Shield forming tunnel form data processing system and method

Abstract

The invention discloses a shield forming tunnel form data processing system and method, and relates to the technical field of tunnel data processing. The shield forming tunnel form data processing method specifically comprises the following steps: S1, collecting and preprocessing data of a plurality of sections in a shield forming tunnel; s2, calculating the offset of the center of each segment; s3, continuously measuring and analyzing a deformation trend; s4, predicting offset change by using a linear regression model; s5, predicting the final offset of the duct piece by using the trained model; the removed abnormal values are recorded and analyzed, so that abnormal factors of segments in the tunnel can be screened, potential problems can be found and solved in time, further deterioration of the problems is prevented, and safety and stability of the tunnel are ensured; the deformation is calculated and the deformation trend is analyzed by using a time sequence, so that the deformation trend can be found in time, a subsequent detection plan can be formulated, the deformation problem can be found and processed in time through continuous monitoring, and the stability and the safety of the tunnel are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel data processing, and in particular to a shield-shaped tunnel morphology data processing system and method. Background Art

[0002] Shield tunneling refers to a method of dark excavation that uses shield machinery to maintain the stability of the excavation surface, excavates the soil under the protection of the shield shell, assembles prefabricated segments at the tail to form a tunnel structure, and finally uses jacks to support the assembled lining and use its reaction force to push the shield forward. Shield machinery can automatically excavate, assemble segments, and push the shield forward, greatly saving manpower. And because of the automated operation of shield machinery, the construction speed is relatively fast, which can shorten the construction period. Shield tunneling can form a tunnel in one excavation, reducing the trouble of multiple excavations and supports, and can be "tailor-made" according to the cross-sectional size, burial depth conditions, and basic conditions of the surrounding rock of the tunnel construction object, and is suitable for various complex geological conditions.

[0003] The segments in a shield-type tunnel are the main structure of the tunnel lining, and their morphological data are crucial to the overall quality and safety of the tunnel. Publication No. CN105335576A discloses a shield-type tunnel cross-section data processing method and system, which processes the data of disorganized cross-section points to form a cross-section measurement results table, thereby simplifying the evaluation of tunnel engineering.

[0004] However, as shown in the above technology, it only solves the problem of intelligent processing of a large amount of tunnel section data to save manpower. Usually, big data processing requires data preprocessing first, such as cleaning, removing outliers, noise, etc., and then supplementing the missing data by the interpolation steps of the above-mentioned comparison file. However, in fact, the abnormal values ​​of tunnel section data are not necessarily caused by detection failures, because they are obtained by equipment scanning, and it is possible to scan the surface abnormalities of the segment caused by problems such as cracks that are not easy to detect and loose segments. At this time, the abnormal values ​​have a certain warning effect. Therefore, the use of common data processing logic is not necessarily appropriate; and there are many reasons for the displacement of the segments in the tunnel. The displacement caused by construction factors may be one-time, but for some displacements caused by geological factors, it will be continuous, so continuous detection and prediction are necessary. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a shield-forming tunnel morphology data processing system and method, which solves the problems still existing in the above-mentioned prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a shield-shaped tunnel morphology data processing method, specifically comprising the following steps: S1 collects data from several sections in the shield-type tunnel, sorts and cleans the data, removes noise and outliers, interpolates missing data, and records the removed outliers for abnormal cause analysis to screen for abnormal factors in the tunnel segments; S2 calculates the offset of each segment center and determines the actual distance of the offset, compares the offset with construction and geological information, and preliminarily analyzes the cause of the offset; S3 measures the diameter of the pipe segment and calculates the roundness error; classifies and sorts the deformation, uses time series to analyze the deformation trend, and formulates a subsequent inspection plan, setting an upper limit on the number of measurements for continuous inspection; S4 combines the time domain factor analysis with the offset law, uses the linear regression model to predict the offset change, selects the prediction model, and uses historical data to train and verify the model; S5 uses the trained model to predict the final displacement of the segment, evaluates the reliability and uncertainty of the prediction results, and provides a confidence interval.

