Shield tail brush deformation and segment shield tail gap monitoring method

Abstract

The invention relates to the technical field of shield construction monitoring, in particular to a shield tail brush deformation and segment shield tail gap monitoring method, which comprises the steps of constructing a multi-dimensional sensing matrix, initializing a monitoring parameter table, adjusting monitoring parameters, dynamically checking, feeding back in real time and the like. Data are collected through the distributed sensors, information is processed by the main control unit in combination with filtering, data fusion, feature extraction and mode recognition algorithms, and results are displayed through the visualization module. The system further comprises an alarm module, a self-diagnosis module, a power management module, an environment adaptation module and other functional modules, and operation stability and safety are guaranteed. According to the invention, high-precision synchronous monitoring can be realized, the construction quality and safety are optimized, and the requirement of shield engineering on efficient monitoring is met.

Description

Technical Field

[0001] This invention belongs to the field of tunnel construction and shield machine monitoring technology, specifically a method for monitoring the deformation of the shield tail brush and the gap between the tunnel segments and the shield tail. Background Technology

[0002] With the development of tunnel boring machine (TBM) construction technology, the monitoring of tail brush deformation and the gap between the tunnel lining segments and the tail shield has become increasingly important for construction safety and quality. However, in practical engineering, existing related technical solutions still have certain limitations in terms of monitoring accuracy, real-time performance, comprehensiveness, and system integration, making it difficult to fully meet the needs of complex working conditions.

[0003] A search revealed that patent CN110529126B proposes a comprehensive early warning device and its working method for the tail shield sealing system of a tunnel boring machine. This technology achieves multi-parameter coupling judgment of the tail shield sealing status by collecting data from multiple directions, including the pressure inside the tail brush cavity, the earth pressure in the shield shell, the tail shield gap, and the shield attitude. Furthermore, it uses a host computer to visualize the sealing safety level of any position in the multiple grease cavities along the circumference of the tail shield. However, this solution primarily focuses on the early warning function of the tail shield sealing system, with limited direct monitoring capability for tail brush deformation and failing to provide a high-precision dynamic tracking method for changes in the gap between the tunnel lining segments and the tail shield. In addition, its reliance on multiple sensors for data acquisition may increase system complexity and negatively impact real-time performance and reliability.

[0004] Patent CN114413775B proposes a method for measuring the tail gap of a tunnel boring machine (TBM) based on dual-line laser vision. This technology utilizes dual-line laser vision to acquire the intersection points of target feature lines by collecting images returned by two laser beams. It then combines the projection relationship of the two laser lines onto the tail shell and tunnel segments to calculate and extract key points for real-time automated measurement. However, this solution primarily focuses on measuring the tail gap and does not address the monitoring of tail brush deformation, lacking comprehensive analytical capabilities for tail brush status changes. Furthermore, its measurement accuracy may be affected by factors such as the laser installation location and ambient light interference, limiting its adaptability to complex operating conditions.

[0005] The aforementioned problems indicate that existing technologies for monitoring tail brush deformation and segment-to-tail gap still require further improvement in terms of monitoring range, accuracy, real-time performance, and system integration. Therefore, this invention aims to provide a method for monitoring tail brush deformation and segment-to-tail gap, achieving synchronous, high-precision monitoring of both tail brush deformation and segment-to-tail gap, optimizing data acquisition and processing procedures, and improving real-time performance and reliability, thereby better meeting the safety and quality control requirements of tunnel boring machine (TBM) construction. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method for monitoring the deformation of the shield tail brush and the gap between the tunnel segments and the shield tail. This method integrates a multi-source data acquisition and processing mechanism, combined with dynamic calibration and real-time feedback strategies, to achieve high-precision synchronous monitoring of the shield tail brush deformation and the gap between the tunnel segments and the shield tail. It optimizes the data acquisition and processing flow, improves the overall system performance, and meets the safety and quality control requirements of tunnel boring machine (TBM) construction.

