Data acquisition preprocessing method for tunnel excavation construction

Abstract

The invention relates to the technical field of data acquisition and preprocessing, in particular to a data acquisition and preprocessing method for tunnel excavation construction. By analyzing the influence of environmental factors on the sensor and the working state of the sensor, drawing a curve chart of operation indexes changing along with time according to the influence and the working state; the state of the sensor is dynamically evaluated by monitoring the change curve of the operation index of the sensor along with time, the derivation analysis system for the tangent line of the curve judges whether the sensor is abnormal or not, the monitoring period is dynamically adjusted according to the abnormal condition, the acquisition frequency is increased when the state is abnormal, and the problem is timely captured and processed. Therefore, the response speed to the sensor problem in the tunnel excavation process is increased; the confidence degree of the data collected by the sensors is judged according to the abnormal values of the sensors, the sensors are helped to screen more reliable data in tunnel excavation construction, the data quality is ensured, and the safety of tunnel excavation construction is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of data acquisition preprocessing, and in particular to a data acquisition preprocessing method for tunnel excavation construction. Background Art

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

[0003] At present, data collected in the tunnel excavation process is usually used directly, and the accuracy of the sensor itself is often ignored. In the actual tunnel excavation process, due to the harsh environment in which the sensor is located (the tunnel construction environment is usually accompanied by high humidity, dust, vibration, temperature changes, etc.), and long-term operation, it will malfunction, fatigue, drift or reduce sensitivity, etc., making the collected data incorrect and in time, making it difficult to ensure the safety of tunnel excavation construction. Summary of the invention

[0004] The purpose of the present invention is to provide a data collection preprocessing method for tunnel excavation construction to solve the problems mentioned in the above background technology.

[0005] The object of the present invention can be achieved by the following technical solution: A data collection preprocessing method for tunnel excavation construction comprises the following steps:

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

[0007] Step 2: retrieve the sensor information and environmental information corresponding to each sensor in the area at each collection time, and make a comprehensive analysis of the sensors in the area and the environment in which the sensors are located to determine the working status of the sensors, thereby obtaining the operation index of the sensors at each collection time; construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the operation index as the vertical coordinate, input the operation index into the value coordinate axis according to its corresponding collection time, and record the point of the operation index in the coordinate axis as the index point, and use a smooth curve to connect the index points in sequence to obtain a curve graph of the sensor's operation index changing with time;

[0008] Step 3: Dynamic monitoring and analysis is performed through the curve of the sensor's operating index changing over time to determine whether the sensor is abnormal, and dynamic adjustment of monitoring and confidence judgment of data are performed accordingly; specifically:

[0009] Perform dynamic monitoring and analysis based on the curve of the operation index changing over time to obtain the abnormal value of the sensor;

[0010] Set different types of sensors to correspond to a safety zone respectively; extract the sensor type and compare it with all the set sensor types to match the corresponding safety zone;

[0011] Compare and analyze the abnormal value of the sensor with its corresponding safety interval. When the abnormal value is greater than the upper limit of the safety interval, mark the data collected by the sensor as lightly trusted data, obtain the type, number and location of the sensor, and send it to the corresponding engineer for correction and maintenance;

[0012] When the abnormal value is within the safe range, the monitoring period of the sensor is adjusted and the data collected by the sensor is marked as moderately confident data; the specific adjustment steps are:

[0013] The current monitoring period of the sensor is obtained, denoted as K', and the abnormal value corresponding to the monitoring period is denoted as Aq', and the latest monitoring period is obtained by formulating the calculation with the latest abnormal value Aq; the specific calculation formula is:

[0014]

[0015] And send the latest monitoring cycle to Step 1;

[0016] When the abnormal value is smaller than the lower limit of the safety interval, no adjustment is required, the current monitoring cycle can be maintained, and the data collected by the sensor is marked as highly confident data.

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

[0018] The operation index of each sensor in the area is retrieved as time goes by, and the tangent line is drawn at each index point. The tangent expression is obtained by data fitting, and the tangent derivative is obtained by derivation. The tangent derivative corresponding to each index point is recorded as Ai; the tangent derivatives greater than zero are summed up to obtain the state increase trend value recorded as q1, and the tangent derivatives less than zero are summed up and the absolute value is taken to obtain the state decrease trend value recorded as q2; the operation derivative Yi, tangent derivative A1, state increase trend value q1, and state decrease trend value q2 at each acquisition time are normalized and their values ​​are taken, and the values ​​are analyzed by formula calculation to obtain the abnormal value Aq; the specific calculation formula is:

[0019]

[0020] Among them, μ1, μ2, and μ3 are the set proportional constants respectively.

