Small-diameter tmb cutterhead-shield collaborative geological prediction system and method

Abstract

This invention relates to the field of intelligent tunneling equipment technology, specifically to a small-diameter TBM cutterhead-shield collaborative geological prediction system and method. The system includes a tunneling status acquisition module, a cutterhead force analysis module, a shield attitude assessment module, a tunneling coordination analysis module, and a geological prediction judgment module. In this invention, by integrating real-time data monitoring and multi-dimensional calculations, the tunneling system achieves collaborative operation. It collects cutterhead rotation speed, propulsion cylinder thrust, and torque data to calculate the propulsion speed to torque ratio. Combined with the difference rate, it generates a tunneling stability coefficient. It analyzes the cutting tool position and force matrix to quantify the force distribution on the cutterhead. It uses laser target coordinates, attitude tilt angle, and cylinder displacement difference to analyze the shield attitude coordination. It constructs a multi-dimensional linear correlation matrix to reveal the system coupling degree. Through coupling degree mutation interval analysis, it achieves real-time geological prediction feedback, improving the accuracy and reaction speed of anomaly identification, reducing reliance on experience, and enhancing prediction reliability.

Description

Technical Field

[0001] This invention relates to the field of intelligent tunneling equipment technology, and in particular to a small-diameter TBM cutterhead-shield collaborative geological prediction system and method. Background Technology

[0002] The field of intelligent tunneling equipment technology encompasses tunnel boring machines (TBMs) and their auxiliary devices used in tunnel construction. The core of this technology focuses on the design and application of mechanical equipment, primarily including the structural components of the TBM such as the cutterhead, propulsion system, and support structure, as well as the associated monitoring and control mechanisms. This field systematically covers the entire process from equipment assembly to on-site operation, such as the TBM's propulsion method in rock strata, the cutting principle of the cutters, and the stability control of the surrounding rock. It also involves material selection and mechanical analysis to adapt to the construction needs of different geological environments. Among these, the small-diameter TBM cutterhead-shield collaborative geological prediction system refers to a prediction device integrated into a tunnel boring machine. The technical aspects addressed by this patent cover the real-time detection and analysis of geological conditions, specifically collecting rock strata information through vibration sensors on the cutterhead and pressure sensors on the shield, solving the problem of identifying the strata ahead through mechanical linkage and data fusion.

[0003] In existing technologies, tunneling monitoring typically relies on a single sensor to collect data, lacking cross-analysis of multi-dimensional data and real-time feedback. In complex geological environments, fluctuations in a single parameter cannot effectively distinguish between mechanical and geological changes. For example, abnormal fluctuations in cutterhead torque may be caused by ground hardness or mechanical jamming, which traditional methods cannot differentiate in a timely manner. Attitude monitoring often focuses only on changes in shield position, failing to dynamically correlate with cutterhead stress and geological information, resulting in a delayed response. Tunneling coordination analysis also lacks comprehensive calculations of multiple factors, making it difficult to identify potential anomalies. This leads to geological predictions relying on manual verification and post-construction comparison, affecting construction efficiency and the accuracy of geological assessments. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a small-diameter TBM cutterhead-shield collaborative geological prediction system and method.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a small-diameter TBM cutterhead-shield coordinated geological prediction system comprising: The tunneling status acquisition module acquires continuous monitoring data of cutterhead rotation speed, propulsion cylinder thrust and cutterhead torque, calculates the ratio of propulsion speed to torque, compares the ratio with the circumferential difference rate of propulsion force, and obtains the tunneling stability coefficient. The cutterhead force analysis module, based on the tunneling stability coefficient, calls the position of the cutterhead cutting tool and the cutting force output to calculate the total force per unit cutting section and obtain the cutterhead force balance. The shield attitude assessment module uses the coordinates of the laser target at the tail of the shield, the attitude tilt angle and the displacement difference of the propulsion cylinder to calculate the rate of change of the angle between the shield's central axis and the tunnel's design axis, based on the force balance of the cutterhead, and to obtain the shield's attitude coordination. Based on the shield attitude coordination degree, the tunneling coordination analysis module calls the synchronization sequence of cutterhead torque, propulsion cylinder thrust and propulsion speed, calculates the linear correlation matrix of the three, filters out combinations exceeding the threshold, compares the proportion of consecutive occurrences, and obtains the intelligent tunneling coupling degree. The geological prediction judgment module, based on the intelligent tunneling coupling degree, calls the continuous sequence of tunneling stability coefficients, identifies the coupling degree mutation interval, determines the proportion that exceeds the stratum disturbance benchmark range, and obtains the geological prediction recognition rate.

[0006] As a further aspect of the present invention, the tunneling stability coefficient includes the propulsion speed fluctuation rate, torque load coefficient, and propulsion force distribution uniformity coefficient; the cutterhead force balance includes the cutting force distribution coefficient, the tool force ratio, and the unit cutting load concentration; the shield attitude coordination includes the shield axis offset angle, attitude inclination angle change rate, and propulsion cylinder displacement difference coefficient; the intelligent tunneling coupling degree includes the propulsion force-torque correlation coefficient, dynamic response synchronization rate, and multi-parameter linear correlation rate; and the geological prediction recognition rate includes the stratum disturbance recognition accuracy, coupling degree abrupt change detection rate, and geological anomaly judgment accuracy.

[0007] As a further aspect of the present invention, the tunneling status acquisition module includes: The speed monitoring submodule acquires continuous monitoring data of cutter head speed, propulsion cylinder thrust and cutter head torque, matches the cutter head speed and propulsion cylinder thrust in time series, calculates the ratio of their change rates in adjacent time periods, analyzes the fluctuation range of the ratio and judges its stability, and generates the cutter head running stability coefficient. Based on the cutterhead running stability coefficient, the thrust calculation submodule calls the propulsion cylinder thrust and cutterhead torque monitoring data to calculate the circumferential distribution value of each propulsion cylinder thrust, analyze the thrust distribution deviation and calculate the degree of circumferential difference, compare its deviation from the overall thrust average value, and generate the thrust circumferential difference rate. The tunneling ratio evaluation submodule calls the propulsion speed and torque ratio monitoring data based on the propulsion force circumferential difference rate, calculates the corresponding change relationship between the ratio fluctuation amplitude and the circumferential difference rate, judges its synchronous trend, and compares it with the stable benchmark value to generate the tunneling stability coefficient.

[0008] As a further aspect of the present invention, the force analysis module for the cutter head includes: Based on the tunneling stability coefficient, the cutting force calculation submodule calls the tool position parameters and the output of the cutting force sensor, extracts the coordinates of the tool action point and the contact boundary, calculates the angle difference between adjacent tools to obtain the angle spacing data, calculates the normal and tangential force components according to the tool sequence, and accumulates them in the unit segment to generate the total force value of the unit segment. The force zone comparison submodule calls the total force value of the unit segment and the tool angle spacing data, divides the interval according to the circumferential direction, calculates the force difference between adjacent intervals and counts the deviation of the difference distribution, and generates the circumferential force difference rate of the tool head. The force balance assessment submodule calculates the offset trend of the force difference at each angle and statistically analyzes its balance in the circumferential distribution based on the circumferential force difference rate of the cutterhead and the tool angle spacing sequence, thereby obtaining the force balance of the cutterhead.

