Pipe jacking machine geological prediction method based on thermal imaging technology

Abstract

The application discloses a pipe jacking machine geological prediction method based on thermal imaging technology and belongs to the technical field of geological identification, and specifically comprises the following steps: acquiring infrared camera images of a pipe jacking machine in a pushing process in real time, pre-processing the infrared camera images, obtaining camera images of a pushing area and a to-be-pushed area and converting the camera images into corresponding temperature data; calculating surface temperature difference values of any pushing area after pushing and when not pushing and marking the surface temperature difference values as characteristic values, constructing a three-dimensional coordinate with the temperature when not pushing as a horizontal axis, the temperature after pushing as a vertical axis and the characteristic values as a vertical axis, generating characteristic points corresponding to all pushing areas, clustering the characteristic points to generate a plurality of category clusters; and analyzing geological features of the pushing areas corresponding to each category cluster to obtain a geological feature model corresponding to each category cluster; the application realizes real-time monitoring and prediction of strata in front of a pipe jacking machine, thereby improving the safety and efficiency of pipe jacking construction and reducing geological risks and engineering costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological identification, and particularly relates to a pipe jacking machine geological prediction method based on thermal imaging technology. BACKGROUND

[0002] The pipe jacking machine is a machine for tunnel excavation under the protection of a shield, which adopts manual excavation, mechanical or hydraulic crushing methods. The pipe jacking machine is placed at the front end of the pipe jacking section, and the main functions are: 1. excavating the soil in the front, while maintaining the stability of the water and soil pressure in the front; 2. controlling the posture of the pipe jacking machine through the deviation correction device to ensure that the pipe section is pushed in according to the designed axis direction.

[0003] However, in the process of pushing the pipe jacking machine, there will be different properties of soil in the underground, and there will also be gangue or rock faults formed by geological activities. At this time, if the pipe jacking machine is pushed at the set speed, the wear of the drill bit will be aggravated, and even the drill bit will be broken and the cutting part will be damaged, which seriously affects the safety of the pipe jacking machine construction. The use of infrared thermal imaging technology to realize the cutting mode recognition of the pipe jacking machine can enable the pipe jacking machine to recognize the properties of the soil being pushed in the process of pushing, so as to automatically adjust its working state to adapt to the change of the pushing mode, avoid the occurrence of the above dangerous situations, and provide technical support for the reliable operation of the pipe jacking machine. SUMMARY

[0004] The purpose of the present application is to provide a pipe jacking machine geological prediction method based on thermal imaging technology, which solves the following technical problems:

[0005] In the process of pushing the pipe jacking machine, there will be different properties of soil in the underground, and there will also be gangue or rock faults formed by geological activities. At this time, if the pipe jacking machine is pushed at the set speed, the wear of the drill bit will be aggravated, and even the drill bit will be broken and the cutting part will be damaged, which seriously affects the safety of the pipe jacking machine construction.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] The pipe jacking machine geological prediction method based on thermal imaging technology comprises the following steps:

[0008] S1, real-time acquisition of infrared camera images of the pipe jacking machine in the process of pushing, pre-processing of the infrared camera images to obtain infrared camera images of the pushing area and infrared camera images of the area to be pushed, and converting the infrared camera images of the pushing area and the infrared camera images of the area to be pushed into corresponding surface temperature data;

[0009] S2, select any advancing area, calculate the surface temperature difference of the advancing area after advancing and before advancing and mark it as a characteristic value, obtain the characteristic values of all advancing areas, respectively, the surface temperature before advancing is the horizontal axis, the surface temperature after advancing is the vertical axis, and the characteristic value is the vertical axis, construct a three-dimensional coordinate, generate the characteristic points corresponding to all advancing areas, select any characteristic point as the center, set the clustering radius R, calculate the characteristic point density L within the clustering radius R, obtain the average characteristic point density within the clustering radius R of all characteristic points, mark the average characteristic point density as MinL, if there is any characteristic point within the radius R whose characteristic point density L is greater than MinL, then mark the characteristic point as a core point, and generate a category cluster with the core point as the center;

[0010] S3, analyze the geological characteristics of the advancing area corresponding to each category cluster to obtain a geological characteristic model corresponding to each category cluster.

[0011] As a further scheme of the present application: in S1, the rotating speed of the pipe jacking machine cutter head and the advancing speed remain unchanged during the advancing process.