[0007] Preferably, the steps of step S1 specifically include: S1.1 Data collection: Use high-precision measurement equipment to collect cross-sectional data at key locations in the tunnel and establish a cross-sectional data set U (u1, u2, ..., u n ), where u represents the data of each section, and u includes the center position O, diameter D, and thickness T of the segment. The center position O of the segment is represented by the coordinates (X, Y, Z); the frequency of data collection is determined according to the construction progress and geological conditions; S1.2 Data classification and sorting: Classify data by type, including location data and size data; and sort each type of data by time sequence or cross-sectional position; S1.3 Data preprocessing: clean the raw data, remove noise and outliers, calculate the mean μ and standard deviation σ of the data set, and use linear interpolation or Lagrange interpolation to interpolate missing data; The calculation formula for the average value μ is: ; Where n is the number of data points in the dataset, u p is the pth data point in U in the dataset; The calculation formula of standard deviation σ is: .

[0008] Preferably, when the acquisition device scans a crack in a pipe segment, or when a pipe segment partially falls off, the data will be obviously abnormal. Therefore, the identified abnormal values ​​are recorded and marked while being eliminated for subsequent judgment of the specific situation of the abnormal value. The specific steps are as follows: a1 Outlier judgment: For each data point u in the data set U, calculate the Z value: Z=σu - μ; if |Z|>Z thresh , the corresponding data point is regarded as an outlier; a2 Outlier record: Remove the identified outliers from the original data set and record them in the outlier record set A rec A rec The information recorded in the includes: the outlier itself, the time of collection, the location of collection, and the type of outlier; a3 Continuous outlier judgment: traverse A rec , check whether there are consecutive N adj The data of the collection points with or above N are all abnormal values. adj The threshold value of the number of adjacent collection points is set to determine the situation of continuous outliers; a4 Warning trigger: If there are continuous outliers, the position corresponding to the outliers is added to the warning set A warn , and trigger the early warning mechanism to notify relevant personnel to handle it; a5 Subsequent processing: For A warn If segment displacement occurs at the same time, the abnormal value data will be used as the analysis data for the subsequent segment displacement causes; then manual inspection or other equipment will be arranged to further confirm the position corresponding to the abnormal value.

[0009] Preferably, the continuous outlier judgment condition is: ; Where [p, q] represents the index range of continuous collection points, N represents the set of natural numbers, that is, the set of all positive integers; the condition p < q means that the starting index p is less than the ending index q, that is, [p, q] is a continuous data point interval, not a single point or reverse interval; k is any integer between [p, q], and k∈A rec Indicates that the data point corresponding to index k exists in the outlier record set A rec In the example, there is a data point identified as an outlier; N adj is the threshold of the number of adjacent collection points, q - p+1≥N adj Indicates that the number of consecutive outliers from p to j is at least N adj .

[0010] Preferably, step S2 specifically includes: S2.1 Calculate the offset: Use vector subtraction to calculate the offset of each segment center relative to the design position ΔP = (ΔX, ΔY, ΔZ); calculate the modulus length of the offset , represents the actual distance of the offset; S2.2 Preliminary analysis of the cause of the offset: Compare the offset with the construction records and geological survey reports to preliminarily determine the cause of the offset; record the time t, position (X′, Y′, Z′) and offset |ΔP| of the offset event.

[0011] Preferably, step S3 specifically includes: S3.1 Diameter measurement and calculation: Measure the diameter of the segment at multiple angles to obtain the diameter array D j ={D j 1 , D j 2 , ..., D j m}, D j Represents the j-th section data u in the data set U j The diameter array of multiple collected points in the circle; and calculate the roundness error , where max(D) is D j The maximum diameter value in, min (D) is D j The minimum diameter value in, avg(D) is D j The average value of all diameter values ​​in; S3.2 Classification and sorting of deformation: Classify deformation according to the degree and direction of deformation, including uniform deformation and local deformation; and sort deformation, including by degree and direction of deformation; S3.3 Introducing the timing factor: Record the time t of each measurement to form a time series {t1, t2, ..., t n}, use the time series analysis method to analyze the deformation change trend over time; follow-up inspection plan: formulate a follow-up inspection plan, including inspection time, inspection location, and inspection frequency; set an upper limit M of the number of measurements for each offset position.

[0012] Preferably, step S4 specifically includes: S4.1 Continuous offset detection: Set the threshold ε to determine whether a certain measurement value of the offset position has a new offset compared to the previous measurement stop , if a new detection is performed at a certain offset position, there is |ΔP new |-|ΔP old | < ε stop , it means that there is no new offset in the measurement position compared with the last measurement, so the measurement is stopped at the position without new offset, and the measurement is continued at other positions until the number of measurements reaches the upper limit M; where ΔP new Indicates the offset of the new measurement ΔP, ΔP old Indicates the offset ΔP of the previous measurement; S4.2 Analysis of migration law: Combine time domain factors to analyze the migration law and use the linear regression model P′ = a + bt predicts the change trend of the offset over time, where P′ is the predicted offset, t is the time, a is the intercept, and b is the slope.