[0007] The technical solution adopted in this invention is: a method for monitoring the deformation of the shield tail brush and the gap between the shield tail segments, characterized by including the following steps: constructing a multi-dimensional sensing matrix and initializing the monitoring parameter table;

[0008] The steps for receiving user instructions and adjusting monitoring parameters;

[0009] The steps to determine whether the current monitoring parameters meet the preset conditions;

[0010] The step of mapping the adjusted monitoring parameters to a multidimensional sensing matrix;

[0011] The steps to implement monitoring functions based on a multi-dimensional sensing matrix; wherein:

[0012] The steps of constructing the multidimensional sensing matrix and initializing the monitoring parameter table include initializing the monitoring parameter table upon first use and updating the monitoring parameter table according to user instructions; the steps of updating the monitoring parameter table according to user instructions include the following:

[0013] The steps for the main control unit to receive instructions to adjust monitoring parameters;

[0014] The step of sending the received adjustment instructions to the monitoring parameter table for comparison;

[0015] The steps to determine whether parameter adjustments can be made based on the comparison results;

[0016] If the comparison results allow for adjustment of the monitoring parameter, the monitoring parameter table is modified, the newly added parameter information is input, and then the newly added parameter information is output to the storage module to form a new monitoring parameter table; otherwise, the original monitoring parameter is not changed.

[0017] The initialization monitoring parameter table mentioned above sets the default monitoring parameter values ​​when the system starts up.

[0018] Each parameter in the monitoring parameter table has a unique definition; when adjusting the monitoring parameters, it is necessary to check whether other parameters have already been defined with this value. If so, the user will be prompted that this definition is not allowed.

[0019] The redefinition of the monitoring parameters can be repeated with other parameters.

[0020] The steps for implementing the monitoring function based on the multidimensional sensing matrix include:

[0021] Implementation of shield tail brush deformation monitoring: The main control unit first queries the physical location and logical identifier of each monitoring point in the sensor array according to the monitoring parameter table, and then obtains the deformation status information of the monitoring point corresponding to the logical identifier.

[0022] The implementation of segment shield tail gap monitoring: The main control unit first queries the physical location and logical identifier of each measurement point in the measurement unit according to the monitoring parameter table, obtains the gap measurement information corresponding to the logical identifier, and then generates the gap change curve according to the measurement information.

[0023] It also includes a step of automatic verification of monitoring parameters, which involves verifying the monitoring parameters of shield tail brush deformation and segment shield tail gap respectively. If the verification is abnormal, the monitoring parameters are restored to the default value.

[0024] The monitoring parameter table is stored in the storage module of the main control unit.

[0025] The storage module is a non-volatile memory, which stores the storage address of the function of each monitoring point and measurement point in the monitoring parameter table, as well as the storage address of the verification value of the configuration of the monitoring point or measurement point.

[0026] The multidimensional sensing matrix consists of multiple distributed sensors, each of which is connected to the main control unit through a dedicated communication interface. The dedicated communication interface uses differential signal transmission to reduce the impact of electromagnetic interference on signal transmission.

[0027] The distributed sensors include strain gauges, displacement sensors, and pressure sensors; the strain gauges are installed on the tail brush base of the shield tail and are used to detect the deformation state of the tail brush; the displacement sensors are installed in the gap between the shield tail shell and the tube segments and are used to measure the gap change; the pressure sensors are installed on the inner wall of the tail brush cavity and are used to monitor the pressure distribution in the tail brush cavity.

[0028] The strain gauge is fixed to the surface of the tail brush base of the shield with epoxy resin and connected to the main control unit through a shielded cable; the displacement sensor is adsorbed to the inside of the tail shell of the shield with a magnetic base and connected to the main control unit through a flexible wire; the pressure sensor is fixed to the inner wall of the tail brush cavity with a threaded connection and connected to the main control unit through an armored cable.

[0029] After receiving the raw data from the distributed sensors, the main control unit preprocesses the data using a filtering algorithm. The filtering algorithm employs a moving average filtering method, which removes noise interference by weighting and averaging multiple sets of continuously collected data.

[0030] After completing data preprocessing, the main control unit integrates multi-source data through a data fusion algorithm. The data fusion algorithm uses the Kalman filter method to calculate the optimal estimate by weighting different sensor data.

[0031] After completing data fusion, the main control unit analyzes the monitoring data using a feature extraction algorithm. The feature extraction algorithm employs wavelet transform to extract key features reflecting the deformation of the shield tail brush and the change in the gap between the shield segments by performing multi-scale decomposition on the monitoring data.

