TBM cutterhead blockage state determination method and device, equipment and medium

Abstract

The application discloses a method and device for judging the blockage state of a cutter head of a TBM, equipment and a medium, and the method comprises the following steps: collecting time sequence data of the cutter head through a sensor, and pre-processing the time sequence data to obtain torque, thrust, speed and vibration signals corresponding to the cutter head; determining vibration characteristics corresponding to the cutter head according to the vibration signals; differentiating the torque to obtain a blockage torque corresponding to the cutter head; determining a blockage factor corresponding to the cutter head according to the thrust, the speed, the vibration characteristics and the blockage torque, and judging the blockage state of the cutter head. The embodiment of the application can improve the sensitivity of the judgment of the blockage state of the cutter head.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of tunnel boring machine (TBM) construction technology, and particularly to a method, device, equipment, and medium for determining the blockage status of the cutterhead of a TBM. Background Technology

[0002] In the field of tunnel engineering, due to the special construction environment of TBMs operating at steep inclines (i.e., inclines of 35 degrees or more), the geological conditions are complex and variable, the rock structure is unstable, and there are various unfavorable factors such as groundwater and faults. In soft rock and fractured strata, the surrounding rock has poor self-stability, is prone to deformation and compression of the shield and cutterhead, increasing frictional resistance. At the same time, in order to overcome the downward trend of the TBM equipment and maintain the tunneling posture in steep inclines, the TBM needs greater thrust and torque, which further increases the interaction force between the TBM cutterhead and the rock mass, as well as between the excavated soil and the cutterhead. When the TBM encounters soft rock (such as mudstone and shale) and groundwater, the soft rock is easily softened by groundwater, causing the TBM cutterhead to become clogged. Furthermore, fault fracture zones may bring the risk of surrounding rock collapse and a large amount of debris flowing into the cutterhead, further aggravating the clog. In other words, gravity causes the slag to accumulate in front of the cutterhead; soft rock forms a sticky slurry when it comes into contact with water, which easily adheres to the cutterhead structure; the high-pressure and high-friction environment causes these deposits to be compacted and hardened. Ultimately, the slag discharge channels such as the cutterhead bucket and chute become blocked, leading to a surge in torque, poor slag discharge, and even equipment shutdown.

[0003] Traditional methods for monitoring TBM cutterhead blockage rely primarily on manual observation and limited single-parameter threshold alarms. However, manual observation is greatly affected by the working environment, visibility, and personnel experience, and cannot achieve continuous, quantitative monitoring. If the monitoring method is based on the cutterhead's vibration characteristics, these characteristics are simultaneously influenced by the hard rock strata encountered during tunneling and the vibrations caused by the blockage. Therefore, directly using vibration characteristics as monitoring data often results in the system issuing an alarm only when blockage has already formed and significantly impacted tunneling efficiency and safety, missing the optimal window for preventative intervention. This leads to inaccurate and delayed assessments of the cutterhead's blockage status. Summary of the Invention

[0004] This application provides a method, apparatus, device, and medium for determining the blockage status of a TBM cutterhead, which can improve the sensitivity of determining the blockage status of the cutterhead.

[0005] In a first aspect, embodiments of this application provide a method for determining the blockage state of a cutterhead, applied to a blockage state identification system. The blockage state identification system includes a TBM (Tunnel Boring Machine), and sensors are installed on the cutterhead of the TBM, including: The timing data of the cutter head is collected by the sensor, and the timing data is preprocessed to obtain the torque, thrust, speed and vibration signals corresponding to the cutter head. The vibration characteristics of the cutter head are determined based on the vibration signal. Differentiating the torque yields the clogging torque corresponding to the cutter head; The blockage factor corresponding to the cutter head is determined based on the thrust, the speed, the vibration characteristics, and the blockage torque, and the blockage state of the cutter head is judged.

[0006] Secondly, embodiments of this application provide a device for determining the blockage status of a TBM cutterhead, applied to a blockage status identification system. The blockage status identification system includes a TBM tunneling machine, and sensors are installed on the cutterhead of the TBM, including: The acquisition module is used to acquire timing data of the cutter head through the sensor, and preprocess the timing data to obtain the torque, thrust, speed and vibration signals corresponding to the cutter head; A first calculation module is used to determine the vibration characteristics corresponding to the cutter head based on the vibration signal. The second calculation module is used to differentiate the torque to obtain the blocking torque corresponding to the cutter head; The judgment module is used to determine the blockage factor corresponding to the cutter head based on the thrust, the speed, the vibration characteristics and the blockage torque, and to determine the blockage state of the cutter head.