[0012] As a further scheme of the present application: in S1, the specific process of the pretreatment is:

[0013] The infrared camera image is subjected to gray scale processing to obtain a gray scale image, the gray scale image is subjected to noise reduction processing, the gray scale image after noise reduction is subjected to edge detection, and the gray scale image after edge detection is subjected to binaryzation processing, the area to be advanced is divided into black, and the advancing area is divided into white, and a binaryzation image is generated.

[0014] As a further scheme of the present application: in S2, the specific process of setting the clustering radius R is:

[0015] Any characteristic point is selected and taken as the center, the Euclidean distance I between the characteristic point and any characteristic point is calculated, the sum of each Euclidean distance is obtained as M, the clustering radius R is obtained based on the numerical value M, and the calculation formula is as follows:

[0016]

[0017]

[0018] Wherein, M is the sum of all Euclidean distance data values of the characteristic points, and I is the Euclidean distance between any two characteristic points.

[0019] As a further scheme of the present application: in S2, the calculation formula of the characteristic point density within the clustering radius R is:

[0020] L=3N / (4πR 3 );

[0021] Wherein N is the number of feature points existing within the clustering radius R.

[0022] As a further scheme of the present application: in S2, if the density L of any non-core point within the clustering radius R in the category cluster also exceeds MinL, the category cluster generated by the non-core point is merged with the original category cluster to generate several category clusters.

[0023] As a further scheme of the present application: in S3, it further includes determining the geological feature model corresponding to each category cluster by manual operation, and dividing the geological feature model into normal geological feature model and abnormal geological model.

[0024] As a further scheme of the present application: it further includes acquiring temperature data in the pushing process in real time and bringing it into the geological feature model for identification, and issuing a warning if the identification result is an abnormal geological model.

[0025] The beneficial effects of the present application are:

[0026] Firstly, by installing a thermal imager inside the cutter head of the pipe jacking machine, the heat distribution generated by the cutter head when cutting different soil layers is monitored in real time. Secondly, during the pushing process of the pipe jacking machine, the thermal imager acquires infrared thermal images of the front stratum and converts these images into digital data. Then, the acquired digital data is analyzed using computer processing technology, and the temperature characteristics of the stratum are extracted. According to the temperature distribution law of the stratum, clustering is performed and the clustered stratum is analyzed geologically to construct a geological model. The real-time acquired temperature data is compared and analyzed with the constructed model to infer and predict the physical properties of the stratum, such as rock type. Finally, when a possible geological obstacle or abnormal situation is detected, the system can issue an alarm and record relevant data to guide subsequent pipe jacking machine operation. The operator can adjust the pushing speed and direction of the pipe jacking machine, as well as the rotation speed of the pipe jacking machine cutter head, according to the real-time analysis results provided by the system, to reduce or avoid the impact of geological obstacles. The use of thermal imaging technology realizes real-time monitoring and prediction of the stratum in front of the pipe jacking machine, and real-time adjustment of pipe jacking parameters according to the monitoring results, which not only improves the safety and efficiency of pipe jacking construction, but also effectively reduces geological risks and engineering costs. BRIEF DESCRIPTION OF DRAWINGS

[0027] The present application will be further described below with reference to the accompanying drawings.

[0028] Figure 1 is a flowchart of the pipe jacking machine geological prediction method based on thermal imaging technology of the present application. DETAILED DESCRIPTION

[0029] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0030] Please refer to Figure 1 The present application is a pipe jacking machine geological prediction method based on thermal imaging technology. A thermal imager is installed inside the cutter head of the pipe jacking machine, and the method comprises the following steps:

[0031] S1, real-time acquisition of infrared camera images of the pipe jacking machine during the pushing process, pre-processing of the infrared camera images, obtaining infrared camera images of the pushed area and infrared camera images of the to-be-pushed area, and converting the infrared camera images of the pushed area and the infrared camera images of the to-be-pushed area into corresponding surface temperature data;

[0032] S2, selecting any pushing area, calculating the surface temperature difference between the pushing area after pushing and before pushing and marking it as a characteristic value, obtaining the characteristic values of all pushing areas, taking the surface temperature before pushing as the horizontal axis, the surface temperature after pushing as the vertical axis, and the characteristic value as the vertical axis, constructing a three-dimensional coordinate, generating the characteristic points corresponding to all pushing areas, selecting any characteristic point as the center, setting the clustering radius R, calculating the characteristic point density L within the clustering radius R, obtaining the average characteristic point density within the clustering radius R of all characteristic points, marking the average characteristic point density as MinL, if there is any characteristic point within the radius R whose characteristic point density L is greater than MinL, marking the characteristic point as a core point, and generating a category cluster with the core point as the center;

[0033] S3, geological feature analysis of the pushing area corresponding to each category cluster is performed to obtain the geological feature model corresponding to each category cluster.