[0013] Preferably, step S5 specifically includes: S5.1 Prediction model training: Use historical offset data to train and validate the model; S5.2 Predict the final offset: Use the trained prediction model to predict the final offset P of the segment. final ; S5.3 Assess the reliability and uncertainty of predictions using confidence intervals [P lower , P upper ] indicates the range of the prediction results, and the confidence interval [P lower , P upper The calculation steps of ] are: S5.3.1 Calculate the standard error of the prediction: The standard error SEE of the model fitting, that is, the standard deviation of the residuals, is expressed as: ; Where H is the sample size, that is, the number of H samples collected at H time points, h = (1, 2, ..., H); P h is the actual observed offset; is the model at time t h The predicted value at time , that is, the predicted value at the hth time point; For the new time point t new The standard error of the predicted value SEP is expressed using the following approximate formula: ; in, is the mean of the sample time; Confidence interval: The confidence interval is calculated using the standard error of the predicted value, expressed as: Confidence interval = (a + bt new ) ±1.96×SEP; Among them, z 0.025 ≈ 1.96 is the quantile of the standard normal distribution; The calculated confidence interval is expressed as [P lower , P upper ].

[0014] The present invention also discloses a shield-forming tunnel morphology data processing system, comprising: Data acquisition and preprocessing module, used to acquire and preprocess the cross-section data in the shield tunnel; The offset calculation and analysis module is used to calculate the offset of each segment center relative to the designed position, determine the actual distance of the offset, compare the offset with the construction records and geological survey reports, and preliminarily analyze the cause of the offset; The measurement and trend analysis module is used to measure, calculate, classify and sort the diameter of the segments, and introduce time series factors to perform deformation trend analysis; Analysis and prediction of displacement patterns, used to predict segment displacement patterns in combination with deformation trend analysis and establish a prediction model; The prediction model training and evaluation module is used to train and evaluate the prediction model and use the trained prediction model to perform formal offset prediction.

[0015] Preferably, it also includes: Cloud platforms, used to access and use system resources via the Internet; Cloud storage module, used to store all information of the collection and processing process; Data visualization module, used to display tunnel morphology data in the form of graphics, images or animations, including 3D visualization of cross-section data, dynamic display of offsets and graphical analysis of deformation trends; Data analysis and report generation module, used to generate detailed reports based on analysis results, including data summary, analysis results, and recommended measures; The system integration and interface module is used to integrate with the construction management system and geological survey system, and provides an open interface to allow third-party applications or services to access the system.

[0016] The present invention provides a shield-shaped tunnel morphology data processing system and method. Compared with the prior art, it has the following beneficial effects: 1. The shield-forming tunnel morphology data processing method records and analyzes the removed outliers, which helps to screen the abnormal factors of the tunnel segments so as to timely discover and solve potential problems and prevent them from further deteriorating, thereby ensuring the safety and stability of the tunnel. Calculating the offset of the center of each segment and comparing it with the construction and geological information can preliminarily analyze the cause of the offset, which helps the construction team to adjust the construction plan in time to avoid or reduce the impact of the offset. Calculating deformation and using time series to analyze deformation trends can help to timely discover deformation trends and formulate subsequent inspection plans. Through continuous monitoring, deformation problems can be discovered and dealt with in a timely manner to ensure the stability and safety of the tunnel.

[0017] 2. The shield-forming tunnel morphology data processing method, the outlier processing in the data preprocessing step and the continuous outlier judgment mechanism effectively improve the quality and accuracy of the data. By recording and analyzing outliers instead of directly removing outliers without analysis, potential problems such as cracks or partial detachment of segments can be discovered and handled in a timely manner, thereby avoiding the impact of these problems on tunnel safety and stability. The method also achieves timely response and handling of abnormal conditions of segments in the tunnel through the triggering of the early warning mechanism and subsequent processing steps. This not only helps to ensure the safe operation of the tunnel, but also provides important reference information for the construction team so that they can adjust the construction plan and monitoring plan according to the actual situation.

