impact response through vibration sensors, suppress excessive vibration in real time, and prevent secondary damage to the weak surrounding rock.

[0082] The present invention proposes an intelligent operation control system for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock, which can also perform the method described in any of the first aspects.

[0083] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent operation of a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock, characterized in that, include: By deploying lidar, inertial measurement units, surrounding rock pressure sensors, and environmental sensors on the dual-arm rock drilling rig, the system acquires tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration, and humidity information to form an operational environment database. Based on the aforementioned operating environment database, and combined with the strength level of the surrounding rock in the tunnel and the distribution characteristics of weak surrounding rock, the intelligent control system dynamically adjusts the attitude parameters, drilling frequency, drilling pressure, and rock entry angle of the double-arm rock drilling rig to achieve adaptive matching of rock breaking parameters. At different stages of operation, high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking mode utilizing the gravity of the free surface are used separately or in combination. By monitoring the rock-breaking impact response using vibration sensors, excessive vibration can be suppressed in real time to prevent secondary damage to weak surrounding rock.

2. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 1, characterized in that, The system utilizes lidar, inertial measurement units, and surrounding rock sensors deployed on a dual-arm rock drilling rig to acquire tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration, and humidity information, forming a working environment database, including: The tunnel cross-section point cloud was scanned by lidar and the cross-sectional geometric parameters were fitted. Attitude data is acquired through an inertial measurement unit, and the geometric parameters are corrected for attitude. The integrity of the surrounding rock is estimated by measuring contact pressure and stress fluctuations using a surrounding rock pressure sensor. Dust concentration and humidity levels are obtained through environmental sensors; The geometric parameters, surrounding rock integrity index, dust concentration, and humidity information are stored in the work environment database.

3. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 1, characterized in that, Based on the operational environment database and considering the strength grade of the surrounding rock and the distribution characteristics of weak surrounding rock, the intelligent control system dynamically adjusts the attitude parameters, drilling frequency, drilling pressure, and rock-entry angle of the boom of the double-arm rock drilling rig to achieve adaptive matching of rock-breaking parameters, including: Read the tunnel cross-sectional geometric parameters and the surrounding rock integrity index from the working environment database, and compare them with the surrounding rock strength grading standard to determine the tunnel surrounding rock strength level; The initial values ​​of the attitude parameters, drilling frequency, drilling pressure, and rock entry angle of the double-arm drilling rig boom are set according to the parameter table based on the tunnel surrounding rock strength level. During operation, feedback results from vibration and pressure sensors are collected to monitor rock-breaking impact response, fracture propagation rate, and energy consumption level. When the parameters deviate from the preset range, parameter correction is triggered. Specifically, when the fracture propagation rate is below the threshold, the drilling frequency is increased or the rock entry angle is increased. When the vibration intensity or energy consumption level exceeds the limit, the drilling pressure is reduced or the boom posture is adjusted.

4. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 1, characterized in that, At different operational stages, high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free face are employed individually or in combination, including: High-frequency impact rock breaking mode is used in the initial rock breaking stage to quickly form fractures; During the fracture propagation stage, a combination of bucket tooth wedge-pry rock-breaking mode and high-frequency impact rock-breaking mode is used to promote fracture propagation. During the rock breaking and unloading stage, the free-face gravity automatic rock breaking mode is mainly used to achieve natural rock spalling. In the final shaping stage, high-frequency impact rock breaking mode was re-adopted and combined with prying action to ensure the quality of cross-section forming.

5. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 4, characterized in that, The method further includes: Within the preset evaluation window, the following indicators are continuously calculated: fracture propagation rate, rock breaking energy consumption per unit volume, vibration intensity and frequency band distribution, slag particle size distribution and output, and contact stiffness. When at least two indicators stably cross the corresponding threshold and continue to reach the preset time or number of times, it is determined that the transition state has entered the adjacent target stage. In the transition state, the rock breaking parameters of the two adjacent stages are linearly or piecewise S-shaped interpolated according to the weights, and the shortest smoothing time is set to avoid abrupt changes that could cause secondary damage to the surrounding rock.

6. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 1, characterized in that, By monitoring the rock-breaking impact response using vibration sensors, excessive vibration can be suppressed in real time to prevent secondary damage to weak surrounding rock, including: The triaxial acceleration signal of the vibration sensor, the surrounding rock pressure sensor and the energy consumption monitoring data are fused from multiple sources to establish a vibration-energy consumption-surrounding rock damage coupling model. Based on the vibration-energy consumption-surrounding rock damage coupling model, the surrounding rock damage risk index is calculated. When the risk index reaches the warning level, adaptive control is executed. The adaptive control includes reducing the chiseling pressure, fine-tuning the boom posture, and maintaining the chiseling frequency within a safe range. Vibration trends are predicted using a vibration trend prediction model trained on historical data. When both the prediction results and real-time monitoring indicate an increase in risk, a protection mode is entered to forcibly reduce the intensity of operations or suspend operations.

7. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 6, characterized in that, The specific processing steps of the vibration-energy consumption-surrounding rock damage coupling model include: Input the triaxial acceleration signal, the surrounding rock pressure sensor, and the energy consumption monitoring data; Frequency band energy, surrounding rock damage factor, and energy consumption per unit volume are extracted through signal processing. The frequency band energy, the surrounding rock damage factor, and the unit volume energy consumption characteristics are integrated into a comprehensive damage index using weighted fuzzy logic, and the surrounding rock damage risk index is output.

8. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 6, characterized in that, The specific processing steps of the vibration trend prediction model include: Historical vibration amplitude, dominant frequency distribution, and energy consumption trends are used as inputs; The temporal characteristics of vibration signals are modeled using a long short-term memory network; Output the vibration trend for future periods and compare it with the real-time monitoring results. When both the prediction and the real-time results indicate an increased risk, trigger the protection mode.

9. The intelligent operation method for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock as described in claim 1, characterized in that, The method further includes: The intelligent control system monitors the power output of the diesel power system and hydraulic system, and dynamically adjusts the energy distribution based on optimization algorithms to minimize energy consumption during the rock breaking process.

10. An intelligent operation control system for a dual-arm drilling rig suitable for large-section tunnels in weak surrounding rock, characterized in that, include: The database construction unit is used to acquire tunnel cross-sectional geometric parameters, surrounding rock integrity indicators, environmental dust concentration and humidity information by using lidar, inertial measurement unit, surrounding rock pressure sensor and environmental sensor deployed on the double-arm rock drilling rig, and to form an operational environment database. The adaptive matching unit is used to dynamically adjust the attitude parameters, drilling frequency, drilling pressure and rock entry angle of the boom of the double-arm rock drilling rig according to the working environment database and in combination with the strength level of the surrounding rock of the tunnel and the distribution characteristics of the weak surrounding rock, so as to realize the adaptive matching of rock breaking parameters. The rock breaking mode selection module allows for the use of high-frequency impact rock breaking, bucket tooth wedging and prying rock breaking, and automatic rock breaking modes utilizing the gravity of the free surface, either individually or in combination, at different operational stages. The damage monitoring unit is used to monitor the rock-breaking impact response through vibration sensors, suppress excessive vibration in real time, and prevent secondary damage to weak surrounding rock.

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