A TBM tunneling machine tunneling state monitoring method, system, device and medium

Abstract

The application discloses a TBM tunneling machine tunneling state monitoring method, system, device and medium, relates to the TBM tunnel geological identification and cutter maintenance technical field, and the application sets up multiple different types of accelerometers on the cutter of the TBM tunneling machine to obtain vibration data of the TBM tunneling machine, respectively establishes independent response models for the vibration data, TBM tunneling machine operation data, cutter data, geological data and construction data, to obtain the nonlinear relationship between the vibration data and each data parameter, and simultaneously establishes a comprehensive response model according to the nonlinear relationship, to establish the mutual feedback mechanism between the vibration data and the influence factors, to calculate the specific numerical range of each influence factor according to the vibration data, so as to judge the tunneling state of the TBM tunneling machine; the multiple accelerometers designed in the application are less affected by the metal cutter, and can judge the tunneling state of the TBM tunneling machine according to the mutual feedback mechanism between the obtained vibration data and the related data.

Description

Technical Field

[0001] This invention relates to the field of TBM tunnel geological identification and cutterhead maintenance technology, and particularly to a method, system, equipment and medium for monitoring the tunneling status of a TBM tunneling machine. Background Technology

[0002] During TBM tunnel construction, especially for long and deep tunnels, it is often difficult to fully identify adverse geological conditions along the route in the early stages of construction, such as weak fracture zones, jointed zones, faults, and karst fissures. These factors can easily lead to serious accidents such as collapses, water inrushes, and mudslides during construction, posing a great threat to construction safety and potentially causing casualties. On the other hand, it is difficult to monitor the cutterhead of the TBM in real time during operation, often resulting in situations where the cutterhead is damaged but the TBM continues to excavate. This greatly reduces the efficiency of TBM excavation and causes serious damage to the TBM cutterhead.

[0003] Furthermore, the rapid tunneling speed of TBMs requires that the identification of geological bodies be fast, real-time, and highly accurate to adapt to the rapid changes in geological conditions ahead of the tunnel face. Currently, during the tunneling process of TBMs, the metal cutterhead of the TBM causes serious interference to electromagnetic detection. At the same time, the TBM occupies a large space in the tunnel face area, almost filling the entire tunnel cross-section. This increases the difficulty of collecting geological data for electromagnetic detection. Moreover, the current judgment of the TBM status is usually based on individual data, making it difficult to judge through the relationship between TBM machine parameters and surrounding rock parameters, thus making it difficult to accurately obtain the real-time status information of the TBM. Summary of the Invention

[0004] This invention provides a method, system, device, and medium for monitoring the tunneling status of a TBM (Tunnel Boring Machine), which can solve the problem in the prior art that it is difficult to judge the real-time status information of the TBM by the relationship between the TBM machine parameters and the surrounding rock parameters.

[0005] This invention provides a method for monitoring the tunneling status of a TBM (Tunnel Boring Machine), comprising the following steps:

[0006] Before the TBM starts operating, acquire the cutterhead data of the TBM; acquire the geological data and construction data of the geological body to be mined by the TBM; acquire the vibration data of the TBM in the x, y and z dimensions during operation; acquire the operating data of the TBM during operation.

[0007] Determine the nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data;

[0008] Determine the integrated response model based on vibration data, operational data, cutterhead data, geological data, and construction data;

[0009] Based on various nonlinear response relationships and a comprehensive response model, the influencing factors of vibration data are obtained; when the real-time vibration data changes, the changing state of the influencing factors is acquired; when the real-time influencing factors change, the changing state of the real-time vibration data is acquired; and based on the mutual changing state between vibration data and influencing factors, a feedback mechanism between vibration data and influencing factors is formed; wherein, the influencing factors include operational data, cutterhead data, geological data, and construction data;

[0010] Based on the feedback mechanism between vibration data and influencing factors, the numerical range of each influencing factor is obtained through real-time vibration data to determine the tunneling status of the TBM.