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

[0022] 301: Retrieve environmental information of sensors in the area, and perform monitoring and analysis based on the information to measure the impact of environmental factors on the sensors, thereby obtaining an environmental impact value;

[0023] 302: Retrieving sensor information of sensors in the area, and performing monitoring and analysis based on the information to measure the working state of the sensor itself, thereby obtaining a working state value;

[0024] 303: The environmental impact value Hi and the working state value PMi of the sensor at each collection time are calculated and analyzed by formula to obtain the operation index Yi of the sensor at each collection time, and the calculation formula is:

[0025]

[0026] Among them, γ1 and γ2 are the set proportional constants respectively.

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

[0028] Retrieve the environmental information of the sensors in the area at each collection time, including the environmental temperature, environmental humidity and vibration amplitude, and record them as Ti, Si and Fi respectively, where i = 1, 2, 3 ... I, I is a positive integer, I is the total number of collection times, and i is any one of the collection times;

[0029] Different types of sensors are set to correspond to a standard operating parameter, and 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 acquisition time, which is recorded as BT; similarly, the ambient humidity is compared with the standard ambient humidity range to obtain the calibration ambient humidity at each acquisition time, which is recorded as BS;

[0031] The ambient temperature Ti, ambient humidity Si, calibration temperature BT, calibration humidity BS, and vibration amplitude Fi at each acquisition time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the environmental impact value Hj at each acquisition time. The specific calculation formula is:

[0032]

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

[0034] Preferably, the steps for obtaining the calibration temperature are:

[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 collection moment can be obtained.

[0036] Preferably, the specific process of measuring the working status of the sensor itself is as follows:

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

[0038] The actual power P i, working temperature G i, response speed Vi, sensitivity coefficient M, standard power BP and standard working temperature BG of the sensor at each acquisition time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the working state value PM i of the sensor at each acquisition time. The specific calculation formula is:

[0039]

[0040] Among them, β1, β2, and β3 are the set proportional constants respectively.

[0041] Beneficial effects of the present invention:

[0042] 1. By retrieving the sensor information and environmental information of each sensor in the area at different collection times, and through normalization and formula calculation methods, the system analyzes the impact of environmental factors on the sensor and the working status of the sensor itself. In particular, by comparing the actual working parameters of the sensor with the standard parameters, the system can determine whether the sensor is working normally or whether it is interfered by environmental factors, providing data support for judging the confidence level of the data collected by the sensor;

[0043] 2. Dynamically evaluate the status of the sensor by monitoring the curve of the sensor's operating index changing over time. The derivative analysis system of the tangent of the curve determines whether the sensor is abnormal. The monitoring cycle is dynamically adjusted according to the abnormal situation to ensure that the acquisition frequency is increased when the status is abnormal, and the problem is captured and processed 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 themselves can be judged, helping them to select more reliable data in tunnel excavation construction, ensuring data quality and ensuring the safety of tunnel excavation construction;

[0045] In summary, this method not only improves the accuracy and reliability of data collection during tunnel excavation, but also provides a strong guarantee for construction safety. Through real-time monitoring and dynamic adjustment, it can effectively cope with the challenges brought by complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] See also Figure 1 As shown, the present invention is a data collection preprocessing method for tunnel excavation construction, comprising the following steps:

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

[0051] Step 2: retrieve the sensor information and environmental information corresponding to each sensor in the area at each collection time, and conduct a comprehensive analysis of the sensors in the area and the environment in which the sensors are located to determine the working status of the sensors; the specific sensor information includes actual power, operating temperature, response speed (the response speed here refers to the speed at which the sensor responds to changes in the input signal, which reflects the sensitivity and timeliness of the sensor to changes in the environment or conditions); the environmental information includes the ambient temperature, ambient humidity and vibration amplitude of the environment in which the sensor is located; it should be noted that because the continuous mechanical vibration during the tunnel excavation process will affect the stability of the sensor (mechanical vibration will cause noise or interference in the sensor output signal, especially in sensitive sensors such as accelerometers, pressure sensors, etc., these noises will cause the collected data to fluctuate greatly and it is difficult to stably reflect the actual situation) or cause looseness (continuous vibration will cause the installation position of the sensor to be slightly displaced or loosened, and this looseness will affect the accuracy and measurement direction of the sensor, thereby causing deviations in the collected data), the collected data is likely to have errors; specifically:

