Low-carbon energy-saving shield machine state monitoring system based on internet of things

Abstract

The application provides a low-carbon and energy-saving shield machine state monitoring system based on the Internet of Things, and relates to the technical field of shield machine monitoring; the application forms a structured data set by collecting shield machine operation state parameters and construction environment data in real time through multiple sensors and marking time stamps, dynamically identifies the construction environment category, divides the core and secondary parameters and processes them in stages according to this, determines the safety level of the current state of the shield machine based on the safety level threshold range, simultaneously identifies the energy efficiency related core parameters to determine the energy efficiency level, is helpful to accurately master the operation state of the shield machine according to different construction environments, guarantees the construction safety, can also effectively evaluate the energy efficiency, and effectively supports the low-carbon and energy-saving operation of the shield construction process.

Description

Technical Field

[0001] This invention relates to the field of tunnel boring machine monitoring technology, and in particular to a low-carbon and energy-saving tunnel boring machine status monitoring system based on the Internet of Things. Background Technology

[0002] The geological environments of tunnels and underground engineering projects are complex and varied, encompassing different types of strata, including soft soil, gravel, hard rock, and high-water-pressure formations. Under different geological environments, the operating status and stress conditions of tunnel boring machines (TBMs) vary significantly. For example, in soft soil, TBMs may face problems such as poor soil stability and susceptibility to settlement; while in hard rock, cutter wear and tunneling resistance are the main challenges. However, traditional TBM condition monitoring methods often employ a uniform monitoring model, failing to fully consider the characteristics of different construction environments and unable to accurately monitor key factors in specific environments. This results in monitoring data that cannot effectively reflect the actual operating status of the TBM in that environment, making it difficult to provide accurate basis for construction decisions.

[0003] Currently, safety assessments of tunnel boring machines (TBMs) primarily rely on threshold judgments for single parameters. This method ignores the interrelationships between different parameters and the impact of the construction environment on safety levels. In reality, the safety status of a TBM is a complex systemic issue, influenced by a combination of factors. For example, exceeding a threshold for a core parameter may not necessarily lead to a dangerous situation; a comprehensive analysis combining other relevant parameters and the current construction environment is required. Traditional safety assessment methods, lacking this comprehensive consideration, result in inaccurate assessments and are prone to misjudgments or omissions.

[0004] Therefore, it is necessary to provide an IoT-based low-carbon and energy-saving tunnel boring machine condition monitoring system to solve the above-mentioned technical problems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an Internet of Things-based low-carbon and energy-saving tunnel boring machine status monitoring system to solve the problems of insufficient accuracy in existing technologies due to the complex and diverse construction environments, the lack of specificity in traditional monitoring, the lack of comprehensive consideration in safety assessments.

[0006] The present invention provides a low-carbon and energy-saving tunnel boring machine condition monitoring system based on the Internet of Things, comprising: The data acquisition module is used to collect parameter data of the current operating status of the tunnel boring machine and the current construction environment data in real time through multiple sensors; The category classification module is used to dynamically identify the environmental category of the current construction environment based on the current construction environment data and through a preset environmental bias threshold. The hierarchical processing module is used to divide the core parameters and secondary parameters from the parameter data of the current operating status of the tunnel boring machine according to the environmental category of the current construction environment, and to perform hierarchical processing on the core parameters and secondary parameters to obtain the processed data. The judgment and identification module is used to determine the safety level of the tunnel boring machine's current state based on the processed data and according to a pre-set safety level threshold range. The energy efficiency classification module is used to identify the core parameters related to energy efficiency from the processed data and output the current energy efficiency level through the preset energy efficiency level threshold range.

[0007] Preferably, the step of collecting parameter data of the current operating status of the tunnel boring machine and the current construction environment data in real time through multiple sensors specifically includes: The shield machine uses various IoT sensors in different systems to collect parameter data of its current operating status at a preset frequency, including vibration data, temperature data, pressure data, or current / voltage data. By deploying environmental sensing sensors around the tunnel boring machine in the construction area, current construction environment data is collected at a preset frequency, including groundwater level, earth pressure, surrounding rock stress, temperature and humidity, or harmful gas concentration data. The parameter data of the current operating status and the data of the current construction environment are timestamped to form a structured dataset of the current operating status parameter data and the current construction environment dataset.

