Shield tunneling machine tool bit abrasion detection method, water jet cleaning control method and shield tunneling machine

Abstract

The embodiment of the invention provides a shield tunneling machine tool bit abrasion detection method, a water jet cleaning control method and a shield tunneling machine, and relates to the technical field of shield tunneling machine tool state monitoring. The detection method comprises the steps that tool bit measurement parameters are collected, a time average value is calculated, and a parameter average set is obtained; determining a mechanical input power, a volume excavation rate and a slurry belt heat power based on the parameter average set; after changing a new cutter, calibrating a baseline mean value, a standard deviation and an average mechanical work capacity in the continuous baseline window; calculating the weighted residual ratio and the standardized quantity of the working energy of the current ring section to generate a wear result; the measurement effectiveness is verified through the energy closing ratio, and a final result is output. The real-time performance, the accuracy and the anti-interference capability of tool wear monitoring can be improved.

Description

Technical Field

[0001] This application relates to the field of tunnel boring machine (TBM) cutter condition monitoring technology, and in particular to a method for detecting cutter head wear, a water jet cleaning control method, and a TBM. Background Technology

[0002] In underground engineering projects such as urban rail transit, highway tunnels, and water conservancy channels, tunnel boring machines (TBMs) undertake multiple high-intensity operations, including continuous tunneling, rock cutting, and excavated soil transportation. The cutterheads are constantly exposed to high-pressure contact, intense friction, and rock and soil impact, and their wear directly determines tunneling efficiency, propulsion stability, and construction safety. As cutter wear gradually accumulates, mechanical input power increases, heat generation in the mud system increases, and propulsion speed begins to decrease. These phenomena often overlap with changes in geological formations, fluctuations in mud properties, and minor equipment vibrations, making it difficult to determine whether abnormal cutter wear exists on the construction site using a single physical quantity.

[0003] Existing technologies for wear condition assessment primarily rely on vibration analysis, image recognition, or manual experience. These methods typically suffer from issues such as response lag, weak anti-interference capabilities, and a lack of closed-loop verification of physical quantities, making them ill-suited for the real-time, high-precision requirements of complex environments. Furthermore, various sensors in the system are susceptible to drift or anomalies due to mud contamination and electromagnetic interference, leading to distorted energy estimations. Without effective calibration methods, tool wear assessments are prone to misjudgments or omissions, ultimately resulting in delayed replacement decisions, increased construction risks, and higher maintenance costs. Summary of the Invention

[0004] To address one of the aforementioned technical deficiencies, this application provides a method for detecting wear on the cutterhead of a tunnel boring machine.

[0005] The first aspect of this application provides a method for detecting cutterhead wear in a tunnel boring machine, including: S1. Collect the set of measurement parameters of the shield machine cutterhead and calculate the time average value of each parameter in the set of measurement parameters to obtain the parameter average set. The set of measurement parameters includes: torque, cutterhead speed, propulsion thrust, propulsion speed, mud flow rate and circulation inlet and outlet temperature difference. S2. Determine the mechanical input power, volumetric excavation rate, and mud heat power of the current ring segment based on the parameter average set; S3. Within the continuous baseline window after the cutter is replaced, calculate the baseline mean, baseline standard deviation, and baseline average mechanical work quantity of the energy closure residual ratio based on the mechanical input power, volume excavation rate, and mud heat power of each ring segment. S4. Calculate the energy closure residual ratio of the current loop segment, and determine the work-weighted residual ratio based on the energy closure residual ratio of the current loop segment and the baseline average mechanical work-power quantity. S5. Generate wear results based on the work-weighted residual ratio, baseline mean, and baseline standard deviation; S6. Calculate the energy closure ratio of the current loop segment, verify the validity of the measurement based on the energy closure ratio of the current loop segment, and generate the final result.

[0006] A second aspect of this application provides a water jet cleaning control method based on the above method, comprising: Based on the cutter head cleaning grid parameter set, the cutter head surface is cut into strips radially according to structural units and then subdivided according to tangential angles. The center point of the sub-region is corrected with the test frame positioning reference. Then, it is reordered and numbered according to the direction of the cutter head rotation area to obtain the cutter head cleaning grid parameter set. Based on the cutter head cleaning grid parameter set, the installation point and direction of the water jet nozzle are read, and the distance and direction between the center point of the sub-area and the pressure node of the test water supply pipeline are combined to form an energy value. Then, the effective coverage range is determined by a threshold to obtain the jet coverage energy distribution set. Based on the spray coverage energy distribution set, the Hungarian allocation algorithm is used to calculate the cleaning time according to the thickness of the sub-region adhesion layer and the pressure value. The time values ​​are formed into a matrix and multiple values ​​are compared in the same row to determine the nozzle correspondence. All matching items are combined into a unified sequence to obtain the nozzle cleaning assignment result set. Based on the nozzle cleaning assignment result set, the particle swarm optimization algorithm is used to add fine-tuning amount to the nozzle installation point, and then the distance is compared with the center point of the cutter head structure unit. The reachability is judged by the angle between the nozzle direction and the normal vector of the cleaning area. The reachable units are recorded to obtain the nozzle arrangement adjustment parameter set. Based on the nozzle arrangement adjustment parameter set, the nozzle start-up time is read according to the test, the assigned sub-region time is arranged into the time axis and aligned with the cutter head rotation area reference, and then integrated into a continuous time period and uniformly sorted to obtain the nozzle start-up and stop sequence table.

[0007] According to a third aspect of the embodiments of this application, a tunnel boring machine is provided, including a control system for performing any of the methods described above.

[0008] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs an energy balance-based tool wear detection system through a process of constructing a parameter average set, determining core energy parameters, calibrating the baseline window, calculating the weighted residual ratio of the function quantity, generating wear results, and verifying the validity of the measurement. This solves the problem of difficulty in timely and accurate determination of tool wear status under complex tunneling conditions. By systematically analyzing existing tunneling parameters, it achieves real-time monitoring of wear status, forms a complete detection link, effectively avoids interference caused by formation fluctuations and changes in working conditions, and significantly reduces the misjudgment rate and missed judgment rate of wear determination.

[0009] 2. This invention collects key operating parameters within a single tunneling time window and performs time averaging to accurately construct a parameter average set. Based on this set, core energy parameters such as mechanical input power, volumetric excavation rate, and mud heat power are determined, providing a stable and reliable data foundation for wear detection. Through comprehensive analysis of multi-dimensional energy parameters, it can more comprehensively reflect the interaction between the cutter and the formation and the energy dissipation law, clearly distinguish the differences in energy dissipation caused by normal operating condition fluctuations such as formation variation and equipment vibration and cutter wear, and significantly improve the anti-interference ability of the detection method.

[0010] 3. This invention constructs a continuous baseline window after tool replacement, calibrating the baseline mean, baseline standard deviation, and baseline average mechanical work quantity that reflect normal working conditions. This provides a personalized benchmark for wear condition determination. At the same time, by weighting the work quantity, the influence of the energy contribution segment is strengthened, and the interference of random fluctuations is reduced. Furthermore, the validity of the measurement data is verified by comparing the energy closure ratio with a preset threshold, ensuring the reliability and rigor of the wear detection results and avoiding incorrect judgments based on invalid data. Attached Figure Description

[0011] Figure 1 A schematic flowchart illustrating the shield machine cutterhead wear detection method provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of the tunnel boring machine cutterhead provided in an embodiment of this application; Figure 3 This is a structural schematic diagram of the central region of the tunnel boring machine cutterhead provided in an embodiment of this application; Figure 4 This is a partial sectional view of the cutterhead of a tunnel boring machine provided in an embodiment of this application; Figure 5 This is a schematic diagram of the tunnel boring machine provided in an embodiment of this application. Detailed Implementation

[0012] This embodiment provides a method for detecting cutterhead wear in tunnel boring machines. (See also...) Figure 1 Specifically, including: S1. Collect the set of measurement parameters of the shield machine cutterhead and calculate the time average value of each parameter in the set of measurement parameters to obtain the parameter average set. The set of measurement parameters includes: torque, cutterhead speed, propulsion thrust, propulsion speed, mud flow rate and circulation inlet and outlet temperature difference. In an embodiment of the present invention, a set of measurement parameters of the tunnel boring machine cutterhead is collected, and the time average value of each parameter in the measurement parameter set is calculated to obtain a parameter average set, including: Within the tunneling time window of a single ring segment, a set of measurement parameters of the tunnel boring machine cutterhead is collected to obtain a time series set of measurement parameters, which includes: torque, cutterhead rotation speed, propulsion thrust, propulsion speed, mud flow rate and circulation inlet and outlet temperature difference; The time series of the measured parameters are averaged separately to obtain the parameter average set, which includes: average torque, average cutterhead speed, average propulsion thrust, average propulsion speed, average mud flow rate, and average mud inlet and outlet temperature difference. Specifically, the measurement parameter set refers to the set of key physical quantities used to characterize the tunneling state of the cutterhead during the wear detection process of the tunnel boring machine (TBM) cutterhead. Its time series set is a dynamic data record of the changes of each parameter over time within a single tunneling segment's time window. Among these, torque is the moment resisting the cutting resistance of the rock and soil when the cutterhead rotates, directly related to the interaction strength between the cutterhead and the rock and soil; cutterhead rotation speed is the angular velocity of the cutterhead rotation, reflecting the operating condition of the cutterhead; propulsion thrust is the axial driving force of the TBM during tunneling, reflecting the combined load of the stratum reaction force and the cutting resistance of the cutterhead; propulsion speed is the tunneling rate of the TBM along the tunnel axis, determining the tunneling time of a segment and related to the volumetric excavation efficiency; mud flow rate is the conveying rate of the circulating mud, used for carrying excavated material and cooling the cutterhead; and the temperature difference between the circulating mud entering and exiting the cutterhead cooling zone reflects the heat exchange and energy dissipation between the cutterhead and the mud. By performing time averaging on the time series of the measured parameters, the average representative value of each parameter in the corresponding loop segment can be obtained. This provides basic data for the calculation of energy parameters such as mechanical input power, volume excavation rate, and mud heat power, thereby enabling quantitative detection of cutter head wear status.

[0013] Specifically, the tunneling time window for a single ring segment is determined based on the pre-set ring width and real-time propulsion speed of the tunnel boring machine (TBM). That is, the start and end times of the tunneling time window correspond to the start and end times of the cutterhead completing the tunneling of that ring segment, and the duration is the ratio of the ring width to the propulsion speed. Within this tunneling time window, the TBM's sensor system continuously collects a set of measurement parameters. Torque is collected using a strain gauge torque sensor, cutterhead rotation speed is collected using a photoelectric encoder, propulsion thrust is calculated using a hydraulic pressure sensor, propulsion speed is collected using a laser displacement sensor, mud flow rate is collected using an electromagnetic flowmeter, and the temperature difference between the circulating inlet and outlet is collected using a platinum resistance temperature sensor. The acquisition frequency of each parameter is no less than 10 Hz, thus forming a time series set of measurement parameters that continuously change over time. When performing time averaging on the time series of the measured parameters, an integral averaging method is used. That is, the time series of each parameter is integrated within the tunneling time window, and then divided by the duration of the time window to obtain the average representative value of each parameter in that loop segment. Among them, the average torque is the integral average of the torque time series, the average cutterhead speed is the integral average of the cutterhead speed time series, the average propulsion thrust is the integral average of the propulsion thrust time series, the average propulsion speed is the integral average of the propulsion speed time series, the average mud flow rate is the integral average of the mud flow rate time series, and the average mud inlet and outlet temperature difference is the integral average of the circulating inlet and outlet temperature difference time series. These average representative values ​​together constitute the parameter average set, providing stable basic data for subsequent energy parameter calculations.

[0014] S2. Determine the mechanical input power, volumetric excavation rate, and mud heat power of the current ring segment based on the parameter average set; In embodiments of the present invention, determining the mechanical input power, volumetric excavation rate, and slurry thermal power of the current ring segment based on the parameter average set includes: Multiply the average torque by the average cutter head speed to obtain the rotational power; Multiply the average thrust by the average thrust speed to obtain the thrust power; The mechanical input power is obtained by summing the rotational power and the propulsion power. Specifically, based on the average torque and average cutterhead rotation speed in the parameter averaging set, multiplying the two yields the rotational power input when the cutterhead rotates. This power reflects the mechanical power required for the cutterhead to overcome the cutting resistance of the rock and soil. Using the average propulsion thrust and average propulsion speed in the parameter averaging set, multiplying the two yields the propulsion power during axial tunneling of the tunnel boring machine. This power reflects the mechanical power required to overcome the ground reaction force and the cutting resistance of the cutterhead to maintain the propulsion speed. Then, the rotational power and propulsion power are added together to obtain the mechanical input power. This power is the total mechanical power input by the main drive system and propulsion system of the tunnel boring machine in the corresponding loop segment, and is the core input energy data for subsequent energy closure analysis.

