Tunnel maintenance big data analysis and prediction method based on 3D scanning

Abstract

The invention discloses a tunnel maintenance big data analysis and prediction method based on 3D scanning, and the method comprises the following steps: 1, data collection: carrying out the full-coverage scanning of the interior of a tunnel through 3D scanning equipment, generating high-precision three-dimensional point cloud data, and storing the collected point cloud data in a big data platform as the basis of subsequent analysis; the method has the beneficial effects that data acquisition is performed by using a high-precision three-dimensional scanning technology, comprehensive health assessment and fault prediction are performed on the tunnel structure through a big data analysis and machine learning model, and the maintenance period and method of the tunnel are optimized, so that the safety and the service life of the tunnel are improved, and the tunnel maintenance cost is reduced. The system is suitable for daily monitoring and maintenance management of various large tunnels such as highway tunnels, railway tunnels and the like, is particularly suitable for tunnel environments which are complex in structure and difficult to manually detect, and can effectively improve the efficiency and safety of tunnel maintenance through advanced 3D scanning and data analysis technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel maintenance management, and in particular to a tunnel maintenance big data analysis and prediction method based on 3D scanning. Background Art

[0002] With the rapid development of transportation infrastructure, tunnels, as an important part of transportation hubs, are becoming increasingly important for maintenance and management. Traditional tunnel inspection methods rely on manual or simple equipment, which are inefficient and inaccurate. Especially in complex tunnel structures, manual inspections are prone to miss potential structural hazards. At the same time, the demand for tunnel maintenance is gradually increasing. How to achieve accurate inspection, scientific analysis and reasonable prediction of tunnels has become an urgent problem to be solved.

[0003] In recent years, 3D scanning technology has been increasingly used in tunnel inspection due to its high precision and high coverage. However, there are still certain technical bottlenecks in effectively integrating these 3D data and using them for long-term tunnel maintenance prediction. Summary of the invention

[0004] The purpose of the present invention is to provide a tunnel maintenance big data analysis and prediction method based on 3D scanning in order to solve the above problems, use high-precision three-dimensional scanning technology to collect data, and use big data analysis and machine learning models to conduct comprehensive health assessment and fault prediction of the tunnel structure, optimize the tunnel maintenance cycle and methods, and thus improve the safety and service life of the tunnel.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] A tunnel maintenance big data analysis and prediction method based on 3D scanning, which includes the following steps:

[0007] Step 1: Data collection: Use 3D scanning equipment to fully scan the interior of the tunnel to generate high-precision 3D point cloud data, and store the collected point cloud data in the big data platform as the basis for subsequent analysis;

[0008] Step 2: Data screening: Process the collected 3D point cloud data to ensure the accuracy of the data;

[0009] Step 3: Feature extraction: The processed data will be compared with the historical monitoring data of the tunnel to identify possible abnormal areas in the tunnel structure;

[0010] Step 4: Data analysis and prediction: Based on historical data and real-time 3D scanning data, big data analysis methods are used to evaluate the structural health of the tunnel, and machine learning algorithms are used to predict possible structural problems that may occur in the tunnel in the future;

[0011] Step 5: Visualization: The analysis and prediction results are presented through visualization tools to generate a three-dimensional model and health assessment report of the tunnel, helping engineers to intuitively understand the current status and future trends of the tunnel.

[0012] Furthermore, during the data collection process in step 1, a data collection module is used to perform an all-round scan of the interior of the tunnel through a 3D scanning device to generate high-precision three-dimensional point cloud data of the tunnel surface; the device can be fixed on a track inside the tunnel and perform mobile scanning along the tunnel to ensure full coverage of the tunnel structure.

[0013] Furthermore, after the point cloud data is collected in step 2, it is pre-processed by a data cleaning module to remove invalid points caused by noise, equipment errors, etc.

[0014] Furthermore, in step 3, the currently collected point cloud is aligned with the historical data by using data alignment technology to identify new or extended structural anomalies in the tunnel.