[0009] As a further aspect of the present invention, the shield attitude assessment module includes: The force and attitude synchronization submodule calls the force balance of the cutterhead, collects the three-dimensional coordinates of the laser target at the tail of the shield and records the time tag, detects the displacement signal of the propulsion cylinder and calculates the displacement difference, matches the force balance with the attitude tilt angle time series, compares the parameter time deviation and calculates the synchronization correction coefficient, integrates the corrected attitude and force parameters to form a unified matrix, and generates the attitude force synchronization matrix value. The axis angle change calculation submodule calls the shield axis vector and tunnel axis vector of the attitude force synchronization matrix value to calculate the angle value of each advancement period. It calculates the change rate based on the angle difference of continuous periods, calls the cutterhead force balance difference coefficient and compares it with the angle change rate, extracts the angle change trend sequence, and obtains the shield axis angle change rate. The attitude coordination determination submodule calls the force parameters of the shield axis angle change rate and the attitude force synchronization matrix value to calculate the propulsion cycle coordination change coefficient, determine the execution interval of the difference between the coordination coefficient and the attitude reference coefficient, generate a coordination index sequence based on the difference distribution, and obtain the shield attitude coordination.

[0010] As a further aspect of the present invention, the tunneling coordination analysis module includes: The parameter synchronization sequence preparation submodule calls the shield attitude coordination degree, extracts the node average value of the time series of cutterhead torque and propulsion cylinder thrust, calculates the node speed difference of the propulsion speed sequence, matches the three sequences according to the shield attitude coordination degree and corrects the time deviation, so that they remain synchronized in the same propulsion cycle, and obtains the tunneling synchronization sequence set. The correlation rate matrix construction submodule calls the tunneling synchronization sequence set to calculate three sets of difference values: cutterhead torque and propulsion cylinder thrust, propulsion cylinder thrust and propulsion speed, and cutterhead torque and propulsion speed. Based on the difference changes, it calculates the linear correlation rate, constructs the correlation rate matrix, and filters variable combinations that exceed the threshold to obtain effective correlation combination matrix values. The coupling degree determination submodule calls the effective correlation combination matrix value, counts the frequency of occurrence of variable combinations in continuous periods, calculates the proportion of consecutive occurrences in adjacent periods and generates a stability sequence, performs weighted and normalized processing, and obtains the intelligent tunneling coupling degree.

[0011] As a further aspect of the present invention, the geological prediction determination module includes: The coupling degree mutation identification submodule obtains the tunneling stability coefficient change sequence based on the intelligent tunneling coupling degree, calculates the coupling degree change difference in each cycle and extracts the change amplitude, compares it with the mutation threshold, identifies the mutation point based on the node change rate, and generates a mutation interval identifier. The disturbance interval judgment submodule calls the mutation interval identifier, detects the coupling degree time series value in each interval, calculates the proportion that exceeds the formation disturbance reference range, compares it with the disturbance reference threshold, judges the interval exceeding the standard state, and generates the disturbance exceeding the standard proportion; The prediction recognition rate calculation submodule, based on the disturbance exceeding the standard ratio, calls the stratigraphic disturbance benchmark range, counts the number of exceeding intervals and the total number of intervals, calculates the average trend of the exceeding ratio, generates the geological disturbance recognition rate distribution, and obtains the geological prediction recognition rate.

[0012] As a further aspect of the present invention, the propulsion speed is calculated by combining propulsion cylinder displacement data with a time series, specifically using the following formula: , in, It is the speed of propulsion. It's a change in the position of the oil needle. It is the change of time; The torque ratio is the ratio of the cutter head torque to the cutter head rotation speed, used to characterize the change in cutting load per unit rotation speed. The specific formula is as follows: , in, It is the torque ratio. It's the torque of the cutter head. It is the rotational speed of the cutter head.

[0013] As a further aspect of the present invention, the tool position parameters are determined by the tool head design model and assembly calibration data, and can be dynamically updated in conjunction with the real-time correction information of the attitude monitoring system. The cutting force sensor is installed between the back of the tool and the tool head support structure. It is a triaxial force sensor used to collect the normal force, tangential force and radial force components of the tool.

[0014] The small-diameter TBM cutterhead-shield collaborative geological prediction method includes the following steps: S1: Collect continuous monitoring data of cutterhead rotation speed, propulsion cylinder thrust and cutterhead torque, calculate the ratio of propulsion speed to torque, input the two sequences into a time series clustering model for fluctuation analysis, calculate the synchronous change ratio based on the clustering results, and generate the tunneling stability coefficient; S2: Based on the tunneling stability coefficient, call the cutter head cutting tool position and cutting force output data, input the tool cutting force matrix into the force zoning analysis algorithm, calculate the total force of each section and perform distribution difference calculation to obtain the cutter head force balance; S3: Based on the cutterhead force balance, call the coordinates of the laser target at the tail of the shield, the attitude tilt angle and the displacement difference of the propulsion cylinder, calculate the rate of change of the angle between the shield's central axis and the tunnel's design axis, and perform a corresponding analysis with the cutterhead force balance to generate the shield's attitude coordination degree. S4: Based on the shield attitude coordination degree, call the synchronization sequence data of cutterhead torque, propulsion cylinder thrust and propulsion speed, construct the correlation rate matrix and input it into the multiple linear regression model to perform parameter correlation calculation, filter out the combination exceeding the threshold and count the proportion of consecutive occurrences to obtain the intelligent tunneling coupling degree; S5: Based on the intelligent tunneling coupling degree, call the continuous sequence of tunneling stability coefficients, identify the coupling degree change range, compare it with the ground disturbance benchmark range, calculate and output the geological prediction recognition rate.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by integrating real-time data monitoring and multi-dimensional calculations, the tunneling system achieves collaborative operation. It collects data on cutterhead rotation speed, propulsion cylinder thrust, and torque to calculate the ratio of propulsion speed to torque. Combining this with the difference rate, it generates a tunneling stability coefficient. It analyzes the cutting tool position and force matrix to quantify the force distribution on the cutterhead. It utilizes laser target coordinates, attitude tilt angle, and cylinder displacement differences to analyze the shield's attitude coordination. A multi-dimensional linear correlation matrix is ​​constructed to reveal the system's coupling degree. Through coupling degree mutation interval analysis, it achieves real-time geological forecast feedback, improving the accuracy and speed of anomaly identification, reducing reliance on experience, and enhancing forecast reliability. Attached Figure Description

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the system sub-modules of the present invention; Figure 3 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Please see Figure 1 The small-diameter TBM cutterhead-shield collaborative geological prediction system includes: The tunneling status acquisition module acquires continuous monitoring data of cutterhead rotation speed, propulsion cylinder thrust and cutterhead torque, calculates the ratio of propulsion speed to torque, compares the ratio with the circumferential difference rate of propulsion force, and obtains the tunneling stability coefficient. The cutterhead force analysis module uses the tunneling stability coefficient to call the position of the cutterhead cutting tool and the cutting force output to calculate the total force per unit cutting section and obtain the force balance of the cutterhead. The shield attitude assessment module uses the coordinates of the laser target at the tail of the shield, the attitude tilt angle and the displacement difference of the propulsion cylinder to calculate the rate of change of the angle between the shield's central axis and the tunnel's design axis, based on the force balance of the cutterhead, and to obtain the shield's attitude coordination. Based on the shield attitude coordination degree, the tunneling coordination analysis module calls the synchronization sequence of cutterhead torque, propulsion cylinder thrust and propulsion speed, calculates the linear correlation matrix of the three, filters out combinations exceeding the threshold, compares the proportion of consecutive occurrences, and obtains the intelligent tunneling coupling degree. The geological prediction judgment module uses the intelligent tunneling coupling degree to call the continuous sequence of tunneling stability coefficients, identify the coupling degree mutation interval, determine the proportion that exceeds the stratum disturbance benchmark range, and obtain the geological prediction recognition rate. The tunneling stability coefficient includes the propulsion speed fluctuation rate, torque load coefficient, and propulsion force distribution uniformity coefficient; the cutterhead force balance includes the cutting force distribution coefficient, the tool force ratio, and the concentration of unit cutting load; the shield attitude coordination includes the shield axis offset angle, attitude inclination angle change rate, and propulsion cylinder displacement difference coefficient; the intelligent tunneling coupling degree includes the propulsion force-torque correlation coefficient, dynamic response synchronization rate, and multi-parameter linear correlation rate; the geological prediction recognition rate includes the accuracy of stratum disturbance recognition, coupling degree abrupt change detection rate, and geological anomaly judgment accuracy.