[0015] Obtain the tunnel cross-sectional area, and calculate the volumetric excavation rate by multiplying the tunnel cross-sectional area by the average advance speed. Obtain the mud density and mud specific heat at constant pressure. Based on the product of the average mud flow rate, mud density, mud specific heat at constant pressure, and average mud inlet and outlet temperature difference, obtain the mud heat-carrying power. Specifically, the volumetric excavation rate refers to the volume of rock and soil excavated by the tunnel boring machine per unit time. It is obtained by multiplying the tunnel cross-sectional area by the average advance speed. The tunnel cross-sectional area is determined by the diameter of the tunnel boring machine cutterhead. The average advance speed is the average representative value of the advance speed in the parameter average set. This parameter is directly related to the volume scale of rock and soil fracturing and is one of the core inputs for calculating the equivalent fracturing energy density of rock mass. The heat-carrying power of the slurry refers to the power corresponding to the heat carried away by the circulating slurry from the cutter head and the slag per unit time. It is obtained by multiplying the slurry density, the slurry specific heat at constant pressure, the average slurry flow rate, and the average temperature difference between the slurry inlet and outlet. The slurry density is the mass density of the circulating slurry (determined by the slurry mix ratio), the slurry specific heat at constant pressure is the specific heat capacity of the slurry under constant pressure (reflecting the slurry's heat absorption capacity), the average slurry flow rate is the average representative value of the slurry flow rate in the parameter average set, and the average temperature difference between the slurry inlet and outlet is the temperature difference between the slurry inlet and outlet in the parameter average set. This parameter reflects the heat dissipation during the cutting and friction process of the cutter head and is a key component of the thermal energy term in the energy closure.

[0016] Specifically, when obtaining the tunnel cross-sectional area, this area is determined by the diameter of the tunnel boring machine (TBM) cutterhead. The calculation formula is: the tunnel cross-sectional area equals pi multiplied by the square of half the cutterhead diameter. This parameter is an inherent design parameter of the TBM and can be directly obtained from the TBM's technical specifications. Then, the tunnel cross-sectional area is multiplied by the average advance speed from the parameter averaging set to obtain the volumetric excavation rate. This rate characterizes the volume of soil and rock excavated by the TBM per unit time, measured in cubic meters per second. When obtaining the mud density and specific heat at constant pressure, the mud density is determined by the mud mix ratio used (e.g., the composition ratio of bentonite mud), and the specific heat at constant pressure is determined by the thermophysical properties of the mud. Both can be obtained through laboratory testing or from the mud product specifications. Then, the average mud flow rate from the parameter averaging set is multiplied by the mud density, specific heat at constant pressure, and average temperature difference between mud inlet and outlet to obtain the mud heat-carrying power. This power characterizes the heat carried away by the circulating mud from the cutterhead and excavated soil per unit time, measured in watts.

[0017] S3. Within the continuous baseline window after the cutter is replaced, calculate the baseline mean, baseline standard deviation, and baseline average mechanical work quantity of the energy closure residual ratio based on the mechanical input power, volume excavation rate, and mud heat power of each ring segment. In an embodiment of the present invention, within a continuous baseline window after the cutter head is replaced, the baseline mean, baseline standard deviation, and baseline average mechanical work quantity are calculated based on the mechanical input power, volumetric excavation rate, and mud heat power of each segment, including: Replace the cutterhead of the tunnel boring machine. After the cutterhead replacement is completed, select continuous ring segments to form a continuous baseline window. Specifically, when replacing the cutterhead of a tunnel boring machine (TBM), the TBM is first stopped and put into a shutdown maintenance mode to ensure the cutterhead is stationary. Simultaneously, the soil chamber pressure balancing system is activated to maintain the stability of the excavation face and ensure the safety of the cutterhead replacement operation. Operators enter the soil chamber through the airlock to disassemble the worn cutterhead and replace it with brand new ones. During the replacement process, the stability of the cutterhead installation and its fit with the cutterhead must be checked to ensure that the geometric parameters and mechanical properties of the new cutterhead meet the design requirements. After the cutterhead replacement is completed, the soil chamber door is closed, the TBM's tunneling system is restored, and the cutterhead is started in a low-thrust, low-speed mode for a short-distance trial excavation. All operating parameters are monitored to ensure they are normal. Once the equipment's operating status is stable and the tunneling parameters have stabilized, the baseline window selection stage begins. During the initial tunneling process after cutter replacement, continuously excavated segments are selected to form a continuous baseline window. The selected segments must meet the following conditions: stable geological conditions (no hard interlayers, karst caves, or other significant geological changes); normal operation of the mud circulation system (no abnormal fluctuations in mud flow rate, density, temperature, and other parameters); and stable tunneling parameters (no sudden changes in thrust, torque, advance speed, etc.). Typically, no fewer than five continuous segments are selected to ensure the representativeness of the baseline data. Within the continuous baseline window, energy parameters such as mechanical input power, volumetric excavation rate, and mud thermal power are continuously collected for each segment. This provides a stable initial data foundation for subsequent baseline calibration and energy closure residual ratio calculation, thereby ensuring the accuracy and reliability of cutter wear detection.

[0018] The mechanical input power is used as the observed value, and the sum of the products of the mud heat power, the equivalent rock mass fracturing energy density, and the volumetric excavation rate is used as the model value. The equivalent rock mass fracturing energy density is determined by minimizing the deviation. Specifically, firstly, the calculated mechanical input power, mud zone thermal power, and volumetric excavation rate of each segment within the continuous baseline window are extracted. The mechanical input power of each segment is determined as the observed value, and the sum of the products of the mud zone thermal power, the equivalent rock mass fracturing energy density, and the volumetric excavation rate of that segment is determined as the model value. To minimize the bias, the least squares method is used to construct the objective function, which is the sum of the squares of the differences between the observed values ​​and the corresponding model values ​​of each segment. The objective function is:

[0019] In the formula, For the sum of squared deviations, The equivalent fracturing energy density of the rock mass. For continuous baseline windows, For the first Mechanical input power of each ring segment, For the first The heat capacity of the mud in each ring segment, For the first The volumetric excavation rate of each ring segment; The core principle of the objective function is the law of energy conservation, meaning that the mechanical input power of the tunnel boring machine (TBM) is mainly consumed in two aspects: rock fracturing and heat dissipation. The slurry heat power directly reflects the heat dissipation carried away by the circulating slurry during the cutting and friction process, and is a directly measurable energy dissipation term. The product of the equivalent rock fracturing energy density and the volumetric excavation rate characterizes the fracturing energy required to excavate soil and rock per unit time; this energy consumption is directly related to the geological characteristics. Within the continuous baseline window after cutter replacement, initial cutter wear is negligible, and energy dissipation is mainly due to normal cutting and inherent system losses. At this point, minimizing the deviation between the mechanical input power (observed value) and the slurry heat power + rock fracturing power (model value) effectively eliminates the influence of measurement noise and short-term operating condition fluctuations, thereby determining the equivalent rock fracturing energy density that best fits the geological conditions of this baseline window. This parameter serves as the benchmark for subsequent energy closure analysis, accurately distinguishing between normal energy dissipation and additional dissipation caused by cutter wear, providing a reliable energy balance reference for the quantitative detection of cutter wear.

[0020] Specifically, the first partial derivative of the objective function with respect to the equivalent fracturing energy density of the rock mass is calculated and set to zero. The analytical solution for the equivalent fracturing energy density of the rock mass is then obtained by solving the resulting linear equation. Considering the physical meaning of the equivalent fracturing energy density, its value must be non-negative. If the analytical solution result is negative, it is corrected to zero to ensure the physical rationality of the parameters. The equivalent fracturing energy density of the rock mass determined through the above process is a constant within a continuous baseline window. It comprehensively reflects the equivalent volumetric energy required for rock and soil fracturing under the corresponding formation conditions within that baseline window, providing core parameter support for the subsequent calculation of the energy closure residual ratio.

[0021] Within a continuous baseline window, the energy closure residual ratio of each segment within the continuous baseline window is calculated based on the thermal power of the mud belt, the volumetric excavation rate, the mechanical input power, and the equivalent fracturing energy density of the rock mass. The formula for calculating the energy closure residual ratio is as follows:

[0022] In the formula, For the first The energy closure residual ratio of each loop segment For the first Mechanical input power of each ring segment, For the first The heat capacity of the mud in each ring segment, The equivalent fracturing energy density of the rock mass. For the first The volumetric excavation rate of each ring segment; Specifically, the energy closure residual ratio is a dimensionless index used in shield tunneling machine cutterhead wear detection to quantify the proportion of additional energy dissipation in the mechanical input power that is not explained by the heat power of mud and the rock breaking power. Its core function is to reflect the degree of additional frictional dissipation caused by cutterhead wear. The energy closure residual ratio originates from energy balance analysis: the numerator is the difference between the mechanical input power and the breaking power driven by the heat power of mud plus the equivalent rock breaking energy density. This difference quantifies the additional energy dissipation in the mechanical input power that is not explained by normal rock and soil breaking and mud heat dissipation, and this additional dissipation is mainly caused by the additional friction generated by cutter wear; the denominator uses the mechanical input power as a normalization benchmark to convert the proportion of additional dissipation into a dimensionless index, making the degree of cutter wear under different segments and different tunneling conditions comparable, thereby achieving quantitative detection of the cutterhead wear state and providing an intuitive and rigorous basis for subsequent wear judgment.

[0023] The arithmetic mean and standard deviation of the energy closure residual ratio of each loop segment within the continuous baseline window are calculated to obtain the baseline mean and baseline standard deviation. Within a continuous baseline window, the mechanical work done by each segment is calculated based on the mechanical input power and the tunneling time of a single segment, and their arithmetic mean is obtained to obtain the baseline average mechanical work done. Specifically, within the continuous baseline window, the energy closure residual ratio is first calculated for each segment. Then, the arithmetic mean of these energy closure residual ratios is calculated to obtain the baseline mean. This mean represents the average level of the energy closure residual ratio within the baseline window when there is no significant tool wear. Simultaneously, the standard deviation of these energy closure residual ratios is calculated to obtain the baseline standard deviation, which reflects the fluctuation range of the energy closure residual ratio under normal operating conditions within the baseline window. Next, for each segment within the continuous baseline window, the mechanical work done is the product of the mechanical input power of that segment and the tunneling time of a single segment (the tunneling time is determined by the ratio of the ring width to the advance speed). After calculating the mechanical work done for each segment, it is arithmetically averaged to obtain the baseline average mechanical work done. This value serves as the energy accumulation benchmark for subsequently determining the weighted window for work done, ensuring the rationality of the weighted residual ratio calculation and providing data support for the stabilization detection of tool wear.

[0024] S4. Calculate the energy closure residual ratio of the current loop segment, and determine the work-weighted residual ratio based on the energy closure residual ratio of the current loop segment and the baseline average mechanical work-power quantity. In an embodiment of the present invention, the energy closure residual ratio of the current loop segment is calculated, and the work done weighted residual ratio is determined based on the energy closure residual ratio of the current loop segment and the baseline average mechanical work done ratio, including: Calculate the energy closure residual ratio and mechanical work quantity of the current loop segment and the preset number of loop segments upstream in time; Specifically, first, the segment currently being excavated or already completed is identified as the current segment. Simultaneously, a preset number of segments upstream in time is set. This number is selected based on the stability of the geological formation and fluctuations in excavation conditions, typically ranging from 5 to 10. Segments upstream in time refer to consecutive segments preceding the current segment in the excavation sequence. For each segment in the current segment and its preset upstream segments, the energy closure residual ratio and mechanical work output are calculated. When calculating the energy closure residual ratio, the mechanical input power, mud heat power, volumetric excavation rate, and the determined equivalent rock fracture energy density of each segment are used. The energy closure residual ratio is calculated using the formula: mechanical input power minus the sum of the products of mud heat power, equivalent rock fracture energy density, and volumetric excavation rate, divided by the mechanical input power. When calculating the mechanical work done, the tunneling time for each segment is first obtained. This time is determined by the ratio of the segment width to the average advance speed of the corresponding segment's parameter set. Then, the mechanical input power of each segment is multiplied by its tunneling time to obtain the mechanical work done for each segment. Through this process, the energy closure residual ratio and mechanical work done for the current segment and its preset upstream segments are calculated, providing basic data for determining the subsequent weighted window.

[0025] Starting from the current loop segment, the mechanical work done by each loop segment is accumulated sequentially upstream in time. The set of loop segments with the smallest accumulated mechanical work done that first reaches or exceeds the baseline average mechanical work done is selected as the weighted window. Specifically, starting with the current loop segment as the accumulation point, the mechanical work done in that loop segment is extracted as the initial cumulative value. This initial cumulative value is compared with the baseline average mechanical work done. If the initial cumulative value has reached or exceeded the baseline average mechanical work done, the current loop segment forms a separate weighted window. If the initial cumulative value has not reached the baseline average mechanical work done, the next loop segment immediately upstream is selected, its mechanical work done is extracted, and added to the initial cumulative value to obtain a new cumulative value. This new cumulative value is then compared with the baseline average mechanical work done again. If the baseline average mechanical work done is still not reached, the next adjacent loop segment is selected upstream, and the above accumulation and comparison process is repeated. That is, each time, the mechanical work done by a new upstream loop segment is added to the cumulative value, and it is determined whether the cumulative value has reached or exceeded the baseline average mechanical work done. When the cumulative mechanical performance first meets the condition of reaching or exceeding the baseline average mechanical performance, the accumulation operation stops. At this time, the set of all segments participating in the accumulation, including the current segment and the upstream segments included in the accumulation process, is the minimum set of segments that meet the condition. This set is determined as the weighted window.

