A data acquisition and preprocessing method for tunnel excavation construction

Abstract

This invention relates to the field of data acquisition and preprocessing technology, specifically a data acquisition and preprocessing method for tunnel excavation construction. It analyzes the impact of environmental factors on sensors and their own operating status, and plots a curve showing the change of operating index over time. By monitoring the change curve of the sensor's operating index over time, the sensor's status is dynamically evaluated. The derivative analysis of the curve's tangent is used to determine if the sensor is abnormal, and the monitoring cycle is dynamically adjusted based on the abnormality. This ensures that the acquisition frequency is increased when abnormalities occur, allowing for timely detection and handling of problems, thereby improving the response speed to sensor issues during tunnel excavation. Furthermore, the method uses abnormal sensor values ​​to determine the confidence level of the data acquired by the sensor itself, helping to filter more reliable data during tunnel excavation construction, ensuring data quality, and guaranteeing the safety of tunnel excavation construction.

Description

Technical Field

[0001] This invention relates to the field of data acquisition and preprocessing technology, specifically to a data acquisition and preprocessing method for tunnel excavation construction. Background Technology

[0002] Data acquisition during tunnel excavation refers to the real-time acquisition of data related to construction status, environment, and equipment operation through various sensors, monitoring equipment, and systems during tunnel construction. This data is used to monitor construction progress, ensure safety, optimize resource allocation, and prevent and respond to potential risks during construction; therefore, the accuracy and timeliness of data collected during tunnel excavation are particularly important.

[0003] In current tunnel excavation processes, data acquisition is often used directly without considering the accuracy of the sensors themselves. In real-world tunnel excavation, the harsh environment in which sensors operate (tunnel construction environments are typically accompanied by high humidity, dust, vibration, and temperature changes) and prolonged operation can lead to malfunctions, fatigue, drift, or decreased sensitivity. This results in insufficient accuracy and timeliness of the acquired data, making it difficult to ensure the safety of tunnel excavation. Summary of the Invention

[0004] The purpose of this invention is to provide a data acquisition and preprocessing method for tunnel excavation construction, so as to solve the problems mentioned in the background art.

[0005] The objective of this invention can be achieved through the following technical solution: a data acquisition and preprocessing method for tunnel excavation construction, comprising the following steps:

[0006] Step 1: Divide the tunnel into several areas, and install several sensors in each area. Collect and store the sensor information and environmental information according to the latest monitoring cycle.

[0007] Step 2: Retrieve sensor information and environmental information for each sensor in the area at each acquisition time, and comprehensively analyze the sensors and their environment in the area to determine the working status of the sensors. This yields the sensor's operating index at each acquisition time. Construct a two-dimensional rectangular coordinate system with time as the horizontal axis and the operating index as the vertical axis. Input the operating index into the coordinate axis according to its corresponding acquisition time, and record the points of the operating index on the coordinate axis as index points. Connect the index points sequentially with a smooth curve to obtain the curve of the sensor's operating index changing over time.

[0008] Step 3: Dynamically monitor and analyze the sensor's operating index over time to determine if there are any anomalies, and adjust the monitoring accordingly and assess the confidence level of the data; specifically:

[0009] Dynamic monitoring and analysis are performed based on the curve of the operating index changing over time to obtain abnormal values ​​of the sensors;

[0010] Set a safe zone for each type of sensor; extract the sensor type and compare it with all the set sensor types to match the corresponding safe zone;

[0011] The abnormal values ​​of the sensor are compared and analyzed with their corresponding safe range. When the abnormal value is greater than the upper limit of the safe range, the data collected by the sensor is marked as slightly confident data. The type, number and location of the sensor are obtained and sent to the corresponding engineer for calibration and maintenance.

[0012] When outliers are within the safe range, adjust the sensor's monitoring cycle and mark the data collected by the sensor as moderately confident data; the specific adjustment steps are as follows:

[0013] The current monitoring period of the sensor is denoted as K', and the corresponding outlier is denoted as Aq'. These are then used to calculate the latest monitoring period using a formula:

[0014]

[0015] Send the latest monitoring cycle to Step1;

[0016] When the outlier is less than the lower limit of the safe range, no adjustment is needed; simply maintain the current monitoring cycle and mark the data collected by the sensor as highly confident data.