[0011] Preferably, acquiring vibration data of the TBM tunneling machine in the x, y, and z dimensions during operation includes:

[0012] Two MEMS accelerometers and two piezoelectric accelerometers are arranged at equal intervals on the rear side of the TBM cutterhead. After the TBM starts running, the MEMS accelerometers and piezoelectric accelerometers collect vibration data at different positions of the cutterhead.

[0013] The vibration data includes: vibration signals in the x, y and z directions during the operation of the TBM tunneling machine, the time history of acceleration values ​​over time, loop variance, maximum acceleration of each loop, root mean square acceleration of each loop, crest factor and skewness.

[0014] Preferably, the operating data, cutterhead data, geological data, and construction data of the TBM tunneling machine are as follows:

[0015] The TBM tunneling machine operating data includes: cutterhead thrust, cutterhead rotation speed, cutterhead torque, penetration depth, tunneling speed, effective support force of the support shoe, and total thrust of the TBM tunneling machine;

[0016] The TBM cutterhead data includes: cutterhead replacement time, loss status, damage type, and damage location;

[0017] The geological data of the TBM tunneling machine includes: uniaxial compressive strength of rock, tensile strength of rock, abrasion resistance of rock, brittleness of rock, rock mass integrity coefficient, groundwater status and surrounding rock engineering geological conditions;

[0018] The TBM tunneling machine construction data includes: construction progress, slag removal data, advanced forecast information, ambient temperature and humidity.

[0019] Preferably, determining the nonlinear response relationship between vibration data and operational data, the nonlinear response relationship between vibration data and cutterhead data, the nonlinear response relationship between vibration data and geological data, and the nonlinear response relationship between vibration data and construction data includes:

[0020] A recurrent neural network (RNN) model was constructed and trained on the TBM tunneling machine's vibration and operation data, cutterhead data, geological data, and construction data respectively, to obtain response models for vibration and operation data, cutterhead data, geological data, and construction data respectively.

[0021] In the response models corresponding to vibration data and operation data, cutterhead data, geological data and construction data respectively, the Pearson correlation coefficient statistical method is used to calculate the correlation between each parameter variable of vibration data and each parameter variable of TBM tunneling machine operation data, cutterhead data, geological data and construction data.

[0022] Based on the correlation, nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data were established using a random forest machine learning model.

[0023] Preferably, the feedback mechanism between vibration data and influencing factors includes:

[0024] Based on the vibration and operation data of the TBM tunneling machine, cutterhead data, geological data, and construction data, a recurrent neural network (RNN) model is trained to obtain a comprehensive response model.

[0025] Based on the real-time operating data, cutterhead data, geological data, and construction data of the TBM tunneling machine during operation, as well as the nonlinear response relationship between the TBM tunneling machine vibration data and the operating data, cutterhead data, geological data, and construction data, the data whose thresholds are higher than the set thresholds in each of the control vibration data characteristic parameters are set as the influencing factors of the control vibration data characteristic parameters using the comprehensive response model.

[0026] The influencing factors are a subset of data from operational data, cutterhead data, geological data, and construction data.

[0027] The interaction process between vibration data characteristic parameters and influencing factors during the operation of the TBM tunneling machine is obtained, and this interaction process is set as the mutual feedback mechanism between vibration data characteristic parameters and influencing factors.

[0028] This invention also provides a TBM tunneling machine tunneling status monitoring system, comprising:

[0029] The vibration monitoring module is used to acquire cutterhead data of the TBM before the TBM starts operating; acquire geological and construction data of the geological body to be mined by the TBM; acquire vibration data of the TBM in the x, y, and z dimensions during operation; and acquire operating data of the TBM during operation.

[0030] The deep learning module is used to determine the nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data.