[0026] Within the weighted window, the energy closure residual ratio of each loop segment is weighted and averaged using the mechanical work done in each loop segment as the weight, to obtain the work done weighted residual ratio. Specifically, within the defined weighted window, the mechanical work done and the corresponding energy closure residual ratio for each segment within the window are extracted. The sum of the mechanical work done for all segments within the weighted window is calculated to obtain the total mechanical work done. For each segment, its mechanical work done is divided by the total mechanical work done to obtain its weight within the weighted window, which reflects the proportion of mechanical work contribution of the corresponding segment. Then, the energy closure residual ratio for each segment is multiplied by its weight to obtain the weighted residual ratio for each segment. The weighted residual ratios of all segments are summed to obtain the weighted residual ratio of the mechanical work done. This weighted residual ratio is a dimensionless indicator used in shield tunneling machine cutterhead wear detection to stably reflect the overall trend of energy dissipation deviation from the baseline in the current and adjacent segments. Its core is to assign differentiated weights to different segments based on the mechanical work done, thereby strengthening the impact of key data on the results. This result strengthens the influence of segments with significant energy contributions on the residual ratio by assigning higher weights to segments with large mechanical work outputs, reduces the interference of random fluctuations on the results, and makes the obtained work output weighted residual ratio more stable in reflecting the overall trend of energy dissipation deviation from the baseline in the current and adjacent segments, providing a reliable basis for subsequent tool wear condition determination.

[0027] S5. Generate wear results based on the work-weighted residual ratio, baseline mean, and baseline standard deviation; In embodiments of the present invention, wear results are generated based on the work-weighted residual ratio, baseline mean, and baseline standard deviation, including: The standardized quantity is obtained by subtracting the baseline mean from the weighted residual ratio of the function quantity and then dividing by the baseline standard deviation. Specifically, after calculating the work-weighted residual ratio and determining the baseline mean and standard deviation, the work-weighted residual ratio, baseline mean, and standard deviation for the current segment are extracted from the corresponding data storage module. The work-weighted residual ratio is used as the divisor; the baseline mean is subtracted to obtain the difference. This difference is then divided by the baseline standard deviation. This arithmetic operation yields the standardized quantity. This standardized quantity is a dimensionless index; its physical meaning is a standardized expression of the deviation of the work-weighted residual ratio from the baseline mean, measured in units of the baseline standard deviation. This eliminates the dimensional influence caused by differences in tunneling conditions across different segments, providing a unified quantitative comparison benchmark for tool wear states under different times and geological conditions. This provides an objective and comparable basis for subsequently determining whether tool wear is significant using preset standard thresholds, ensuring the accuracy and reliability of wear detection results.

[0028] The standardized quantity is compared with a preset standard threshold. If the standardized quantity is greater than the preset standard threshold, a wear result with significant wear is generated; otherwise, a wear result with insignificant wear is generated. Specifically, after the standardized quantity calculation is completed, a preset standard threshold is retrieved from the system's preset threshold database. This threshold is based on the statistical distribution characteristics of the energy closure residual ratio under normal operating conditions within the baseline window, and is determined in conjunction with industry standards and engineering experience for tunnel boring machine cutter wear detection. It is typically set to 2.0 to ensure that normal fluctuation interference is excluded at a 95% confidence level. The standardized quantity of the current loop segment is compared with the preset standard threshold. If the standardized quantity is greater than the preset standard threshold, it indicates that the energy-weighted residual ratio deviates significantly from the baseline level, and is judged that the additional energy dissipation caused by cutter wear exceeds the normal range, thus generating a significant wear result. If the standardized quantity is less than or equal to the preset standard threshold, it indicates that the current energy dissipation state is consistent with the normal operating conditions within the baseline window, and no significant additional dissipation related to cutter wear is detected, thus generating an insignificant wear result. This process achieves an objective judgment of the cutter wear state through quantitative comparison, ensuring the reliability and industry applicability of the wear detection results.

[0029] S6. Calculate the energy closure ratio of the current loop segment, verify the validity of the measurement based on the energy closure ratio of the current loop segment, and generate the final result; In an embodiment of the present invention, the energy closure ratio of the current loop segment is calculated, the validity of the measurement is verified based on the energy closure ratio of the current loop segment, and a final result is generated, including: The energy closure ratio of the current ring segment is calculated based on the mud zone thermal power, equivalent rock mass fracturing energy density, volumetric excavation rate, and mechanical input power. The formula for calculating the energy closure ratio is as follows:

[0030] In the formula, For the first The energy closure ratio of each loop segment For the first Mechanical input power of each ring segment, For the first The heat capacity of the mud in each ring segment, The equivalent fracturing energy density of the rock mass. For the first The volumetric excavation rate of each ring segment; Specifically, this energy closure ratio originates from the verification logic of energy conservation. By dividing the sum of the thermal power of the mud and the crushing power driven by the equivalent crushing energy density of the rock mass (representing the energy dissipation term) by the mechanical input power (representing the total input energy term), the resulting ratio quantifies the degree of energy dissipation. This ratio reflects how much of the mechanical input power in this segment has been explained by the thermal and crushing channels. If the ratio is stable within a reasonable range, it indicates that the main dissipation of mechanical input power is effectively explained by the thermal power of the mud and the crushing of the rock mass, and the measurement system and calculation process are reliable. If the ratio deviates from the reasonable range, it indicates that there is an anomaly in the measurement or unidentified fluctuation in the working condition, and that the measurement of parameters such as torque, thrust, and mud flow rate is abnormal. The metering equipment needs to be calibrated to provide key verification for the validity of the wear detection results and ensure the accuracy of the final wear determination.

[0031] If the energy closure ratio of the current loop segment is greater than the preset energy closure threshold, a measurement anomaly is generated, and the final result requires metrological calibration; otherwise, the wear result is taken as the final result. Specifically, first, the combined uncertainty upper limit shown on the equipment's metrology certificate is read. The value of the combined uncertainty upper limit plus 1 is used as the energy closure threshold. The combined uncertainty upper limit is the upper bound value obtained by quantifying, combining, and amplifying the various uncertainty sources in the measurement chain. It is used to express the maximum deviation range that the measurement chain may produce under a specified confidence probability. It is constructed by obtaining the combined uncertainty from independent uncertainty components such as sensor range deviation, random noise drift, temperature drift, calibration residuals, and quantization errors in the data acquisition link through uncertainty combination rules. Then, the combined result is amplified according to a specified coverage factor so that the final value can cover the possible range of the true value of the measurement result within a given confidence interval. This upper limit is usually issued by a qualified metrology institution based on calibration results and is clearly stated in the metrology certificate, serving as the basis for determining whether the measurement result is within the allowable metrological range.

[0032] Specifically, the energy closure ratio is defined as the ratio of energy dissipation to mechanical input power. Mechanical input power represents the upper limit of energy supply to the system; therefore, ideally, the energy dissipation should not exceed the mechanical input power, and the energy closure ratio should physically not exceed one. When the energy closure ratio is slightly higher than one, it usually does not indicate a violation of the law of energy conservation, but rather a deviation caused by the uncertainty of the measurement chain itself, including errors in mechanical power measurement, flow rate measurement, temperature difference measurement, and deviations in the determination of specific heat at constant pressure. Since these errors act together in different channels, judging whether the energy closure ratio exceeds one alone cannot reflect whether the actual deviation is within the allowable range of metrology. Therefore, it is necessary to combine the combined uncertainty upper limit given in the metrological certificate, using one as the physical boundary, and then superimpose this uncertainty upper limit, so that one plus the combined uncertainty upper limit becomes the upper bound allowed for the measurement chain within the specified confidence interval. The upper limit of combined uncertainty indicates that even if the explained power deviates from the mechanical input power in an unfavorable direction, as long as it does not exceed this allowable upper limit, it can still be considered a measurement consistency state that conforms to energy conservation. If it exceeds this upper limit, it means that the deviation has exceeded the reliable range of the measurement chain and should be preferentially judged as a measurement anomaly. If the energy closure ratio of the current loop is less than or equal to the set energy closure threshold, it means that the measurement data conforms to the energy dissipation logic under normal operating conditions. At this time, the previously judged results of significant or insignificant wear are taken as the final results to ensure that the tool wear detection results are based on reliable measurement data, thus ensuring the accuracy of wear detection and the effectiveness of engineering decisions.

[0033] Based on the above, this embodiment also provides a method for monitoring mud cake on a tunnel boring machine cutterhead to adapt to situations where the cutterhead experiences wear. The monitoring method provided in this embodiment includes the following steps: S1. Monitoring Area Division: Based on the shield cutterhead structure and mud cake accumulation pattern, the monitoring area is divided into a primary monitoring area, a secondary monitoring area, and a tertiary monitoring area. The primary monitoring area is the cutterhead edge area; the secondary monitoring area is the cutterhead transition area; and the tertiary monitoring area is the cutterhead center area. The primary monitoring area is the range of 70%-100% of the cutterhead radius, where the cutting linear velocity is the highest and the mud cake accumulation rate is the fastest. The secondary monitoring area is the range of 30%-70% of the cutterhead radius, and the tertiary monitoring area is the range of 0%-30% of the cutterhead radius and the inner wall of the soil chamber. S2. Sensor Selection and Layout: In the primary monitoring zone, a set of three-unit "vibration-temperature-stress" sensors is arranged at 15° intervals. Earth pressure sensors are densely arranged at the cutterhead connection. The vibration sensor is an IEPE type accelerometer, and the temperature sensor is a PT1000 platinum resistance sensor. Each set includes one IEPE type accelerometer, one PT1000 platinum resistance temperature sensor, and one strain gauge stress sensor. At the same time, two sets of earth pressure sensors are densely arranged at the connection between the cutterhead and the cutter head to capture pressure changes caused by mud cake. In the secondary monitoring zone, a "vibration-temperature" dual-unit sensor is arranged at 30° intervals to reduce the redundancy of stress sensors. In the tertiary monitoring zone, a laser displacement sensor, a high-definition industrial camera, an LED supplementary lighting module, and a moisture content sensor are arranged. One laser displacement sensor is arranged at the center of the cutter head to monitor the thickness of mud cake accumulation in the central area. Three high-definition industrial cameras are evenly arranged along the circumference of the inner wall of the soil chamber, and the LED supplementary lighting module is used to acquire mud cake images. One moisture content sensor is arranged at the inlet of the screw conveyor to obtain soil physical property parameters. S3. Monitoring System Construction: A three-level system of "perception layer - transmission layer - processing layer" is established. The perception layer is the aforementioned sensor, the transmission layer adopts a hybrid transmission method of "wireless + wired", and the processing layer includes edge computing nodes and cloud servers. The edge nodes are responsible for data preprocessing, and the cloud servers are responsible for data storage and predictive calculations.

[0034] Wireless transmission uses 5G industrial modules; wired transmission uses industrial Ethernet.

[0035] This embodiment also provides a method for predicting mud cake on a tunnel boring machine cutterhead, including the following steps: A1. Multi-source data preprocessing: Sensor data is processed using an improved wavelet threshold denoising algorithm, data synchronization is achieved through timestamp alignment, and missing data is filled in using linear interpolation. The improved wavelet thresholding denoising algorithm employs a continuously differentiable threshold function, using an exponential term to achieve a smooth transition at the threshold, thereby reducing signal distortion. The specific strategy is as follows: Improved Wavelet Thresholding Denoising: Traditional wavelet thresholding functions suffer from distortion at signal abrupt changes. This invention improves the hard thresholding function into a continuously differentiable thresholding function, as shown in the following formula: , in, The coefficients are those obtained after wavelet decomposition. These are the denoised coefficients; Threshold and , For data length, The standard deviation of noise. ; This is an adjustment factor (ranging from 0.8 to 1.2, adaptively adjusted according to data noise intensity). This improved function achieves a smooth transition at the threshold through an exponential term, reducing signal distortion. Data synchronization and completion: Multi-sensor data synchronization is achieved using timestamp alignment, with time error controlled within 50ms; missing data is completed using linear interpolation, and a backup sensor data switching mechanism is triggered when the missing rate exceeds 10%.