[0017] Preferably, the specific process of dynamic monitoring and analysis based on the curve of the operating index changing over time is as follows:

[0018] The operating index curves of each sensor within the region are retrieved over time. Tangent lines are drawn at each index point, and the tangent line expression is obtained through data fitting. The derivative of the tangent line expression is calculated, and the tangent line derivative corresponding to each index point is denoted as Ai. The tangent line derivatives greater than zero are summed to obtain the state increase trend value, denoted as q1, and the tangent line derivatives less than zero are summed and their absolute values ​​are taken to obtain the state decrease trend value, denoted as q2. The operating derivative Yi, tangent line derivative A1, state increase trend value q1, and state decrease trend value q2 at each acquisition time are normalized, and their values ​​are taken. The values ​​are then calculated and analyzed using a formula to obtain the outlier value Aq. The specific calculation formula is as follows:

[0019]

[0020] Where μ1, μ2, and μ3 are set proportional constants.

[0021] Preferably, the specific process for comprehensively analyzing the sensors within the area and the environment in which the sensors are located is as follows:

[0022] 301: Retrieve environmental information from sensors within the area and conduct monitoring and analysis based on this information to measure the impact of environmental factors on the sensors, thereby obtaining the environmental impact value;

[0023] 302: Retrieve sensor information from sensors within the area and perform monitoring and analysis based on this information to measure the working status of the sensors themselves, thereby obtaining the working status value.

[0024] 303: The sensor's operating index Yi at each acquisition time is obtained by formulaically calculating and analyzing the environmental impact value Hi and the operating status value Pmi of the sensor at each acquisition time. The calculation formula is as follows:

[0025]

[0026] Where γ1 and γ2 are set proportional constants.

[0027] Preferably, the specific process for measuring the impact of environmental factors on the sensor is as follows:

[0028] The environmental information of the sensors in the area at each acquisition time is retrieved, which specifically includes the ambient temperature, ambient humidity and vibration amplitude, and are denoted as Ti, Si and F i respectively, where i = 1, 2, 3... I, I is a positive integer, I is the total number of acquisition times, and i is any one of the acquisition times;

[0029] Set a standard operating parameter for each type of sensor. The specific standard operating parameters include standard power, standard operating temperature, standard ambient temperature range, and standard ambient humidity range.

[0030] The ambient temperature is compared with the standard ambient temperature range to obtain the calibration temperature at each sampling time, and it is denoted as BT; similarly, the ambient humidity is compared with the standard ambient humidity range to obtain the calibration ambient humidity at each sampling time, and it is denoted as BS.

[0031] The ambient temperature Ti, ambient humidity Si, calibration temperature BT, calibration humidity BS, and vibration amplitude Fi at each data acquisition time were normalized and their values ​​were taken. The environmental impact value Hj at each data acquisition time was then calculated and analyzed using a formula. The specific calculation formula is as follows:

[0032]

[0033] α1, α2, and α3 are the set proportional constants.

[0034] Preferably, the step of obtaining the calibration temperature is as follows:

[0035] The ambient temperature is compared and analyzed with the standard ambient temperature range. When the ambient temperature is greater than the upper limit of the standard ambient temperature range, the upper limit of the standard ambient temperature range is used as the calibration temperature of the ambient temperature. When the ambient temperature is within the standard ambient temperature range, the ambient temperature is used as the calibration temperature of the ambient temperature. When the ambient temperature is less than the lower limit of the standard ambient temperature range, the lower limit of the standard ambient temperature range is used as the calibration temperature of the ambient temperature. Thus, the calibration temperature at each sampling time can be obtained.

[0036] Preferably, the specific process for measuring the sensor's own operating status is as follows:

[0037] Retrieve sensor information at each acquisition time, including actual power, operating temperature, and response speed, and denote them as Pi, Gi, and Vi, respectively; set a temperature sensitivity coefficient corresponding to different types of sensors, obtain the sensor type, and compare it with all set sensor types to match the corresponding temperature sensitivity coefficient, which is denoteed as M;

[0038] The actual power Pi, operating temperature Gi, response speed Vi, sensitivity coefficient M, standard power BP, and standard operating temperature BG of the sensor at each acquisition time are normalized and their values ​​are taken. The values ​​are then used to perform formulaic calculations and analysis to obtain the sensor's operating state value PMi at each acquisition time. The specific calculation formula is as follows:

[0039]

[0040] Where β1, β2, and β3 are set proportional constants.