[0031] Determine the integrated response model based on vibration data, operational data, cutterhead data, geological data, and construction data;

[0032] Based on various nonlinear response relationships and a comprehensive response model, the influencing factors of vibration data are obtained; when the real-time vibration data changes, the changing state of the influencing factors is acquired; when the real-time influencing factors change, the changing state of the real-time vibration data is acquired; and based on the mutual changing state between vibration data and influencing factors, a feedback mechanism between vibration data and influencing factors is formed; wherein, the influencing factors include operational data, cutterhead data, geological data, and construction data;

[0033] The early warning module is used to determine the tunneling status of the TBM by obtaining the numerical range of each influencing factor through real-time vibration data based on the feedback mechanism between vibration data and influencing factors.

[0034] This invention also provides an electronic device, including a memory and a processor;

[0035] The memory is used to store computer programs;

[0036] When the processor executes the computer program stored in the memory, it implements the steps of the TBM tunneling status monitoring method described above.

[0037] This invention also provides a computer-readable storage medium, characterized in that it is used to store a computer program, which, when executed by a processor, implements the steps of a TBM tunneling status monitoring method as described above.

[0038] This invention provides a method, system, equipment, and medium for monitoring the tunneling status of a TBM (Tunnel Boring Machine). Compared with the prior art, its advantages are as follows:

[0039] This invention acquires vibration data of a TBM by installing multiple different types of accelerometers on the cutterhead of the TBM. Independent response models are then established for the vibration data, along with TBM operating data, cutterhead data, geological data, and construction data, to obtain the nonlinear relationships between the vibration data and each data parameter. Simultaneously, a comprehensive response model is established based on these nonlinear relationships. Within this comprehensive response model, the influencing factors controlling the characteristic parameters of the vibration data are identified based on the nonlinear relationships. A feedback mechanism between the vibration data and these influencing factors is established, allowing the specific numerical ranges of each influencing factor to be calculated from the vibration data, thereby determining the tunneling status of the TBM. Unlike traditional technologies that rely on acquiring a series of data parameters for judgment, this invention first establishes the nonlinear response relationship between the vibration data of the TBM during operation and various other data. Then, based on the corresponding nonlinear response relationship and real-time operating data, it identifies the influencing factors among the data that control the characteristic parameters of the vibration data under a comprehensive response model. This establishes a feedback mechanism between the vibration data and the influencing factors, allowing the specific numerical range of each influencing factor to be calculated solely from the vibration data, thereby determining the tunneling status. This contrasts with traditional technologies that require analyzing a series of parameters to make a judgment.

[0040] Furthermore, the multiple different types of accelerometers set up in this invention avoid the influence of static gravity, and at the same time cover normal frequencies and extremely low frequencies in terms of recording frequency, which can more comprehensively reflect the vibration of the TBM tunneling machine. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall process of a TBM tunneling status monitoring method provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the system principle of a TBM tunneling status monitoring method and system provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the system principle breakdown of a TBM tunneling status monitoring method and system provided in an embodiment of the present invention.

[0044] Figure 4 A schematic diagram of the cutterhead arrangement accelerometer of a TBM tunneling machine tunneling status monitoring method and system provided in this embodiment of the invention;

[0045] Figure 5 This is a schematic diagram of an electronic device for a TBM tunneling status monitoring method and system provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] See Figure 2 This invention provides a method and system for monitoring the tunneling status of a TBM (tunnel boring machine), including a vibration monitoring module 100, a data processing module 200, a database module 300, a deep learning module 400, and an early warning module 500.

[0048] The vibration monitoring module 100 is equipped with two different types of accelerometers to collect vibration signals during the operation of the TBM. The vibration monitoring module 100 controls the opening and closing of the two different types of accelerometers so that the accelerometers record static gravity before the TBM is running, so as to eliminate the influence of static gravity in the vibration signal during the operation of the TBM and ensure the accuracy and reliability of the recorded vibration signal.

[0049] like Figure 3 As shown, the vibration monitoring module 100 includes a MEMS accelerometer module 101 and a piezoelectric accelerometer module 102. The MEMS accelerometer module 101 is used to measure static gravity and record the extremely low frequency vibration signal of the TBM. The piezoelectric accelerometer module 102 cannot record extremely low frequency signals, but has higher sensitivity and measurement range. Each module in the MEMS accelerometer module 101 and the piezoelectric accelerometer module 102 is equipped with two accelerometers so that when one accelerometer fails, the other accelerometer can work normally, and the data recorded by the two accelerometers can be mutually verified and supplemented.