[0007] Thirdly, an electronic device provided according to an embodiment of this application includes: At least one processor; At least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, any of the first aspects are implemented.

[0008] Thirdly, according to the embodiments of the application, a computer-readable storage medium is provided, storing computer-executable instructions, which are used to perform any of the first aspects.

[0009] In summary, the above embodiments of this application include: acquiring timing data of the cutter head through sensors, and preprocessing the timing data to obtain the torque, thrust, speed and vibration signals corresponding to the cutter head; determining the vibration characteristics corresponding to the cutter head based on the vibration signals; differentiating the torque to obtain the clogging torque corresponding to the cutter head; determining the clogging factor corresponding to the cutter head based on the thrust, speed, vibration characteristics and clogging torque, and judging the clogging state of the cutter head. This application first determines the vibration characteristics of the cutterhead based on the vibration signal. These vibration characteristics can reflect the vibration caused by cutterhead blockage in the frequency domain. The torque is differentiated to obtain the blockage torque of the cutterhead. This blockage torque directly reflects whether the cutterhead is blocked, eliminating the interference of hard rock layers on the judgment of cutterhead blockage status, thus providing an effective real-time characteristic indicator for identifying cutterhead blockage. Then, the blockage factor of the cutterhead is determined based on thrust, velocity, vibration characteristics, and blockage torque to judge the blockage status of the cutterhead. Considering thrust and velocity, the blockage torque is introduced to eliminate the interference of hard rock layers on the judgment of cutterhead blockage status. The blockage factor is calculated in the frequency domain in conjunction with the vibration characteristics, ensuring that the blockage factor accurately reflects the actual cutterhead blockage status, thereby improving the sensitivity of cutterhead blockage judgment. Attached Figure Description

[0010] Figure 1 This is a flowchart of the steps of a method for determining the blockage state of a TBM cutterhead according to an embodiment of this application; Figure 2 This is a flowchart of a method for determining the blockage state of a TBM cutterhead according to an embodiment of this application; Figure 3 This application provides a schematic diagram of the blockage factor distribution curve of a TBM in a 680m tunneling section, according to one embodiment. Figure 4 This application provides a schematic diagram of the blockage factor distribution curve of a TBM in a 550m tunneling section, according to one embodiment. Figure 5 This is a hardware schematic diagram of an electronic device provided in one embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0012] It is understandable that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0013] In the field of tunnel engineering, due to the special construction environment of TBMs operating at steep inclines (i.e., inclines of 35 degrees or more), the geological conditions are complex and variable, the rock structure is unstable, and there are various unfavorable factors such as groundwater and faults. In soft rock and fractured strata, the surrounding rock has poor self-stability, is prone to deformation and compression of the shield and cutterhead, increasing frictional resistance. At the same time, in order to overcome the downward trend of the TBM equipment and maintain the tunneling posture in steep inclines, the TBM needs greater thrust and torque, which further increases the interaction force between the TBM cutterhead and the rock mass, as well as between the excavated soil and the cutterhead. When the TBM encounters soft rock (such as mudstone and shale) and groundwater, the soft rock is easily softened by groundwater, causing the TBM cutterhead to become clogged. Furthermore, fault fracture zones may bring the risk of surrounding rock collapse and a large amount of debris flowing into the cutterhead, further aggravating the clog. In other words, gravity causes the slag to accumulate in front of the cutterhead; soft rock forms a sticky slurry when it comes into contact with water, which easily adheres to the cutterhead structure; the high-pressure and high-friction environment causes these deposits to be compacted and hardened. Ultimately, the slag discharge channels such as the cutterhead bucket and chute become blocked, leading to a surge in torque, poor slag discharge, and even equipment shutdown.