[0018] 3. The shield-forming tunnel morphology data processing method can more accurately grasp the morphological changes of the tunnel and discover potential problems in a timely manner by accurately calculating the offset of the center of each segment and introducing time series factors to analyze the deformation trend. At the same time, the time domain factor is combined to conduct an in-depth analysis of the offset law, and a linear regression model is used for prediction, which improves the accuracy and reliability of the prediction. In addition, the method also sets an upper limit on the number of measurements to avoid unnecessary waste of resources, and introduces a confidence interval in the prediction results, providing more comprehensive information support for the construction team. In summary, this method has obvious advantages in data processing, analysis and prediction, which helps to improve the safety and efficiency of tunnel construction and operation and reduce potential risks.

[0019] 4. The shield tunnel morphology data processing system integrates multiple functional modules such as data acquisition, preprocessing, analysis, prediction and visualization, realizing the comprehensive management and efficient use of shield tunnel morphology data. Through the cloud platform and cloud storage, remote access and storage of data are realized, which improves the sharing and security of data. The data visualization module makes the analysis results more intuitive and easy to understand, which is helpful for decision-making. In addition, the system integration and interface module enhances the scalability and compatibility of the system, facilitates seamless connection with other construction management and geological survey systems, and improves the intelligent level of overall construction management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram of the process of Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the abnormal value judgment logic of the third embodiment of the present invention; Figure 4 This is a schematic diagram of the process of Embodiment 3 of the present invention; Figure 5 It is a system module block diagram of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] The present invention discloses a shield-shaped tunnel morphology data processing method and provides the following three technical solutions: Figure 1 The first embodiment is shown: specifically comprising the following steps: S1 collects data from several sections in the shield-shaped tunnel, sorts and cleans the data, removes noise and outliers, interpolates missing data, and records the removed outliers for abnormal cause analysis to screen out abnormal factors of the tunnel segments; S2 calculates the offset of each segment center and determines the actual distance of the offset, compares the offset with construction and geological information, and preliminarily analyzes the cause of the offset; S3 measures the diameter of the pipe segment and calculates the roundness error; classifies and sorts the deformation, uses time series to analyze the deformation trend, and formulates a subsequent inspection plan, setting an upper limit on the number of measurements for continuous inspection; S4 combines the time domain factor analysis with the offset law, uses the linear regression model to predict the offset change, selects the prediction model, and uses historical data to train and verify the model; S5 uses the trained model to predict the final displacement of the segment, evaluates the reliability and uncertainty of the prediction results, and provides a confidence interval.

[0023] Recording and analyzing the removed outliers helps to screen the abnormal factors of the segments in the tunnel so as to timely discover and solve potential problems and prevent them from further deteriorating, thus ensuring the safety and stability of the tunnel. Calculating the offset of each segment center and comparing it with construction and geological information can preliminarily analyze the cause of the offset, which helps the construction team to adjust the construction plan in time to avoid or reduce the impact of the offset. Calculating deformation and using time series to analyze deformation trends can help to timely discover deformation trends and formulate subsequent inspection plans. Through continuous monitoring, deformation problems can be discovered and dealt with in a timely manner to ensure the stability and safety of the tunnel.

[0024] Figure 2-Figure 3 The second embodiment is shown, which is mainly different from the first embodiment in that step S1 specifically includes: S1.1 Data collection: Use high-precision measurement equipment to collect cross-sectional data at key locations in the tunnel and establish a cross-sectional data set U (u1, u2, ..., un ), where u represents the data of each section, and u includes the segment center position O, diameter D, and thickness T. The segment center position O is represented by the coordinates (X, Y, Z); the data collection frequency is determined according to the construction progress and geological conditions to ensure the timeliness and representativeness of the data; S1.2 Data classification and sorting: Classify data by type, including location data and size data; and sort each type of data by time sequence or cross-sectional location to facilitate subsequent analysis; S1.3 Data preprocessing: clean the raw data, remove noise and outliers, such as using the 3σ principle to eliminate outliers, calculate the mean μ and standard deviation σ of the data set, and use linear interpolation or Lagrange interpolation to interpolate missing data; The calculation formula for the average value μ is: ; Where n is the number of data points in the dataset, u p is the pth data point in U in the dataset; The calculation formula of standard deviation σ is: .