[0032] After completing feature extraction, the main control unit classifies the monitoring results using a pattern recognition algorithm. The pattern recognition algorithm employs a support vector machine method, which learns from historical data to establish a classification model for the deformation of the shield tail brush and the change in the gap between the shield tail segments.

[0033] After completing pattern recognition, the main control unit presents the monitoring results to the user through the visualization module. The visualization module uses three-dimensional graphics display technology to show the deformation of the tail brush and the changing trend of the gap between the shield segments through dynamic curves and three-dimensional models.

[0034] The main control unit also includes an alarm module, which alerts the user via an audible and visual alarm device when the monitoring result exceeds a preset threshold. The audible and visual alarm device includes a buzzer and an LED indicator, which are respectively installed on the main control unit panel.

[0035] The main control unit is connected to the remote monitoring center via a wireless communication module; the wireless communication module uses LoRa communication technology to achieve remote data transmission through a low-power wide area network.

[0036] After receiving monitoring data from the main control unit, the remote monitoring center performs in-depth data mining through a big data analysis platform. The big data analysis platform adopts the Hadoop distributed computing framework and extracts potential patterns through parallel processing of massive amounts of data.

[0037] The remote monitoring center also includes a prediction module, which predicts the future trends of shield tail brush deformation and segment-to-shield gap by analyzing historical data. The prediction module uses time series analysis to calculate the probability distribution of future changes by modeling the time series of monitoring data.

[0038] After completing the prediction, the remote monitoring center provides optimization suggestions to users through the decision support module. The decision support module adopts expert system technology and generates specific construction guidance schemes by regularizing construction specifications and experience knowledge.

[0039] The main control unit and the remote monitoring center transmit data via an encryption protocol; the encryption protocol uses the AES symmetric encryption algorithm to ensure data security through encryption and decryption of transmitted data.

[0040] The main control unit also includes a self-diagnostic module, which periodically checks the hardware and software status to detect potential faults. The self-diagnostic module uses a heartbeat detection mechanism to determine the operating status of key components by periodically polling them.

[0041] After completing self-diagnosis, the main control unit stores the operating status information in the storage module through the log recording module; the log recording module adopts a circular writing method and avoids data overflow through dynamic management of storage space.

[0042] The main control unit also includes a power management module, which ensures stable system operation by monitoring the power supply voltage and current in real time; the power management module adopts a dual-redundancy design and improves system reliability through a main and backup power switching mechanism.

[0043] The main control unit controls the internal temperature through a heat dissipation module; the heat dissipation module adopts a combination of air cooling and heat pipes to reduce the system operating temperature through effective heat conduction and dissipation.

[0044] The main control unit also includes an environmental adaptation module, which assesses the stability of the system's operating environment by monitoring environmental parameters such as temperature, humidity, and vibration. The environmental adaptation module uses a multi-parameter sensor array to generate an environmental status report by collecting environmental data in real time.

[0045] After completing the environmental adaptation assessment, the main control unit dynamically adjusts the system parameters through the adaptive adjustment module. The adaptive adjustment module uses a fuzzy control algorithm to generate the optimal adjustment strategy through fuzzy reasoning of the environmental parameters. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the system architecture of the method for monitoring shield tail brush deformation and segment-to-shield gap of the present invention.

[0047] Figure 2 This is a schematic diagram showing the composition and distribution of a multidimensional sensing matrix.

[0048] Figure 3 This is the data processing flowchart for the main control unit.

[0049] Figure 4 This is a schematic diagram of the functional modules of the remote monitoring center.

[0050] Figure 5This is a schematic diagram illustrating the working principle of the adaptive adjustment module. Detailed Implementation

[0051] The shield tail brush deformation and segment shield tail gap monitoring method of the present invention is realized through the collaborative work of the main control unit, distributed sensors, storage module and remote monitoring center. Figure 1 The system architecture diagram is shown, in which the main control unit is the core control component, responsible for receiving and processing data from distributed sensors, storing the processing results in the storage module, and interacting with the remote monitoring center via a wireless communication module. The distributed sensors include strain gauges, displacement sensors, and pressure sensors, which are installed at different locations on the shield tail structure to collect data related to the deformation of the shield tail brush and the gap between the shield segments.