[0034] First, by installing a thermal imager inside the cutter head of the pipe jacking machine, the heat distribution generated by the cutter head cutting different soil layers is monitored in real time. Second, during the pushing process of the pipe jacking machine, the thermal imager obtains infrared thermal images in front of the stratum and converts these images into digital data. Then, using computer processing technology, the obtained digital data is analyzed to extract the temperature characteristics and distribution of the stratum. According to the temperature distribution of the stratum, the bottom layer is clustered and geologically analyzed, a geological model is constructed, the real-time temperature data is compared and analyzed with the constructed model to infer and predict the physical properties of the stratum, such as rock type. Finally, when a possible geological obstacle or abnormal situation is detected, the system can issue an alarm and record relevant data to guide subsequent pipe jacking machine operation. Operators can adjust the pushing speed and direction of the pipe jacking machine, the rotating speed of the cutter head of the pipe jacking machine according to the real-time analysis results provided by the system to reduce or avoid the impact of geological obstacles. The use of thermal imaging technology realizes real-time monitoring and prediction of the stratum in front of the pipe jacking machine, and real-time adjustment of pipe jacking parameters according to monitoring results, which not only improves the safety and efficiency of pipe jacking construction, but also effectively reduces geological risks and engineering costs.

[0035] In a preferred embodiment of the present application, in S1, the rotating speed of the drill bit of the pipe jacking machine during the pushing process and the pushing speed remain unchanged.

[0036] It can be understood that the geological parameter properties of the pushing area of the pipe jacking machine during the pushing process are constantly changing, so that the characteristic value of the pushing area is only related to the geological parameters of the pushing area when the rotating speed of the drill bit of the pipe jacking machine and the pushing speed remain unchanged. It can be understood that the characteristic value obtained after pushing loose sand and dense clay layers is different.

[0037] In a preferred embodiment of the present application, in S1, the specific process of preprocessing is:

[0038] The infrared camera image is subjected to gray scale processing to obtain a gray scale image, the gray scale image is subjected to noise reduction processing, the noise-reduced gray scale image is subjected to edge detection, and the edge-detected gray scale image is subjected to binaryzation processing, the area to be pushed is divided into black, the pushing area is divided into white, and a binaryzation image is generated.

[0039] Converting the infrared camera image into a grayscale image simplifies the image data, reduces the computational complexity, and makes the subsequent processing (such as noise reduction and edge detection) more efficient. Noise reduction on the grayscale image can remove noise or interference in the image, improve the quality of the image, and avoid image noise interference with the effect of edge detection, resulting in inaccurate final results. Edge detection on the noise-reduced grayscale image is used to extract significant boundaries in the image. Edge detection helps identify the contours and shapes of objects in the image and is an important step in image analysis. Common edge detection algorithms include the Canny algorithm and the Sobel operator. The grayscale image after edge detection is binarized to generate a binary image. This step divides the image into black and white regions, simplifying the image representation and facilitating further analysis. By dividing the to-be-pushed region into black and the pushed region into white, different regions are clearly represented and distinguished.

[0040] In a preferred embodiment of the present application, in S2, the specific process of setting the clustering radius R is as follows:

[0041] Select any feature point and calculate the Euclidean distance I between the feature point and any feature point. Sum each Euclidean distance to obtain M. Based on the value M, the clustering radius R is obtained, and the calculation formula is as follows:

[0042]

[0043]

[0044] Where M is the sum of all Euclidean distance data values of the feature points, and I is the Euclidean distance between any two feature points.

[0045] In a preferred embodiment of the present application, in S2, the calculation formula for calculating the density of feature points within the clustering radius R is as follows:

[0046] L=3N / (4πR 3 );

[0047] Where N is the number of feature points within the clustering radius R.

[0048] In a preferred embodiment of the present application, in S2, if the density L of any non-core point within the clustering radius R is also greater than MinL, the non-core point is merged with the original category cluster to generate several category clusters.

[0049] In a preferred embodiment of the present application, in S3, the method further comprises determining the geological feature model corresponding to each category cluster by artificial judgment, and dividing the geological feature model into a normal geological feature model and an abnormal geological model.