[0036] A2. Feature Enhancement and Fusion: Extract statistical features from sensor data and image features from mud cake images, assign weights to the fused features through a channel-spatial dual attention mechanism, and output enhanced features; The specific strategies for feature enhancement and fusion in A2 are as follows: A21. Statistical Feature Extraction: Extract time-domain features (mean, variance, peak value, kurtosis) and frequency-domain features (dominant frequency, spectral energy) from sensor time-series data (vibration, temperature, pressure, etc.), for a total of 12 statistical features; extract 2-dimensional features, namely rate of change and cumulative deviation, from moisture content data; A22. Image Feature Extraction: An improved CNN model (with a residual module) is used to extract features from the mud pie image. The model structure includes three convolutional layers (kernel sizes of 3×3, 3×3, and 5×5, all with a stride of 1), two pooling layers (max pooling, with a 2×2 kernel), and one fully connected layer, outputting 64-dimensional image features. The introduction of the residual module solves the gradient vanishing problem in deep CNNs, as shown in the following formula: ,in: For the input feature map, , For convolution weights, , For bias terms, For ReLU activation function, Output for the residual module; A23. Improved Attention Mechanism Enhancement: A channel-spatial dual attention mechanism (CSAM) is introduced to assign weights to the fused statistical features and image features, highlighting key features of mud cake formation (such as the vibration peak at the cutterhead edge, temperature abrupt change rate, and mud cake grayscale features in the image). The channel attention weights are calculated as follows: ; Spatial attention weights are calculated as follows: ; in, As a feature of fusion, For global average pooling, It is a two-dimensional convolution. This is element-wise multiplication. , The output features are channel and spatial attention features, respectively, and the final output is an 80-dimensional enhanced feature.

[0037] A3. Spatiotemporal Fusion Prediction: The enhanced features are input into the "CNN-LSTM-ELM" fusion model. CNN extracts spatial correlation features, LSTM is improved to extract temporal correlation features, ELM optimizes the accuracy of the output layer, and the ELM optimized by the chaotic particle swarm algorithm outputs the preliminary prediction results. The improved LSTM model optimizes the forget gate by introducing a nonlinear transformation of the hidden layer from the previous time step, thereby enhancing the ability to filter redundant temporal information. Chaotic particle swarm optimization algorithm enhances particle diversity through logistic mapping and optimizes the input layer weights and biases of ELM; Specifically: CNN spatial feature extraction: The 80-dimensional enhanced features are reshaped into an 8×10 feature matrix, which is then input into two convolutional layers (3×3 kernels, stride 1) and outputs a 32-dimensional spatial feature vector. LSTM temporal feature extraction: 32-dimensional spatial features are input into an improved LSTM model according to the time series (time window size is 20, i.e., the 21st data group is predicted from the first 20 data groups). A forget gate optimization mechanism is introduced, and the forget gate output formula is as follows: ,in, This is the output of the hidden layer from the previous time step. Input for the current time. , This is the weight matrix. For bias terms, This is an adjustment coefficient (with a value of 0.1-0.3). The sigmoid activation function is used. This improvement enhances the forget gate's ability to filter redundant temporal information by introducing a nonlinear transformation of the hidden layer from the previous time step. The LSTM outputs a 16-dimensional temporal feature vector. ELM Output Optimization: The 16-dimensional temporal features are input into the Extreme Learning Machine (ELM). The input layer weights and biases of the ELM are optimized using the Chaotic Particle Swarm Optimization (CPSO) algorithm to avoid local optima caused by random initialization. The fitness function of CPSO is the sum of squared prediction errors of the ELM. Particle diversity is enhanced through a chaotic mapping (Logistic mapping). The formula for the Logistic mapping is as follows: ,in, For the first Substitute chaotic variables, For control parameters, when When the system is in a completely chaotic state, the optimized ELM outputs the probability of cake formation P (0≤P≤1) and the prediction time T (unit: min).

[0038] A4. Error Correction and Early Warning: A rolling window error correction mechanism is used to correct the prediction results, and graded early warning information is output based on the corrected probability of mud cake formation.

[0039] A rolling window error correction mechanism is adopted, using the errors of the previous 5 predictions as feedback to correct the current prediction result. The correction formula is as follows: , ,in, , For correction factor, , The first The probability and time of actual mud cake formation; when When, trigger an alert, according to Values ​​are used for tiered early warning.

[0040] The tiered early warning system includes: Level 1: probability of mud cake formation 0.7-0.85, warning time 15-20 min; Level 2: probability 0.85-0.95, warning time 10-15 min; Level 3: probability ≥0.95, warning time 5-10 min.

[0041] Based on the above technical solutions, after testing the mud cake, a method for analyzing the mud cake parameters of the tunnel boring machine cutterhead can also be performed, including: S1. Based on multiple sets of temperature sensors arranged on the surface of the tunnel boring machine cutterhead, temperature data is collected in real time at a preset acquisition rate to obtain real-time temperature data at different locations on the surface of the tunnel boring machine cutterhead. S2. Obtain the real-time tunneling parameters of the tunnel boring machine during the tunneling process, and input the real-time tunneling parameters into the pre-trained tunnel boring machine temperature prediction model to obtain the predicted temperature data of the tunnel boring machine cutterhead surface. S3. Calculate the temperature deviation value between the real-time temperature data and the predicted temperature data to obtain the temperature anomaly location data on the surface of the tunnel boring machine cutterhead. S4. Analyze the temperature anomaly location data based on the preset cutterhead area and mud cake formation relationship rules to obtain the area data where mud cake may form. Analyze the area data based on real-time tunneling parameters to obtain the mud cake location data. S5. Obtain the temperature deviation value of the temperature anomaly location data corresponding to the mud cake location data, quantify and evaluate the thickness level of the mud cake based on the temperature deviation value, and integrate the thickness level and mud cake location data into a mud cake parameter analysis report. The tunnel boring machine operator adjusts the tunneling strategy or cleans the cutterhead based on the mud cake parameter analysis report.

[0042] In this embodiment, a tunnel boring machine (TBM) is a mechanical device used for underground tunnel construction. Its cutterhead is responsible for cutting materials such as soil and rock. The cutterhead surface typically has multiple cutters and sensors for cutting and moving the soil during the tunneling process. Temperature sensors are installed on the TBM cutterhead surface to monitor changes in surface temperature in real time. Temperature data helps determine the working status and wear of the cutterhead. Tunneling parameters refer to a series of data generated by the TBM during tunneling, such as thrust, cutting force, rotational speed, and cutterhead torque. These parameters reflect the working conditions during tunneling. The temperature prediction model is a model trained through machine learning that predicts the temperature change of the TBM cutterhead under given tunneling parameters based on historical data (such as tunneling parameters and cutterhead temperature). The report analyzes the following: Temperature anomaly locations refer to areas where the cutterhead surface temperature differs significantly from the predicted value, identified by calculating temperature deviations. These locations are considered abnormally high or low temperatures. Mud cake refers to the mud-like substance that forms on the cutterhead surface during tunneling due to the adhesion of soil and water. Excessive mud cake accumulation can affect the cutterhead's efficiency and even lead to machine malfunctions. Mud cake location refers to the specific location of mud cake formation on the cutterhead surface. By analyzing temperature anomaly locations and mud cake formation patterns, it's possible to predict which areas are prone to mud cake formation. The mud cake parameter analysis report provides a detailed analysis of the cutterhead's condition based on temperature deviations, mud cake location, and thickness levels. This report helps operators understand the mud cake situation and take timely measures.

[0043] The shield cutterhead mud cake parameter analysis method proposed in this embodiment, compared with Embodiment 1, includes real-time tunneling parameters such as propulsion speed, cutterhead torque and shield machine thrust.

[0044] In this embodiment, cutterhead torque refers to the rotational torque borne by the cutterhead of the tunnel boring machine during the tunneling process. It is an important indicator when the cutterhead of the tunnel boring machine is working, reflecting the resistance when the cutterhead cuts through soil and rock. A larger cutterhead torque means that there are problems such as mud cake formation.

[0045] In an optional embodiment, the tunnel boring machine temperature prediction model is a deep learning model consisting of an input layer, a hidden layer, and an output layer. The input layer receives real-time tunneling parameters, the hidden layer extracts and nonlinearly maps parameter features through a multi-layer neural network, and the output layer outputs the corresponding predicted temperature data of the cutterhead surface. During the model training phase, historical tunneling parameters and corresponding measured temperature data of the cutterhead surface are used as training samples, and the model parameters are optimized through a backpropagation algorithm to minimize the mean square error between the predicted temperature and the measured temperature, thereby improving the prediction accuracy of the model.

[0046] It should be noted that backpropagation is an algorithm used to train neural networks, aiming to optimize network parameters by minimizing the error between the output and the true label; mean squared error is a commonly used metric to measure the difference between the predicted result and the actual value. It is the average of the squares of the errors between the predicted and the true values. In tunnel boring machine temperature prediction, mean squared error can be used to quantify the difference between the temperature predicted by the model and the actual temperature; training samples refer to the dataset used in the model training process. It consists of input data (such as tunneling parameters) and corresponding actual outputs (such as cutterhead temperature data). Through these samples, the model learns the relationship between the input and output and optimizes its prediction ability based on historical data.

[0047] In an optional embodiment, real-time tunneling parameters are input into a pre-trained tunnel boring machine (TBM) temperature prediction model to obtain predicted temperature data for the TBM cutterhead surface, including: A1. Standardize the real-time tunneling parameters to obtain standardized tunneling parameter data. Input the standardized tunneling parameter data into the shield machine temperature prediction model. The standardization process includes normalizing the mean and variance of the real-time tunneling parameters such as the propulsion speed, cutterhead torque and shield machine thrust to eliminate the influence of differences in the magnitude of different parameters on the model prediction results. A2. A multi-layer neural network based on hidden layers extracts and nonlinearly maps the parameter features of standardized tunneling parameter data layer by layer to obtain parameter feature vectors; A3. Input the parameter feature vector into the output layer, and obtain the predicted temperature data of each region on the surface of the cutter head based on the output of the output layer.

[0048] In an optional embodiment, temperature deviation values ​​are calculated from real-time temperature data and predicted temperature data to obtain temperature anomaly location data on the surface of the tunnel boring machine cutterhead, including: B1. Compare the real-time temperature data and the predicted temperature data point by point according to the corresponding location to obtain the difference between the real-time temperature data and the predicted temperature data at each location. B2. Based on the preset temperature deviation threshold, the difference is judged, and the position corresponding to the difference exceeding the temperature deviation threshold is marked as the temperature abnormal position. All temperature abnormal positions are integrated to obtain temperature abnormal position data.

[0049] It should be noted that point-by-point comparison refers to a one-to-one comparison between real-time temperature data and predicted temperature data. The real-time temperature value and predicted temperature value at each location are compared separately, and the difference between them is calculated. The temperature deviation threshold is a preset tolerance range used to determine whether the difference between the predicted temperature and the actual temperature is within the normal range. An abnormal temperature location refers to a location in the point-by-point comparison where the difference between the real-time temperature and the predicted temperature exceeds the preset temperature deviation threshold. The temperature deviation at that location exceeds the normal range, reflecting the possible presence of mud cake at that location. The abnormal temperature location data refers to all the specific locations marked as having abnormal temperatures during the comparison process. Integrating the data from these abnormal locations can help operators identify potential problems such as mud cake on the surface of the tunnel boring machine cutterhead and take timely maintenance or adjustment measures.

[0050] In an optional embodiment, the relationship rules between the cutterhead area and the mud cake formation include: the central area of ​​the cutterhead, due to the dense cutting tools and concentrated stress, is identified as a potential mud cake formation area when the temperature anomaly location data shows a continuous negative deviation exceeding a threshold, and the real-time tunneling parameters show a decrease in propulsion speed and an increase in cutterhead torque. The edge area of ​​the cutterhead, due to its large friction area with the surrounding rock, is identified as a potential mud cake formation area if the temperature anomaly location data shows a local positive deviation accompanied by torque fluctuations, and the real-time tunneling parameters show no significant change in the tunnel boring machine thrust.

[0051] It should be noted that the cutterhead area refers to different parts of the tunnel boring machine (TBM) cutterhead. The cutterhead is an important part of the TBM, responsible for cutting the surrounding rock. The cutterhead area is usually divided into a central area and an edge area. Increased torque usually means increased cutting resistance of the cutterhead. This may be due to mud cake accumulation, increased hardness of the surrounding rock, or other reasons that lead to poor cutting performance. A continuously increasing cutterhead torque may be a signal of mud cake formation. A decrease in propulsion speed means that the TBM's propulsion speed has slowed down for a period of time. This is because the cutterhead is blocked by mud cake, thus requiring more force to maintain the propulsion process. Positive deviation means that the real-time temperature data is higher than the predicted temperature data, that is, the actual temperature is greater than the predicted temperature. If a local positive deviation occurs in the edge area of ​​the cutterhead, it may mean that the friction between this area and the surrounding rock has increased, leading to a rise in temperature, indicating the possible formation of mud cake.

[0052] In an optional embodiment, temperature anomaly location data is analyzed based on preset rules governing the relationship between the cutterhead region and cake formation to obtain data on areas where cake may form, including: C1. Divide the temperature anomaly location data according to the preset partitions of the cutter head, and determine the distribution location of temperature anomaly points in each partition. The preset partitions include the central area, the edge area, and the transition area. C2. Based on the relationship rules between the cutterhead region and mud cake formation, the correlation between the distribution location of temperature anomalies and real-time tunneling parameters is matched one by one. If the change in real-time tunneling parameters corresponding to the distribution location of temperature anomalies meets the conditions for mud cake formation in the relationship rules between the cutterhead region and mud cake formation, then the distribution location of temperature anomalies is marked as a region that may form mud cakes. All regions that may form mud cakes are integrated to obtain the region data that may form mud cakes.