[0052] Analysis of the impact of environmental factors:

[0053] Different types of sensors are set to correspond to a standard working parameter. The specific standard working parameters include standard power, standard working temperature, standard ambient temperature range and standard ambient humidity range. The standard power and standard working temperature are recorded as BP and BG. It should be noted that the standard working parameters refer to the various working conditions determined by the sensor during the design and manufacturing process. These conditions can ensure that the sensor can play the best performance and accuracy in various application scenarios. These parameters are determined by factors such as the material selection, internal structure design and process technology of the sensor. If the sensor exceeds these standard ranges during operation, the accuracy and reliability of its data collection will be affected, and may even cause equipment damage or failure. For example, high humidity or low humidity environment will affect the physical properties of the electronic components and materials of the sensor. For example, excessive humidity will cause the insulation performance of electrical insulation materials to decrease, thereby increasing the risk of leakage and affecting the electrical performance of the sensor. The output signal of the sensor may fluctuate or become unstable as a result. Although the impact of low humidity is relatively small, static electricity problems will increase in extremely low humidity environments, especially in electronic sensors. Static electricity accumulation will cause signal interference or damage to electronic components.

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

[0055] Compare and analyze the ambient temperature with the standard ambient temperature interval. When the ambient temperature is greater than the upper limit of the standard ambient temperature interval, the upper limit of the standard ambient temperature interval is used as the calibration temperature of the ambient temperature; when the ambient temperature is within the standard ambient temperature interval, 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 interval, the lower limit of the standard ambient temperature interval 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, the ambient humidity is compared and analyzed with the standard ambient humidity interval. When the ambient humidity is greater than the upper limit of the standard ambient humidity interval, the upper limit of the standard ambient humidity interval is used as the calibration humidity of the ambient humidity; when the ambient humidity is within the standard ambient humidity interval, the ambient humidity is used as the calibration humidity of the ambient humidity; when the ambient humidity is less than the lower limit of the standard ambient humidity interval, the lower limit of the standard ambient humidity interval is used as the calibration humidity of the ambient humidity; thus, the calibrated ambient humidity at each acquisition time can be obtained and recorded as BS;

[0057] The ambient temperature, ambient humidity, calibration temperature, calibration humidity, and vibration amplitude at each collection time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the environmental impact value Hj at each collection time. The specific calculation formula is:

[0058]

[0059] Among them, α1, α2, and α3 are respectively set proportional constants. It can be seen from the formula that when the ambient temperature and ambient humidity deviate 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 at this time has a greater impact on the sensor, and the environmental impact value is greater; when the vibration amplitude is larger, it means that the vibration is more severe, the impact on the sensor is greater, and the environmental impact value is greater;

[0060] Analysis of the sensor's own status:

[0061] The actual power, operating temperature, and response speed of the sensor at each acquisition moment are retrieved and recorded 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 conditions of the sensor. Different types of sensors are set to correspond to a temperature sensitivity coefficient, and the sensor type is obtained, and is compared with all set sensor types to match the corresponding temperature sensitivity coefficient, which is recorded as M.

[0062] The actual power P i, working temperature G i, response speed Vi, sensitivity coefficient M, standard power BP and standard working temperature BG of the sensor at each acquisition time are normalized and their values ​​are taken. The values ​​are calculated and analyzed by formula to obtain the working state value PM i of the sensor at each acquisition time. The specific calculation formula is:

[0063]

[0064] Among them, β1, β2, and β3 are respectively the set proportional constants; from the formula, it can be seen that when the actual power and working temperature are closer to the standard power and standard working temperature, the working state of the sensor is more normal, and the working state value is larger; otherwise, the working state value is smaller; when the corresponding speed is larger, it means that the sensitivity of the sensor is greater, and the working state value is larger;

[0065] The environmental impact value Hi and the working state value PMi of the sensor at each collection time are calculated and analyzed by formula to obtain the operation index Yi of the sensor at each collection time. The calculation formula is:

[0066]

[0067] Among them, γ1 and γ2 are the set proportional constants respectively;

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

[0069] By retrieving the sensor information and environmental information of each sensor in the area at different collection times, and through normalization and formula calculation methods, the impact of environmental factors on the sensors and the working status of the sensors themselves are analyzed. In particular, by comparing the actual working parameters of the sensors with the standard parameters, the system can determine whether the sensors are working properly or whether they are interfered by environmental factors, providing data support for judging the confidence level of the data collected by the sensors.