[0041] The beneficial effects of this invention are:

[0042] 1. By retrieving sensor information and environmental information from various sensors in the area at different acquisition times, and through normalization and formulaic calculation methods, the system analyzes the impact of environmental factors on the sensors and the working status of the sensors themselves. In particular, by comparing the actual working parameters of the sensors with the standard parameters, the system can determine whether the sensors are working normally or whether they are interfered with by environmental factors, providing data support for judging the confidence level of the data collected by the sensors.

[0043] 2. The sensor status is dynamically evaluated by monitoring the change curve of the sensor's operating index over time. The derivative analysis of the curve tangent is used to determine whether there is any abnormality in the sensor. The monitoring cycle is dynamically adjusted according to the abnormality to ensure that the acquisition frequency is increased when the status is abnormal, so as to capture and deal with the problem in time, thereby improving the response speed to sensor problems during tunnel excavation.

[0044] 3. Based on the abnormal values ​​of the sensors, the confidence level of the data collected by the sensors can be judged, which helps them to screen more reliable data in tunnel excavation construction, ensure data quality, and ensure the safety of tunnel excavation construction.

[0045] In summary, this method not only improves the accuracy and reliability of data acquisition during tunnel excavation, but also provides strong protection for construction safety. Through real-time monitoring and dynamic adjustment, it can effectively cope with the challenges brought by complex environments. Attached Figure Description

[0046] The invention will now be further described with reference to the accompanying drawings.

[0047] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figure 1 As shown, the present invention is a data acquisition and preprocessing method for tunnel excavation construction, comprising the following steps;

[0050] Step 1: Divide the tunnel into several areas, and install several sensors in each area. Collect and store the sensor information and environmental information according to the latest monitoring cycle.

[0051] Step 2: Retrieve sensor information and environmental information for each sensor within the area at each acquisition time, and comprehensively analyze the sensors and their environment to determine their operating status. Specific sensor information includes actual power, operating temperature, and response speed (response speed refers to the speed at which the sensor reacts to changes in the input signal, reflecting its sensitivity and timeliness to changes in the environment or conditions). Environmental information includes ambient temperature, humidity, and vibration amplitude. It should be noted that continuous mechanical vibration during tunnel excavation can affect sensor stability (mechanical vibration can cause noise or interference in the sensor's output signal, especially in sensitive sensors such as accelerometers and pressure sensors; this noise can cause large fluctuations in the acquired data, making it difficult to stably reflect the actual situation) or cause loosening (continuous vibration can cause slight displacement or loosening of the sensor's installation position; this loosening can affect the sensor's accuracy and measurement direction, leading to deviations in the acquired data), making the acquired data more likely to contain errors. Specifically:

[0052] Analysis of the impact on environmental factors:

[0053] Different types of sensors are assigned standard operating parameters, including standard power, standard operating temperature, standard ambient temperature range, and standard ambient humidity range. Standard power and standard operating temperature are denoted as BP and BG, respectively. It's important to note that standard operating parameters refer to the operating conditions determined during the sensor's design and manufacturing process. These conditions ensure the sensor performs optimally and accurately in various application scenarios. These parameters are determined by factors such as the sensor's material selection, internal structure design, and manufacturing technology. If the sensor operates outside these standard ranges, the accuracy and reliability of its data acquisition will be affected, potentially leading to equipment damage or malfunction. For example, high or low humidity environments can affect the physical properties of the sensor's electronic components and materials. Excessive humidity can reduce the insulation performance of electrical insulating materials, increasing the risk of leakage and affecting the sensor's electrical performance. The sensor's output signal may therefore fluctuate or become unstable. While the impact of low humidity is relatively minor, extremely low humidity environments can increase static electricity issues, especially in electronic sensors. Static electricity buildup can cause signal interference or damage to electronic components.

[0054] The ambient temperature, ambient humidity, and vibration amplitude at each acquisition time are denoted as Ti, Si, and Fi, where i = 1, 2, 3...I, I is a positive integer, I is the total number of acquisition times, and i is any one of the acquisition times.

[0055] The ambient temperature is compared and analyzed with the standard ambient temperature range. When the ambient temperature is greater than the upper limit of the standard ambient temperature range, the upper limit of the standard ambient temperature range is used as the calibration temperature of the ambient temperature. When the ambient temperature is within the standard ambient temperature range, the ambient temperature is used as the calibration temperature of the ambient temperature. When the ambient temperature is less than the lower limit of the standard ambient temperature range, the lower limit of the standard ambient temperature range is used as the calibration temperature of the ambient temperature. Thus, the calibration temperature at each acquisition time can be obtained and recorded as BT.