[0052] Strain gauges are fixed to the surface of the tail brush base using epoxy resin adhesive, and their shielded cables are connected to the main control unit to ensure stable signal transmission. Displacement sensors are magnetically attached to the inside of the tail shell, and flexible wires connect them to the main control unit, enabling real-time measurement of changes in the gap between the tail shell and the tunnel segments. Pressure sensors are fixed to the inner wall of the tail brush cavity via threaded connections, and armored cables connect them to the main control unit to monitor the pressure distribution within the tail brush cavity. These sensors collectively form a multi-dimensional sensing matrix, such as... Figure 2 As shown, each sensor is connected to the main control unit through a dedicated communication interface. The communication interface uses differential signal transmission to reduce the impact of electromagnetic interference on the signal.

[0053] After receiving the raw data collected by the distributed sensors, the main control unit first preprocesses the data using a filtering algorithm. The filtering algorithm employs a moving average filtering method, which removes noise interference by weighted averaging of multiple continuously collected data sets. The preprocessed data then enters the data fusion stage. The main control unit uses a Kalman filter to integrate the multi-source data, calculating the optimal estimate by assigning weights to data from different sensors. After data fusion, the main control unit analyzes the monitoring data using a feature extraction algorithm. This algorithm employs wavelet transform, extracting key features reflecting the deformation of the shield tail brush and changes in the gap between the shield segments through multi-scale decomposition of the monitoring data.

[0054] The main control unit further classifies the monitoring results using a pattern recognition algorithm. This algorithm employs a support vector machine method, establishing a classification model for shield tail brush deformation and segment-to-shield gap changes by learning from historical data. After classification, the main control unit presents the monitoring results to the user through a visualization module. This module uses 3D graphics display technology, showcasing the changing trends of shield tail brush deformation and segment-to-shield gap through dynamic curves and 3D models. When the monitoring results exceed a preset threshold, the main control unit issues an audible and visual warning via an alarm module. The alarm device includes a buzzer and LED indicators, each mounted on the main control unit panel.

[0055] The main control unit connects to the remote monitoring center via a wireless communication module using LoRa technology to achieve remote data transmission through a low-power wide-area network. After receiving the monitoring data from the main control unit, the remote monitoring center performs in-depth data mining using a big data analytics platform. This platform employs the Hadoop distributed computing framework to extract potential patterns through parallel processing of massive amounts of data. The remote monitoring center also includes a prediction module that analyzes historical data to predict future trends in shield tail brush deformation and segment-to-shield gap. This prediction module uses time series analysis to calculate the probability distribution of future changes by modeling the time series of monitoring data. After completing the prediction, the remote monitoring center provides optimization suggestions to the user through a decision support module. This module uses expert system technology to generate specific construction guidance plans through the rule-based representation of construction specifications and experiential knowledge.

[0056] The main control unit and the remote monitoring center transmit data via an encrypted protocol using the AES symmetric encryption algorithm. Data security is ensured through encryption and decryption. The main control unit also includes a self-diagnostic module that periodically checks the hardware and software status to detect potential faults. This module employs a heartbeat detection mechanism, periodically polling key components to determine their operational status. After completing the self-diagnosis, the main control unit stores the operational status information in a storage module via a log recording module. The log recording module uses a circular write method, dynamically managing storage space to prevent data overflow.

[0057] The main control unit also includes a power management module, which ensures stable system operation by real-time monitoring of the supply voltage and current. The power management module employs a dual-redundancy design, improving system reliability through a primary / backup power switching mechanism. A heat dissipation module controls the internal temperature of the main control unit, using a combination of air cooling and heat pipes to effectively conduct and dissipate heat, reducing the system operating temperature. An environmental adaptation module monitors environmental parameters such as temperature, humidity, and vibration to assess the stability of the system's operating environment. This module uses a multi-parameter sensor array to generate environmental status reports by collecting environmental data in real time. After completing the environmental adaptation assessment, the main control unit dynamically adjusts system parameters through an adaptive adjustment module. This module uses a fuzzy control algorithm to generate the optimal adjustment strategy through fuzzy inference of environmental parameters.