[0050] It can be understood that in the pushing process of the pipe jacking machine, due to the different properties of the soil in the underground, and the mixture of gangue or rock faults formed by geological activities. At this time, if the pipe jacking machine is pushed at the set speed, the wear of the drill bit will be aggravated, and even the drill bit will be broken and the cutting part will be damaged, which seriously affects the safety of pipe jacking construction. Therefore, the geological feature model can be constructed according to the temperature data change in the actual pushing process of the pipe jacking machine, and then the artificial judgment is carried out, so as to determine the abnormal geological model, so as to effectively predict and prevent the geological obstacles, reduce the equipment damage, reduce the cost of maintenance and replacement of parts, improve the safety and efficiency of pipe jacking construction, and effectively reduce the geological risk and engineering cost.

[0051] In a preferred embodiment of the present application, real-time temperature data during pushing is obtained and brought into the geological feature model for identification. If the identification result is an abnormal geological model, a warning is issued.

[0052] It can be understood that the real-time acquired temperature data is compared and analyzed with the model based on the geological feature model, to infer and predict the physical properties of the stratum, such as rock type, sand type, etc. When it is detected that there may be a geological obstacle or abnormal situation, the pushing speed and direction of the pipe jacking machine and the rotating speed of the pipe jacking machine cutter head can be adjusted according to the real-time analysis result provided by the system, so as to reduce or avoid the influence of the geological obstacle.

[0053] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still belong to the patent coverage of the present application.

Claims

1. A pipe jacking machine geological prediction method based on thermal imaging technology, characterized in that, A heat imager is installed inside a cutter head of a pipe jacking machine, and the following steps are included: S1, real-time acquisition of infrared camera images of the pipe jacking machine during the pushing process, preprocessing of the infrared camera images to obtain infrared camera images of the pushing area and infrared camera images of the area to be pushed, and conversion of the infrared camera images of the pushing area and the infrared camera images of the area to be pushed into corresponding surface temperature data; S2, selecting any pushing area, calculating the surface temperature difference between the pushing area after pushing and before pushing and marking it as a characteristic value, obtaining the characteristic values of all pushing areas, constructing a three-dimensional coordinate with the surface temperature before pushing as the horizontal axis, the surface temperature after pushing as the vertical axis and the characteristic value as the vertical axis, generating characteristic points corresponding to all pushing areas, selecting any characteristic point as the center, setting a clustering radius R, calculating the density L of the characteristic points within the clustering radius R, obtaining the average density of the characteristic points within the clustering radius R of all characteristic points, marking the average density of the characteristic points as MinL, and if the density L of any characteristic point within the radius R is greater than MinL, marking the characteristic point as a core point and generating a class cluster with the core point as the center; S3, geological feature analysis of the pushing area corresponding to each class cluster to obtain a geological feature model corresponding to each class cluster.

2. The pipe jacking machine geology prediction method based on thermal imaging technology according to claim 1, characterized in that, In S1, the rotating speed and pushing speed of the cutter head of the pipe jacking machine during the pushing process remain unchanged.

3. The pipe jacking machine geological prediction method based on thermal imaging technology according to claim 1, characterized in that, In S1, the specific process of preprocessing is as follows: The infrared camera image is subjected to gray scale processing to obtain a gray scale image, the gray scale image is subjected to noise reduction processing, the noise-reduced gray scale image is subjected to edge detection, and the edge-detected gray scale image is subjected to binaryzation processing, the area to be pushed is divided into black, the pushing area is divided into white, and a binaryzation image is generated.

4. The pipe jacking machine geology prediction method based on thermal imaging technology according to claim 1, characterized in that, In S2, the specific process of setting the clustering radius R is as follows: Any characteristic point is selected and taken as the center, the Euclidean distance I between the characteristic point and any characteristic point is calculated, the sum of each Euclidean distance is obtained as M, the clustering radius R is obtained based on the numerical value M, and the calculation formula is as follows: ； ； Wherein, M is the sum of the Euclidean distance data values of all characteristic points, and I is the Euclidean distance between any two characteristic points.

5. The method of claim 1, wherein the method further comprises: In S2, the calculation formula of the density of the characteristic points within the clustering radius R is as follows: L = 3N / (4πR 3 ); Wherein, N is the number of characteristic points within the clustering radius R.

6. The method of claim 1, wherein the method further comprises: In S2, if the density L of the non-core point within the clustering radius R in any class cluster is also greater than MinL, the class cluster generated by the non-core point is merged with the original class cluster to generate several class clusters.

7. The method of claim 1, wherein the method further comprises: In S3, the geological feature model corresponding to each class cluster is also determined by artificial judgment, and the geological feature model is divided into a normal geological feature model and an abnormal geological model.

8. The method of claim 1, wherein the method further comprises: Real-time acquisition of temperature data during the pushing process and identification of the geological feature model are also included, and a warning is issued if the identification result is an abnormal geological model.

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