[0053] It should be noted that one-to-one matching means comparing the location of each temperature anomaly point with the real-time tunneling parameters one by one to determine whether the point meets the conditions for mud cake formation. If the temperature anomaly data at a certain location matches the changes in the tunneling parameters and conforms to the rules of mud cake formation, then that location can be considered a potential mud cake formation area.

[0054] In an optional embodiment, regional data is analyzed based on real-time tunneling parameters to obtain mud cake location data, including: D1. Obtain the changing trend characteristics of propulsion speed, cutterhead torque and tunnel boring machine thrust in real-time tunneling parameters; D2. Conduct correlation analysis between regional data and trend characteristics; D3. If the cutterhead torque corresponding to the regional data exceeds the preset torque threshold, the propulsion speed shows a continuous downward trend, and the tunnel boring machine thrust remains stable, then the regional data is determined to be the location of the mud cake, so as to obtain the mud cake location data.

[0055] It should be noted that correlation analysis refers to comparing and correlating different types of data to find patterns or relationships between them. Correlation analysis compares regional data with real-time tunneling parameters (such as advance speed, cutterhead torque, and tunnel boring machine thrust) to identify whether there is a specific relationship between different regions and different parameters. For example, if the data of a certain region meets certain tunneling characteristics (such as a continuous decrease in advance speed and cutterhead torque exceeding a threshold), it can be inferred that mud cake phenomenon may have occurred in that region. Determining the location of mud cake in regional data means analyzing the regional data and combining it with the changing trend characteristics of tunneling parameters (such as cutterhead torque exceeding a threshold, decrease in advance speed, and stable thrust) to determine whether the region meets the conditions for mud cake formation. If all these conditions are met, the location can be determined as a region where mud cake may form.

[0056] In an optional embodiment, the thickness grade of the mud cake is quantitatively assessed based on the temperature deviation value, and the thickness grade and mud cake location data are integrated into a mud cake parameter analysis report, including: E1. Pre-set the mapping relationship between temperature deviation value and mud cake thickness grade. The mapping relationship includes the fact that the larger the absolute value of the temperature deviation value, the higher the corresponding mud cake thickness grade. E2. Match the temperature deviation value and mapping relationship corresponding to the mud cake location data to determine the thickness level of each mud cake location; E3. Integrate the mud cake location data, the thickness grade corresponding to the mud cake location data, and the characteristic parameters related to mud cake formation in the real-time tunneling parameters into a mud cake parameter analysis report. The characteristic parameters include the cutterhead torque change rate and the decrease in propulsion speed. The mud cake parameter analysis report includes the specific coordinate range of the mud cake area, the thickness grade assessment results, and the corresponding data basis.

[0057] It should be noted that thickness level matching refers to comparing the temperature deviation value corresponding to the cake location data with a preset mapping relationship to determine the cake thickness level of each cake location. The purpose of this process is to judge the severity of the cake through the temperature deviation value and further evaluate the cake area. The corresponding data basis refers to the data on which the cake parameter analysis report is based, including temperature deviation value, cutterhead torque change rate, and feed speed decrease, to ensure the accuracy and traceability of the report.

[0058] In an optional embodiment, the tunnel boring machine operator adjusts the tunneling strategy or performs cutterhead cleaning based on the mud cake parameter analysis report, including: F1. When the mud cake parameter analysis report shows that the mud cake is located in the central area and the thickness level reaches the preset first threshold, the tunnel boring machine operator can reduce the propulsion speed and appropriately increase the cutterhead rotation speed, while starting the cutterhead water spray system for local cleaning. F2. When the mud cake parameter analysis report shows that the mud cake location is in the edge area and the thickness level reaches the preset second threshold, the tunnel boring machine operator can keep the current tunneling parameters unchanged and continuously monitor the temperature deviation value and the changing trend of the tunneling parameters. F3. When the mud cake parameter analysis report shows that the mud cake covers multiple areas and the thickness level reaches the preset third threshold, the tunnel boring machine operator should immediately stop tunneling and use mechanical cleaning or chemical soil modification to completely remove the mud cake from the cutterhead surface. After cleaning is completed, tunneling should be restarted.

[0059] It should be noted that chemically modified slag refers to a method of altering soil properties by adding specific chemical agents. The purpose is to make the slag cake easier to decompose or change its properties through chemical reactions, so that it can be removed more easily. This method is suitable for situations where the slag cake problem is more complex or where mechanical cleaning is difficult to solve.

[0060] Based on the above technical solutions, after the mud cake is arranged, monitored, and predictively analyzed, the following water jet cleaning control method for the tunnel boring machine cutterhead can also be adopted, including the following steps: S1: Based on the cutter head cleaning grid parameter set, the cutter head surface is cut into strips radially according to structural units and then subdivided according to tangential angles. The center point of the sub-region is corrected with the test frame positioning reference. Then, it is reordered and numbered according to the direction of the cutter head rotation area to obtain the cutter head cleaning grid parameter set. S2: Based on the cutter head cleaning grid parameter set, read the water jet nozzle installation point and direction, synthesize the distance and direction between the sub-area center point and the water supply pipeline pressure node into an energy value, and then determine the effective coverage range with a threshold to obtain the jet coverage energy distribution set. S3: Based on the spray coverage energy distribution set, the Hungarian allocation algorithm is used to calculate the cleaning time according to the thickness of the sub-region adhesion layer and the pressure value. The time quantities are formed into a matrix and multiple values ​​are compared in the same row to determine the nozzle correspondence. All matching items are combined into a unified sequence to obtain the nozzle cleaning assignment result set. S4: Based on the nozzle cleaning assignment result set, the particle swarm optimization algorithm is used to add fine-tuning amount to the nozzle installation point, and then compare the distance with the center point of the cutter head structure unit. The reachability is judged by the angle between the nozzle direction and the normal vector of the cleaning area. The reachable units are recorded to obtain the nozzle arrangement adjustment parameter set. S5: Based on the nozzle arrangement adjustment parameter set, read the nozzle start time according to the test, arrange the assigned sub-area time into the time axis and align it with the cutter head rotation area reference, then integrate it into a continuous time period and organize it uniformly to obtain the nozzle start and stop sequence table.

[0061] The cutterhead cleaning grid parameter set includes sub-region number, sub-region coordinate points, and sub-region position sequence; the spray coverage energy distribution set includes sub-region energy value, coverage mark, and direction superposition value; the nozzle cleaning assignment result set includes nozzle number, sub-region number, and cleaning time; the nozzle arrangement adjustment parameter set includes position adjustment amount, direction correction amount, and reachability mark; and the nozzle start-stop sequence table includes start time, stop time, and nozzle sequence number.

[0062] The specific steps for generating the tool shroud cleaning mesh parameter set are as follows: Based on the cutter head cleaning grid parameter set, the cutter head surface is divided into several radial strips according to the change from the outer edge to the center. Then, tangential partitions are drawn according to a fixed angle. The actual position of the center point of each sub-region is re-determined using the test frame positioning point to generate a sub-region positioning dataset. Based on the sub-region positioning dataset, the numbering order of all sub-regions is rearranged according to the direction of the tool head rotation area. The rearranged numbers are arranged in a matrix to form a unified index, thus obtaining the tool head cleaning grid parameter set. Based on the cutter head cleaning grid parameter set, a radial slicing numerical partitioning algorithm is used to continuously take points along a fixed step distance from the outer edge of the cutter head to the center, and the areas between each point are sequentially marked as radial strips. Then, on each radial strip, tangential regions are generated by rotating segment by segment with a fixed angular step distance, and the center point angle of each tangential region is recorded. Then, the three-axis coordinates of the test frame positioning points are used to perform three-way translation on each center point, and the displacement is superimposed sequentially to form the corrected center point position. All corrected positions are arranged in the generation order to generate a sub-region positioning dataset. Based on the sub-region positioning dataset, the rotation zone numbering rearrangement algorithm is used to perform angle conversion on the center point coordinates of each sub-region, and the converted angles are arranged in order according to the starting direction of the cutter head rotation zone. The arranged sub-regions are then renumbered. Then, a numbering matrix is ​​established with the number of radial strips as the row structure basis and the number of tangential partitions as the column structure basis. The corresponding numbers are filled into the matrix positions one by one, and all row and column information is sorted to obtain the cutter head cleaning mesh parameter set.

[0063] The specific steps for generating the jet coverage energy distribution set are as follows: Based on the cutter head cleaning mesh parameter set, the installation point position and spray direction of the water jet nozzle are extracted, and then the energy quantity is combined with the straight distance and direction between the center point of each sub-area and the pressure node of the water supply pipeline, and recorded as the same sequence to generate the energy base set. Based on the energy base set, the energy in the sequence is compared with a set threshold in turn. Sub-regions that reach the threshold are included in the effective coverage range. The coverage results are then classified into separate sequences according to the numbering order to obtain the jet coverage energy distribution set. Based on the cutterhead cleaning mesh parameter set, an energy combination operation algorithm is used to execute a three-axis coordinate value retrieval command on the water jet nozzle installation point position and a direction vector analysis command on the spray direction. Then, a three-axis difference command is executed on the coordinates of the center point of each sub-zone and the coordinates of the pressure node of the water supply pipeline. The difference components are combined into a distance vector in a fixed order, and the distance vector length is used as the distance quantity. The distance quantity and the spray direction vector are extracted one by one. After extraction, a multiplication and addition command is executed with the distance quantity as the first factor and the angle quantity as the second factor to form the energy quantity value of a single sub-zone. Then, all energy quantity values ​​are added to the sequence buffer in the order of sub-zone number, and a one-time read command is executed on the buffer to form a continuous energy quantity sequence, generating an energy base set. Based on the energy base set, the energy threshold comparison algorithm is used to perform the comparison command on the energy in the sequence in numerical order, and a fixed threshold is used as the comparison benchmark. For the energy greater than the threshold, the valid identifier is written and the corresponding sub-region number is written to the coverage buffer. Then, the contents of the buffer are rearranged in ascending order of sub-region number. After rearrangement, all valid numbers are written into the coverage record sequence in a single column structure to form an independent data group, thus obtaining the jet coverage energy distribution set.

[0064] The specific steps for generating the nozzle cleaning assignment result set are as follows: Based on the energy distribution set of the spray coverage, the Hungarian allocation algorithm is used to extract the thickness of the attachment layer of each sub-region from the record table one by one, and the pressure value under the same number is sequentially substituted into the thickness amount to convert it into time amount. Then, the converted time amount is arranged into a continuous sequence according to the order of the sub-region to generate a time amount sequence set. Based on the time sequence set, the sequence is divided into multiple rows according to the number of nozzles. Then, the position of the identifier is determined in each row according to the time magnitude. These identifiers are grouped together according to the nozzle arrangement order to generate the nozzle corresponding identifier set. Based on the nozzle corresponding identifier set, the nozzle number and sub-area number are retrieved in pairs according to the actual landing point of the identifier. Then, the corresponding time quantity is inserted into the same sequence to form a continuous record. The records are then merged into the set in the overall order to obtain the nozzle cleaning assignment result set. Based on the spray coverage energy distribution set, the Hungarian allocation algorithm is used to execute a read command for the thickness of the adhesion layer in each sub-region of the record table according to the sub-region number. The read thickness is written into the thickness sequence in numerical order. Then, the pressure value retrieval command is called to write the pressure values ​​with the same number into the pressure sequence in sequence. Next, using the thickness sequence as input, a conversion command is executed item by item according to the number. The corresponding value in the pressure sequence is used as the conversion factor to form the time quantity, and the time quantity is written into the time block buffer in an indexed manner. Then, in the Hungarian allocation algorithm, a row minimum value subtraction command is executed on the time block buffer. The minimum value is subtracted from each item in the corresponding row to form a row difference matrix, and then the process is continued. The minimum value subtraction command is used to subtract the minimum value from each item in the corresponding column to form a column difference matrix. The position of zero value is found in the difference matrix and written into the zero value mark list according to the row and column of the zero value. Then, the zero value mark list is covered by the cover command, and the cover index is recorded in the row direction and column direction respectively. If the coverage is insufficient, the minimum difference is recorded in the uncovered position and the minimum difference subtraction and cover position addition commands are executed to update the difference matrix. Then, the zero value search and row and column coverage actions are repeated until the coverage meets the assignment scale. The row and column numbers in the difference matrix are written into the index sequence according to the zero value distribution to generate the time quantity sequence set. Based on the time sequence set, the Hungarian allocation algorithm is used to split the sequence according to the number of nozzles, and the generated row structure is sorted according to the time size. The original index of each item during the sorting process is written into the identifier buffer. Then, the position registration command is executed on the identifier buffer row by row, and the identifiers in each row are merged into identifier groups according to the nozzle number order. After merging, all identifier groups are written into the identifier set in order, and an identifier mapping table is constructed. After the entire mapping table is written, the identifier corresponding sequence is formed, and the nozzle corresponding identifier set is generated. Based on the nozzle corresponding identifier set, the Hungarian allocation algorithm is used to execute the read command on the row and column numbers in the identifier, retrieve the corresponding nozzle number according to the read number, retrieve the corresponding sub-area number according to the same number, execute the pairing command after retrieval, write the pairing number into the pairing list, execute the insertion command on the time quantity with the same row number in the pairing list, and form a continuous time record sequence after insertion. Then, all record sequences are merged into a unified set in sequence, and the set alignment command is executed to form the final pairing result group, thus obtaining the nozzle cleaning assignment result set.