[0058] The storage module is a non-volatile memory that stores the storage addresses of the functions of each monitoring point and measurement point in the monitoring parameter table, as well as the storage addresses of the verification values ​​for the configurations of each monitoring point or measurement point. The monitoring parameter table is stored in the main control unit's storage module. The definition of each parameter is unique. When adjusting a monitoring parameter, it is necessary to check whether another parameter has already been defined with this value. If so, the user is prompted that this definition is not allowed. Monitoring parameters can be redefined repeatedly with other parameters. Upon first use, the monitoring parameter table is initialized with the default monitoring parameter values ​​set at system startup. The monitoring parameter table is updated according to user instructions. This includes the main control unit receiving instructions to adjust monitoring parameters and sending the received adjustment instructions to the monitoring parameter table for comparison. Based on the comparison result, it is determined whether the parameter can be adjusted. If the comparison result allows the adjustment of the monitoring parameter, the monitoring parameter table is modified, the newly added parameter information is input, and then the newly added parameter information is output to the storage module to form a new monitoring parameter table. Otherwise, the original monitoring parameters are not changed.

[0059] The main control unit implements monitoring functions based on a multi-dimensional sensing matrix. For shield tail brush deformation monitoring, the main control unit queries the monitoring parameter table to find the physical location and logical identifier of each monitoring point in the sensor array, thus obtaining the deformation status information of the monitoring point corresponding to that logical identifier. For segment-to-shield gap monitoring, the main control unit queries the monitoring parameter table to find the physical location and logical identifier of each measurement point in the measurement unit, obtains the gap measurement information corresponding to that logical identifier, and then generates a gap change curve based on this measurement information. The automatic verification of monitoring parameters involves verifying the monitoring parameters for shield tail brush deformation and segment-to-shield gap separately. If the verification is abnormal, the monitoring parameters are restored to their default values.

[0060] The main control unit completes high-precision synchronous monitoring of the tail brush deformation and the gap between the tunnel segments and the tail through the above steps. The entire process, from data acquisition to processing, analysis, prediction and optimization suggestions, is automated and intelligent, meeting the safety and quality control requirements of tunnel boring machine construction.

[0061] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention will be further explained below in conjunction with a specific application scenario.

[0062] During tunnel boring machine (TBM) construction, the main control unit (MCU) collects real-time data on the deformation of the tail brush and the gap between the tunnel segments and the tail section using distributed sensors. First, strain gauges are installed on the surface of the tail brush base, fixed with epoxy resin, and connected to the MCU via shielded cables. The resistance of the strain gauges changes with the deformation of the tail brush base, and the MCU calculates the deformation of the tail brush based on this resistance change. Simultaneously, displacement sensors are magnetically attached to the inside of the tail shell, connected to the MCU by flexible wires, enabling real-time measurement of the gap change between the tail shell and the tunnel segments. The displacement sensor outputs a voltage value, which the MCU analyzes to calculate the actual gap distance. Pressure sensors are fixed to the inner wall of the tail brush cavity via threaded connections, connected to the MCU by armored cables, and used to monitor the pressure distribution within the tail brush cavity. The pressure data output by the pressure sensors, after processing by the MCU, reflects the changing trend of the pressure state within the tail brush cavity.

[0063] After receiving the raw data from the distributed sensors, the main control unit first preprocesses the data using a filtering algorithm. The filtering algorithm employs a moving average filtering method, which removes noise interference by weighted averaging of multiple continuously acquired data sets. For example, the strain gauge resistance value acquired within a certain time period may fluctuate; the main control unit calculates the stable resistance value within that time period using the moving average filtering method, thereby improving data reliability. After data preprocessing, the main control unit enters the data fusion stage, using a Kalman filter to integrate the multi-source data. The Kalman filter calculates the optimal estimate by assigning weights to data from different sensors. For example, when strain gauges and displacement sensors simultaneously acquire data related to the deformation of the shield tail brush, the main control unit calculates the combined deformation based on the weighted allocation of both, thereby improving the accuracy of the monitoring results.