[0019] Please see Figure 2 The tunneling status acquisition module includes: The speed monitoring submodule acquires continuous monitoring data of cutter head speed, propulsion cylinder thrust and cutter head torque, matches the cutter head speed and propulsion cylinder thrust in time series, calculates the ratio of their change rates in adjacent time periods, analyzes the fluctuation range of the ratio and judges its stability, and generates the cutter head running stability coefficient. First, the speed monitoring submodule acquires continuous monitoring data of the cutterhead speed, propulsion cylinder thrust, and cutterhead torque. The time series of these three data sets is obtained through real-time sensors. For example, at time node T1, the cutterhead speed is 80 rpm, the propulsion cylinder thrust is 250 kN, and the cutterhead torque is 450 Nm; at time node T2, the cutterhead speed is 85 rpm, the propulsion cylinder thrust is 260 kN, and the cutterhead torque is 470 Nm. Next, the time series of the cutterhead speed and propulsion cylinder thrust are matched. Assuming that at time node T1, the cutterhead speed is 80 rpm and the propulsion cylinder thrust is 250 kN, while at T2, the cutterhead speed is 85 rpm and the propulsion cylinder thrust is 260 kN, the changes in these two data points between these two nodes are calculated. The rate of change ratio is calculated by the difference between time points and the difference between magnitudes. Assuming a time interval of 10 seconds, the rate of change of the cutter head rotation speed is (85-80) / 10 = 0.5 rpm / s, and the rate of change of the propulsion cylinder thrust is (260-250) / 10 = 1.0 kN / s. The ratio of the rates of change is 0.5 / 1.0 = 0.5. Next, the fluctuation range of the ratio is analyzed. Assuming that the ratio fluctuates within the range of [0.5, 1.5] over multiple consecutive cycles, the fluctuation range is 1.0. By calculating the fluctuation range of the ratio, its stability is judged. If the fluctuation range is less than the set stability threshold (e.g., 1.2), the system is considered stable; otherwise, instability is considered to exist. Finally, the cutter head operation stability coefficient is generated. The thrust calculation submodule is based on the cutterhead running stability coefficient. It calls the thrust of the propulsion cylinder and the torque monitoring data of the cutterhead to calculate the circumferential distribution value of the thrust of each propulsion cylinder, analyzes the thrust distribution deviation and calculates the degree of circumferential difference, compares its deviation from the overall thrust average value, and generates the circumferential difference rate of thrust. First, based on the cutterhead's operational stability coefficient, the thrust of the propulsion cylinders and the torque monitoring data of the cutterhead are retrieved. Assuming the cutterhead's operational stability coefficient is 0.95, indicating a relatively stable working state, the circumferential distribution value of the thrust of each propulsion cylinder is calculated. The circumferential distribution value refers to the distribution of the thrust of the propulsion cylinders at different angular positions on the cutterhead. Assuming there are 10 cylinders on the cutterhead, and the thrust data of each cylinder is (250kN, 260kN, 270kN, 255kN, 265kN, 250kN, 275kN, 260kN, 280kN, 290kN), the circumferential distribution value of the thrust of each cylinder can be calculated. For example, the thrust of the first group of cylinders is 250kN, the second group is 260kN, and so on. Next, the thrust distribution deviation is analyzed. The deviation refers to the... The difference between the thrust of each cylinder and the average thrust value is calculated. Assuming the average thrust is 270kN, the deviation is the difference between the thrust of each cylinder and 270kN. For example, if the thrust of the first group of cylinders is 250kN, the deviation is 250kN - 270kN = -20kN. Next, the circumferential difference is calculated by taking the standard deviation of the thrust deviation of each cylinder. Assuming the standard deviation of the deviation data of 10 cylinders is 15kN, the circumferential difference is 15kN. Then, the deviation from the overall thrust average is compared. The deviation of the overall thrust is the ratio of the circumferential difference to the average thrust. Assuming the average thrust is 270kN, the thrust deviation is calculated to be 15 / 270 = 5.56%. Finally, the circumferential difference rate of the thrust is generated. The tunneling ratio evaluation submodule calls the propulsion speed and torque ratio monitoring data based on the circumferential difference rate of propulsion force, calculates the corresponding change relationship between the ratio fluctuation amplitude and the circumferential difference rate, judges its synchronous trend, and compares it with the stable benchmark value to generate the tunneling stability coefficient. First, based on the circumferential difference rate of propulsion force, the monitoring data of the ratio of propulsion speed to torque is retrieved. Assuming the monitoring data for the ratio of propulsion speed to cutterhead torque is (2.0, 2.1, 2.2, 2.0, 2.3, 2.4), the fluctuation range of the ratio is calculated. The fluctuation range refers to the difference between the maximum and minimum values ​​of the ratio within a certain period. For example, in the above data, the maximum value of the ratio is 2.4, and the minimum value is 2.0, then the fluctuation range is 2.4 - 2.0 = 0.4. Next, the corresponding change relationship of the circumferential difference rate is calculated. Assuming that the circumferential difference rate is 5.56% in a certain period, the circumferential difference rate needs to be... The fluctuation range of the ratio of the tunneling rate to the propulsion speed to the cutterhead torque is compared to analyze the relationship between the two. If the fluctuation range of the ratio is large and the circumferential difference rate is high, it indicates that there is a large instability in the tunneling state; otherwise, it indicates that the tunneling state is relatively stable. Then, the synchronization trend of the ratio is judged. If the fluctuation range of the ratio and the change of the circumferential difference rate tend to be consistent, it indicates that the propulsion state is stable; otherwise, it indicates that there is an incoordination. Finally, it is compared with the stability benchmark value. Assuming that the stability benchmark value is set to 0.2, if the fluctuation range of the ratio exceeds this value and is inconsistent with the change trend of the circumferential difference rate, the tunneling state can be considered unstable. Finally, the tunneling stability coefficient is generated. The propulsion speed is calculated using the propulsion cylinder displacement data and time series, with the specific formula as follows: , in, It is the speed of propulsion. It's a change in the position of the oil needle. It is the change of time; Assumption: Displacement changes of the hydraulic cylinder during the two measurements: Initial displacement S1 = 1250 mm Final displacement S2 = 1370 mm Displacement increment: =S2-S1=1370-1250=120 mm If the time intervals corresponding to S1 and S2 are: =6s; Substitute into the formula to calculate: ; At this point, the propulsion speed calculated using the above steps is 20 mm / s; The torque ratio is the ratio of the cutter head torque to the cutter head speed, used to characterize the change in cutting load per unit speed. The specific formula is: , in, It is the torque ratio. It's the torque of the cutter head. It is the rotational speed of the cutter head; Assumption: In the speed monitoring submodule, it was detected that It is 80 rpm. It is 450 Nm; Substitute into the formula to calculate: =450 / 80≈5.62; At this point, the torque ratio calculated using the above steps is 5.62; Please see Figure 2 The force analysis module for the cutter head includes: The cutting force calculation submodule is based on the tunneling stability coefficient. It calls the tool position parameters and the output of the cutting force sensor, extracts the coordinates of the tool action point and the contact boundary, calculates the angle difference between adjacent tools to obtain the angle spacing data, calculates the normal and tangential force components according to the tool sequence, and accumulates them in the unit segment to generate the total force value of the unit segment. First, based on the established tunneling stability coefficient, the key factors affecting cutting force are determined. The stability coefficient reflects the stability of the soil layer; a lower value indicates soft soil, while a higher value indicates hard soil. For example, the stability coefficient is 0.6 in soft soil and 1.2 in hard rock. Next, the tool position parameters and cutting force sensor output data are used to obtain tool position information in real time, such as the tool's specific coordinates and contact depth with the soil layer. Assuming that at a certain moment, the tool's coordinates are (X1, Y1, Z1) and the contact depth is d1, this data reflects the tool's current position and working condition. Based on this, the coordinates of the tool's point of action and the contact boundary are extracted. The contact boundary between the tool and the soil is obtained through real-time data transmission and analysis. For example, if the tool's contact point is P1 with coordinates (X1, Y1, Z1) and the contact boundary is boundary B1, the distance d2 between the tool's point of action and the contact boundary can be calculated using the following formula: , Where (XB,YB,ZB) are the coordinates of the contact boundary.