[0014] Traditional methods for monitoring TBM cutterhead blockage rely primarily on manual observation and limited single-parameter threshold alarms. However, manual observation is greatly affected by the working environment, visibility, and personnel experience, and cannot achieve continuous, quantitative monitoring. If the monitoring method is based on the cutterhead's vibration characteristics, these characteristics are simultaneously influenced by the hard rock strata encountered during tunneling and the vibrations caused by the blockage. Therefore, directly using vibration characteristics as monitoring data often results in the system issuing an alarm only when blockage has already formed and significantly impacted tunneling efficiency and safety, missing the optimal window for preventative intervention. This leads to inaccurate and delayed assessments of the cutterhead's blockage status.

[0015] Based on this, embodiments of this application provide a method, apparatus, device, and medium for determining the blockage state of a TBM cutterhead, which can improve the sensitivity of determining the blockage state of the cutterhead.

[0016] For example, in this embodiment, the vibration characteristics of the cutterhead are first determined based on the vibration signal. These vibration characteristics can reflect the vibration caused by cutterhead blockage in the frequency domain. The torque is differentiated to obtain the blockage torque of the cutterhead. The blockage torque can directly reflect whether the cutterhead is blocked, eliminating the interference of hard rock layers on the judgment of the cutterhead blockage state, thus providing an effective real-time characteristic index for identifying cutterhead blockage. Then, the blockage factor of the cutterhead is determined based on the thrust, velocity, vibration characteristics, and blockage torque to judge the blockage state of the cutterhead. The thrust and velocity are considered, and the blockage torque is introduced to eliminate the interference of hard rock layers on the judgment of the cutterhead blockage state. The blockage factor is calculated in combination with the vibration characteristics in the frequency domain, so that the blockage factor can accurately reflect the actual cutterhead blockage state, thereby improving the sensitivity of the cutterhead blockage state judgment.

[0017] This application provides a method for determining the blockage state of a TBM cutterhead, applied to a blockage state identification system. The blockage state identification system includes a TBM tunneling machine, and sensors are installed on the cutterhead of the TBM. Figure 1 and Figure 2 As shown, including but not limited to the following steps: In step S100, the timing data of the cutter head is collected by the sensor, and the timing data is preprocessed to obtain the torque, thrust, speed and vibration signals corresponding to the cutter head.

[0018] In some embodiments, preprocessing the time-series data to obtain the torque, thrust, speed, and vibration signals corresponding to the cutterhead includes: cleaning the initial torque, initial thrust, and initial speed of the time-series data to obtain intermediate torque, intermediate thrust, and intermediate speed; sequentially performing power frequency notch filtering, bandpass filtering, and noise reduction on the initial vibration signal of the time-series data to obtain intermediate vibration signals; and performing abrupt change screening on the intermediate torque, intermediate thrust, intermediate speed, and intermediate vibration signals based on statistical principles to obtain torque, thrust, speed, and vibration signals.

[0019] It is understandable that the time-series data collected by the sensors is subject to unavoidable disturbances such as sensor transient failures or communication interference, resulting in outliers or missing values. This embodiment cleans the initial torque, initial thrust, and initial velocity of the time-series data to remove outliers or missing values ​​caused by unavoidable disturbances such as sensor transient failures or communication interference, thus obtaining intermediate torque, intermediate thrust, and intermediate velocity. The initial vibration signal is first subjected to a 50Hz power frequency notch filter, then a 5-20Hz bandpass filter to retain the low-frequency characteristic band strongly correlated with cutterhead blockage. Finally, wavelet thresholding is used for noise reduction to obtain the intermediate vibration signal. Finally, based on statistical principles, abrupt changes are filtered out from the intermediate torque, intermediate thrust, intermediate velocity, and intermediate vibration signal. That is, if the i-th data point is greater than the sum of the (i-1)-th and (i+1)-th data points, the i-th data point is considered to be abrupt and is removed, thus obtaining the torque, thrust, velocity, and vibration signals. This improves the data quality of the torque, thrust, velocity, and vibration signals.

[0020] Specifically, the abrupt changes in torque, thrust, velocity, and vibration signals satisfy the following expression: ; in, For the i-th data, For the (i-1)th data, This is the (i+1)th data point.

[0021] Step S110: Determine the vibration characteristics of the cutter head based on the vibration signal.

[0022] It is understood that vibrations generated by the same mechanical parts or fault types have specific characteristic frequencies. In this application embodiment, frequency decomposition is performed on the collected vibration signals to separate vibrations caused by different reasons, and the vibration signal with the highest correlation to cutter head blockage is selected as the vibration signal of cutter head blockage in the frequency domain.