[0064] After data fusion, the main control unit analyzes the monitoring data using a feature extraction algorithm. This algorithm employs wavelet transform to extract key features reflecting the deformation of the tail brush and changes in the gap between the shield tail and the tunnel segments through multi-scale decomposition of the monitoring data. For example, by performing wavelet transform on the gap change data collected by displacement sensors, the frequency and amplitude characteristics of the gap changes can be extracted. These features reflect the dynamic changes in the gap between the shield tail shell and the tunnel segments. Subsequently, the main control unit classifies the monitoring results using a pattern recognition algorithm. This algorithm uses a support vector machine (SVM) method to establish a classification model for the deformation of the tail brush and changes in the gap between the shield tail and the tunnel segments by learning from historical data. For example, when the detected deformation exceeds a certain threshold, the SVM method can classify the deformation as an abnormal state and trigger an alarm module.

[0065] The main control unit presents the monitoring results to the user through a visualization module. This module employs 3D graphics display technology, showcasing the deformation of the shield tail brush and the changing trends of the segment-to-shield gap through dynamic curves and 3D models. For example, the visualization module can generate a dynamic curve showing the change in the segment-to-shield gap over time, while simultaneously displaying the deformation state of the shield tail brush through a 3D model. When the monitoring results exceed a preset threshold, the main control unit issues an audible and visual warning via the alarm module. A buzzer and LED indicator lights are mounted on the main control unit panel. When an abnormal state is detected, the buzzer sounds an alarm, and the LED indicator lights flash, reminding operators to take timely action.

[0066] The main control unit connects to the remote monitoring center via a wireless communication module using LoRa technology to achieve remote data transmission through a low-power wide-area network. After receiving the monitoring data from the main control unit, the remote monitoring center performs in-depth data analysis using a big data analytics platform. This platform employs the Hadoop distributed computing framework, extracting potential patterns through parallel processing of massive amounts of data. For example, by analyzing historical data, the platform can identify the correlation between shield tail brush deformation and pressure within the tail brush cavity, providing a basis for optimizing construction parameters. The remote monitoring center also includes a prediction module that predicts future trends in shield tail brush deformation and the gap between the shield and the tunnel lining segments based on historical data analysis. This prediction module uses time series analysis to calculate the probability distribution of future changes by modeling the time series of monitoring data. For example, when a continuous increase in shield tail brush deformation is detected, the prediction module can predict the deformation trend over a future period based on historical data and generate an early warning.

[0067] After completing the prediction, the remote monitoring center provides optimization suggestions to users through the decision support module. The decision support module employs expert system technology, generating specific construction guidance schemes through the rule-based representation of construction specifications and experiential knowledge. For example, when the prediction module predicts that the deformation of the shield tail brush may exceed the safety threshold, the decision support module can generate suggestions to adjust the shield advance speed or grouting volume, helping operators optimize construction parameters. Data transmission between the main control unit and the remote monitoring center is conducted via an encrypted protocol using the AES symmetric encryption algorithm, ensuring data security through encryption and decryption.

[0068] The main control unit also includes a self-diagnostic module, which periodically checks the hardware and software status to detect potential faults. The self-diagnostic module employs a heartbeat detection mechanism, periodically polling key components to determine their operational status. For example, when the main control unit detects a signal interruption from a displacement sensor, the self-diagnostic module records the fault and generates a fault report. After completing the self-diagnosis, the main control unit stores the operational status information in a storage module through a log recording module. The log recording module uses a circular write method, dynamically managing the storage space to prevent data overflow.

[0069] The main control unit also includes a power management module, which ensures stable system operation through real-time monitoring of the supply voltage and current. The power management module employs a dual-redundancy design, improving system reliability through a primary / backup power switching mechanism. For example, when the primary power supply fails, the power management module automatically switches to the backup power supply to ensure continuous system operation. The heat dissipation module controls the internal temperature of the main control unit, using a combination of air cooling and heat pipes to effectively conduct and dissipate heat, thus reducing the system operating temperature. The environmental adaptation module monitors environmental parameters such as temperature, humidity, and vibration to assess the stability of the system's operating environment. For example, when the ambient temperature is too high, the environmental adaptation module generates a high-temperature warning and dynamically adjusts system parameters through the adaptive adjustment module. The adaptive adjustment module uses a fuzzy control algorithm to generate the optimal adjustment strategy through fuzzy inference of environmental parameters. For example, when the ambient temperature rises, the adaptive adjustment module automatically adjusts the fan speed of the heat dissipation module to maintain the normal operating temperature of the main control unit.