[0015] Furthermore, in step 4, when analyzing and predicting the data, the data analysis module is used to generate a health assessment report of the tunnel structure by integrating and analyzing historical data and real-time collected data. The machine learning model is used to train data such as crack expansion and structural deformation to predict possible structural problems that may occur in the tunnel in the future; for example, by analyzing the expansion speed and deformation curvature of the crack, the failure risk of the tunnel is predicted, and corresponding maintenance strategies are formulated.

[0016] Furthermore, the analysis results in step 5 are presented through a three-dimensional visualization platform, and users can view the distribution of cracks and deformations in the virtual tunnel model; the visualization platform also supports dynamic display of historical change trends of tunnel structures to help engineers predict future maintenance needs.

[0017] Furthermore, the three-dimensional point cloud data extracted in step 1 contains detailed information such as surface morphology, cracks, deformation, etc. of the tunnel structure.

[0018] Furthermore, the tunnel monitoring data in step 2 may come from sensor equipment of a tunnel health monitoring system.

[0019] Furthermore, the data screening in step 2 involves cleaning and organizing the collected 3D point cloud data, including data noise reduction, outlier processing and parameter extraction, to ensure the accuracy and availability of the data.

[0020] Furthermore, the prediction content in step 4 includes crack expansion trend, deformation speed, potential failure risk, etc.

[0021] Furthermore, the visualization in step 5 shows the historical changes and prediction results of the tunnel structure through 3D models, charts and curves, so as to intuitively express the data change characteristics and structural health status, which can provide a scientific basis for the maintenance and management of the tunnel and improve the safety performance and service life of the tunnel.

[0022] Furthermore, the present invention is applicable to the daily monitoring and maintenance management of various large-scale tunnels such as highway tunnels and railway tunnels, especially to tunnel environments with complex structures that are difficult to detect manually. This method can effectively improve the efficiency and safety of tunnel maintenance through advanced 3D scanning and data analysis technology.

[0023] The beneficial effects of the present invention are:

[0024] 1. The present invention utilizes 3D scanning technology to conduct comprehensive and accurate structural monitoring of tunnels, capture tiny cracks and deformations, and avoid the limitations of manual detection.

[0025] 2. The present invention automatically processes massive data through a big data platform and machine learning algorithms, reducing the complexity of manual operations while improving the accuracy and efficiency of analysis.

[0026] 3. Based on the combination of historical data and structural models, the present invention can effectively predict future maintenance needs and reduce the risk of sudden structural damage.

[0027] 4. The present invention displays the analysis results through three-dimensional models and intuitive charts, which helps decision makers quickly understand the health status of the tunnel and optimize the maintenance plan. DETAILED DESCRIPTION

[0028] A tunnel maintenance big data analysis and prediction method based on 3D scanning, which includes the following steps:

[0029] Step 1: Data collection: Use 3D scanning equipment to fully scan the interior of the tunnel to generate high-precision 3D point cloud data, and store the collected point cloud data in the big data platform as the basis for subsequent analysis;

[0030] Step 2: Data screening: Process the collected 3D point cloud data to ensure the accuracy of the data;

[0031] Step 3: Feature extraction: The processed data will be compared with the historical monitoring data of the tunnel to identify possible abnormal areas in the tunnel structure;

[0032] Step 4: Data analysis and prediction: Based on historical data and real-time 3D scanning data, big data analysis methods are used to evaluate the structural health of the tunnel, and machine learning algorithms are used to predict possible structural problems that may occur in the tunnel in the future;

[0033] Step 5: Visualization: The analysis and prediction results are presented through visualization tools to generate a three-dimensional model and health assessment report of the tunnel, helping engineers to intuitively understand the current status and future trends of the tunnel.

[0034] In this embodiment, during the data collection process in step 1, a data collection module is used to perform an all-round scan of the interior of the tunnel through a 3D scanning device to generate high-precision three-dimensional point cloud data of the tunnel surface; the device can be fixed on a track inside the tunnel and perform mobile scanning along the tunnel to ensure full coverage of the tunnel structure.

[0035] In this embodiment, after the point cloud data is collected in step 2, it is pre-processed by a data cleaning module to remove invalid points caused by noise, equipment errors, etc.