[0056] Similarly, by comparing the ambient humidity with the standard ambient humidity range, when the ambient humidity is greater than the upper limit of the standard ambient humidity range, the upper limit of the standard ambient humidity range is used as the calibration humidity; when the ambient humidity is within the standard ambient humidity range, the ambient humidity is used as the calibration humidity; when the ambient humidity is less than the lower limit of the standard ambient humidity range, the lower limit of the standard ambient humidity range is used as the calibration humidity; thus, the calibration ambient humidity at each sampling time can be obtained and recorded as BS.

[0057] The ambient temperature, ambient humidity, calibration temperature, calibration humidity, and vibration amplitude at each data acquisition time were normalized and their values ​​were taken. The environmental impact value Hj at each data acquisition time was then calculated and analyzed using a formula. The specific calculation formula is as follows:

[0058]

[0059] Where α1, α2, and α3 are set proportional constants. As can be seen from the formula, when the ambient temperature and humidity deviate more from the standard ambient temperature range and standard ambient humidity range of the sensor, it means that the environment in which the sensor is located has a greater impact on the sensor, and the environmental impact value is greater. When the vibration amplitude is greater, it means that the vibration is more intense, and the impact on the sensor is greater, and the environmental impact value is greater.

[0060] Sensor self-state analysis:

[0061] The actual power, operating temperature, and response speed of the sensor at each acquisition time are retrieved and denoted as Pi, Gi, and Vi, respectively. It should be noted that different types of sensors have different sensitivities to temperature due to differences in the types of built-in electronic components. The specific temperature sensitivity coefficient is set by a person skilled in the art based on a comprehensive assessment of the specific sensor. A temperature sensitivity coefficient is set for each different type of sensor. The sensor type is obtained and compared with all the set sensor types to match the corresponding temperature sensitivity coefficient, denoted as M.

[0062] The actual power Pi, operating temperature Gi, response speed Vi, sensitivity coefficient M, standard power BP, and standard operating temperature BG of the sensor at each acquisition time are normalized and their values ​​are taken. The values ​​are then used to perform formulaic calculations and analysis to obtain the sensor's operating state value PMi at each acquisition time. The specific calculation formula is as follows:

[0063]

[0064] Where β1, β2, and β3 are set proportional constants; as can be seen from the formula, when the actual power and operating temperature are closer to the standard power and standard operating temperature, it means that the sensor is in a more normal working state, and the operating state value is larger; conversely, the operating state value is smaller; when the response speed is larger, it means that the sensor is more sensitive, and the operating state value is larger.

[0065] The environmental impact value Hi and the operating status value Pi of the sensor at each data acquisition time are calculated and analyzed using a formula to obtain the sensor's operating index Yi at each data acquisition time. The calculation formula is as follows:

[0066]

[0067] Where γ1 and γ2 are set proportional constants;

[0068] A two-dimensional rectangular coordinate system is constructed with time as the horizontal axis and the operating index as the vertical axis. The operating index is input into the coordinate axis according to its corresponding acquisition time, and the points of the operating index in the coordinate axis are recorded as index points. A smooth curve is used to connect the index points in sequence to obtain the curve of the sensor's operating index changing with time, and the curve is displayed in real time. Using a smooth curve is more suitable than a straight line to represent the natural changes in the sensor's operating status, so that tunnel excavation and construction personnel can monitor the operating status of each sensor in the area in real time in the background.

[0069] By retrieving sensor and environmental information from various sensors within the area at different acquisition times, and through normalization and formulaic calculations, the system analyzes the impact of environmental factors on the sensors and their own operating status. In particular, by comparing the actual operating parameters of the sensors with standard parameters, the system can determine whether the sensors are working properly or whether they are affected by environmental factors, providing data support for judging the confidence level of the data collected by the sensors.