[0020] Assumption: Tool position and depth data: The tool coordinates are (X1,Y1,Z1)=(10,20,30); The contact depth between the cutting tool and the soil layer is d1 = 5 cm; Coordinates of the tool's point of action and the contact boundary: The point of application of the tool is P1=(X1,Y1,Z1)=(10,20,30); The contact boundary is B1=(XB,YB,ZB)=(15,25,35); Substitute into the formula to calculate: ≈8.66cm; The distance between the tool's point of action and the contact boundary, obtained through the above calculation steps, is 8.66 cm. The above calculation formulas can provide accurate cutting force analysis and optimization paths for actual tunneling operations; Next, the angle difference between adjacent tools is calculated. The angle difference refers to the difference in angle between two adjacent tools on the cutter head. By calculating the angle difference between two tools, assuming the angles of adjacent tools are 20° and 25° respectively, the angle difference is 5°. After obtaining the angle difference data, the spacing between tools can be determined, and further angle spacing data can be obtained. Then, the normal and tangential force components are calculated according to the tool sequence. Through the force analysis of each tool, the normal force and tangential force of each tool in operation are calculated. Finally, based on these force data, the total force value of the unit segment is generated by accumulating them within the unit segment. The total force value is calculated using the following formula: , Where F1A is the normal force of tool A, T1A and T1B are the tangential forces, F1B is the normal force of tool B, and Ftotal is the total force value. Assumption: The normal force of tool A is F1A = 200N; The tangential force is T1A = 150 N; The normal force of tool B is F1B = 180N; The tangential force is T1B = 140 N; Substitute into the formula to calculate: Ftotal=200+150+180+140=670N; At this point, the total force obtained through the above calculation steps is 670N; The force zone comparison submodule calls the total force value of the unit segment and the tool angle spacing data, divides the intervals according to the circumferential direction, calculates the force difference between adjacent intervals and counts the deviation of the difference distribution, and generates the circumferential force difference rate of the tool head. First, the total force value per unit segment and the tool angle spacing data are retrieved. The total force value per unit segment includes the force on each tool within that segment. For example, the total force on the tool within a unit segment is 100N. Next, the cutter head is divided into circumferential intervals. The circumferential distribution of the cutter head means dividing it into multiple intervals evenly according to the tool angles. Assuming the cutter head angle is 360° and the angle spacing between each tool is 10°, the cutter head can be divided into 36 intervals. Then, the force difference between adjacent intervals is calculated. The force difference can be obtained by comparing the total force in two adjacent intervals. For example, the total force in interval 1 is 100N. N, and the total force in interval 2 is 120N, so the force difference is 20N; next, the deviation of the difference distribution is calculated. The deviation is obtained by calculating the standard deviation of the force difference across all intervals. The standard deviation reflects the distribution of force differences in each interval. Assuming the standard deviation of the force difference across all intervals is 15N, it indicates a large force difference; finally, the circumferential force difference rate of the cutterhead is generated. The force difference rate is calculated as the ratio of the force difference across all intervals to the average force value. For example, assuming the average force on the cutterhead in a certain period is 100N, and the standard deviation of the force difference is 15N, then the force difference rate is 15 / 100 = 15%; The force balance assessment submodule calculates the offset trend of the force difference at each angle and statistically analyzes its balance in the circumferential distribution based on the circumferential force difference rate of the cutterhead and the tool angle spacing sequence, thereby obtaining the force balance of the cutterhead. First, based on the circumferential force difference rate of the cutter head and the tool angle spacing sequence, the distribution of force on the cutter head in the circumferential direction is analyzed. Assuming the circumferential force difference rate of the cutter head is 15%, it indicates that there is a large force difference during the operation of the cutter head. Next, the offset trend of the force difference at each angle is calculated. The offset trend reflects the change trend of force on each tool at different angular positions. For example, assuming the force difference of the tool at 0° to 90° is 2N, and the force difference of the tool at 180° to 270° is 5N, the balance of the force distribution on the cutter head can be analyzed by calculating the change trend of the force difference. Next, the degree of balance in the circumferential distribution is statistically analyzed. The degree of balance reflects the uniformity of the force on the cutter head at different circumferential positions. Assuming the degree of force balance of the cutter head in the circumferential direction is 80%, it indicates that the force on the cutter head is relatively balanced. Finally, the force balance degree of the cutter head is obtained. The tool position parameters are determined by the tool head design model and assembly calibration data, and can be dynamically updated by combining real-time correction information from the attitude monitoring system; The cutting force sensor is installed between the back of the tool and the tool head support structure. It is a triaxial force sensor used to collect the normal force, tangential force and radial force components of the tool. Please see Figure 2 The shield attitude assessment module includes: The force and attitude synchronization submodule calls the force balance of the cutterhead, collects the three-dimensional coordinates of the laser target at the tail of the shield and records the time tag, detects the displacement signal of the propulsion cylinder and calculates the displacement difference, matches the force balance with the attitude tilt angle time series, compares the parameter time deviation and calculates the synchronization correction coefficient, integrates the corrected attitude and force parameters to form a unified matrix, and generates the attitude force synchronization matrix value. First, based on the force balance of the cutterhead, the force balance of the cutterhead during operation is obtained. Assuming a force balance of 80%, it indicates that the force on the cutterhead is relatively uniform. Next, the three-dimensional coordinates of the laser target at the tail of the shield are acquired. Through the laser target acquisition system, the spatial position data of the shield tail is obtained in real time. This data includes X, Y, and Z coordinate values, and a time label is added to each data point. For example, at time T1, the target's three-dimensional coordinates are (X1, Y1, Z1), and at time T2, the target's three-dimensional coordinates are (X2, Y2, Z2). Simultaneously, the displacement signal of the propulsion cylinder is detected. The cylinder displacement data can be monitored in real time through sensors. Assuming that at time T1 the cylinder displacement is L1, and at time T2 the displacement is L2, the displacement difference is L2 - L1. Next, based on the acquired force balance and attitude tilt... The angular time series is used to match the force balance with the attitude tilt angle at each time point. This allows us to obtain the variation pattern of force and attitude at each point in the time series. For example, if the force balance is 75% at time T1 and 80% at time T2, matching these two values ​​forms a dynamic data sequence. Then, the time deviation of the parameters is compared, the time deviation of each data point is analyzed, and a synchronization correction coefficient is calculated. This coefficient is used to correct the asynchrony between data. For example, if there is a 2-second time deviation in the tilt angle data at time T1, the correction coefficient can be adjusted by comparing the time difference and the amount of change. Finally, the corrected attitude and force parameters are integrated to form a unified matrix. In the integrated data matrix, each row contains the time point, the corrected attitude data, the corrected force data, etc. Finally, the attitude and force synchronization matrix value is generated. The axis angle change calculation submodule calls the shield axis vector and tunnel axis vector of the attitude force synchronization matrix value to calculate the angle value of each advancement period. It calculates the rate of change based on the angle difference of continuous periods, calls the cutterhead force balance difference coefficient and compares it with the angle change rate, extracts the angle change trend sequence, and obtains the shield axis angle change rate. First, by calling the shield axis vector and tunnel axis vector in the attitude force synchronization matrix, the angle between the actual attitude of the shield and the tunnel axis is determined. Assuming that at a certain moment, the direction vector of the shield axis is (X1, Y1, Z1), and the direction vector of the tunnel axis is (X2, Y2, Z2), the angle between these two can be calculated using the vector angle formula. The specific formula is as follows: , Where A•B is the dot product of two vectors, and |A| and |B| are the magnitudes of the vectors; Assumption: directional vectors of the shield axis and the tunnel axis: The shield axis direction vector A = (X1, Y1, Z1) = (10, 20, 30); The tunnel axis direction vector B = (X2, Y2, Z2) = (15, 25, 35); Calculate the angle between the shield axis and the tunnel axis: The dot product A•B is: A•B=X1X2+Y1Y2+Z1Z2=10×15+20×25+30×35=150+500+1050=1700; The modulus lengths |A| and |B| are: ; ; Substitute into the formula to calculate: cosθ=1700 / 37.42×45.53=1702.991700≈0.998; The included angle is: ; The included angle at this point is 3.86 degrees; Next, the included angle value for each advancement period is calculated. Assuming that the included angle changes from 10° to 15° between time periods T1 and T2, the angle difference is 5°. Further, based on the angle difference across consecutive time periods, the rate of change is calculated. The formula for the rate of change is: Rate of change = Angle difference / Time difference. For example, if the angle difference between T1 and T2 is 5° and the time difference is 10 seconds, then the rate of change is 0.5° / second. Next, the force balance difference coefficient of the cutterhead is called. This coefficient reflects the force difference of the cutterhead at different working stages. For example, if the cutterhead at T1... The force balance at time T1 is 75%, while it is 80% at time T2, so the force difference coefficient is 5%. Next, this force difference coefficient is compared with the angle change rate. Assuming the force difference coefficient is 5% and the angle change rate is 0.5° / second, the relationship between force change and attitude change can be obtained. Finally, the angle change trend sequence is extracted. Through statistical analysis of the angle change rate at multiple time periods, a stable angle change trend is obtained. The average change rate is calculated based on the angle change trend, and finally the shield axis angle change rate is obtained. Assumption: The rate of change sequence for multiple time periods is 0.5 degrees / second, 0.4 degrees / second, and 0.6 degrees / second; Average rate of change = 0.5 + 0.4 + 0.6 / 3 = 0.5 degrees / second At this point, 0.5 degrees / second is the rate of change of the angle between the shield axis and the tunnel axis, which represents the average rate of change of the angle between the shield axis and the tunnel axis per unit time. The attitude coordination determination submodule calls the force parameters of the shield axis angle change rate and attitude force synchronization matrix value, calculates the propulsion cycle coordination change coefficient, determines the execution interval of the difference between the coordination coefficient and the attitude reference coefficient, generates a coordination index sequence based on the difference distribution, and obtains the shield attitude coordination. First, by calling the force parameters in the synchronization matrix of the shield axis angle change rate and attitude force, and combining the changes within the propulsion cycle, the propulsion cycle coordination coefficient is calculated. This coefficient reflects whether the shield attitude and force can maintain good coordination in different propulsion stages. Assuming that the angle change rate is 0.5° / second at a certain point in the propulsion cycle, and the corresponding force parameter is 80N, the coordination coefficient can be obtained by calculating the relationship between the angle change rate and the force change. The specific formula is: Coordination coefficient = Angle change rate / Force change = 0.5° / 80 = 0.00 625° / second / N. For example, by statistically analyzing the changes in angle and force over different time periods, the value of this coefficient can be obtained. Next, the execution range of the difference between the coordination coefficient and the attitude reference coefficient is determined. The reference coefficient can be set through prior experiments or empirical values. Assuming the reference coefficient is 0.3 and the coordination coefficient is 0.5, then the difference = |coordination coefficient - reference coefficient| = |0.5 - 0.3| = 0.2. The smaller the difference, the better the coordination between attitude and force. Next, a coordination index sequence is generated based on the difference distribution. The sequence contains the coordination index value at each time point. The calculation formula for the coordination index is: , in: Ccoord,i is the coordination index for time period Ti; Δθi is the change in the angle between points Ti over time period Ti; ΔFi is the change in force over time period Ti; Assumption: Over a certain time period T1, T2, T3, the rate of change of the included angle and the change of the force are as follows: T1: The rate of change of the included angle Δθ1 = 0.5° / second, and the change of force ΔF1 = 80 N; T2: The rate of change of the included angle Δθ2 = 0.4° / second, and the change in force ΔF2 = 85N; T3: The rate of change of the included angle Δθ3 = 0.6° / second, and the change in force ΔF3 = 75 N; T4: The rate of change of the included angle Δθ4 = 0.3° / second, and the change of force ΔF4 = 90 N; Substitute into the formula to calculate: For T1: Ccoord,1=|0.5-80| / 0.5+80≈0.987; Ccoord,2=|0.4-85| / 0.4+85≈0.991; Ccoord,3=|0.6-75| / 0.6+75≈0.985; Ccoord,4=|0.3-90| / 0.3+90≈0.994; Generate a series of coordination indicators: Ccoord,seq=[0.987,0.991,0.985,0.994]; Calculate the average value of the coordination index: Ccoord,avg=0.987+0.991+0.985+0.994 / 4=0.989; By calculating these indicators, the final value of the shield's attitude coordination is 0.989. At this point, the calculated result of 0.989 is close to 1, indicating that the changes in attitude and force during the advancement process have a high degree of coordination, making it suitable for continued advancement. Please see Figure 2 The tunneling coordination analysis module includes: The parameter synchronization sequence preparation submodule calls the shield attitude coordination degree, extracts the node average value of the time series of cutterhead torque and propulsion cylinder thrust, calculates the node speed difference of the propulsion speed sequence, matches the three sequences according to the shield attitude coordination degree and corrects the time deviation, so that they remain synchronized in the same propulsion cycle, and obtains the tunneling synchronization sequence set. First, the shield attitude coordination data is retrieved. This data represents the matching between the shield's attitude changes and force balance. Assuming that at a certain moment, the shield attitude coordination is 85%, it indicates good coordination between force and attitude. Next, the average values ​​of nodes are extracted from the time series of cutterhead torque and propulsion cylinder thrust. For example, at time node T1, the cutterhead torque is 500 Nm and the propulsion cylinder thrust is 300 kN; at time node T2, the cutterhead torque is 550 Nm and the propulsion cylinder thrust is 320 kN. By calculating the average values ​​of these time nodes, representative values ​​of the cutterhead torque and propulsion cylinder thrust are obtained. For instance, the average cutterhead torque at nodes T1 and T2 is 525 Nm and the average propulsion cylinder thrust is 310 kN. kN; then, the node speed difference of the propulsion speed sequence is calculated. It is assumed that at time node T1, the propulsion speed is 2m / min and at time node T2, the propulsion speed is 2.5m / min, and the propulsion speed difference is 0.5m / min; next, the three sequences (cutterhead torque, propulsion cylinder thrust, and propulsion speed) are matched and the time deviation is corrected according to the shield attitude coordination. It