[0025] When the acquisition equipment scans cracks in the pipe segment, or when the pipe segment has a local detachment problem, the data will be obviously abnormal. Therefore, the identified abnormal values ​​are recorded and marked while being eliminated for subsequent judgment of the specific situation of the abnormal value. The specific steps are as follows: a1 Outlier judgment: For each data point u in the data set U, calculate the Z value: Z=σu - μ; if |Z|>Z thresh ; (Z thresh is the set threshold, such as 3), the corresponding data point is regarded as an outlier; a2 Outlier record: Remove the identified outliers from the original data set and record them in the outlier record set A rec A rec The information recorded in the data includes: the outlier itself, the acquisition time, the acquisition location, the outlier type (such as data anomalies that may be caused by cracks); a3 Continuous outlier judgment: traverse A rec , check whether there are consecutive N adj The data of the collection points with or above N are all abnormal values. adj The threshold value of the number of adjacent collection points is set to determine the situation of continuous outliers; a4 Warning trigger: If there are continuous outliers, the position corresponding to the outliers is added to the warning set A warn , and trigger the early warning mechanism to notify relevant personnel to handle it; a5 Subsequent processing: For A warnIf segment displacement occurs at the same time, the abnormal value data will be used as the analysis data for the subsequent segment displacement causes; then manual inspection or other equipment will be arranged to further confirm the position corresponding to the abnormal value, such as checking whether there are subtle cracks.

[0026] The conditions for judging continuous outliers are: ; Where [p, q] represents the index range of continuous collection points, N represents the set of natural numbers, that is, the set of all positive integers; the condition p < q means that the starting index p is less than the ending index q, that is, [p, q] is a continuous data point interval, not a single point or reverse interval; k is any integer between [p, q], and k∈A rec Indicates that the data point corresponding to index k exists in the outlier record set A rec In the example, there is a data point identified as an outlier; N adj is the threshold of the number of adjacent collection points, q - p+1≥N adj Indicates that the number of consecutive outliers from p to j is at least N adj .

[0027] The outlier processing and continuous outlier judgment mechanism in the data preprocessing step effectively improve the quality and accuracy of the data. By recording and analyzing outliers instead of directly removing them without analysis, potential problems such as cracks or partial detachment of segments can be discovered and handled in a timely manner, thus avoiding the impact of these problems on tunnel safety and stability. The method also achieves timely response and handling of abnormal conditions of segments in the tunnel through the triggering of the early warning mechanism and subsequent processing steps. This not only helps to ensure the safe operation of the tunnel, but also provides important reference information for the construction team so that they can adjust the construction plan and monitoring plan according to the actual situation.

[0028] Figure 4 The third embodiment is shown, which is mainly different from the first embodiment in that step S2 specifically includes: S2.1 Calculate the offset: Use vector subtraction to calculate the offset of each segment center relative to the design position ΔP = (ΔX, ΔY, ΔZ); calculate the modulus length of the offset , represents the actual distance of the offset; S2.2 Preliminary analysis of the cause of the offset: Compare the offset with the construction records and geological survey reports to preliminarily determine the cause of the offset; record the time t, position (X′, Y′, Z′) and offset |ΔP| of the offset event.

[0029] Step S3 specifically includes: S3.1 Diameter measurement and calculation: Measure the diameter of the segment at multiple angles to obtain the diameter array D j ={D j 1 , D j 2 , ..., D j m}, D j Represents the j-th section data u in the data set U j The diameter array of multiple collected points in the circle; and calculate the roundness error , where max(D) is D j The maximum diameter value in, min (D) is D j The minimum diameter value in, avg(D) is D j The average value of all diameter values ​​in; S3.2 Classification and sorting of deformation: Classify deformation according to the degree and direction of deformation, including uniform deformation and local deformation; and sort deformation, including by degree and direction of deformation; S3.3 Introducing the timing factor: Record the time t of each measurement to form a time series {t1, t2, ..., t n}, use time series analysis methods, such as ARIMA model, to analyze the trend of deformation over time; follow-up inspection plan: formulate a follow-up inspection plan, including inspection time, inspection location, and inspection frequency; set an upper limit M of the number of measurements for each offset position.

[0030] Step S4 specifically includes: S4.1 Continuous offset detection: Set the threshold ε to determine whether a certain measurement value of the offset position has a new offset compared to the previous measurement stop , if a new detection is performed at a certain offset position, there is |ΔP new |-|ΔP old | < ε stop , it means that there is no new offset in the measurement position compared with the last measurement, so the measurement is stopped at the position without new offset, and the measurement is continued at other positions until the number of measurements reaches the upper limit M; where ΔP new Indicates the offset of the new measurement ΔP, ΔP old Indicates the offset ΔP of the previous measurement; S4.2 Analysis of migration law: Combine time domain factors to analyze the migration law and use the linear regression model P′ = a + bt predicts the change trend of the offset over time, where P′ is the predicted offset, t is the time, a is the intercept, and b is the slope.