[0070] Through the above steps, the main control unit completed high-precision synchronous monitoring of the tail brush deformation and the gap between the tunnel segments and the tail shield. The entire process, from data acquisition to processing, analysis, prediction, and the generation of optimization suggestions, was automated and intelligent, meeting the safety and quality control requirements of tunnel boring machine (TBM) construction.

[0071] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring shield tail brush deformation and segment-to-shield gap, characterized in that, Includes the following steps: The steps for constructing a multidimensional sensing matrix and initializing the monitoring parameter table; The steps for receiving user instructions and adjusting monitoring parameters; The steps to determine whether the current monitoring parameters meet the preset conditions; The step of mapping the adjusted monitoring parameters to a multidimensional sensing matrix; The steps to implement monitoring functions based on a multi-dimensional sensing matrix; wherein: The steps of constructing the multidimensional sensing matrix and initializing the monitoring parameter table include initializing the monitoring parameter table upon first use and updating the monitoring parameter table according to user instructions; the steps of updating the monitoring parameter table according to user instructions include the following: The steps for the main control unit to receive instructions to adjust monitoring parameters; The step of sending the received adjustment instructions to the monitoring parameter table for comparison; The steps to determine whether parameter adjustments can be made based on the comparison results; If the comparison results allow for adjustment of the monitoring parameter, the monitoring parameter table is modified, the newly added parameter information is input, and then the newly added parameter information is output to the storage module to form a new monitoring parameter table; otherwise, the original monitoring parameter is not changed.

2. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 1, characterized in that, The initialization monitoring parameter table mentioned above sets the default monitoring parameter values ​​when the system starts up.

3. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 1, characterized in that, Each parameter in the monitoring parameter table has a unique definition; when adjusting the monitoring parameters, it is necessary to check whether other parameters have already been defined with this value. If so, the user will be prompted that this definition is not allowed.

4. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 1, characterized in that, The steps for implementing the monitoring function based on the multidimensional sensing matrix include: Implementation of shield tail brush deformation monitoring: The main control unit queries the physical location and logical identifier of each monitoring point in the sensor array according to the monitoring parameter table, and obtains the deformation status information of the monitoring point corresponding to the logical identifier. The implementation of segment shield tail gap monitoring: The main control unit queries the physical location and logical identifier of each measurement point in the measurement unit according to the monitoring parameter table, obtains the gap measurement information corresponding to the logical identifier, and then generates the gap change curve based on the measurement information.

5. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 1, characterized in that, It also includes a step of automatic verification of monitoring parameters, which involves verifying the monitoring parameters of shield tail brush deformation and segment shield tail gap respectively. If the verification is abnormal, the monitoring parameters are restored to the default value.

6. The method for monitoring shield tail brush deformation and segment-to-shield gap according to any one of claims 1 to 5, characterized in that, The monitoring parameter table is stored in the storage module of the main control unit.

7. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 6, characterized in that, The storage module is a non-volatile memory, which stores the storage address of the function of each monitoring point and measurement point in the monitoring parameter table, as well as the storage address of the verification value of the configuration of the monitoring point or measurement point.

8. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 1, characterized in that, The multidimensional sensing matrix consists of multiple distributed sensors, each of which is connected to the main control unit through a dedicated communication interface; the dedicated communication interface adopts a differential signal transmission method.

9. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 8, characterized in that, The distributed sensors include strain gauges, displacement sensors, and pressure sensors; the strain gauges are installed on the tail brush base of the shield tail and are used to detect the deformation state of the tail brush; the displacement sensors are installed in the gap between the shield tail shell and the tube segments and are used to measure the gap change; the pressure sensors are installed on the inner wall of the tail brush cavity and are used to monitor the pressure distribution in the tail brush cavity.

10. The method for monitoring shield tail brush deformation and segment-to-shield gap according to claim 1, characterized in that, After receiving the raw data from the distributed sensors, the main control unit preprocesses the data using a filtering algorithm. The filtering algorithm employs a moving average filtering method, which removes noise interference by weighting and averaging multiple sets of continuously collected data.

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