[0023] In some embodiments, determining the vibration characteristics of the cutterhead based on the vibration signal includes: performing empirical mode decomposition on the vibration signal to obtain modal components; and calculating the vibration characteristics based on the modal components.

[0024] In some embodiments, the modal components satisfy the following expression: ; in, For modal components, Let represent the change of the i-th component with time t.

[0025] Understandably, the vibration signal is decomposed using Empirical Mode Decomposition (EMD) to obtain multiple Intrinsic Mode Functions (IMFs), which are modal components. Considering the probability that the vibration caused by the tool head blockage is usually low-frequency, the multiple modal components are sorted in descending order, and the first three modal components are selected to reflect the low-frequency vibration characteristics of the tool head. Then, the three modal components are added together and divided by the mean of all modal components to obtain the vibration characteristics.

[0026] Specifically, the vibration characteristics satisfy the following expression: ; in, Vibration characteristics, All are modal components. This is the mean.

[0027] Step S120: Differentiate the torque to obtain the blocking torque corresponding to the cutter head.

[0028] It is understandable that when a TBM (Tunnel Boring Machine) is tunneling through coal mine strata, the total torque of its cutterhead can be decomposed into the following three main parts: the cutting torque generated by the cutterhead cutting through the rock strata, the torque generated by the interaction between the cutterhead and the internal rock debris, and the inherent internal friction torque of the cutterhead's mechanical transmission system. The torque satisfies the following expression: ; in, For torque, The cutting torque generated by the cutterhead cutting through the rock strata. This refers to the torque generated by the interaction between the cutterhead and the rock cuttings. This refers to the internal friction torque of the cutter head's own mechanical transmission system.

[0029] Understandably, while existing technologies utilize torque to determine cutterhead blockage, they typically use torque directly as an indicator. However, directly using torque ignores the dynamic changes in torque caused by uneven cutterhead mass due to rock debris falling onto the cutterhead during rock breaking. Therefore, torque cannot accurately reflect the blockage status. During tunneling, if observed on a second-level timescale, the geological conditions can be considered relatively stable. In this case, differentiating the cutterhead torque reveals negligible contributions from the cutting torque directly related to lithology and the inherent internal friction torque (i.e., cutterhead idling), which change minimally in a short time. This means the blockage torque obtained by differentiating the torque primarily reflects the dynamic changes in the movement of rock debris within the cutterhead, directly indicating whether the cutterhead is blocked. Compared to existing technologies that directly use vibration signals for monitoring, this approach can distinguish between cutterhead blockage and encountering hard rock layers, providing an effective real-time characteristic indicator for identifying cutterhead blockage.

[0030] Step S130: Determine the blockage factor corresponding to the cutter head based on the thrust, speed, vibration characteristics and blockage torque, and judge the blockage state of the cutter head.

[0031] In some embodiments, determining the blockage factor corresponding to the cutterhead based on thrust, velocity, vibration characteristics, and blockage torque includes: obtaining weighting factors, wherein the weighting factors include a first factor and a second factor; calculating the propulsion power based on thrust and velocity, and calculating the rotational resistance based on the blockage torque; weighting based on the first factor and vibration characteristics, and weighting based on the second factor and rotational resistance to obtain the blockage factor.

[0032] In some embodiments, the congestion factor satisfies the following expression: ; in, As a blocking factor, As the first factor, To block torque, Let v be the thrust and v be the velocity. As the second factor, This is a vibration characteristic.