[0050] Specifically, such as Figure 4 As shown, the two sets of accelerometers in the vibration monitoring module 100 are located behind the TBM cutterhead and distributed around it to monitor the TBM vibration in real time. There are a total of 4 accelerometer sensors distributed behind the TBM cutterhead, of which MEMS accelerometers are located at A and B, and piezoelectric accelerometers are located at C and D.

[0051] The data processing module 200 is used to digitize and record signals from the analog accelerometer. The data processing module 200 can convert the vibration signals collected by the two accelerometer modules, and record and analyze the converted vibration signals through Simulink software. The software has signal filtering and compression functions, and initially outputs the vibration data of the TBM.

[0052] The data processing module 200 includes an analog-to-digital converter module 201 and a data recording module 202. The data processing module 200 can be configured with the analog-to-digital converter module 201 and the data recording module 202. The analog-to-digital converter module 201 is used to convert the vibration signal measured by the vibration monitoring module 100 into an analog-to-digital signal, and then input the digitized signal into the data recording module 202. The data recording module 202 records and analyzes the vibration data through Simulink software. The vibration result obtained at this time is the vibration data of the TBM during operation. This data is input into the database module 300 for storage and can be called by the deep learning module 400 at any time.

[0053] Database module 300 is used to collect and store all relevant data information, including TBM vibration data module 301, geological data module 302, TBM operation data module 303, cutterhead data module 304 and construction data module 305.

[0054] The database module 300 includes the following modules: TBM vibration data module 301, which includes vibration signals in the x, y, and z dimensions during TBM operation; time history (i.e., the change of acceleration value over time); loop variance (i.e., the variance of acceleration value per revolution of the TBM cutterhead); maximum acceleration (per loop); root mean square acceleration (per loop); crest factor; and skewness. The geological data module 302 includes uniaxial compressive strength of rock, tensile strength of rock, abrasion resistance of rock, brittleness of rock, integrity coefficient of rock mass, groundwater status, and engineering geological conditions of surrounding rock. The TBM operation data module 303 includes cutterhead thrust, cutterhead rotation speed, cutterhead torque, penetration depth, tunneling speed, effective support force of support shoes, and total thrust. The cutterhead data module 304 includes cutterhead replacement time, damage status, failure type, and damage location. The construction data module 305 includes construction progress, slag discharge data, advanced forecast information, ambient temperature, and ambient humidity.

[0055] The deep learning module 400 is used to establish the response relationship between vibration data and other data; it includes a data analysis module 401 and a model update module 402.

[0056] The data analysis module 401 included in the deep learning module 400 is used to establish separate response models for vibration data, geological data, TBM operation data, cutterhead data, and construction data, respectively, to clarify the significant correlation between vibration characteristics and various data parameters. Vibration data can be used to characterize the working state of the TBM and the geological conditions ahead. Based on this, a comprehensive response model between vibration data and various data is established, characteristic parameters of the influence of various related data on vibration data are established, influencing factors controlling vibration parameter characteristics and the mutual feedback mechanism between various influencing factors are discovered, so as to realize the real-time judgment of abnormal factors in the TBM operation process through vibration data. As the TBM continues to operate and the accumulation of various data increases, the model update module 402 will revise the response model based on all data and the actual engineering conditions revealed by TBM excavation, making the response model more accurate and more in line with the actual working conditions. At the same time, the data analysis module 401 will make real-time judgments on the operating status of the TBM based on the latest response model and the latest vibration data.

[0057] The early warning module 500 is used to provide real-time early warning of abnormal situations that occur during TBM operation; it includes the adverse geological conditions early warning module 501, the cutterhead replacement early warning module 502, and the TBM status abnormality early warning module 503.