[0031] Step S5 specifically includes: S5.1 Prediction model training: Use historical offset data to train and verify the model to ensure its accuracy and reliability; S5.2 Predict the final offset: Use the trained prediction model to predict the final offset P of the segment. final ; S5.3 Assess the reliability and uncertainty of predictions using confidence intervals [P lower , P upper ] indicates the range of the prediction results, and the confidence interval [P lower , P upper The calculation steps of ] are: S5.3.1 Calculate the standard error of the prediction: The standard error SEE of the model fitting, that is, the standard deviation of the residuals, is expressed as: ; Where H is the sample size, that is, the number of H samples collected at H time points, h = (1, 2, ..., H); P h is the actual observed offset; is the model at time t h The predicted value at time , that is, the predicted value at the hth time point; For the new time point t new The standard error of the predicted value SEP is expressed using the following approximate formula: ; in, is the mean of the sample time; Confidence interval: The confidence interval is calculated using the standard error of the predicted value, expressed as: Confidence interval = (a + bt new ) ±1.96×SEP; Among them, z 0.025 ≈ 1.96 is the quantile of the standard normal distribution, indicating a confidence level of 95%. Since 1 -0.95=0.05, and 0.05 / 2=0.025, we choose z 0.025 ; The calculated confidence interval is expressed as [P lower , P upper ].

[0032] By accurately calculating the offset of each segment center and introducing time series factors to analyze deformation trends, this method can more accurately grasp the morphological changes of the tunnel and discover potential problems in a timely manner. At the same time, the time domain factor is combined to conduct an in-depth analysis of the offset law and use a linear regression model for prediction, which improves the accuracy and reliability of the prediction. In addition, this method also sets an upper limit on the number of measurements to avoid unnecessary waste of resources, and introduces a confidence interval in the prediction results, providing more comprehensive information support for the construction team. In summary, this method has obvious advantages in data processing, analysis and prediction, which helps to improve the safety and efficiency of tunnel construction and operation and reduce potential risks.

[0033] See also Figure 5 The present invention also discloses a shield-forming tunnel morphology data processing system, comprising: Data acquisition and preprocessing module, used to acquire and preprocess the cross-section data in the shield tunnel; The offset calculation and analysis module is used to calculate the offset of each segment center relative to the designed position, determine the actual distance of the offset, compare the offset with the construction records and geological survey reports, and preliminarily analyze the cause of the offset; The measurement and trend analysis module is used to measure, calculate, classify and sort the diameter of the segments, and introduce time series factors to perform deformation trend analysis; Analysis and prediction of displacement patterns, used to predict segment displacement patterns in combination with deformation trend analysis and establish a prediction model; The prediction model training and evaluation module is used to train and evaluate the prediction model and use the trained prediction model to perform formal offset prediction; Cloud platforms, used to access and use system resources via the Internet; Cloud storage module, used to store all information of the collection and processing process; Data visualization module, used to display tunnel morphology data in the form of graphics, images or animations, including 3D visualization of cross-section data, dynamic display of offsets and graphical analysis of deformation trends; Data analysis and report generation module, used to generate detailed reports based on analysis results, including data summary, analysis results, and recommended measures; The system integration and interface module is used to integrate with the construction management system and geological survey system, and provides an open interface to allow third-party applications or services to access the system.

[0034] The system integrates multiple functional modules such as data acquisition, preprocessing, analysis, prediction and visualization, realizing the comprehensive management and efficient use of shield tunnel morphological data. Through the cloud platform and cloud storage, remote access and storage of data are realized, which improves the sharing and security of data. The data visualization module makes the analysis results more intuitive and easy to understand, which is helpful for decision-making. In addition, the system integration and interface module enhances the scalability and compatibility of the system, facilitates seamless connection with other construction management and geological survey systems, and improves the overall intelligent level of construction management.