[0008] Preferably, the step of dynamically identifying the environmental category of the current construction environment based on current construction environment data and through a preset environmental bias threshold specifically includes: Acquire engineering geological data and historical construction experience data, define the environmental categories of the construction environment, and set corresponding environmental bias thresholds for each environmental category. The environmental categories include soft soil layer, gravel layer, hard rock layer or high water pressure stratum. The current construction environment dataset is compared with the bias threshold of each environment category. Matching is performed using rule matching or a lightweight classification model. If the current construction environment dataset meets the environment bias threshold of the environment category, the current construction environment is determined to belong to that environment category, and a unique current environment category identifier is output.

[0009] Preferably, the steps of dividing the core parameters and secondary parameters from the parameter data of the current operating status of the tunnel boring machine according to the environmental category of the current construction environment, and performing hierarchical processing on the core parameters and secondary parameters to obtain processed data, specifically include: For different environmental categories, analyze the influencing factors of tunnel boring machine operation under different environments, predefine the core parameters and secondary parameters corresponding to different environmental categories, and establish an environmental category-parameter mapping table; Based on the current environment category identifier, the core parameters and secondary parameters are automatically divided from the current running status parameter dataset by querying the environment category-parameter mapping table; The core parameters and secondary parameters are divided and processed separately, including: high-frequency sampling, noise reduction filtering, and outlier removal for the core parameters; moving average processing for the secondary parameters; and integration of the divided core parameters and secondary parameters to form processed data in a unified format.

[0010] Preferably, the step of determining the safety level of the tunnel boring machine's current state based on the processed data and according to a pre-set safety level threshold range specifically includes: The system presets the security level threshold range corresponding to the environment category and calls it. It compares the current values ​​of each core parameter and secondary parameter with the corresponding security level threshold range to obtain the judgment result of the security level of each core parameter and secondary parameter. The security level includes normal, warning or danger. Based on the judgment results of the safety levels of each core parameter and secondary parameter, a weighted average method is used for comprehensive evaluation to obtain the overall safety level of the tunnel boring machine in its current state.

[0011] Preferably, the steps of identifying core energy efficiency-related parameters from the processed data and outputting the current energy efficiency level within a preset energy efficiency level threshold range specifically include: The core parameters related to energy efficiency are identified from the processed data. The preprocessed core parameters related to energy efficiency are compared with the pre-set energy efficiency level threshold range to obtain the energy efficiency level threshold comparison results. Based on the comparison results of energy efficiency level thresholds, the current energy efficiency level of the tunnel boring machine is determined.

[0012] Preferably, the energy efficiency level threshold range includes three energy efficiency level threshold ranges: high efficiency, medium efficiency, and low efficiency.

[0013] Compared with related technologies, the IoT-based low-carbon and energy-saving tunnel boring machine condition monitoring system provided by this invention has the following advantages: This invention uses multiple sensors to collect real-time operating status parameters of the tunnel boring machine (TBM) and construction environment data, and marks them with timestamps to form a structured dataset. It dynamically identifies the construction environment category, divides core and secondary parameters accordingly, and processes them in a hierarchical manner. Based on the safety level threshold range, it determines the current safety level of the TBM and identifies energy efficiency-related core parameters to determine the energy efficiency level. This helps to accurately grasp the operating status of the TBM according to different construction environments, ensure construction safety, effectively assess energy efficiency, and effectively support the low-carbon and energy-saving operation of the TBM construction process. Attached Figure Description

[0014] Figure 1 This is a system block diagram of the IoT-based low-carbon and energy-saving tunnel boring machine condition monitoring system of the present invention. Detailed Implementation

[0015] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0016] Example like Figure 1 As shown, the IoT-based low-carbon and energy-saving tunnel boring machine condition monitoring system includes: The data acquisition module is used to collect parameter data of the current operating status of the tunnel boring machine and the current construction environment data in real time through multiple sensors; The category classification module is used to dynamically identify the environmental category of the current construction environment based on the current construction environment data and through a preset environmental bias threshold. The hierarchical processing module is used to divide the core parameters and secondary parameters from the parameter data of the current operating status of the tunnel boring machine according to the environmental category of the current construction environment, and to perform hierarchical processing on the core parameters and secondary parameters to obtain the processed data. The judgment and identification module is used to determine the safety level of the tunnel boring machine's current state based on the processed data and according to a pre-set safety level threshold range. The energy efficiency classification module is used to identify the core parameters related to energy efficiency from the processed data and output the current energy efficiency level through the preset energy efficiency level threshold range.