[0058] The early warning module 500 includes an adverse geological condition early warning module 501, which predicts the distribution of adverse geological bodies in front of the TBM face based on a response relationship model between vibration data, geological data, and TBM operation data. When relevant parameters of the vibration data exceed a set threshold, it can be determined that there are adverse geological bodies in front of the face. By coupling calculations with geological data and TBM operation data, the early warning level of adverse geological bodies in front of the face is obtained and displayed. The cutterhead replacement early warning module 502 determines the damage state of the cutterhead based on a response relationship model between vibration data and cutterhead data. By using data obtained from accelerometers at four different locations, it determines the specific location of the damaged cutterhead and realizes the replacement of the cutterhead. Real-time monitoring of the disc status: When the set vibration parameters exceed the threshold, the cutterhead replacement early warning module 502 will issue and display different levels of early warning information based on the specific values. The TBM status abnormality early warning module 503 will judge the overall situation of the TBM based on the comprehensive response model. When the vibration data is abnormal and does not meet the early warning standards of the adverse geological conditions early warning module 501 and the cutterhead replacement early warning module 502, it will indicate that the TBM status is abnormal. Based on the specific situation of the TBM shutdown inspection, the abnormal TBM status corresponding to the vibration abnormality will be determined and recorded in the database module 300. When the same vibration abnormality occurs later, the TBM status can be accurately judged.

[0059] Based on the response relationship model of vibration data, geological data, and TBM operation data, the distribution of adverse geological bodies in front of the tunnel face is predicted. By coupling calculation with geological data and TBM operation data, the warning level of adverse geological bodies in front of the tunnel face is obtained and displayed. The warning level is divided into four levels: safe, low risk, high risk, and high risk.

[0060] Based on the response relationship model between vibration data and cutterhead data, the damage state of the cutterhead is determined. Data obtained from accelerometers at four different locations are used to determine the specific location of the damaged cutterhead. The warning levels include four levels: no damage, minor damage, moderate damage, and severe damage.

[0061] The overall TBM condition is assessed based on the comprehensive response model. When vibration data is abnormal and does not meet the warning standards for geological warning and cutterhead warning, it indicates that the TBM is in an abnormal state. The warning levels are divided into four levels: no abnormality, slight abnormality, high abnormality, and severe abnormality.

[0062] like Figure 1 As shown, the specific operation monitoring method includes the following steps:

[0063] Step 1: Collect geological data, cutterhead data, and construction data and enter them into the database, and perform data acquisition and inspection on the vibration monitoring module.

[0064] Step 2: After the TBM starts running, the vibration monitoring module collects vibration data in real time, and the data processing module processes and analyzes the vibration data.

[0065] Step 3: The generated vibration data is entered into the database module, the deep learning module begins to build various response models, and makes a preliminary judgment on the TBM's operating status.

[0066] Step 4: Through continuous operation of the TBM, the established response model presents the results of adverse geological conditions and cutterhead status based on the real-time collected vibration data.

[0067] Step 5: When an anomaly occurs, the actual situation of the anomaly is entered into the database module, and the deep learning module then corrects the response model based on the actual situation.

[0068] Step 6: Establish early warning levels based on the revised response model, and issue early warnings for corresponding anomalies using vibration data.

[0069] This invention employs two types of accelerometers, enabling more accurate acquisition of TBM vibration signals while avoiding the influence of static gravity. It covers both normal and extremely low frequencies in its recording frequency range, avoiding the drawback of traditional methods that fail to record extremely low-frequency vibrations. This provides a more comprehensive reflection of the TBM's vibration status, leading to more accurate judgments.

[0070] This invention achieves a comprehensive integration of vibration signals with geological exploration, cutterhead monitoring, and TBM condition monitoring, overcoming the shortcomings of traditional methods where each function is independent and implemented separately. This greatly shortens TBM downtime, accelerates TBM construction efficiency, and reduces construction costs.

[0071] This invention uses deep learning to build a response model and updates it in real time to reflect actual engineering conditions, avoiding the limitations of traditional general models that are not suitable for different environments. This allows the established response model to effectively collect vibration signals and make accurate judgments in soil and rock tunnels.