[0035] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0036] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A shield-forming tunnel morphology data processing method, characterized in that: The specific steps include: S1 collects data from several sections in the shield-type tunnel, sorts and cleans the data, removes noise and outliers, interpolates missing data, and records the removed outliers for abnormal cause analysis to screen for abnormal factors in the tunnel segments; S2 calculates the offset of each segment center and determines the actual distance of the offset, compares the offset with construction and geological information, and preliminarily analyzes the cause of the offset; S3 measures the diameter of the pipe segment and calculates the roundness error; Classify and sort deformations, use time series to analyze deformation trends, and develop follow-up inspection plans, setting an upper limit on the number of measurements for continuous inspection; S4 combines the time domain factor analysis with the offset law, uses the linear regression model to predict the offset change, selects the prediction model, and uses historical data to train and verify the model; S5 uses the trained model to predict the final displacement of the segment, evaluates the reliability and uncertainty of the prediction results, and provides a confidence interval.

2. A shield-forming tunnel morphology data processing method according to claim 1, characterized in that: The steps of step S1 specifically include: S1.1 Data collection: Use high-precision measurement equipment to collect cross-sectional data at key locations in the tunnel and establish a cross-sectional data set U (u1, u2, ..., u n ), where u represents the data of each section, and u includes the center position O, diameter D, and thickness T of the segment. The center position O of the segment is represented by the coordinates (X, Y, Z); the frequency of data collection is determined according to the construction progress and geological conditions; S1.2 Data classification and sorting: Classify data by type, including location data and size data; and sort each type of data by time sequence or cross-sectional position; S1.3 Data preprocessing: clean the raw data, remove noise and outliers, calculate the mean μ and standard deviation σ of the data set, and use linear interpolation or Lagrange interpolation to interpolate missing data; The calculation formula for the average value μ is: ; Where n is the number of data points in the dataset, u p is the pth data point in U in the dataset; The calculation formula of standard deviation σ is: .

3. A shield-forming tunnel morphology data processing method according to claim 2, characterized in that: When the acquisition equipment scans cracks in the pipe segment, or when the pipe segment has a local detachment problem, the data will be obviously abnormal. Therefore, the identified abnormal values ​​are recorded and marked while being eliminated for subsequent judgment of the specific situation of the abnormal value. The specific steps are as follows: a1 Outlier judgment: For each data point u in the data set U, calculate the Z value: Z=σu-μ; if |Z|>Z thresh , the corresponding data point is regarded as an outlier; a2 Outlier record: Remove the identified outliers from the original data set and record them in the outlier record set A rec A rec The information recorded in the includes: the outlier itself, the time of collection, the location of collection, and the type of outlier; a3 Continuous outlier judgment: traverse A rec , check whether there are consecutive N adj The data of the collection points with or above N are all abnormal values. adj The threshold value of the number of adjacent collection points is set to determine the situation of continuous outliers; a4 Warning trigger: If there are continuous outliers, the position corresponding to the outliers is added to the warning set A warn , and trigger the early warning mechanism to notify relevant personnel to handle it; a5 Subsequent processing: For A warn If segment displacement occurs at the same time, the abnormal value data will be used as the analysis data for the subsequent segment displacement causes; then manual inspection or other equipment will be arranged to further confirm the position corresponding to the abnormal value.

4. A shield-forming tunnel morphology data processing method according to claim 3, characterized in that: The conditions for judging continuous outliers are: ； Where [p, q] represents the index range of continuous collection points, N represents the set of natural numbers, that is, the set of all positive integers; the condition p < q means that the starting index p is less than the ending index q, that is, [p, q] is a continuous data point interval, not a single point or reverse interval; k is any integer between [p, q], and k∈A rec Indicates that the data point corresponding to index k exists in the outlier record set A rec In the example, there is a data point identified as an outlier; N adj is the threshold of the number of adjacent collection points, q-p+1≥N adj Indicates that the number of consecutive outliers from p to j is at least N adj .

5. The shield-forming tunnel morphology data processing method according to claim 1 is characterized by: Step S2 specifically includes: S2.1 Calculate the offset: Use vector subtraction to calculate the offset of each segment center relative to the design position ΔP = (ΔX, ΔY, ΔZ); calculate the modulus length of the offset , represents the actual distance of the offset; S2.2 Preliminary analysis of the cause of the offset: Compare the offset with the construction records and geological survey reports to preliminarily determine the cause of the offset; record the time t, position (X′, Y′, Z′) and offset |ΔP| of the offset event.