[0070] Step 3: Dynamic monitoring and analysis is performed through the curve of the sensor's operating index changing over time to determine whether the sensor is abnormal, and dynamic adjustment of monitoring is performed accordingly; specifically:

[0071] The operation index of each sensor in the area is retrieved as time goes by, and the tangent line is drawn at each index point. The tangent expression is obtained by data fitting, and the tangent derivative is obtained by derivation. The tangent derivative corresponding to each index point is recorded as Ai; the tangent derivatives greater than zero are summed up to obtain the state increase trend value recorded as q1, and the tangent derivatives less than zero are summed up and the absolute value is taken to obtain the state decrease trend value recorded as q2; the operation derivative Yi, tangent derivative A1, state increase trend value q1, and state decrease trend value q2 at each acquisition time are normalized and their values ​​are taken, and the values ​​are analyzed by formula calculation to obtain the abnormal value Aq; the specific calculation formula is:

[0072]

[0073] Among them, μ1, μ2, and μ3 are respectively set proportional constants. It can be seen from the formula that when the state increase trend value of the operation index is smaller and the state decrease trend value is larger, it means that the operation state of the sensor is reduced and the reduction speed is faster, then the abnormal value is larger; when the tangent derivative fluctuation degree is larger, it means that the operation state of the sensor is more unstable, then the abnormal value is larger;

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

[0075] Compare and analyze the abnormal value of the sensor with its corresponding safety interval. When the abnormal value is greater than the upper limit of the safety interval, it means that the sensor has obvious abnormality and the confidence level of the data collected is low. In this case, 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 so that the engineer can perform correction and maintenance operations on it.

[0076] When the abnormal value is within the safe range, it means that the sensor has a slight abnormality. In this case, the monitoring period of the sensor is adjusted and the data collected by the sensor is marked as moderately confident data. The specific adjustment steps are as follows:

[0077] The current monitoring period of the sensor is obtained, denoted as K', and the abnormal value corresponding to the monitoring period is denoted as Aq', and the latest monitoring period is obtained by formulating the calculation with the latest abnormal value Aq; the specific calculation formula is:

[0078]

[0079] And send the latest monitoring cycle to Step 1; From the formula, we can see that when the abnormal value is larger, the corresponding monitoring cycle is smaller, and the data collection is more frequent, which can better capture sensor information and environmental information, thereby improving the ability to respond to abnormal conditions, timely capturing and handling problems, and ensuring the accuracy of monitoring and the reliability of data;

[0080] When the abnormal value is less than the lower limit of the safety interval, it means that the sensor is normal and no adjustment is required. The current monitoring cycle can be maintained and the data collected by the sensor is marked as highly confident data.

[0081] In this way, the data collected by each sensor can be classified according to the confidence level, so as to facilitate the data screening of tunnel excavation construction workers and improve the data quality;

[0082] The status of the sensor is dynamically evaluated by monitoring the curve of the change of the sensor's operating index over time. The derivative analysis system of the tangent of the curve determines whether there is any abnormality in the sensor. The monitoring cycle is dynamically adjusted according to the abnormal situation to ensure that the acquisition frequency is increased when the status is abnormal, and the problem is captured and processed in time, thereby improving the response speed to sensor problems in the tunnel excavation process. At the same time, the confidence level of the data collected by the sensor itself is judged based on the abnormal value of the sensor, helping them to screen more reliable data in tunnel excavation construction, ensure data quality, and ensure the safety of tunnel excavation construction.