[0072] like Figure 5 The diagram shown is a structural schematic of an electronic device provided in an embodiment of the present invention, comprising:

[0073] Memory 1, processor 2, and computer program stored on memory 1 and capable of running on processor 2.

[0074] When processor 2 executes the program, it implements the steps of a TBM tunneling status monitoring method provided in the above embodiments.

[0075] Electronic devices also include:

[0076] Communication interface 3 is used for communication between memory 1 and processor 2.

[0077] Memory 1 is used to store computer programs that can run on processor 2.

[0078] Memory 1 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0079] If memory 1, processor 2, and communication interface 3 are implemented independently, then communication interface 3, memory 1, and processor 2 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0080] If memory 1, processor 2, and communication interface 3 are integrated on a single chip, then memory 1, processor 2, and communication interface 3 can communicate with each other through their internal interfaces.

[0081] Processor 2 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0082] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for monitoring the tunneling status of a TBM (Tunnel Boring Machine), characterized in that, Includes the following steps: Before the TBM starts operating, acquire the cutterhead data of the TBM; acquire the geological data and construction data of the geological body to be mined by the TBM; acquire the vibration data of the TBM in the x, y and z dimensions during operation; Acquire the operating data of the TBM tunneling machine during operation; Determine the nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data; specifically including: A recurrent neural network (RNN) model was constructed and trained on the TBM tunneling machine's vibration and operation data, cutterhead data, geological data, and construction data respectively, to obtain response models for vibration and operation data, cutterhead data, geological data, and construction data respectively. In the response models corresponding to vibration data and operation data, cutterhead data, geological data and construction data respectively, the Pearson correlation coefficient statistical method is used to calculate the correlation between each parameter variable of vibration data and each parameter variable of TBM tunneling machine operation data, cutterhead data, geological data and construction data. Based on the correlation, nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data were established using a random forest machine learning model. Determine the integrated response model based on vibration data, operational data, cutterhead data, geological data, and construction data; Based on various nonlinear response relationships and a comprehensive response model, the influencing factors of vibration data are obtained; when the real-time vibration data changes, the changing state of the influencing factors is acquired; when the real-time influencing factors change, the changing state of the real-time vibration data is acquired; and based on the mutual changing state between vibration data and influencing factors, a feedback mechanism between vibration data and influencing factors is formed; wherein, the influencing factors include operational data, cutterhead data, geological data, and construction data; The feedback mechanism between vibration data and influencing factors includes: Based on the vibration and operation data of the TBM tunneling machine, cutterhead data, geological data, and construction data, a recurrent neural network (RNN) model is trained to obtain a comprehensive response model. Based on the real-time operating data, cutterhead data, geological data, and construction data of the TBM tunneling machine during operation, as well as the nonlinear response relationship between the TBM tunneling machine vibration data and the operating data, cutterhead data, geological data, and construction data, the data whose thresholds are higher than the set thresholds in each of the control vibration data characteristic parameters are set as the influencing factors of the control vibration data characteristic parameters using the comprehensive response model. The influencing factors are a subset of data from operational data, cutterhead data, geological data, and construction data. It also acquires the interaction process between vibration data characteristic parameters and influencing factors during the operation of the TBM tunneling machine, and sets this interaction process as the mutual feedback mechanism between vibration data characteristic parameters and influencing factors; Based on the feedback mechanism between vibration data and influencing factors, the numerical range of each influencing factor is obtained through real-time vibration data to determine the tunneling status of the TBM.

2. The method for monitoring the tunneling status of a TBM (Tunnel Boring Machine) according to claim 1, characterized in that, The acquisition of vibration data of the TBM tunneling machine in the x, y, and z dimensions during operation includes: Two MEMS accelerometers and two piezoelectric accelerometers are arranged at equal intervals on the rear side of the TBM cutterhead. After the TBM starts running, the MEMS accelerometers and piezoelectric accelerometers collect vibration data at different positions of the cutterhead. The vibration data includes: vibration signals in the x, y and z directions during the operation of the TBM tunneling machine, the time history of acceleration values ​​over time, loop variance, maximum acceleration of each loop, root mean square acceleration of each loop, crest factor and skewness.