[0065] The Hungarian allocation algorithm first extracts the valley value of each row of the cleaning time quantity arranged in matrix form and subtracts it from the corresponding items in the row, so that each row forms a difference distribution with zero as the base. Then, it extracts the valley value of each column of the processed matrix and subtracts it from the corresponding items in the column, so that the values ​​in the column synchronously form a new difference matrix. Then, it searches for all positions with zero values ​​in the matrix and covers the rows and columns containing zero values ​​in a straight line. The covered rows and columns are recorded as a cover set. When the number of rows and columns in the cover set is insufficient to cover all zero values, the valley value is extracted from the uncovered positions in the matrix and subtracted from all uncovered positions. At the same time, it is added to all covered positions to form a new difference matrix. The zero value search and row and column covering actions are repeated on the new difference matrix. When the number of rows and columns in the cover set reaches the number of nozzles, the uncovered zero value positions are collected into pairs according to the row and column coordinates and the pairs are used as the pairing indexes corresponding to the nozzles and sub-regions. The Hungarian allocation algorithm, according to the formula:

[0066] in: Indicates the number is The thickness of the target adhesion layer in the blade plate area. Indicates the number is The water jet injection pressure value in the cutter head area, This represents the water jet deposition removal efficiency coefficient. Indicates the number is The duration of water jet action in the blade plate area Indicates the number is The uniformity index of water jet energy coverage in the blade plate area. Indicates the number is The overlap rate index of water jet trajectory coverage in the blade plate area. Indicates the number is The nozzle wear index in the cutter head area. This represents the weighting coefficient for energy coverage uniformity correction. This represents the weighting coefficient for trajectory coverage overlap correction. This indicates the nozzle wear correction weighting factor; Execution process: First, read the target adhesion layer thickness of the sub-region numbered in the order of sub-region numbering on the surface of the cutter head. And retrieve the water jet injection pressure value that matches the sub-zone number from the injection control unit. The system then retrieves the water jet deposition removal efficiency coefficient, which has been determined experimentally, from the calibration parameter set. Then, based on the water jet energy distribution sampling results, the number is calculated as follows: Subregion energy coverage uniformity index And calculate the number based on the water jet trajectory generation model. Sub-region trajectory coverage overlap index At the same time, the number is calculated by accumulating the nozzle operation records. Sub-region nozzle wear index The system then retrieves the energy coverage uniformity correction weighting coefficients obtained through least-squares fitting from the controller parameter area. And trajectory coverage overlap rate correction weight coefficient and nozzle wear correction weighting factor The system will , , , , Substituting all the values ​​into the formula, and applying a joint correction factor that includes three correction factors—energy uniformity, trajectory overlap rate, and nozzle wear—to the original time calculation results, we obtain the result numbered as follows: Water jet action time in the blade plate area Finally, the above process is executed sequentially on all cutter disc areas according to their numbering order to form a complete set of water jet action time sequence.

[0067] The specific steps for generating the nozzle arrangement adjustment parameter set are as follows: Based on the nozzle cleaning assignment result set, the particle swarm optimization algorithm is used to move the coordinates of the nozzle installation point by a certain distance in a fixed direction to form a new set of coordinates. Then, the corresponding nozzle number is appended to these new coordinates to make them a list that can be directly indexed, thus generating the nozzle adjustment coordinate set. Based on the nozzle adjustment coordinate set, the adjusted coordinates of each group are compared with the coordinates of the center point of the cutter head structure unit one by one, the distance between the two points is measured, and the distances are arranged into a record column according to the nozzle number to generate a nozzle distance comparison set. Based on the nozzle distance reference set, the nozzle direction and the normal vector of the cleaning area are taken out in numerical order, the angle between the directions is calculated, and the angle and distance are recorded together as a judgment item. Finally, the numbers that meet the attainable requirements are collected into a list to obtain the nozzle layout adjustment parameter set. Based on the nozzle cleaning assignment result set, the particle swarm optimization algorithm is used to execute the position initialization command for the nozzle installation point coordinates. The three-axis coordinates of each nozzle are written into the particle position vector, the corresponding nozzle number is written into the index vector, the three-axis components of the velocity vector are initialized to zero, and after initialization, the direction offset command is executed for each position vector. The offset distance parameter is used as the three-axis increment to write the offset coordinates into the candidate coordinate list. Then, the nozzle number is appended to the candidate coordinate list to form an indexable record, and the record is written into the particle current coordinate set. After the coordinate set is established, the velocity update command is executed for each particle. The inertia factor is multiplied by the three-axis components of the previous velocity vector, the difference component between the particle's current coordinate and the particle's historical coordinate is multiplied by coefficient one and added to the velocity vector, the difference component between the particle's current coordinate and the group's historical coordinate is multiplied by coefficient two and added to the velocity vector, and the updated velocity vector is added to the current position component by component to obtain the new position coordinates. The new position is written into the coordinate set and the update steps are repeated until the iteration number requirement is reached to generate the nozzle adjustment coordinate set. Based on the nozzle adjustment coordinate set, the particle swarm optimization algorithm is used to execute the coordinate reading command for each set of adjustment coordinates according to the nozzle number. The three-axis components of the adjustment coordinates are calculated item by item with the three-axis components of the center point coordinates of the cutter head structure unit. The difference components are written into the difference buffer. Then, the three-axis quantity accumulation command is executed on the difference buffer to form the distance quantity. The distance quantity is written into the distance record column in the order of nozzle number. During the writing process, the number and the distance quantity are kept in a one-to-one correspondence to form a distance sequence. After all the writing is completed, the distance sequence is used as the input data group of the particle swarm optimization algorithm fitness value to generate the nozzle distance comparison set. Based on the nozzle distance reference set, the particle swarm optimization algorithm is used to execute the direction reading command according to the nozzle number. The three-axis components of the direction vector of each nozzle are written into the direction buffer, and the three-axis components of the normal vector of the cleaning zone are written into the normal buffer. Then, the multiply-add command is executed on the two sets of vectors to form the angle combination value. The combination value is input into the direction difference command to obtain the direction angle. The angle and distance are written into the decision sequence in parallel according to the number. After the decision sequence is written, the number filtering command is executed on the sequence. The numbers that meet the attainable requirements are written into the output list one by one to obtain the nozzle layout adjustment parameter set.

[0068] The particle swarm optimization algorithm first randomly generates position vectors and sets corresponding velocity vectors for each nozzle arrangement scheme within the search space. The position vectors are recorded as the initial coordinate set of the particles, and the velocity vectors as the initial velocity set. Then, based on data such as nozzle installation point coordinates, injection direction angle, cutter head mesh cell coverage, and cleaning time, the cost value of each particle is calculated. This cost value is compared with the particle's historical cost to update the particle's historical adaptive position set. Simultaneously, the particle with the lowest cost value is selected from all particles and recorded as the swarm's adaptive position set. Finally, for each particle, the inertial weight is multiplied by the previous generation's velocity, and the particle's historical position and current position are added. The velocity vector is updated by weighting the difference between the current position and the difference between the current position and the group adaptation position. The updated velocity vector is then added to the current position coordinates dimension by dimension to obtain the new position coordinates. Coordinates that are outside the installation range are truncated by boundary values ​​or corrected by reverse foldback to form a new generation of particle position set. The cost value corresponding to the new position is recalculated and the particle history adaptation position set and the group adaptation position set are updated according to the same rules. The velocity update, position update and cost calculation steps are repeated within the preset number of iterations. When the iteration ends, the nozzle coordinates and injection direction angle in the group adaptation position set are recorded as the output results of the particle swarm optimization algorithm. Particle swarm optimization algorithm, according to the formula:

[0069] in: Indicates the first The iteration number is The nozzle particle velocity vector Indicates the first The iteration number is The nozzle particle velocity vector Indicates the inertia weighting coefficient. Represents individual learning factors. Represents a random number in the interval between zero and one. Indicates the first The iteration number is The individual historical optimal position vector of the nozzle particle. Indicates the first The iteration number is The current position vector of the nozzle particle. This represents the radial position correction weighting coefficient. Indicates the number is The nozzle radial position index, This indicates the tool occlusion correction weight coefficient. Indicates the number is The nozzle-cutter obstruction impact index, Represents the group learning factor. Represents a random number in the interval between zero and one. Indicates the first The global optimal position vector of the nozzle particle swarm in the next iteration. This represents the weighting factor for correcting historical scour intensity deviation. Indicates the number is The nozzle's historical erosion intensity deviation index. This represents the weighting coefficient for correcting jet crosstalk. Indicates the number is The nozzle jet cross-interference index; Execution process: First, initial coordinates are set for each of the multiple nozzles and used as the first... The current position vector of the next iteration At the same time, set the initial velocity vector of the corresponding nozzle. And load the inertia weight coefficient Individual learning factors With group learning factor The controller then calculates the cleaning coverage evaluation value based on the current nozzle coordinates and determines the number as follows. The individual historical best position vector of the nozzle and the global optimal position vector corresponding to all nozzle combinations In the same iteration, the controller obtains the radial position index based on the ratio of the nozzle radial distance to the cutter head radius. The tool obstruction influence index is obtained based on the obstruction angle and minimum distance between the nozzle and the adjacent tool. The historical scouring intensity deviation index was obtained by normalizing the residual thickness deviation of the nozzles in previous cleaning tests. The jet cross-interference index is obtained based on the proportion of overlapping areas between the water jet trajectories of the nozzles. Simultaneously, the radial position correction weighting coefficient is read from the calibration parameter set. Tool occlusion correction weight coefficient Historical scour intensity deviation correction weighting coefficient and the weighting coefficient for jet cross-interference correction The controller then generates a random number for each nozzle. and And and Substituting the individual terms into the formula, , , , , and Substitute the group terms into the formula, and then combine them with the inertia term. Add them together to get the speed update result. Then according to The displacement is decomposed into a unit vector along a fixed direction and superimposed with the current coordinates to form the nozzle coordinates for the next iteration. The controller repeats the above speed update and position update process for all nozzles until the iteration count and fitness convergence conditions are met. Finally, the multiple nozzle coordinates at the termination iteration time are used as the nozzle adjustment coordinate set for the cutterhead water jet cleaning path planning and execution.

[0070] The specific steps for generating the nozzle start-stop timing table are as follows: Based on the nozzle arrangement adjustment parameter set, the nozzle start time is extracted according to the original test record, and the time corresponding to the assigned sub-area is added to the same time axis according to the start order, thereby forming a continuous time arrangement sequence and generating a time axis arrangement set. Based on the time axis arrangement set, the position of the sequence is checked segment by segment according to the time reference of the cutter head rotation area, and the continuous time periods are re-connected into a unified record format according to the nozzle number to obtain the nozzle start and stop sequence table. Based on the nozzle arrangement adjustment parameter set, a time series construction algorithm is used to execute a read command for each nozzle start time in the original test record. The read time values ​​are written into the start buffer according to the nozzle number. Then, the time quantity corresponding to the assigned sub-area is executed with a value retrieval command and written into the sub-area time buffer in numerical order. Then, the time axis establishment command is executed with the time in the start buffer as the reference and the minimum start value is used as the start point of the time axis. The time superposition command is executed for the time quantity of each nozzle. The superimposed time points are written into a unified time axis list in sequence. During the writing process, the continuity marking command is executed on the time axis list. The interval between adjacent time points is written into the interval sequence with a fixed step size. Then, the time points and the interval sequence are combined and written into the arrangement buffer to form a continuous time arrangement sequence. The numbering and labeling command is executed on the arrangement sequence to generate a time axis arrangement set. Based on the time axis arrangement set, a time calibration and reconstruction algorithm is used to execute a read command for each time period in the sequence and use the time reference value set in the cutter head rotation area as the calibration reference for the read time period. The difference between the start time of each time period and the reference value is calculated and the difference is written into the offset list as the calibration offset. The offset list is then applied to all time periods in sequence to execute calibration commands and the calibrated time periods are written into the reconstruction sequence. After the reconstruction sequence is established, a serial command is executed according to the nozzle number to merge all time periods of the same nozzle number into a single continuous structure in sequence. The start time, end time, and nozzle number in each structure are written into a data column of a unified record format in a fixed field order and all data columns are written into the output set in sequence to obtain the nozzle start and stop timing table.