[0070] Step 3: Dynamically monitor and analyze the sensor's operating index over time to determine if there are any abnormalities, and make dynamic adjustments accordingly; specifically:

[0071] The operating index curves of each sensor within the region are retrieved over time. Tangent lines are drawn at each index point, and the tangent line expression is obtained through data fitting. The derivative of the tangent line expression is calculated, and the tangent line derivative corresponding to each index point is denoted as Ai. The tangent line derivatives greater than zero are summed to obtain the state increase trend value, denoted as q1, and the tangent line derivatives less than zero are summed and their absolute values ​​are taken to obtain the state decrease trend value, denoted as q2. The operating derivative Yi, tangent line derivative A1, state increase trend value q1, and state decrease trend value q2 at each acquisition time are normalized, and their values ​​are taken. The values ​​are then calculated and analyzed using a formula to obtain the outlier value Aq. The specific calculation formula is as follows:

[0072]

[0073] Where μ1, μ2, and μ3 are set proportional constants. As can be seen from the formula, the smaller the increasing trend value of the operating index and the larger the decreasing trend value, the greater the decrease in the sensor's operating state and the greater the rate of decrease, and the larger the outlier. The greater the fluctuation of the tangent derivative, the more unstable the sensor's operating state, and the larger the outlier.

[0074] Different types of sensors are assigned a safety range. It should be noted that the more important a certain type of sensor is for tunnel excavation safety early warning, the smaller the upper and lower limits of its corresponding safety threshold. For example, the upper and lower limits of the safety range corresponding to the stress monitoring sensor are smaller than the upper and lower limits of the safety range corresponding to the temperature monitoring sensor, respectively. The sensor types are extracted and compared with all the set sensor types to match the corresponding safety range.

[0075] The abnormal values ​​of the sensor are compared and analyzed with their corresponding safe range. When the abnormal value is greater than the upper limit of the safe range, it indicates that the sensor has a relatively obvious abnormality and the confidence level of the data it collects is low. The data collected by the sensor is then marked as slightly confident data. The type, number and location of the sensor are obtained and sent to the corresponding engineer so that the engineer can perform calibration and maintenance operations.

[0076] When outliers are within the safe range, it indicates a minor anomaly in the sensor. In this case, the sensor's monitoring cycle should be adjusted, and the data collected by the sensor should be marked as moderate confidence data. The specific adjustment steps are as follows:

[0077] The current monitoring period of the sensor is denoted as K', and the corresponding outlier is denoted as Aq'. These are then used to calculate the latest monitoring period using a formula:

[0078]

[0079] The latest monitoring cycle is then sent to Step1. As can be seen from the formula, the larger the outlier, the shorter the corresponding monitoring cycle, and the more frequent the data collection. This allows for better capture of sensor and environmental information, thereby improving the ability to respond to abnormal situations, promptly capturing and handling problems, and ensuring the accuracy of monitoring and the reliability of data.

[0080] When the outlier is less than the lower limit of the safe range, it means that the sensor is normal and no adjustment is needed. Just maintain the current monitoring cycle and mark the data collected by the sensor as highly confident data.

[0081] This allows the data collected by each sensor to be classified according to their confidence level, making it easier for staff to filter data during tunnel excavation and improving data quality.

[0082] By monitoring the change curve of the sensor's operating index over time, the sensor's status is dynamically assessed. The derivative analysis of the curve's tangent is used to determine if the sensor is abnormal, and the monitoring cycle is dynamically adjusted based on the abnormality. This ensures that the acquisition frequency is increased when the status is abnormal, allowing for timely detection and handling of problems, thereby improving the response speed to sensor issues during tunnel excavation. At the same time, the confidence level of the sensor's own data is determined based on the sensor's abnormal values, helping them to filter more reliable data during tunnel excavation construction, ensuring data quality, and guaranteeing the safety of tunnel excavation construction.