3. The method for monitoring the tunneling status of a TBM (Tunnel Boring Machine) according to claim 1, characterized in that, The operating data, cutterhead data, geological data, and construction data of the TBM tunneling machine are as follows: The TBM tunneling machine operating data includes: cutterhead thrust, cutterhead rotation speed, cutterhead torque, penetration depth, tunneling speed, effective support force of the support shoe, and total thrust of the TBM tunneling machine; The TBM cutterhead data includes: cutterhead replacement time, loss status, damage type, and damage location; The geological data of the TBM tunneling machine includes: uniaxial compressive strength of rock, tensile strength of rock, abrasion resistance of rock, brittleness of rock, rock mass integrity coefficient, groundwater status and surrounding rock engineering geological conditions; The TBM tunneling machine construction data includes: construction progress, slag removal data, advanced forecast information, ambient temperature and humidity.

4. A TBM tunneling machine tunneling status monitoring system, characterized in that, include: The vibration monitoring module is used to acquire cutterhead data of the TBM before the TBM starts operating; Acquire geological and construction data of the geological body to be mined by the TBM tunneling machine; acquire vibration data of the TBM tunneling machine in the x, y, and z dimensions during operation; Acquire the operating data of the TBM tunneling machine during operation; The deep learning module is used to determine the nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data; specifically, it includes: A recurrent neural network (RNN) model was constructed and trained on the TBM tunneling machine's vibration and operation data, cutterhead data, geological data, and construction data respectively, to obtain response models for vibration and operation data, cutterhead data, geological data, and construction data respectively. In the response models corresponding to vibration data and operation data, cutterhead data, geological data and construction data respectively, the Pearson correlation coefficient statistical method is used to calculate the correlation between each parameter variable of vibration data and each parameter variable of TBM tunneling machine operation data, cutterhead data, geological data and construction data. Based on the correlation, nonlinear response relationships between vibration data and operational data, vibration data and cutterhead data, vibration data and geological data, and vibration data and construction data were established using a random forest machine learning model. Determine the integrated response model based on vibration data, operational data, cutterhead data, geological data, and construction data; Based on various nonlinear response relationships and a comprehensive response model, the influencing factors of vibration data are obtained; when the real-time vibration data changes, the changing state of the influencing factors is acquired; when the real-time influencing factors change, the changing state of the real-time vibration data is acquired; and based on the mutual changing state between vibration data and influencing factors, a feedback mechanism between vibration data and influencing factors is formed; wherein, the influencing factors include operational data, cutterhead data, geological data, and construction data; The feedback mechanism between vibration data and influencing factors includes: Based on the vibration and operation data of the TBM tunneling machine, cutterhead data, geological data, and construction data, a recurrent neural network (RNN) model is trained to obtain a comprehensive response model. Based on the real-time operating data, cutterhead data, geological data, and construction data of the TBM tunneling machine during operation, as well as the nonlinear response relationship between the TBM tunneling machine vibration data and the operating data, cutterhead data, geological data, and construction data, the data whose thresholds are higher than the set thresholds in each of the control vibration data characteristic parameters are set as the influencing factors of the control vibration data characteristic parameters using the comprehensive response model. The influencing factors are a subset of data from operational data, cutterhead data, geological data, and construction data. It also acquires the interaction process between vibration data characteristic parameters and influencing factors during the operation of the TBM tunneling machine, and sets this interaction process as the mutual feedback mechanism between vibration data characteristic parameters and influencing factors; The early warning module is used to determine the tunneling status of the TBM by obtaining the numerical range of each influencing factor through real-time vibration data based on the feedback mechanism between vibration data and influencing factors.

5. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the steps of the TBM tunneling status monitoring method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the steps of a TBM tunneling status monitoring method as described in any one of claims 1 to 3.

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