[0033] Understandably, when the cutterhead becomes clogged, the cutterhead torque typically increases significantly while maintaining a constant set rotational speed. Because the blockage increases rotational resistance, the drive system must output greater torque to maintain the set speed, resulting in an actual torque value significantly higher than under normal operating conditions. Simultaneously, the uneven load distribution caused by the blockage further exacerbates torque fluctuations, specifically manifested as an increase in the blocked torque, which directly reflects the dynamic fluctuation intensity of the rotating load. Furthermore, the vibration signal during TBM tunneling changes accordingly with geological conditions and operating status. When the cutterhead becomes clogged, the non-uniform load alters the characteristics of the vibration signal, specifically manifested as changes in the signal frequency components (e.g., a relative increase in low-frequency energy) and periodic fluctuations in the time-domain amplitude. The vibration amplitude is also affected by rock strength; generally, the higher the rock strength, the more significant the vibration. This application's embodiment introduces the derivative of torque as the clogging torque, thereby eliminating the interference of hard rock layers on the judgment of cutterhead clogging status. It integrates frequency domain vibration characteristics and calculates the clogging factor by combining a first factor and a second factor, thus improving the sensitivity of cutterhead clogging status judgment and the robust quantitative assessment of common geological changes. This provides a core basis for subsequent clogging level identification and early warning. Under different strata (e.g., soft rock, hard rock) or different operating parameters, the absolute magnitudes of torque and clogging torque differ significantly, and directly comparing the numerical values ​​of clogging torque has no practical physical meaning. This application's embodiment divides the clogging torque by the current instantaneous propulsion power (i.e., the product of thrust and velocity) to achieve dimensionless processing, thereby obtaining the torque fluctuation intensity per unit propulsion power. This eliminates the influence of the overall propulsion power level, making it possible to evaluate the dynamic resistance changes of the cutterhead under different strata and thrust conditions, providing a unified benchmark for lateral comparisons between different operating conditions.

[0034] Specifically, blockage factor data from different tunneling sections were selected for analysis and judgment, referring to... Figure 3 As shown, the blockage factor in this section is mostly below 5, with only a few peak values ​​slightly above 5. This is highly consistent with the actual field measurement data, corresponding to the working conditions of increased tunneling speed and a surge in muck output. It also aligns with the phenomenon of localized blockage risk caused by brief load fluctuations of the cutterhead during actual construction. This indicates that the blockage factor calculation model can effectively reflect the instantaneous state changes of the cutterhead under high-capacity operation. (Refer to...) Figure 4As shown, the blockage factor exhibits a regular increasing trend with increasing tunneling distance: initially in the normal tunneling range (0-5), it gradually enters the mild blockage range (5-10) as geological conditions change; during continuous tunneling, due to the accumulation of excavated soil, the blockage factor further climbs to the moderate blockage range (10-20), eventually exceeding the severe blockage threshold (≥20), triggering the system's emergency shutdown mechanism. This evolution process perfectly matches the data from the abnormal increase in cutterhead torque and the sudden drop in rotational speed monitored on-site, indicating that the blockage factor calculation model proposed in this invention can accurately capture the entire process of cutterhead blockage from its initial initiation to its gradual deterioration, providing a reliable basis for predicting construction risks.

[0035] It is understood that, in this embodiment, a preset model is introduced to train and determine the first and second factors from a large number of historical samples (including normal and various blockage conditions). During training, the system can automatically learn how to scale and balance the contributions of blockage torque and vibration characteristics, ensuring that blockage torque and vibration characteristics contribute to the judgment of blockage status on a relatively uniform basis, thereby improving the accuracy of the blockage factor. The preset model in this embodiment can be a neural network model such as a random forest; the specific architecture of the preset model is not limited and can be selected according to actual needs.

[0036] In some embodiments, the first factor is determined by the following steps: obtaining a training set and a validation set; determining the number of decision trees and the number of node independent variables in the training set through cross-validation; constructing a random forest model using the number of decision trees and the number of node independent variables, and validating the random forest model based on the validation set, thereby obtaining the first factor.

[0037] For example, a strategy combining grid search and K-fold cross-validation is used to determine the number of decision trees (i.e., the total number of decision trees) and the number of node independent variables (i.e., the maximum depth of each tree) in the random forest. In each round of cross-validation evaluation, the negative mean squared error (MSE) is used as a performance metric. Maximizing the performance metric effectively minimizes the MSE, thereby selecting the optimal hyperparameter configuration, i.e., the optimal number of decision trees and node independent variables. After determining the optimal number of decision trees and node independent variables, the model is fitted on the entire training set using these optimal numbers to obtain the random forest model. Subsequently, the random forest model is applied to the validation set to predict the independent variables, obtaining the preset weight factors. This embodiment of the application can utilize random forests for machine learning, thereby improving the reliability of the first factor.

[0038] It is understood that the specific implementation of the second factor is basically the same as the implementation of the first factor described above, and will not be elaborated here.