is assumed that there is a certain time deviation between the time sequences of cutterhead torque and propulsion cylinder thrust, for example, the cutterhead torque lags behind the propulsion cylinder thrust by 0.2 seconds. By analyzing and correcting this deviation, all parameters can be synchronized within the same propulsion cycle. Finally, the time sequences of these three data points after correction are merged to obtain the tunneling synchronization sequence set; The correlation matrix construction submodule calls the tunneling synchronization sequence set to calculate three sets of difference values: cutterhead torque and propulsion cylinder thrust, propulsion cylinder thrust and propulsion speed, and cutterhead torque and propulsion speed. Based on the difference changes, it calculates the linear correlation rate, constructs the correlation matrix, and filters variable combinations that exceed the threshold to obtain the effective correlation combination matrix value. First, the tunneling synchronization sequence data is accessed. This dataset contains corrected time series of cutterhead torque, propeller cylinder thrust, and propeller speed. Next, three sets of difference values ​​are calculated: cutterhead torque versus propeller cylinder thrust, propeller cylinder thrust versus propeller speed, and cutterhead torque versus propeller speed. These differences are calculated by addressing the differences between adjacent time points. For example, regarding the difference between cutterhead torque and propeller cylinder thrust, assuming at time point T1 the cutterhead torque is 500 Nm and the propeller cylinder thrust is 300 kN, while at time point T2 the cutterhead torque is 550 Nm and the propeller cylinder thrust is 320 kN, the difference is 500 Nm - 550 Nm = -50 Nm for cutterhead torque and 300 kN - 320 kN = -20 kN for propeller cylinder thrust. Subsequently, based on these difference values, the linear correlation rate between the three sets of data is calculated. The linear correlation rate can be calculated using the Pearson correlation coefficient formula, specifically: , Where: r is the Pearson correlation coefficient, used to measure the linear relationship between two variables; Xi and Yi are the data points of the two variables, respectively; and This represents the mean of these two variables; Assumption: Between time points T1 and T2, the values ​​of cutter head torque and propulsion cylinder thrust are as follows: At time node T1, the cutter head torque is M1=500 Nm, and the propulsion cylinder thrust is F1=300 kN; At time node T2, the cutter head torque is M2=550 Nm, and the propulsion cylinder thrust is F2=320 kN; The differences between the cutter head torque and the propulsion cylinder thrust are as follows: ΔM=M2-M1=550 Nm-500 Nm=50 Nm; ΔF=F2-F1=320 kN-300 kN=20 kN; Next, the differences between the cutter head torque and the propulsion cylinder thrust are as follows: ΔM = -50 Nm, ΔF = -20 kN; Between time points T1 and T2, the data for propulsion cylinder thrust and propulsion speed are as follows: At time point T1, the thrust of the propulsion cylinder is F1=300 kN and the propulsion speed is v1=2 m / min; At time point T2, the thrust of the propulsion cylinder is F2 = 320 kN, and the propulsion speed is v1 = 2.5 m / min; The differences between the thrust and propulsion speed of the hydraulic cylinder are as follows: ΔF=F2-F1=320 kN-300 kN=20 kN; Δv=v2-v1=2.5 m / min-2 m / min=0.5 m / min; Between time points T1 and T2, the data for cutterhead torque and feed speed are as follows: At time node T1, the cutter head torque is M1=500 Nm and the feed speed is v1=2 m / min; At time point T2, the cutterhead torque is M2 = 550 Nm, and the feed speed is v2 = 2.5 m / min; The differences between the cutter head torque and the feed speed are as follows: ΔM=M2-M1=550 Nm-500 Nm=50 Nm; Δv=v2-v1=2.5 m / min-2 m / min=0.5 m / min; Substitute into the formula to calculate the linear correlation rate: Calculate the Pearson correlation coefficient between the cutter head torque and the propulsion cylinder thrust: The difference data = [(-50, -20), (-100, -30)] = 0.95; This indicates a strong linear relationship between the cutter head torque and the thrust of the propulsion cylinder; Calculate the Pearson correlation coefficient between the propulsion cylinder thrust and the propulsion speed: Assume the difference between the thrust and propulsion speed of the propulsion cylinder is as follows: The difference data = [(20,0.5),(20,0.5)] = 0.98; This indicates that there is a strong linear relationship between the thrust of the propulsion cylinder and the propulsion speed; Calculate the Pearson correlation coefficient between cutterhead torque and feed rate: Assume the difference between the cutter head torque and the feed speed is as follows: The difference data = [(50,0.5),(50,0.5)] = 0.90; This indicates that there is also a strong linear relationship between the cutter head torque and the feed speed; At this point, the matrix value obtained through the above calculation steps is [0.95 0.98 0.90], where each column represents the linear correlation rate between different combinations of variables; Finally, variable combinations that exceed the set threshold are selected. For example, if the set threshold is 0.8 and the correlation rate between the cutter head torque and the propulsion cylinder thrust is 0.95, then the combination that exceeds the threshold is retained, and the effective correlation combination matrix value is obtained. The coupling degree determination submodule calls the effective correlation combination matrix value, counts the frequency of occurrence of variable combinations in continuous periods, calculates the proportion of consecutive occurrences in adjacent periods and generates a stability sequence, performs weighted and normalized processing to obtain the intelligent tunneling coupling degree. First, the effective correlation combination matrix is ​​retrieved. This matrix contains the strongly correlated variable combinations selected in previous steps. Next, the frequency of these variable combinations within consecutive cycles is calculated using the formula: Frequency = Number of occurrences / Total number of cycles. For example, if the effective correlation combination of cutterhead torque and propulsion cylinder thrust occurs four times in five consecutive propulsion cycles, its frequency is 80%. Then, the proportion of consecutive occurrences in adjacent cycles is calculated. For instance, the proportion of this correlation combination is 80% between cycles T1 and T2, and 60% between cycles T2 and T3. Based on these data, a stability sequence is generated. The number of stability sequences... The value reflects the stability of the relevant combination in different periods. For example, in a certain stable period, the stability sequence value is 0.8, indicating that the stability of the relevant combination is relatively high. Finally, weighting and normalization are performed. The values ​​in the stability sequence are weighted according to a certain weight. For example, the weight is 0.7, and the weighted value is 0.56. Then, normalization is performed to convert the value to the range of 0 to 1, and finally the intelligent tunneling coupling degree is obtained. Assuming that the coupling degree value obtained after normalization is 0.85, it means that during the tunneling process, the coupling degree between the cutterhead torque, the propulsion cylinder thrust, and the propulsion speed is relatively high, and it has good collaborative operation capability. Assumption: The weight w is 0.7, which means that for each value in the stability sequence, we multiply it by 0.7 for weighting. Stability sequence 1 = 0.80; The weighted stability value is 1 = 0.80 × 0.7 = 0.56; The weighted value is 0.56; The occurrence rates of related combinations over multiple periods are as follows: Stable sequence = [0.80, 0.60, 0.75, 0.85]; The entire sequence is weighted, and the weighted result is: The weighted stability sequence is [0.56, 0.42, 0.525, 0.595]. Next, normalization is performed. Normalization transforms the weighted stability sequence values ​​into values ​​within the range [0,1]. The specific formula for normalization is: ; Substitute the numerical values ​​into the formula to calculate: ; By normalizing the entire sequence, we obtain: Normalized stability sequence = [0.8, 0.0, 0.5, 1.0]; Finally, based on the normalized stability sequence, we can derive the intelligent tunneling coupling degree. The coupling degree value is usually calculated based on the average value of the normalized stability sequence. Substitute the normalized stability sequence into the calculation: Intelligent tunneling coupling degree = 0.8, 0.0, 0.5, 1.0 / 4 = 0.574; At this point, the intelligent tunneling coupling degree is calculated to be 0.574 through the above steps, which means that the coupling degree between the cutterhead torque, the propulsion cylinder thrust and the propulsion speed is moderately low during the tunneling process. Please see Figure 2 The geological forecasting and judgment module includes: The coupling degree mutation identification submodule obtains the tunneling stability coefficient change sequence based on the intelligent tunneling coupling degree, calculates the coupling degree change difference in each cycle and extracts the change amplitude, compares it with the mutation threshold, identifies the mutation point based on the node change rate, and generates a mutation interval identifier. First, based on the intelligent tunneling coupling degree data, the tunneling stability coefficient change sequence is obtained. Assuming that at a certain moment, the tunneling stability coefficient is 0.75, and in the next cycle, the stability coefficient changes to 0.80, then the change sequence is 0.75, 0.80, representing the change in the stability coefficient between different cycles. Next, the difference in coupling degree change for each cycle is calculated. The difference value can be calculated by the change in coupling degree between adjacent cycles. For example, between cycles T1 and T2, the coupling degree changes from 0.85 to 0.90, then the difference is 0.90 - 0.85 = 0.05. Then, the change amplitude is extracted by calculating the difference between the maximum and minimum change in coupling degree within a cycle. Assuming the change amplitude between cycles T1 and T2 is 0.10, this amplitude can represent the coupling degree within the cycle. The maximum change is then determined. Next, the magnitude of the change is compared to a mutation threshold, assuming a threshold of 0.08. When the magnitude of the change exceeds this threshold, a mutation in coupling degree is considered to have occurred within that period. Then, mutation points are identified based on the node change rate. The node change rate can be determined by calculating the ratio of the change in each node to the time interval. For example, if the coupling degree changes by 0.05 between periods T1 and T2, and the time interval is 10 seconds, the change rate is 0.005 per second. If the change rate exceeds a set rate threshold (say, 0.004 per second), that period can be identified as a mutation point. Finally, mutation interval markers are generated. These markers will determine when a sharp change in coupling degree occurred and will be the focus of subsequent analysis. The disturbance interval judgment submodule calls the mutation interval identifier, detects the coupling degree time series value in each interval, calculates the proportion that exceeds the formation disturbance reference range, compares it with the disturbance reference threshold, judges the interval exceeding the standard state, and generates the disturbance exceeding the standard proportion; First, the abrupt change interval identifier is called to obtain the coupling degree abrupt change points that occur during tunneling. Assuming a sudden change in coupling degree occurs between T2 and T3, the coupling degree time series values ​​within each interval are then detected. For example, if the coupling degree time series within a certain interval is (0.90, 0.88, 0.86, 0.84), the coupling degree decreases over time. Next, the proportion exceeding the formation disturbance reference range is calculated. The disturbance reference range is defined as the normal coupling degree variation range within a certain range. For example, in a certain formation, the disturbance reference range is 0.85 to 0.90. If a value in the time series (e.g., 0.84) is lower than the baseline range, then a disturbance has occurred at that point. The proportion exceeding the baseline range is the ratio of the number of nodes exceeding the range to the total number of nodes. Assuming that in one period, there are 3 nodes exceeding the baseline range and the total number of nodes is 6, then the proportion is 3 / 6 = 50%. Next, the proportion exceeding the disturbance baseline range is compared with the disturbance baseline threshold. Assuming the disturbance baseline threshold is 40%, since the proportion of 50% is greater than this threshold, it can be determined that the interval is out of control. Finally, the disturbance exceedance ratio is generated, which represents the severity of the disturbance in a certain period. The prediction recognition rate calculation submodule is based on the proportion of disturbance exceeding the standard, calls the stratigraphic disturbance benchmark range, counts the number of exceeding intervals and the total number of intervals, calculates the average trend of the exceeding proportion, generates the geological disturbance recognition rate distribution, and obtains the geological prediction recognition rate. First, based on the proportion of disturbances exceeding the standard, the intervals where disturbances exceeded the standard are obtained. Assuming there are 5 intervals in one tunneling cycle, and 3 of them exhibited disturbances exceeding the standard, the ground disturbance baseline range is called to determine whether each exceeding interval truly exceeds the baseline range. Assuming the baseline range is 0.85 to 0.90, and within a certain interval, the coupling degree is 0.82, significantly lower than the baseline range, then that interval is considered an exceeding interval. Next, the number of exceeding intervals is counted relative to the total number of intervals. Assuming that a total of 10 intervals were detected during tunneling, and 3 of them exceeded the standard, then the ratio of exceeding intervals to the total number of intervals is 3 / 10 = 30%. Finally, the average trend of the exceeding proportion is calculated by... By averaging the changes in the proportions of all exceeding the standard intervals, the overall trend of exceeding the standard can be obtained. Assuming that the exceeding proportions are 30%, 40%, and 35% in each period, the average value is 35%. Finally, a geological disturbance identification rate distribution is generated, which represents the frequency and severity of geological disturbances throughout the tunneling process. Assuming that the identification rate distribution is (30%, 35%, 40%) in different periods, the geological prediction identification rate is obtained. This value represents the accuracy and sensitivity of geological disturbance identification during the tunneling process. Assuming that the final geological prediction identification rate is 35%, it indicates that the method has a relatively accurate prediction effect on geological disturbances and can effectively identify potential geological problems. Please see Figure 3 A small-diameter TBM cutterhead-shield collaborative geological prediction method includes the following steps: S1: Collect continuous monitoring data of cutterhead rotation speed, propulsion cylinder thrust and cutterhead torque, calculate the ratio of propulsion speed to torque, input the two sequences into a time series clustering model for fluctuation analysis, calculate the synchronous change ratio based on the clustering results, and generate the tunneling stability coefficient; S2: Based on the tunneling stability coefficient, call the cutter head cutting tool position and cutting force output data, input the tool cutting force matrix into the force zoning analysis algorithm, calculate the total force of each section and perform distribution difference calculation to obtain the cutter head force balance; S3: Based on the cutterhead force balance, call the coordinates of the laser target at the tail of the shield, the attitude tilt angle and the displacement difference of the propulsion cylinder, calculate the rate of change of the angle between the shield's central axis and the tunnel's design axis, and perform a corresponding analysis with the cutterhead force balance to generate the shield's attitude coordination degree. S4: Based on the shield attitude coordination, the synchronous sequence data of cutterhead torque, propulsion cylinder thrust and propulsion speed are called to construct the correlation rate matrix and input into the multiple linear regression model to perform parameter correlation calculation. The combination exceeding the threshold is screened and the proportion of consecutive occurrences is counted to obtain the intelligent tunneling coupling degree. S5: Based on the intelligent tunneling coupling degree, call the continuous sequence of tunneling stability coefficients, identify the coupling degree variation range, compare it with the ground disturbance benchmark range, calculate and output the geological prediction recognition rate.