[0071] A unified recording format is used to create a collection of time segment records for each nozzle, arranged in a fixed field order, including the start time, end time, sub-area number, and nozzle number. Each record is based on a continuous time period and forms an independent entry according to the nozzle number. The time segments within each entry are arranged in order of start time first and end time last. All entries form a data group with the same field structure, enabling the time segments of different nozzles to be read and compared in the same set according to the fixed field positions.

[0072] Based on the above solution, this embodiment also provides a tunnel boring machine, including: a control system, which is used to execute the method described above.

[0073] The tunnel boring machine also includes: the cutterhead and casing. Among them, such as... Figure 2 and Figure 3 As shown, the tunnel boring machine cutterhead includes: cutterhead body 1, first water jet nozzle 13 and second water jet nozzle 14.

[0074] The outer end face of the cutter head body 1 is divided into a central region 11 and a peripheral region, and multiple cutter heads 12 are provided in both the central region and the peripheral region. The cutter heads in the central region are irregularly positioned, while the cutter heads in the peripheral region are arranged radially outward from the central region.

[0075] In this embodiment, six rows of cutter heads are arranged outward from the central region. Each row of cutter heads extends radially outward along the cutter head body 1 and is spaced apart. The six rows of cutter heads are evenly distributed circumferentially. Each cutter head 12 includes two coaxially arranged cutter rings, which are axially symmetrical.

[0076] The first water jet nozzle 13 is located at the intersection of the extension line of the axis of symmetry between the two cutter rings in one cutter head 12 and the extension line of the axis of symmetry between the two cutter rings in an adjacent cutter head. Specifically, as follows... Figure 3 As shown, taking the cutter head 12 located at the lower left and the cutter head 12 located at the upper left as examples: the first water jet nozzle 13 is set at the intersection of the extension line L of the two cutter ring symmetry axes in the lower left cutter head 12 and the extension line L of the two cutter ring symmetry axes in the upper left cutter head 12. Similarly, a similar layout is adopted in the remaining cutter heads.

[0077] The second water jet nozzle 14 is located at the intersection of the line connecting the center of one cutter ring and the edge of the other cutter ring in a cutter head 12, and the extended line connecting the center of one cutter ring and the edge of the other cutter ring in a neighboring cutter head. Specifically, as follows... Figure 3As shown, taking the cutter head 12 located at the lower left and the cutter head 12 located at the upper left as examples: In the cutter head 12 located at the lower left, a second water jet nozzle 14 is installed at the intersection of the extension line K of the line connecting the center of one cutter ring to the edge of the other cutter ring and the extension line K of the line connecting the center of one cutter ring to the edge of the other cutter ring in the cutter head 12 located at the upper left. Similarly, the other two adjacent cutter heads 12 adopt a similar scheme.

[0078] The cutter head 12 includes two types. One type has a gap between two cutter rings, in which case the extensions L and K of the two cutter rings do not coincide, and they form an acute angle. Figure 3 The blade 12 is located in the upper left and lower left corners.

[0079] Another scenario is where there is no gap or a very small gap between the two cutter rings in cutter head 12. In this case, the extension lines L and K of the two cutter rings are considered to coincide. Figure 3 The cutter head 12 is located at the top and the cutter head 12 is located on the right. In this design, for the top cutter head 12, the intersection of its overlapping extension line L (K) with the extension line L of the right cutter head 12 is provided with a first water jet nozzle 13, and the intersection of its extension line K with the extension line K of the upper left cutter head 12 is provided with a second water jet nozzle 14.

[0080] In the two cutter heads 12, the intersection of the extended line L of the cutter ring symmetry axis and the intersection of the extended line K of the line connecting the center of one cutter ring and the edge of the other cutter ring are prone to the accumulation and deposition of slag and soil, which are high-incidence areas for mud cake formation. By setting the first water jet nozzle 13 and the second water jet nozzle 14 at these two locations, high-pressure water jets can be used to flush away the mud cake as soon as it begins to form, effectively preventing further accumulation of mud cake.

[0081] Based on the above scheme, in this embodiment, the first water jet nozzle 13 and the second water jet nozzle 14 are set according to the position of the cutter head 12, which can accurately match the formation pattern of mud cake near each cutter head 12. Compared with the scheme of uniformly arranging water jet nozzles in the prior art, the high-pressure water jets sprayed by the first water jet nozzle 13 and the second water jet nozzle 14 in this embodiment can remove mud cake more efficiently, reduce the obstruction of mud cake to the normal operation of the cutter head body 1 and the cutter head 12, significantly improve the mud cake removal efficiency and effect, and ensure the smoothness and reliability of cutting the soil.

[0082] The technical solution provided in this embodiment includes a cutter head body with multiple cutter heads in the central region of its outer end face. Each cutter head includes two axially symmetrically arranged cutter rings. A first water jet nozzle is located at the intersection of the extension line of the axis of symmetry between the two cutter rings in one cutter head and the extension line of the axis of symmetry between the two cutter rings in an adjacent cutter head. A second water jet nozzle is located at the intersection of the line connecting the center of one cutter ring and the edge of another cutter ring in one cutter head and the extension line connecting the center of one cutter ring and the edge of another cutter ring in an adjacent cutter head. This solution can remove mud cake more efficiently, reduce the obstruction of mud cake to the normal operation of the cutter head body and cutter heads, significantly improve the efficiency and effect of mud cake removal, and ensure the smoothness and reliability of cutting the soil.

[0083] Based on the above technical solution, a third water jet nozzle 15 is also adopted. An outer tangent circle C is drawn with the outer peripheral edge of a cutter head 12, and the outer tangent circle C is internally tangent to the circular envelope of the central region 11 of the cutter head body. If there is no first water jet nozzle 13 or second water jet nozzle 14 within the outer tangent circle C, the third water jet nozzle 15 is disposed at the center of the outer tangent circle C.

[0084] The setting can be based on the position of the cutter head 12 in the central region 11. Figure 3 Taking the cutter head layout shown as an example, the lower right blank area is relatively large. The outer periphery of the cutter head 12 is circumscribed by a circle C, and the circumscribed circle C is internally tangent to the circular envelope of the central area 11 of the cutter head body. Figure 3 A third water jet nozzle 15 can be installed at the center of each of the three circumscribed circles C.

[0085] The third water jet nozzle 15 effectively fills the gap in mud cake prevention that may exist after the arrangement of the first two types of water jet nozzles. It can assist the first water jet nozzle 13 and the second water jet nozzle 14 in providing all-round, no-dead-angle mud cake prevention coverage to the central area, avoiding the local accumulation of mud cake on the central area 11, which would cause the center of gravity of the cutter head body 1 to shift, and improving the stability and safety of the cutter head body 1 when rotating.

[0086] The aforementioned first water jet nozzle 13, second water jet nozzle 14, and third water jet nozzle 15 can all be columnar structures, vertically mounted on the cutterhead body 1. All three nozzles spray water radially along the cutterhead body 1 to ensure that the high-pressure water jet can vertically impact the mud cake, improving the scouring effect. All nozzles are positioned in the central region 11 where the cutter head 12 is not located, thus not affecting the normal operation and design of the cutter head 12, ensuring that the cutting function and mud cake prevention function do not interfere with each other and work synergistically.

[0087] In this embodiment, the cutter head 12 is a double-edged hob, which is a structure containing two cutter rings.

[0088] Reference Figure 4 Considering that the depth of mud cake accumulation is not completely uniform, in this embodiment, the number of the first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 is at least one. In this embodiment, the distance h between the nozzle 19 of the different first water jet nozzles 13, the second water jet nozzles 14, and the third water jet nozzles 15 and the panel of the cutter head body 1 is different, that is, the length of the first water jet nozzles 13, the second water jet nozzles 14, and the third water jet nozzles 15 extending out of the end face of the cutter head is different. They are arranged in a stepped manner on the cross-section of the cutter head, so that the high-pressure water jets ejected by the first water jet nozzles 13, the second water jet nozzles 14, and the third water jet nozzles 15 at different positions can form a multi-layered flushing effect in front of the cutter head body 1, effectively cleaning mud cakes of different accumulation depths. Whether it is shallow mud cake or mud cake that has accumulated to a relatively thick depth, it can be effectively cleaned, effectively enhancing the comprehensiveness and effectiveness of mud cake prevention and control.

[0089] Reference Figure 4 The implementation methods of the first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 in this embodiment will be described. The first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 can adopt the same structure and are collectively referred to as water jet nozzles. The water jet nozzle is provided with a water inlet channel 16, which runs through the water jet nozzle along the axial direction. The other end of the water jet nozzle is provided with a water outlet channel 17, which is perpendicular to the axial direction of the water jet nozzle. Both ends of the water outlet channel 17 pass through the outer peripheral surface of the water jet nozzle. The water outlet channel 17 is connected to the water inlet channel 16, so the water jet nozzle can spray two jets of high-pressure water in opposite directions to achieve bidirectional scouring.

[0090] In practical work, the spray direction at both ends of the water outlet channel 17 can be flexibly adjusted according to the distribution of mud cake on the cutter head 12. For example, both ends can be aimed at the area around the cutter head 12 where mud cake is easy to accumulate, or at least one end can be aimed at one cutter head 12, thereby increasing the coverage area of ​​the high-pressure water jet on the area around the cutter head 12 and the central area 11, enhancing the scouring effect and improving the scouring efficiency.

[0091] In addition, a pulse solenoid valve 18 is installed on the water inlet channel 16 of the water jet nozzle, enabling the water jet nozzle to perform pulse water spraying when needed, thereby improving the efficiency of mud cake removal. Compared with continuous water spraying, pulse water spraying can generate a stronger impact force, and with the same water consumption, the effect of breaking up and removing mud cake is more significant, effectively reducing water consumption and improving the efficiency of mud cake control.

[0092] To achieve intelligent mud cake prevention, this embodiment also includes a control system. The control system monitors the torque of the cutterhead body 1 and adjusts the water pressure, flow rate, and spraying time of each water jet nozzle based on the torque. Torque is a key parameter reflecting the working state of the cutterhead body 1 and the degree of mud cake accumulation. When the torque of the cutterhead body 1 increases, indicating severe mud cake accumulation, the control system can increase the water pressure, flow rate, and spraying time of each water jet nozzle to quickly remove the mud cake and reduce the load on the cutterhead body 1. Conversely, it can decrease these parameters to save energy and extend the equipment's service life.

[0093] In this embodiment, the control system monitors the torque of the cutter head body 1 in several ways, including but not limited to the following: (1) Using a torque sensor, the torque sensor is installed on the drive unit of the drive motor 6 that can drive the cutter head body 1 to rotate. The torque sensor works based on the strain principle or the magnetoelectric principle and can directly measure the torque transmitted by the drive unit. It has the characteristics of high measurement accuracy and fast response speed, and can obtain the torque information of the cutter head body in real time and accurately.

[0094] (2) When the drive motor 6 that drives the cutter head body 1 to rotate is running, the current magnitude and the output torque have a certain correspondence. According to the characteristic curve of the drive motor 6 and the relevant mathematical model, the torque of the cutter head body 1 can be indirectly calculated based on the current of the drive motor 6 by detecting the working current of the drive motor 6. There is no need to install a special torque sensor, and the cost is relatively low. While ensuring a certain measurement accuracy, it effectively reduces the cost investment of the equipment and is suitable for engineering projects with strict cost control.

[0095] (3) If the cutter head body 1 is hydraulically driven, a pressure sensor can be installed in the hydraulic drive system. For example, the pressure sensor can be installed on the oil inlet or return pipe of the hydraulic motor. When the cutter head body 1 is subjected to torque, the pressure in the hydraulic system will change. The pressure sensor can measure this pressure change and obtain the torque value through the corresponding conversion relationship. The pressure sensor is easy to install and requires little modification to the hydraulic drive system.

[0096] On the panel of the cutter body 1, the mud cake usually forms gradually in the central area 11 first, and then accumulates thicker and thicker and gradually expands from the central area 11 to the edge of the cutter body 1.

[0097] Reference Figure 5In this embodiment of the tunnel boring machine, the rear of the cutterhead body 1 is rotatably connected to the shield shell 2. The shield shell 2 is a cylindrical hollow structure. The inner circumferential surface of the shield shell 2 is connected to a shield partition 3 that is parallel to the cutterhead body 1. An installation ring 4 is installed on the side of the shield partition 3 near the cutterhead body 1. The end of the installation ring 4 near the cutterhead body 1 is connected to the outer ring of the shield bearing 5. The inner ring of the shield bearing 5 is connected to the drive part of the drive motor 6. The drive motor 6 is installed on the shield partition 3. The drive part of the drive motor 6 is connected to the cutterhead support arm 8 through a connecting plate 7. The cutterhead support arm 8 is connected to the cutterhead body 1.

[0098] The shield diaphragm 3 has a central hole in the center. The outer cylinder 9 passes through the central hole and is fixedly connected to the shield diaphragm 3. The outer circumferential surface of the outer cylinder 9 has a high-pressure water inlet 011. The inner cylinder 010 is coaxially sleeved inside the outer cylinder 9. The inner cylinder 010 is rotatably connected to the outer cylinder 9. The end of the inner cylinder 010 near the cutterhead body 1 is connected to the connecting plate 7, and the inner cylinder 010 penetrates the connecting plate 7.