[0039] For example, the method for determining the blockage status of the TBM cutterhead may include, but is not limited to, the following steps: The first step is to install sensors on the TBM to collect real-time timing data of the TBM cutterhead, including initial torque, initial thrust, initial speed, and initial vibration signals. The second step is to clean the initial torque, initial thrust, and initial velocity in the time series data, removing outliers and missing values ​​caused by sensor transient failures or communication interference. For the initial vibration signal, a 50Hz power frequency notch filter is first applied, followed by a 5-20Hz bandpass filter to retain the low-frequency characteristic bands that are strongly correlated with cutterhead blockage. Finally, wavelet thresholding is used for noise reduction. To unify the data quality, abrupt value screening based on statistical principles is applied to all the collected data to obtain torque, thrust, velocity, and vibration signals, thereby ensuring the reliability of the input data. The third step is to perform EMD decomposition on the preprocessed vibration signal to extract energy features and obtain 6-8 intrinsic mode functions (IMFs). The first 3 modal components are selected to reflect the low-frequency vibration characteristics of the cutterhead. The fourth step involves constructing a multi-parameter integrated response to cutterhead blockage based on thrust, speed, blockage torque, and vibration characteristics. In this embodiment, the blockage factor integrates the dynamic fluctuations of torque and the frequency domain characteristics of vibration signals, and optimizes the weights using a neural network. This achieves a quantitative assessment that is sensitive to cutterhead blockage and robust to ordinary geological changes, providing a core basis for subsequent blockage level identification and early warning. The fifth step is to construct a mapping table 1 between the blockage factor and the cutterhead status based on the on-site engineering data. Referring to Table 1, the blockage level of the cutterhead is quantitatively determined based on Table 1, and the corresponding tunneling parameter adjustment strategy is linked and matched.

[0040] Table 1

[0041] The embodiments of this application have the following beneficial effects: (I) It abandons the traditional mode of relying on manual experience or single parameter thresholds. By deeply integrating multi-source heterogeneous data such as cutterhead torque, thrust, propulsion speed and vibration signals, a blocking factor (CF) that comprehensively reflects mechanical dynamics and vibration characteristics is constructed. This can effectively eliminate normal interference caused by changes in geological conditions and adjustments in operating parameters, significantly improve the specificity of state recognition, realize the transformation from abnormal alarm to early warning, and provide a key time window for preventive intervention; (II) The proposed blocking factor calculation model performs dimensionless processing of torque change rate and instantaneous propulsion power, and integrates characteristic parameters characterizing low-frequency vibration energy, thereby constructing a robust index that is comparable to different working conditions, and combining it with neural networks. Network training adaptively determines the weights of each feature, enabling the model to learn the optimal decision boundary autonomously from historical data, significantly improving the accuracy of judging different types and degrees of blockage and reducing false alarms and false negatives; (iii) By using blockage factors combined with multi-level threshold quantitative discrimination criteria and directly linking them to hierarchical control strategies, the blockage level can be automatically judged based on the real-time calculated CF value, and corresponding adjustment measures (such as adjusting propulsion parameters, injecting improvers, controlling cutterhead movements, etc.) can be recommended or triggered, thereby directly feeding the state perception results back to construction control, forming an intelligent decision support and execution closed loop, which greatly improves the automation level of TBM construction and the ability to cope with sudden working conditions; (iv) No additional expensive or complex sensing systems are required, resulting in low implementation costs.

[0042] This application provides a device for determining the blockage status of a TBM cutterhead, applied to a blockage status identification system. The blockage status identification system includes a TBM tunneling machine, and sensors are installed on the cutterhead of the TBM, including: The acquisition module is used to acquire timing data of the cutter head through sensors, and to preprocess the timing data to obtain the torque, thrust, speed and vibration signals of the cutter head. The first calculation module is used to determine the vibration characteristics of the cutter head based on the vibration signal. The second calculation module is used to differentiate the torque to obtain the blocking torque corresponding to the cutter head. The judgment module is used to determine the blockage factor corresponding to the cutter head based on the thrust, speed, vibration characteristics and blockage torque, and to judge the blockage state of the cutter head.

[0043] It is understood that the specific implementation of the TBM cutterhead blockage state determination device is basically the same as the embodiment of the TBM cutterhead blockage state determination method described above, and will not be repeated here.

[0044] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method for determining the blockage status of the TBM cutterhead. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0045] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 502 can be implemented in the form of read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 502 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 502, and the processor 501 calls and executes the TBM tool head blockage state determination method of the embodiments of this application. The input / output interface 503 is used to implement information input and output; The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504); The processor 501, memory 502, input / output interface 503, and communication interface 504 are connected to each other within the device via bus 505.