[0017] In the specific implementation process, the data acquisition module executes the following steps: The shield machine uses various IoT sensors in different systems to collect parameter data of its current operating status at preset frequencies, including vibration data, temperature data, pressure data, or current / voltage data.

[0018] Specifically, various types of IoT sensors are installed in key systems of the tunnel boring machine (TBM), such as the cutterhead system, propulsion system, and hydraulic system. These sensors continuously collect various parameter data during the TBM's operation at pre-set frequencies, such as once per second or once per minute. Vibration sensors collect vibration signals generated during operation to reflect the equipment's operational stability and the wear of mechanical components. Temperature sensors measure the temperature of various system components to prevent equipment damage due to overheating. Pressure sensors acquire pressure data from components such as the hydraulic system to ensure that system pressure remains within the normal range. Current / voltage sensors monitor the TBM's power consumption, reflecting the operating status of electrical equipment such as motors.

[0019] By deploying environmental sensing sensors around the tunnel boring machine in the construction area, current construction environment data, including groundwater level, earth pressure, surrounding rock stress, temperature and humidity, or harmful gas concentration data, are collected at a preset frequency.

[0020] Specifically, various environmental sensing sensors are deployed in the construction area surrounding the tunnel boring machine (TBM) to meet different monitoring needs. For example, groundwater level sensors are installed at the bottom of the tunnel to monitor changes in groundwater levels; earth pressure sensors are installed in the soil in front of the TBM to acquire real-time soil pressure data; surrounding rock stress sensors are installed on the tunnel walls to measure the stress state of the surrounding rock; temperature and humidity sensors are installed at different locations inside the tunnel to monitor the temperature and humidity of the construction environment; and hazardous gas concentration sensors, such as those for methane and carbon monoxide, are installed in areas where hazardous gases may be present.

[0021] The parameter data of the current operating status and the data of the current construction environment are timestamped to form a structured dataset of the current operating status parameter data and the current construction environment dataset.

[0022] Specifically, precise timestamps are added to each set of current operating status parameter data and current construction environment data. It should be noted that the timestamps record the specific moment of data collection, ensuring the timeliness and traceability of the data. Then, according to the preset data structure and format, the operating status parameter data and construction environment data with timestamps are sorted and stored separately to form a structured current operating status parameter dataset and current construction environment dataset.

[0023] In the specific implementation process, the category classification module includes the following steps: Obtain engineering geological data and historical construction experience data, define the environmental categories of the construction environment, and set corresponding environmental bias thresholds for each environmental category. The environmental categories include soft soil layer, gravel layer, hard rock layer or high water pressure stratum.

[0024] Specifically, engineering geological data related to the current tunnel boring machine (TBM) construction project is collected, including geological exploration reports, soil test data, and groundwater level measurement records. Based on the collected engineering geological data and professional knowledge of TBM construction, the construction environment is classified and defined. For example, based on soil particle size, cohesion, and other characteristics, areas with soft soil texture and high water content are defined as soft soil layers; areas composed of sand and pebbles with good permeability are classified as sand and pebble layers; areas with high rock hardness and good integrity are classified as hard rock layers; and areas with high groundwater levels and high water pressure are defined as high-pressure strata.

[0025] In this embodiment, four main environmental categories are defined: soft soil layer, gravel layer, hard rock layer, and high water pressure stratum. For soft soil layer, the environmental bias threshold is set as soil moisture content greater than 30% and plasticity index greater than 15; for gravel layer, the threshold is set as sand content greater than 60% and gravel content less than 30% in particle size distribution; hard rock layer is determined based on uniaxial compressive strength of rock greater than 60 MPa; and high water pressure stratum is defined as groundwater level more than 5 meters above the tunnel burial depth.

[0026] The current construction environment dataset is compared with the bias threshold of each environment category. Matching is performed using rule matching or a lightweight classification model. If the current construction environment dataset meets the environment bias threshold of the environment category, the current construction environment is determined to belong to that environment category, and a unique current environment category identifier is output.