6. A shield-forming tunnel morphology data processing method according to claim 5, characterized in that: Step S3 specifically includes: S3.1 Diameter measurement and calculation: Measure the diameter of the segment at multiple angles to obtain the diameter array D j = {D j 1 , D j 2 , ..., D j m }, D j Represents the j-th section data u in the data set U j The diameter array of multiple collected points in the circle; and calculate the roundness error , where max(D) is D j The maximum diameter value in, min (D) is D j The minimum diameter value in, avg(D) is D j The average value of all diameter values ​​in; S3.2 Classification and sorting of deformation: Classify deformation according to the degree and direction of deformation, including uniform deformation and local deformation; and sort deformation, including by degree and direction of deformation; S3.3 Introducing the timing factor: Record the time t of each measurement to form a time series {t1, t2, ..., t n }, use the time series analysis method to analyze the deformation change trend over time; follow-up inspection plan: formulate a follow-up inspection plan, including inspection time, inspection location, and inspection frequency; set an upper limit M of the number of measurements for each offset position.

7. A shield-forming tunnel morphology data processing method according to claim 6, characterized in that: Step S4 specifically includes: S4.1 Continuous offset detection: Set the threshold ε to determine whether a certain measurement value of the offset position has a new offset compared to the previous measurement stop , if a new detection is performed at a certain offset position, there is |ΔP new |-|ΔP old | < ε stop , it means that there is no new offset in the measurement position compared with the last measurement, so the measurement is stopped at the position without new offset, and the measurement is continued at other positions until the number of measurements reaches the upper limit M; where ΔP new Indicates the offset of the new measurement ΔP, ΔP old Indicates the offset ΔP of the previous measurement; S4.2 Analysis of offset rules: The offset rules are analyzed in combination with time domain factors, and the linear regression model P′= a +bt is used to predict the trend of offset changes over time, where P′ is the predicted offset, t is time, a is the intercept, and b is the slope.

8. A shield-forming tunnel morphology data processing method according to claim 7, characterized in that: Step S5 specifically includes: S5.1 Prediction model training: Use historical offset data to train and validate the model; S5.2 Predict the final offset: Use the trained prediction model to predict the final offset P of the segment. final ; S5.3 Assess the reliability and uncertainty of predictions using confidence intervals [P lower , P upper ] indicates the range of the prediction results, and the confidence interval [P lower , P upper The calculation steps of ] are: S5.3.1 Calculate the standard error of the prediction: The standard error SEE of the model fitting, that is, the standard deviation of the residuals, is expressed as: ; Where H is the sample size, that is, the number of H samples collected at H time points, h = (1, 2, ..., H); P h is the actual observed offset; is the model at time t h The predicted value at time , that is, the predicted value at the hth time point; For the new time point t new The standard error of the predicted value SEP is expressed using the following approximate formula: ; in, is the mean of the sample time; Confidence interval: The confidence interval is calculated using the standard error of the predicted value, expressed as: Confidence interval = (a + bt new ) ±1.96×SEP; Among them, z 0.025 ≈ 1.96 is the quantile of the standard normal distribution; The calculated confidence interval is expressed as [P lower , P upper ].

9. A shield-shaped tunnel morphology data processing system, used to implement the shield-shaped tunnel morphology data processing method according to any one of claims 1 to 8, characterized in that: include: Data acquisition and preprocessing module, used to acquire and preprocess the cross-section data in the shield tunnel; The offset calculation and analysis module is used to calculate the offset of each segment center relative to the designed position, determine the actual distance of the offset, compare the offset with the construction records and geological survey reports, and preliminarily analyze the cause of the offset; The measurement and trend analysis module is used to measure, calculate, classify and sort the diameter of the segments, and introduce time series factors to perform deformation trend analysis; Analysis and prediction of displacement patterns, used to predict segment displacement patterns in combination with deformation trend analysis and establish a prediction model; The prediction model training and evaluation module is used to train and evaluate the prediction model and use the trained prediction model to perform formal offset prediction.

10. A shield-forming tunnel morphology data processing system according to claim 9, characterized in that: Also includes: Cloud platforms, used to access and use system resources via the Internet; Cloud storage module, used to store all information of the collection and processing process; Data visualization module, used to display tunnel morphology data in the form of graphics, images or animations, including 3D visualization of cross-section data, dynamic display of offsets and graphical analysis of deformation trends; Data analysis and report generation module, used to generate detailed reports based on analysis results, including data summary, analysis results, and recommended measures; The system integration and interface module is used to integrate with the construction management system and geological survey system, and provides an open interface to allow third-party applications or services to access the system.

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