[0099] The first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 are all connected to one end of the high-pressure water inlet pipe 1 012 via a rotary joint. The other end of the high-pressure water inlet pipe 1 012 passes through the connecting plate 7 and extends into the gap between the outer cylinder 9 and the inner cylinder 010. This gap is connected to the high-pressure water inlet 011. The high-pressure water inlet 011 is connected to one end of the high-pressure water inlet pipe 2 013. The other end of the high-pressure water inlet pipe 2 013 is connected to the water storage tank 014. The water storage tank 014 is connected to the air pressure pump 015 via the high-pressure water inlet pipe 3 016. The high-pressure water inlet pipe 2 013 is equipped with a high-pressure water valve 017, and the high-pressure water inlet pipe 3 016 is equipped with a pressure gauge 018.

[0100] The first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 on the cutterhead body 1 can spray high-pressure water in the radial direction of the cutterhead body 1. At the same time, the cutterhead body 1 can rotate under the drive of the drive motor 6, excavating and removing mud cake at the same time. This improves the construction efficiency of the tunnel boring machine in complex geological conditions such as rich mud soil layers, effectively reduces problems such as cutterhead wear and equipment failure caused by mud cake accumulation, reduces construction costs, shortens the construction cycle, and significantly improves the overall performance and engineering applicability of the tunnel boring machine.

[0101] After the tunnel boring machine (TBM) is started, the drive motor 6 begins to work. The drive unit of the drive motor 6 is connected to the cutterhead support arm 8 via the connecting plate 7. The cutterhead support arm 8 is in turn connected to the cutterhead body 1. Therefore, the power of the drive motor 6 can be transmitted to the cutterhead body 1, causing the cutterhead body 1 to rotate around the central axis of the shield bearing 5. Several cutter heads 12 set in the central area 11 of the cutterhead body 1 rotate accordingly, excavating clay strata, clayey sandy soil strata, etc., cutting the soil into fine sand particles and debris.

[0102] The air pump 015 is connected to the water storage tank 014 via the high-pressure water inlet pipe 3 016, pressurizing the water storage tank 014. The pressure gauge 018 on the high-pressure water inlet pipe 3 016 monitors the pressure provided by the air pump 015 in real time, ensuring that the water supply pressure is stable within a suitable range, thus guaranteeing the stable spraying of the subsequent high-pressure water jet. High-pressure water flows out from the water storage tank 014, passing through the high-pressure water inlet pipe 2 013. The high-pressure water valve 017 on the high-pressure water inlet pipe 2 013 controls the on / off state and flow rate of the high-pressure water. The high-pressure water enters the high-pressure water inlet 011 of the outer cylinder 9. The inner cylinder 010 is coaxially sleeved inside the outer cylinder 9, and the inner cylinder 010 is rotatably connected to the outer cylinder 9. After entering the gap between the outer cylinder 9 and the inner cylinder 010, the high-pressure water is delivered to the first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 through the high-pressure water inlet pipe 1 012. As the cutter head body 1 rotates continuously, the high-pressure water inlet pipe 1012 is connected to the first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 through a rotary joint, ensuring a continuous supply of high-pressure water during the rotation of the cutter head body 1.

[0103] The first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15 can spray high-pressure water along the radial direction of the cutterhead body 1. These nozzles are designed based on the position of the cutter head 12, and the high-pressure water jets they spray precisely impact areas on the cutterhead body 1 where mud cake easily forms, such as the intersection of the extended lines L of the cutter head 12's cutter ring symmetry axis, the intersection of the extended line K connecting the center of the outer cutter ring and any corner of the inner cutter ring, and the center of a specific circumscribed circle C. All three nozzles can achieve pulsed water spraying, enhancing the breaking and removal of mud cake and reducing water consumption. Through the high-pressure water jet flushing, the mud cake is broken and dispersed, preventing it from covering the cutter head and clogging the cutter cylinder, reducing cutter wear, ensuring normal cutter rotation and cutting function, and maintaining stable tunneling of the cutterhead body 1.

[0104] During the excavation of soil by the cutterhead body 1, the control system monitors the torque of the cutterhead body 1 in real time. During normal excavation, the torque of the cutterhead body 1 remains within a relatively stable range. However, as mud cake gradually accumulates on the cutterhead body 1, the resistance it experiences increases, and the torque also rises accordingly. Based on the monitored torque changes, the control system automatically adjusts the water pressure, flow rate, and spraying time of the first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15. For example, when the increased torque indicates heavier mud cake accumulation, the control system increases the water pressure and flow rate of the first water jet nozzle 13, the second water jet nozzle 14, and the third water jet nozzle 15, and appropriately extends the spraying time to enhance the flushing and breaking up of the mud cake; conversely, when the torque returns to normal, the corresponding parameters are reduced to save energy and water resources.

Claims

1. A method for detecting cutterhead wear in a tunnel boring machine, characterized in that, include: S1. Collect the set of measurement parameters of the shield machine cutterhead and calculate the time average value of each parameter in the set of measurement parameters to obtain the parameter average set. The set of measurement parameters includes: torque, cutterhead speed, propulsion thrust, propulsion speed, mud flow rate and circulation inlet and outlet temperature difference. S2. Determine the mechanical input power, volumetric excavation rate, and mud heat power of the current ring segment based on the parameter average set; S3. Within the continuous baseline window after the cutter is replaced, calculate the baseline mean, baseline standard deviation, and baseline average mechanical work quantity of the energy closure residual ratio based on the mechanical input power, volume excavation rate, and mud heat power of each ring segment. S4. Calculate the energy closure residual ratio of the current loop segment, and determine the work-weighted residual ratio based on the energy closure residual ratio of the current loop segment and the baseline average mechanical work-power quantity. S5. Generate wear results based on the work-weighted residual ratio, baseline mean, and baseline standard deviation; S6. Calculate the energy closure ratio of the current loop segment, verify the validity of the measurement based on the energy closure ratio of the current loop segment, and generate the final result.

2. The method for detecting cutterhead wear of a tunnel boring machine according to claim 1, characterized in that, Collect a set of measurement parameters for the tunnel boring machine cutterhead, and calculate the time average value of each parameter in the set to obtain a parameter average set, including: Within the tunneling time window of a single ring segment, a set of measurement parameters of the tunnel boring machine cutterhead is collected to obtain a time series set of measurement parameters, which includes: torque, cutterhead rotation speed, propulsion thrust, propulsion speed, mud flow rate and circulation inlet and outlet temperature difference; The time series of the measured parameters are averaged over time to obtain the parameter average set, which includes: average torque, average cutterhead speed, average propulsion thrust, average propulsion speed, average mud flow rate, and average mud inlet and outlet temperature difference.

3. The method for detecting cutterhead wear of a tunnel boring machine according to claim 2, characterized in that, The mechanical input power, volumetric excavation rate, and mud heat power of the current ring segment are determined based on the parameter average set, including: Multiply the average torque by the average cutter head speed to obtain the rotational power; Multiply the average thrust by the average thrust speed to obtain the thrust power; The mechanical input power is obtained by summing the rotational power and the propulsion power. Obtain the tunnel cross-sectional area, and calculate the volumetric excavation rate by multiplying the tunnel cross-sectional area by the average advance speed. Obtain the mud density and mud specific heat at constant pressure. Based on the product of the average mud flow rate, mud density, mud specific heat at constant pressure, and average mud inlet and outlet temperature difference, obtain the mud heat-carrying power.

4. The method for detecting cutterhead wear of a tunnel boring machine according to claim 1, characterized in that, Within the continuous baseline window after the cutter replacement, the baseline mean, baseline standard deviation, and baseline average mechanical work quantity are calculated based on the mechanical input power, volumetric excavation rate, and mud zone thermal power of each segment, including: Replace the cutterhead of the tunnel boring machine. After the cutterhead replacement is completed, select continuous ring segments to form a continuous baseline window. The mechanical input power is used as the observed value, and the sum of the products of the mud heat power, the equivalent rock mass fracturing energy density, and the volumetric excavation rate is used as the model value. The equivalent rock mass fracturing energy density is determined by minimizing the deviation. Within the continuous baseline window, the energy closure residual ratio of each segment within the continuous baseline window is calculated based on the thermal power of the mud belt, the volumetric excavation rate, the mechanical input power, and the equivalent fracturing energy density of the rock mass. The arithmetic mean and standard deviation of the energy closure residual ratio of each loop segment within the continuous baseline window are calculated to obtain the baseline mean and baseline standard deviation. Within a continuous baseline window, the mechanical work done by each segment is calculated based on the mechanical input power and the tunneling time of a single segment, and their arithmetic mean is obtained to obtain the baseline average mechanical work done.

5. The method for detecting cutterhead wear of a tunnel boring machine according to claim 4, characterized in that, Calculate the energy closure residual ratio of the current loop segment, and determine the work-weighted residual ratio based on the current loop segment's energy closure residual ratio and the baseline average mechanical work quantity, including: Calculate the energy closure residual ratio and mechanical work quantity of the current loop segment and the preset number of loop segments upstream in time; Starting from the current loop segment, the mechanical work done by each loop segment is accumulated sequentially upstream in time. The set of loop segments with the smallest accumulated mechanical work done that first reaches or exceeds the baseline average mechanical work done is selected as the weighted window. Within the weighted window, the energy closure residual ratio of each loop segment is weighted and averaged using the mechanical work done in each loop segment as the weight, to obtain the work done weighted residual ratio.

6. The method for detecting cutterhead wear of a tunnel boring machine according to claim 1, characterized in that, Wear results are generated based on the function-weighted residual ratio, baseline mean, and baseline standard deviation, including: The standardized quantity is obtained by subtracting the baseline mean from the weighted residual ratio of the function quantity and then dividing by the baseline standard deviation. The standardized quantity is compared with a preset standard threshold. If the standardized quantity is greater than the preset standard threshold, a wear result with significant wear is generated; otherwise, a wear result with insignificant wear is generated.

7. The method for detecting cutterhead wear of a tunnel boring machine according to claim 1, characterized in that, Calculate the energy closure ratio of the current loop segment, verify the validity of the measurement based on the energy closure ratio of the current loop segment, and generate the final results, including: The energy closure ratio of the current ring segment is calculated based on the mud zone thermal power, equivalent rock mass fracturing energy density, volumetric excavation rate, and mechanical input power. The formula for calculating the energy closure ratio is as follows: ； In the formula, For the first The energy closure ratio of each loop segment For the first Mechanical input power of each ring segment, For the first The heat capacity of the mud in each ring segment, The equivalent fracturing energy density of the rock mass. For the first The volumetric excavation rate of each ring segment; If the energy closure ratio of the current loop segment is greater than the preset energy closure threshold, a measurement anomaly is generated, and the final result requires metrological calibration; otherwise, the wear result is taken as the final result.

8. The method for detecting cutterhead wear of a tunnel boring machine according to claim 5, characterized in that, The formula for calculating the energy closure residual ratio is: ； In the formula, For the first The energy closure residual ratio of each loop segment For the first Mechanical input power of each ring segment, For the first The heat capacity of the mud in each ring segment, The equivalent fracturing energy density of the rock mass. For the first The volumetric excavation rate of each ring segment.

9. A method for controlling water jet cleaning of a tunnel boring machine cutterhead based on any one of claims 1-8, characterized in that, include: Based on the cutter head cleaning grid parameter set, the cutter head surface is cut into strips radially according to structural units and then subdivided according to tangential angles. The center point of the sub-region is corrected with the test frame positioning reference. Then, it is reordered and numbered according to the direction of the cutter head rotation area to obtain the cutter head cleaning grid parameter set. Based on the cutter head cleaning grid parameter set, the installation point and direction of the water jet nozzle are read, and the distance and direction between the center point of the sub-area and the pressure node of the test water supply pipeline are combined to form an energy value. Then, the effective coverage range is determined by a threshold to obtain the jet coverage energy distribution set. Based on the spray coverage energy distribution set, the Hungarian allocation algorithm is used to calculate the cleaning time according to the thickness of the sub-region adhesion layer and the pressure value. The time values ​​are formed into a matrix and multiple values ​​are compared in the same row to determine the nozzle correspondence. All matching items are combined into a unified sequence to obtain the nozzle cleaning assignment result set. Based on the nozzle cleaning assignment result set, the particle swarm optimization algorithm is used to add fine-tuning amount to the nozzle installation point, and then the distance is compared with the center point of the cutter head structure unit. The reachability is judged by the angle between the nozzle direction and the normal vector of the cleaning area. The reachable units are recorded to obtain the nozzle arrangement adjustment parameter set. Based on the nozzle arrangement adjustment parameter set, the nozzle start-up time is read according to the test, the assigned sub-region time is arranged into the time axis and aligned with the cutter head rotation area reference, and then integrated into a continuous time period and uniformly sorted to obtain the nozzle start-up and stop sequence table.

10. A tunnel boring machine, characterized in that, Includes a control system for performing the method according to any one of claims 1-9.

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