[0027] Specifically, key data related to the bias thresholds of each environmental category are extracted from the current construction environment dataset. For example, to determine whether it is a soft soil layer, data such as soil moisture content and plasticity index are extracted and processed to ensure that the data format and units are consistent with the threshold settings. In this embodiment, according to pre-set rules, the extracted data is compared one by one with the bias thresholds of each environmental category. If the current construction environment data meets all the threshold conditions of a certain environmental category, then the current construction environment is determined to belong to that environmental category. For example, the rule for determining a soft soil layer is that the soil moisture content is greater than 30% and the plasticity index is greater than 15. When the extracted current soil moisture content is 32% and the plasticity index is 18, the rule is met, and it is determined to be a soft soil layer.

[0028] In the specific implementation process, the hierarchical processing module executes the following steps: For different environmental categories, the influencing factors of tunnel boring machine operation under different environments are analyzed, the core parameters and secondary parameters corresponding to different environmental categories are predefined and the environmental category-parameter mapping table is established.

[0029] Specifically, historical data on tunnel boring machines (TBMs) operating under different environments is obtained. Parameters accounting for more than one-third of the total parameters affecting construction safety and efficiency under different environments are identified as influencing factors. The remaining two-thirds are considered ordinary factors. These influencing parameters are then designated as core parameters for different environmental categories, while those with relatively minor impacts are designated as secondary parameters. For example, in soft soil layers, earth pressure, TBM propulsion pressure, and cutterhead torque have a significant impact on construction safety and efficiency and are defined as core parameters; while TBM shell temperature and pressure of some auxiliary systems have a smaller impact and are designated as secondary parameters. In hard rock layers, rock hardness, cutter rotation speed, and propulsion speed are core parameters, while hydraulic system oil temperature can be considered as secondary parameters. Using spreadsheet software such as Excel or a dedicated database management system, an environment category-parameter mapping table is created. This table contains columns for environment category, core parameters, and secondary parameters, clearly listing the core and secondary parameters corresponding to each environment category for easy subsequent querying and use.

[0030] Based on the current environment category identifier, the core parameters and secondary parameters are automatically divided from the current running status parameter dataset by querying the environment category-parameter mapping table.

[0031] The core parameters and secondary parameters are divided and processed separately, including: high-frequency sampling, noise reduction filtering, and outlier removal for the core parameters; moving average processing for the secondary parameters; and integration of the divided core parameters and secondary parameters to form processed data in a unified format.

[0032] Specifically, for core parameters, the sampling frequency is adjusted, and the number of sampling points is increased to more accurately capture parameter changes. For example, for a rapidly changing core parameter like cutterhead torque, the sampling frequency is increased from once per second to five times per second. Next, filtering algorithms (such as mean filtering and median filtering) are used to denoise the sampled data, removing random noise and interference signals to improve data accuracy. For example, median filtering of the collected temperature data can effectively eliminate abnormal spikes caused by temporary sensor malfunctions or external interference. Finally, a reasonable threshold range is set, and data points exceeding the threshold range are identified as outliers and removed. For example, for the tunnel boring machine's propulsion pressure, the normal operating range is between 10-20 MPa; when the collected data exceeds this range, it is considered an outlier and removed. For secondary parameters, a moving average is applied. By setting a sliding window size, the average value of the data within the window is calculated and used to replace the current data point, thereby smoothing the data curve and reducing data fluctuations. For example, for hydraulic system oil temperature, the sliding window size is set to 5, meaning the current data point and the average of its four preceding data points are used as the processed data. The core and secondary parameters, after hierarchical processing, are integrated into a unified data format to form a complete dataset, facilitating subsequent safety level assessments and energy efficiency analyses.

[0033] In the specific implementation process, the judgment and identification module executes the following steps: The system presets the security level threshold range corresponding to the environment category and calls it. It then compares the current values ​​of each core parameter and secondary parameter with the corresponding security level threshold range to obtain the judgment result of the security level of each core parameter and secondary parameter. The security level includes normal, warning, or danger.

[0034] Specifically, based on the design parameters, operating principles, and industry safety standards of the tunnel boring machine (TBM), and considering the characteristics of different environmental categories (such as soft soil, gravel, hard rock, and high-water-pressure strata), the safety ranges for each core and secondary parameter under each environmental category are determined. In this embodiment, in a soft soil environment, the normal operating range for the core parameter of the TBM's propulsion pressure is set at 10-15 MPa. When the propulsion pressure is below 10 MPa, effective propulsion may be impossible, posing a risk to the construction progress, thus setting it as a warning level. When the propulsion pressure is above 15 MPa, it may cause excessive compression of the surrounding soil, leading to dangers such as soil collapse, thus setting it as a hazard level. For secondary parameters such as the TBM's outer shell temperature, the normal range is set at 30-50℃. Temperatures below 30℃ or above 50℃ are respectively set as warning levels. These set safety level threshold ranges are stored in a dedicated database. The current values ​​of each core and secondary parameter are obtained from the processed data, and the same operation is performed on all core and secondary parameters sequentially to obtain the safety level judgment result for each parameter.