[0021] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A small-diameter TBM cutterhead-shield coordinated geological prediction system, characterized in that: The system includes: The tunneling status acquisition module acquires continuous monitoring data of cutterhead rotation speed, propulsion cylinder thrust and cutterhead torque, calculates the ratio of propulsion speed to torque, compares the ratio with the circumferential difference rate of propulsion force, and obtains the tunneling stability coefficient. The cutterhead force analysis module, based on the tunneling stability coefficient, calls the position of the cutterhead cutting tool and the cutting force output to calculate the total force per unit cutting section and obtain the cutterhead force balance. The shield attitude assessment module uses the coordinates of the laser target at the tail of the shield, the attitude tilt angle and the displacement difference of the propulsion cylinder to calculate the rate of change of the angle between the shield's central axis and the tunnel's design axis, based on the force balance of the cutterhead, and to obtain the shield's attitude coordination. Based on the shield attitude coordination degree, the tunneling coordination analysis module calls the synchronization sequence of cutterhead torque, propulsion cylinder thrust and propulsion speed, calculates the linear correlation matrix of the three, filters out combinations exceeding the threshold, compares the proportion of consecutive occurrences, and obtains the intelligent tunneling coupling degree. The geological prediction judgment module, based on the intelligent tunneling coupling degree, calls the continuous sequence of tunneling stability coefficients, identifies the coupling degree mutation interval, determines the proportion that exceeds the stratum disturbance benchmark range, and obtains the geological prediction recognition rate.

2. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 1, characterized in that: The tunneling stability coefficient includes the propulsion speed fluctuation rate, torque load coefficient, and propulsion force distribution uniformity coefficient; the cutterhead force balance includes the cutting force distribution coefficient, cutter force ratio, and unit cutting load concentration; the shield attitude coordination includes the shield axis offset angle, attitude inclination angle change rate, and propulsion cylinder displacement difference coefficient; the intelligent tunneling coupling degree includes the propulsion force-torque correlation coefficient, dynamic response synchronization rate, and multi-parameter linear correlation rate; the geological prediction recognition rate includes the accuracy of stratum disturbance recognition, coupling degree abrupt change detection rate, and geological anomaly judgment accuracy.

3. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 1, characterized in that: The tunneling status acquisition module includes: The speed monitoring submodule acquires continuous monitoring data of cutter head speed, propulsion cylinder thrust and cutter head torque, matches the cutter head speed and propulsion cylinder thrust in time series, calculates the ratio of their change rates in adjacent time periods, analyzes the fluctuation range of the ratio and judges its stability, and generates the cutter head running stability coefficient. Based on the cutterhead running stability coefficient, the thrust calculation submodule calls the propulsion cylinder thrust and cutterhead torque monitoring data to calculate the circumferential distribution value of each propulsion cylinder thrust, analyze the thrust distribution deviation and calculate the degree of circumferential difference, compare its deviation from the overall thrust average value, and generate the thrust circumferential difference rate. The tunneling ratio evaluation submodule calls the propulsion speed and torque ratio monitoring data based on the propulsion force circumferential difference rate, calculates the corresponding change relationship between the ratio fluctuation amplitude and the circumferential difference rate, judges its synchronous trend, and compares it with the stable benchmark value to generate the tunneling stability coefficient.

4. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 1, characterized in that: The force analysis module for the cutter head includes: Based on the tunneling stability coefficient, the cutting force calculation submodule calls the tool position parameters and the output of the cutting force sensor, extracts the coordinates of the tool action point and the contact boundary, calculates the angle difference between adjacent tools to obtain the angle spacing data, calculates the normal and tangential force components according to the tool sequence, and accumulates them in the unit segment to generate the total force value of the unit segment. The force zone comparison submodule calls the total force value of the unit segment and the tool angle spacing data, divides the interval according to the circumferential direction, calculates the force difference between adjacent intervals and counts the deviation of the difference distribution, and generates the circumferential force difference rate of the tool head. The force balance assessment submodule calculates the offset trend of the force difference at each angle and statistically analyzes its balance in the circumferential distribution based on the circumferential force difference rate of the cutterhead and the tool angle spacing sequence, thereby obtaining the force balance of the cutterhead.

5. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 1, characterized in that: The shield attitude assessment module includes: The force and attitude synchronization submodule calls the force balance of the cutterhead, collects the three-dimensional coordinates of the laser target at the tail of the shield and records the time tag, detects the displacement signal of the propulsion cylinder and calculates the displacement difference, matches the force balance with the attitude tilt angle time series, compares the parameter time deviation and calculates the synchronization correction coefficient, integrates the corrected attitude and force parameters to form a unified matrix, and generates the attitude force synchronization matrix value. The axis angle change calculation submodule calls the shield axis vector and tunnel axis vector of the attitude force synchronization matrix value to calculate the angle value of each advancement period. It calculates the change rate based on the angle difference of continuous periods, calls the cutterhead force balance difference coefficient and compares it with the angle change rate, extracts the angle change trend sequence, and obtains the shield axis angle change rate. The attitude coordination determination submodule calls the force parameters of the shield axis angle change rate and the attitude force synchronization matrix value to calculate the propulsion cycle coordination change coefficient, determine the execution interval of the difference between the coordination coefficient and the attitude reference coefficient, generate a coordination index sequence based on the difference distribution, and obtain the shield attitude coordination.

6. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 1, characterized in that: The tunneling coordination analysis module includes: The parameter synchronization sequence preparation submodule calls the shield attitude coordination degree, extracts the node average value of the time series of cutterhead torque and propulsion cylinder thrust, calculates the node speed difference of the propulsion speed sequence, matches the three sequences according to the shield attitude coordination degree and corrects the time deviation, so that they remain synchronized in the same propulsion cycle, and obtains the tunneling synchronization sequence set. The correlation rate matrix construction submodule calls the tunneling synchronization sequence set to calculate three sets of difference values: cutterhead torque and propulsion cylinder thrust, propulsion cylinder thrust and propulsion speed, and cutterhead torque and propulsion speed. Based on the difference changes, it calculates the linear correlation rate, constructs the correlation rate matrix, and filters variable combinations that exceed the threshold to obtain effective correlation combination matrix values. The coupling degree determination submodule calls the effective correlation combination matrix value, counts the frequency of occurrence of variable combinations in continuous periods, calculates the proportion of consecutive occurrences in adjacent periods and generates a stability sequence, performs weighted and normalized processing, and obtains the intelligent tunneling coupling degree.

7. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 1, characterized in that: The geological prediction and determination module includes: The coupling degree mutation identification submodule obtains the tunneling stability coefficient change sequence based on the intelligent tunneling coupling degree, calculates the coupling degree change difference in each cycle and extracts the change amplitude, compares it with the mutation threshold, identifies the mutation point based on the node change rate, and generates a mutation interval identifier. The disturbance interval judgment submodule calls the mutation interval identifier, detects the coupling degree time series value in each interval, calculates the proportion that exceeds the formation disturbance reference range, compares it with the disturbance reference threshold, judges the interval exceeding the standard state, and generates the disturbance exceeding the standard proportion; The prediction recognition rate calculation submodule, based on the disturbance exceeding the standard ratio, calls the stratigraphic disturbance benchmark range, counts the number of exceeding intervals and the total number of intervals, calculates the average trend of the exceeding ratio, generates the geological disturbance recognition rate distribution, and obtains the geological prediction recognition rate.

8. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 3, characterized in that: The propulsion speed is calculated using propulsion cylinder displacement data and a time series, with the specific formula as follows: ， in, It is the speed of propulsion. It's a change in the position of the oil needle. It is the change of time; The torque ratio is the ratio of the cutter head torque to the cutter head rotation speed, used to characterize the change in cutting load per unit rotation speed. The specific formula is as follows: ， in, It is the torque ratio. It's the torque of the cutter head. It is the rotational speed of the cutter head.

9. The small-diameter TBM cutterhead-shield collaborative geological prediction system according to claim 4, characterized in that: The tool position parameters are determined by the tool head design model and assembly calibration data, and can be dynamically updated in conjunction with the real-time correction information of the attitude monitoring system. The cutting force sensor is installed between the back of the tool and the tool head support structure. It is a triaxial force sensor used to collect the normal force, tangential force and radial force components of the tool.

10. A small-diameter TBM cutterhead-shield collaborative geological prediction method, characterized in that, The small-diameter TBM cutterhead-shield collaborative geological prediction system according to any one of claims 1-9 is executed, comprising the following steps: S1: Collect continuous monitoring data of cutterhead rotation speed, propulsion cylinder thrust and cutterhead torque, calculate the ratio of propulsion speed to torque, input the two sequences into a time series clustering model for fluctuation analysis, calculate the synchronous change ratio based on the clustering results, and generate the tunneling stability coefficient; S2: Based on the tunneling stability coefficient, call the cutter head cutting tool position and cutting force output data, input the tool cutting force matrix into the force zoning analysis algorithm, calculate the total force of each section and perform distribution difference calculation to obtain the cutter head force balance; S3: Based on the cutterhead force balance, call the coordinates of the laser target at the tail of the shield, the attitude tilt angle and the displacement difference of the propulsion cylinder, calculate the rate of change of the angle between the shield's central axis and the tunnel's design axis, and perform a corresponding analysis with the cutterhead force balance to generate the shield's attitude coordination degree. S4: Based on the shield attitude coordination degree, call the synchronization sequence data of cutterhead torque, propulsion cylinder thrust and propulsion speed, construct the correlation rate matrix and input it into the multiple linear regression model to perform parameter correlation calculation, filter out the combination exceeding the threshold and count the proportion of consecutive occurrences to obtain the intelligent tunneling coupling degree; S5: Based on the intelligent tunneling coupling degree, call the continuous sequence of tunneling stability coefficients, identify the coupling degree change range, compare it with the ground disturbance benchmark range, calculate and output the geological prediction recognition rate.

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