[0046] In some embodiments, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for determining the blockage state of the TBM's cutterhead.

[0047] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0048] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0049] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0050] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0051] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0052] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0053] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0054] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0055] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0056] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0057] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for determining the blockage state of the cutterhead in a TBM, characterized in that, An application is made in a blockage status identification system, the system comprising a TBM (tunnel boring machine), wherein sensors are mounted on the cutterhead of the TBM, including: The timing data of the cutter head is collected by the sensor, and the timing data is preprocessed to obtain the torque, thrust, speed and vibration signals corresponding to the cutter head. The vibration characteristics of the cutter head are determined based on the vibration signal. Differentiating the torque yields the clogging torque corresponding to the cutter head; The blockage factor corresponding to the cutter head is determined based on the thrust, the speed, the vibration characteristics, and the blockage torque, and the blockage state of the cutter head is judged.

2. The method for determining the blockage state of the cutterhead in a TBM according to claim 1, characterized in that, Determining the vibration characteristics of the cutter head based on the vibration signal includes: Empirical mode decomposition is performed on the vibration signal to obtain modal components; The vibration characteristics are calculated based on the modal components.

3. The method for determining the blockage state of the cutterhead in a TBM according to claim 2, characterized in that, The modal components satisfy the following expression: ； in, For the modal components, Let represent the change of the i-th component with time t.

4. The method for determining the blockage state of the cutterhead in a TBM according to claim 1, characterized in that, The step of determining the blockage factor corresponding to the cutter head based on the thrust, the velocity, the vibration characteristics, and the blockage torque includes: Obtain weighting factors, wherein the weighting factors include a first factor and a second factor; The propulsion power is calculated based on the thrust and the speed, and the rotational resistance is calculated in conjunction with the blocking torque; The blockage factor is obtained by weighting the first factor and the vibration characteristics, and then by combining the second factor and the rotational resistance.

5. The method for determining the blockage state of the cutterhead in a TBM according to claim 4, characterized in that, The blocking factor satisfies the following expression: ； in, The blocking factor, For the first factor, The blocking torque, Let v be the thrust and v be the velocity. The second factor, The vibration characteristic is described above.

6. The method for determining the blockage state of the cutterhead in a TBM according to claim 4, characterized in that, The first factor is determined through the following steps: Obtain the training set and validation set; The number of decision trees and the number of node independent variables are determined through cross-validation in the training set. A random forest model is constructed using the number of decision trees and the number of node independent variables, and the random forest model is validated based on the validation set to obtain the first factor.

7. The method for determining the blockage state of the cutterhead in a TBM according to claim 1, characterized in that, The preprocessing of the time-series data to obtain the torque, thrust, speed, and vibration signals corresponding to the cutterhead includes: The initial torque, initial thrust, and initial velocity of the time series data are cleaned to obtain intermediate torque, intermediate thrust, and intermediate velocity. The initial vibration signal of the time series data is sequentially subjected to power frequency notch filtering, bandpass filtering and noise reduction processing to obtain the intermediate vibration signal; The intermediate torque, intermediate thrust, intermediate velocity, and intermediate vibration signal are subjected to abrupt change screening based on statistical principles to obtain the torque, thrust, velocity, and vibration signal.

8. A device for determining the blockage status of the cutter head in a TBM, characterized in that, An application is made in a blockage status identification system, the system comprising a TBM (tunnel boring machine), wherein sensors are mounted on the cutterhead of the TBM, including: The acquisition module is used to acquire timing data of the cutter head through the sensor, and preprocess the timing data to obtain the torque, thrust, speed and vibration signals corresponding to the cutter head; A first calculation module is used to determine the vibration characteristics corresponding to the cutter head based on the vibration signal. The second calculation module is used to differentiate the torque to obtain the blocking torque corresponding to the cutter head; The judgment module is used to determine the blockage factor corresponding to the cutter head based on the thrust, the speed, the vibration characteristics and the blockage torque, and to determine the blockage state of the cutter head.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the method for determining the blockage state of the cutterhead of the TBM as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the method for determining the blockage status of the cutterhead of any one of the TBMs described in claims 1 to 7.

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