[0035] Based on the judgment results of the safety levels of each core parameter and secondary parameter, a weighted average method is used for comprehensive evaluation to obtain the overall safety level of the tunnel boring machine in its current state.

[0036] Specifically, weights are assigned based on the degree of influence of each parameter on the safety status of the tunnel boring machine (TBM). Core parameters, due to their significant impact on the TBM's operational safety and construction efficiency, are typically assigned higher weights; secondary parameters, with relatively smaller impacts, are assigned lower weights. In this embodiment, core parameters such as cutterhead torque and propulsion pressure play a crucial role in the safety status during TBM operation, and their weights are set to 0.3 and 0.3 respectively. Secondary parameters such as TBM shell temperature and pressure of some auxiliary systems can have weights set to 0.1 and 0.1, etc. It should be noted that the sum of all parameter weights is 1. Based on the safety level assessment results of each parameter, they are converted into numerical forms. For example, the normal level is set to 1, the warning level to 2, and the danger level to 3. A weighted average is used, with the formula: Overall safety level value = Σ (parameter safety level value × parameter weight). That is, the safety level value of each parameter is multiplied by its corresponding weight, and then all products are summed to obtain the overall safety level value.

[0037] In the specific implementation process, the energy efficiency classification module includes the following steps: Core parameters related to energy efficiency are identified from the processed data. The preprocessed core parameters related to energy efficiency are compared with the pre-set energy efficiency level threshold range to obtain the energy efficiency level threshold comparison results. The energy efficiency level threshold range includes three energy efficiency level threshold ranges: high efficiency, medium efficiency, and low efficiency.

[0038] Specifically, based on the energy efficiency standards of the tunnel boring machine industry and historical data experience, threshold ranges are defined for different energy efficiency levels. For example, in this embodiment, for the parameter of motor power, the threshold range for high efficiency is set to 80%-100% of the motor's rated power; that is, when the motor power is within this range, the tunnel boring machine is considered to be operating efficiently in terms of this parameter. The threshold range for medium efficiency is 60%-80%, and the threshold range for low efficiency is below 60%. These defined energy efficiency threshold ranges are then stored in a dedicated database or configuration file for easy access later.

[0039] Based on the comparison results of energy efficiency level thresholds, the current energy efficiency level of the tunnel boring machine is determined.

[0040] Specifically, the current value of each energy efficiency-related core parameter is compared with the corresponding energy efficiency level threshold range. For example, if the current motor power is 75% of the rated power, comparing it with the set motor power energy efficiency level threshold range will show that the parameter is at the medium efficiency level. The same operation is performed on all energy efficiency-related core parameters to obtain the energy efficiency level threshold comparison result for each parameter.

[0041] In this embodiment, in a tunnel boring machine (TBM) construction project, the core energy efficiency-related parameters identified are cutterhead speed, propulsion pressure, and motor power. The current cutterhead speed is identified as 3.5 r / min, propulsion pressure as 15 MPa, and motor power as 70% of rated power. The preset threshold ranges for cutterhead speed are: high efficiency 4-5 r / min, medium efficiency 3-4 r / min, and low efficiency below 3 r / min; high efficiency propulsion pressure 16-20 MPa, medium efficiency 12-16 MPa, and low efficiency below 12 MPa; and high efficiency motor power 80%-100%, medium efficiency 60%-80%, and low efficiency below 60%. Through comparison, the cutterhead speed, propulsion pressure, and motor power are all at the medium efficiency level.

[0042] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0043] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0044] It should also be noted that 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. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A low-carbon, energy-saving tunnel boring machine condition monitoring system based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect parameter data of the current operating status of the tunnel boring machine and the current construction environment data in real time through multiple sensors; The category classification module is used to dynamically identify the environmental category of the current construction environment based on the current construction environment data and through a preset environmental bias threshold. The hierarchical processing module is used to divide the core parameters and secondary parameters from the parameter data of the current operating status of the tunnel boring machine according to the environmental category of the current construction environment, and to perform hierarchical processing on the core parameters and secondary parameters to obtain the processed data. The judgment and identification module is used to determine the safety level of the tunnel boring machine's current state based on the processed data and according to a pre-set safety level threshold range. The energy efficiency classification module is used to identify the core parameters related to energy efficiency from the processed data and output the current energy efficiency level through the preset energy efficiency level threshold range.