[0083] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A data collection preprocessing method for tunnel excavation construction, characterized in that: The following steps are involved: Step 1: Divide the tunnel into several areas, install several sensors in each area, collect sensor information and environmental information according to the latest monitoring cycle received, and store them; Step 2: retrieve the sensor information and environmental information corresponding to each sensor in the area at each collection time, and make a comprehensive analysis of the sensors in the area and the environment in which the sensors are located to determine the working status of the sensors, thereby obtaining the operation index of the sensors at each collection time; construct a two-dimensional rectangular coordinate system with time as the horizontal coordinate and the operation index as the vertical coordinate, input the operation index into the value coordinate axis according to its corresponding collection time, and record the point of the operation index in the coordinate axis as the index point, and use a smooth curve to connect the index points in sequence to obtain a curve graph of the sensor's operation index changing with time; Step 3: Dynamic monitoring and analysis is performed through the curve of the sensor's operating index changing over time to determine whether the sensor is abnormal, and dynamic adjustment of monitoring and confidence judgment of data are performed accordingly; specifically: Perform dynamic monitoring and analysis based on the curve of the operation index changing over time to obtain the abnormal value of the sensor; Set different types of sensors to correspond to a safety zone respectively; extract the sensor type and compare it with all the set sensor types to match the corresponding safety zone; Compare and analyze the abnormal value of the sensor with its corresponding safety interval. When the abnormal value is greater than the upper limit of the safety interval, mark the data collected by the sensor as lightly trusted data, obtain the type, number and location of the sensor, and send it to the corresponding engineer for correction and maintenance; When the abnormal value is within the safe range, the monitoring period of the sensor is adjusted and the data collected by the sensor is marked as moderately confident data; the specific adjustment steps are: Get the current monitoring cycle of the sensor and the abnormal value corresponding to the monitoring cycle, and calculate the latest monitoring cycle with the latest abnormal value, and send the latest monitoring cycle to Step 1; When the abnormal value is smaller than the lower limit of the safety interval, no adjustment is required, the current monitoring cycle can be maintained, and the data collected by the sensor is marked as highly confident data.

2. A data collection and preprocessing method for tunnel excavation construction according to claim 1, characterized in that: The specific process of dynamic monitoring and analysis based on the curve of operation index changing over time is as follows: Retrieve the curve of the operation index of each sensor in the area changing with time, draw a tangent line at each index point, use data fitting to get the tangent expression, and calculate the derivative of the tangent expression to get the tangent derivative, thereby getting the tangent derivative corresponding to each index point; sum up the tangent derivatives greater than zero to get the state increase trend value, and sum up the tangent derivatives less than zero and take the absolute value to get the state decrease trend value; The running derivative, tangent derivative, state increase trend value, and state decrease trend value at each acquisition moment are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formulas to obtain abnormal values.

3. The data collection and preprocessing method for tunnel excavation construction according to claim 1 is characterized in that: The specific process of comprehensive analysis of the sensors in the area and the environment in which the sensors are located is as follows: 301: Retrieve environmental information of sensors in the area, and perform monitoring and analysis based on the information to measure the impact of environmental factors on the sensors, thereby obtaining an environmental impact value; 302: Retrieving sensor information of sensors in the area, and performing monitoring and analysis based on the information to measure the working state of the sensor itself, thereby obtaining a working state value; 303: Performing formulaic calculation and analysis on the environmental impact value and the working status value of the sensor at each collection time to obtain the operation index of the sensor at each collection time.

4. A data collection preprocessing method for tunnel excavation construction according to claim 3, characterized in that: The specific process of measuring the impact of environmental factors on the sensor is as follows: Retrieve environmental information of sensors in the area at each collection time, including environmental temperature, environmental humidity and vibration amplitude; Different types of sensors are set to correspond to a standard operating parameter, and the specific standard operating parameters include standard power, standard operating temperature, standard ambient temperature range and standard ambient humidity range; Compare and analyze the ambient temperature with the standard ambient temperature range to obtain the calibrated temperature at each collection time; similarly, compare the ambient humidity with the standard ambient humidity range to obtain the calibrated ambient humidity at each collection time; The ambient temperature, ambient humidity, calibrated temperature, calibrated humidity, and vibration amplitude at each collection time are normalized and their values ​​are taken. The values ​​are then calculated and analyzed using a formula to obtain the environmental impact value at each collection time.

5. A data collection and preprocessing method for tunnel excavation construction according to claim 4, characterized in that: The steps to obtain the calibration temperature are: 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 collection moment can be obtained.

6. The data collection preprocessing method for tunnel excavation construction according to claim 3 is characterized in that: The specific process of measuring the working status of the sensor itself is as follows: Retrieve sensor information at each acquisition moment, including actual power, operating temperature, and response speed; set different types of sensors to correspond to a temperature sensitivity coefficient, 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 calculated and analyzed by formulas to obtain the working status value of the sensor at each acquisition time.

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