[0036] In this embodiment, step 3 is to align the currently collected point cloud with the historical data through data alignment technology to identify new or expanded structural anomalies in the tunnel.

[0037] In this embodiment, when analyzing and predicting data in step 4, a data analysis module is used to generate a health assessment report of the tunnel structure by integrating and analyzing historical data and real-time collected data. A machine learning model is used to train data such as crack expansion and structural deformation to predict possible structural problems that may occur in the tunnel in the future; for example, by analyzing the expansion speed and deformation curvature of the crack, the failure risk of the tunnel is predicted, and a corresponding maintenance strategy is formulated.

[0038] In this embodiment, the analysis results in step 5 are presented through a three-dimensional visualization platform, and the user can view the distribution of cracks and deformations in the virtual tunnel model; the visualization platform also supports dynamic display of historical change trends of tunnel structures to help engineers predict future maintenance needs.

[0039] In this embodiment, the three-dimensional point cloud data extracted in step 1 includes detailed information such as the surface morphology, cracks, and deformation of the tunnel structure.

[0040] In this embodiment, the tunnel monitoring data in step 2 may come from sensor equipment of a tunnel health monitoring system.

[0041] In this embodiment, the data screening in step 2 involves cleaning and organizing the collected 3D point cloud data, including data noise reduction, outlier processing and parameter extraction, to ensure the accuracy and availability of the data.

[0042] In this embodiment, the prediction content in step 4 includes crack expansion trend, deformation speed, potential failure risk, etc.

[0043] In this embodiment, the visualization in step 5 displays the historical changes and prediction results of the tunnel structure through 3D models, charts and curves, so as to intuitively express the data change characteristics and structural health status, provide a scientific basis for the maintenance and management of the tunnel, and improve the safety performance and service life of the tunnel.

[0044] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A tunnel maintenance big data analysis and prediction method based on 3D scanning, characterized by: It includes the following steps: Step 1: Data collection: Use 3D scanning equipment to fully scan the interior of the tunnel to generate high-precision 3D point cloud data, and store the collected point cloud data in the big data platform as the basis for subsequent analysis; Step 2: Data screening: Process the collected 3D point cloud data to ensure the accuracy of the data; Step 3: Feature extraction: The processed data will be compared with the historical monitoring data of the tunnel to identify possible abnormal areas in the tunnel structure; Step 4: Data analysis and prediction: Based on historical data and real-time 3D scanning data, big data analysis methods are used to evaluate the structural health of the tunnel, and machine learning algorithms are used to predict possible structural problems that may occur in the tunnel in the future; Step 5: Visualization: The analysis and prediction results are presented through visualization tools to generate a three-dimensional model and health assessment report of the tunnel, helping engineers to intuitively understand the current status and future trends of the tunnel.

2. The method for analyzing and predicting tunnel maintenance big data based on 3D scanning according to claim 1, characterized in that: The three-dimensional point cloud data extracted in step 1 contains detailed information such as the surface morphology, cracks, deformation, etc. of the tunnel structure.

3. The method for analyzing and predicting tunnel maintenance big data based on 3D scanning according to claim 1 is characterized in that: The tunnel monitoring data in step 2 may come from sensor equipment of a tunnel health monitoring system.

4. The method for analyzing and predicting tunnel maintenance big data based on 3D scanning according to claim 1, characterized in that: The data screening in step 2 involves cleaning and organizing the collected 3D point cloud data, including data noise reduction, outlier processing and parameter extraction, to ensure the accuracy and availability of the data.

5. The method for analyzing and predicting tunnel maintenance big data based on 3D scanning according to claim 1 is characterized in that: The prediction content in step 4 includes crack expansion trend, deformation speed, potential failure risk, etc.

6. The method for analyzing and predicting tunnel maintenance big data based on 3D scanning according to claim 1, characterized in that: The visualization in step 5 shows the historical changes and prediction results of the tunnel structure through 3D models, charts and curves, so as to intuitively express the data change characteristics and structural health status, provide a scientific basis for the maintenance and management of the tunnel, and improve the safety performance and service life of the tunnel.