2. The low-carbon, energy-saving tunnel boring machine condition monitoring system based on the Internet of Things as described in claim 1, characterized in that, The steps of collecting real-time parameter data of the tunnel boring machine's current operating status and current construction environment data through multiple sensors specifically include: The shield machine uses various IoT sensors in different systems to collect parameter data of its current operating status at a preset frequency, including vibration data, temperature data, pressure data, or current / voltage data. By deploying environmental sensing sensors around the tunnel boring machine in the construction area, current construction environment data is collected at a preset frequency, including groundwater level, earth pressure, surrounding rock stress, temperature and humidity, or harmful gas concentration data. The parameter data of the current operating status and the data of the current construction environment are timestamped to form a structured dataset of the current operating status parameter data and the current construction environment dataset.

3. The low-carbon, energy-saving tunnel boring machine condition monitoring system based on the Internet of Things as described in claim 1, characterized in that, The step of dynamically identifying the environmental category of the current construction environment based on current construction environment data and through a preset environmental bias threshold specifically includes: Acquire engineering geological data and historical construction experience data, define the environmental categories of the construction environment, and set corresponding environmental bias thresholds for each environmental category. The environmental categories include soft soil layer, gravel layer, hard rock layer or high water pressure stratum. The current construction environment dataset is compared with the bias threshold of each environment category. Matching is performed using rule matching or a lightweight classification model. If the current construction environment dataset meets the environment bias threshold of the environment category, the current construction environment is determined to belong to that environment category, and a unique current environment category identifier is output.

4. The low-carbon, energy-saving tunnel boring machine condition monitoring system based on the Internet of Things according to claim 1, characterized in that, The steps of dividing the shield tunneling machine's current operating status parameter data into core parameters and secondary parameters based on the current construction environment category, and then performing hierarchical processing on the core and secondary parameters to obtain processed data, specifically include: For different environmental categories, analyze the influencing factors of tunnel boring machine operation under different environments, predefine the core parameters and secondary parameters corresponding to different environmental categories, and establish an environmental category-parameter mapping table; Based on the current environment category identifier, the core parameters and secondary parameters are automatically divided from the current running status parameter dataset by querying the environment category-parameter mapping table; The core parameters and secondary parameters are divided and processed separately, including: high-frequency sampling, noise reduction filtering, and outlier removal for the core parameters; moving average processing for the secondary parameters; and integration of the divided core parameters and secondary parameters to form processed data in a unified format.

5. The low-carbon, energy-saving tunnel boring machine condition monitoring system based on the Internet of Things according to claim 1, characterized in that, The steps for determining the safety level of the tunnel boring machine's current state based on the processed data and according to a pre-set safety level threshold range specifically include: The system presets the security level threshold range corresponding to the environment category and calls it. It compares the current values ​​of each core parameter and secondary parameter with the corresponding security level threshold range to obtain the judgment result of the security level of each core parameter and secondary parameter. The security level includes normal, warning or danger. Based on the judgment results of the safety levels of each core parameter and secondary parameter, a weighted average method is used for comprehensive evaluation to obtain the overall safety level of the tunnel boring machine in its current state.

6. The low-carbon, energy-saving tunnel boring machine condition monitoring system based on the Internet of Things according to claim 1, characterized in that, The steps of identifying core energy efficiency-related parameters from the processed data and outputting the current energy efficiency level within a preset energy efficiency level threshold range specifically include: The core parameters related to energy efficiency are identified from the processed data. The preprocessed core parameters related to energy efficiency are compared with the pre-set energy efficiency level threshold range to obtain the energy efficiency level threshold comparison results. Based on the comparison results of energy efficiency level thresholds, the current energy efficiency level of the tunnel boring machine is determined.

7. The IoT-based low-carbon energy-saving tunnel boring machine condition monitoring system according to claim 6, characterized in that, The energy efficiency level threshold range includes three energy efficiency level threshold ranges: high efficiency, medium efficiency, and low efficiency.

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