[0083] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A data acquisition and preprocessing method for tunnel excavation construction, characterized in that, Includes the following steps: Step 1: Divide the tunnel into several areas, and install several sensors in each area. Collect and store the sensor information and environmental information according to the latest monitoring cycle. Step 2: Retrieve sensor information and environmental information for each sensor within the area at each data acquisition time. Then, comprehensively analyze this information regarding the sensors and their surrounding environment to determine their operational status. This yields the sensor's operational index at each data acquisition time. Specifically: 301: Retrieve environmental information from sensors within the area and conduct monitoring and analysis based on this information to measure the impact of environmental factors on the sensors, thereby obtaining the environmental impact value; 302: Retrieve sensor information from sensors within the area and perform monitoring and analysis based on this information to measure the working status of the sensors themselves, thereby obtaining the working status value. 303: The operating index of the sensor at each acquisition time is obtained by formulaically calculating and analyzing the environmental impact value and working status value of the sensor at each acquisition time; A two-dimensional rectangular coordinate system is constructed with time as the horizontal axis and the operating index as the vertical axis. The operating index is input into the coordinate axis according to its corresponding acquisition time, and the points of the operating index in the coordinate axis are recorded as index points. A smooth curve is used to connect the index points in sequence to obtain the curve of the sensor's operating index changing with time. Step 3: Dynamically monitor and analyze the sensor's operating index over time to determine if there are any anomalies, and adjust the monitoring accordingly and assess the confidence level of the data; specifically: Dynamic monitoring and analysis are performed based on the curve of the operating index changing over time to obtain abnormal values ​​of the sensors; specifically: The operating index of each sensor in the region is retrieved as a function of time. Tangent lines are drawn at each index point. Tangent line expressions are obtained by fitting the data. The derivative of the tangent line expression is calculated to obtain the tangent line derivative, from which the tangent line derivative corresponding to each index point can be obtained. The tangent line derivatives with values ​​greater than zero are summed to obtain the state increase trend value, and the tangent line derivatives with values ​​less than zero are summed and their absolute values ​​are taken to obtain the state decrease trend value. The running derivative, tangent derivative, state increase trend value, and state decrease trend value at each acquisition time are normalized and their values ​​are taken. The values ​​are then calculated and analyzed using formulas to obtain outliers. Set a safe zone for each type of sensor; extract the sensor type and compare it with all the set sensor types to match the corresponding safe zone; The abnormal values ​​of the sensor are compared and analyzed with their corresponding safe range. When the abnormal value is greater than the upper limit of the safe range, the data collected by the sensor is marked as slightly confident data. The type, number and location of the sensor are obtained and sent to the corresponding engineer for calibration and maintenance. When outliers are within the safe range, adjust the sensor's monitoring cycle and mark the data collected by the sensor as moderately confident data; the specific adjustment steps are as follows: Obtain the current monitoring cycle of the sensor and the corresponding outlier for that monitoring cycle, and calculate the latest monitoring cycle by formulating the calculation with the latest outlier, and send the latest monitoring cycle to Step1; When the outlier is less than the lower limit of the safe range, no adjustment is needed; simply maintain the current monitoring cycle and mark the data collected by the sensor as highly confident data.

2. The data acquisition and preprocessing method for tunnel excavation construction according to claim 1, characterized in that, The specific process for measuring the impact of environmental factors on sensors is as follows: Retrieve environmental information from sensors within the area at each acquisition time, specifically including ambient temperature, ambient humidity, and vibration amplitude; Set a standard operating parameter for each type of sensor. The specific standard operating parameters include standard power, standard operating temperature, standard ambient temperature range, and standard ambient humidity range. The ambient temperature is compared with the standard ambient temperature range to obtain the calibration temperature at each sampling time; similarly, the ambient humidity is compared with the standard ambient humidity range to obtain the calibration ambient humidity at each sampling time. The ambient temperature, ambient humidity, calibration temperature, calibration humidity, and vibration amplitude at each acquisition time were normalized and their values ​​were taken. The values ​​were then calculated and analyzed using formulas to obtain the environmental impact values ​​at each acquisition time.

3. The data acquisition and preprocessing method for tunnel excavation construction according to claim 2, characterized in that, The steps to obtain the calibration temperature are as follows: The ambient temperature is compared and analyzed with the standard ambient temperature range. When the ambient temperature is greater than the upper limit of the standard ambient temperature range, the upper limit of the standard ambient temperature range is used as the calibration temperature of the ambient temperature. When the ambient temperature is within the standard ambient temperature range, the ambient temperature is used as the calibration temperature of the ambient temperature. When the ambient temperature is less than the lower limit of the standard ambient temperature range, the lower limit of the standard ambient temperature range is used as the calibration temperature of the ambient temperature. Thus, the calibration temperature at each sampling time can be obtained.

4. The data acquisition and preprocessing method for tunnel excavation construction according to claim 1, characterized in that, The specific process for measuring the sensor's own operating status is as follows: Retrieve sensor information at each acquisition time, including actual power, operating temperature, and response speed; set a temperature sensitivity coefficient corresponding to different types of sensors, obtain the sensor type, and compare it with all set sensor types to match the corresponding temperature sensitivity coefficient. The actual power, operating temperature, response speed, sensitivity coefficient, standard power, and standard operating temperature of the sensor at each acquisition time are normalized and their values ​​are taken. The values ​​are then used to perform formulaic calculations and analyses to obtain the sensor's operating status values ​​at each acquisition time.

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