Intelligent early warning method and system for tunnel face risk based on infrared temperature variance

Abstract

The invention relates to the technical field of tunnel face risk intelligent early warning, in particular to an infrared temperature variance-based tunnel face risk intelligent early warning method and system, and the method comprises the steps: calculating a first difference value between a current infrared temperature variance and an initial infrared temperature variance of each grid region; if the first difference value exceeds a first set threshold value, a second difference value between the current average infrared temperature and the initial average infrared temperature of each grid area is calculated, if the second difference value exceeds a set threshold value, it is indicated that the current grid area is a fracture risk area, and if the second difference value does not exceed the set threshold value, it is indicated that the current grid area is a water inrush risk area; determining a fracture risk surface and a water inrush risk surface; according to the intersection condition of the fracture risk face, the water inrush risk face and the unexcavated tunnel face, the fracture risk area and the water inrush risk area of the unexcavated tunnel face are determined. And the abnormal change condition of the tunnel face surface temperature is represented through the infrared temperature variance, so that the possible risk condition of the tunnel face is analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent early warning technology for tunnel face risks, and particularly to an intelligent early warning method and system for tunnel face risks based on infrared temperature variance. Background Art

[0002] In tunnel engineering, monitoring the stability of the tunnel face is crucial for the construction safety of tunnel engineering. Early warning of tunnel face risks can timely detect potential safety hazards in tunnel construction, so as to take corresponding preventive and disposal measures in a timely manner. On the one hand, it helps to avoid casualties, equipment damage and project delays caused by geological disasters, thereby reducing economic losses and ensuring the safety of construction personnel. On the other hand, it can reduce construction interruptions caused by handling emergencies and improve construction efficiency.

[0003] During the tunnel excavation process, by analyzing the risk situations of a series of already excavated tunnel faces, the position of the bad geological body in front of the tunnel face and the integrity of its surrounding rock structure can be understood, the possibility of risks occurring in the subsequent tunnel face can be predicted, and a basis can be provided for the design of support parameters and the optimization of construction plans.

[0004] However, due to the harsh construction environment of the tunnel face, with high noise, much dust, and often large mechanical equipment operating, the traditional early warning and monitoring system is not suitable for the construction site of the tunnel face, and can only conduct risk early warning on the current tunnel face, and cannot intelligently predict the risk occurrence situation of the future tunnel face based on the risk situations that have occurred in the already excavated tunnel faces during the tunnel advancement process. Summary of the Invention

[0005] To solve the deficiencies of the prior art, the present invention provides an intelligent early warning method and system for tunnel face risks based on infrared temperature variance;

[0006] On the one hand, an intelligent early warning method for tunnel face risks based on infrared temperature variance is provided, including:

[0007] Obtain the infrared image of the tunnel face; preprocess the infrared image of the tunnel face; perform grid processing on the preprocessed tunnel face image to obtain a number of grid regions; calculate the average infrared temperature and infrared temperature variance of each grid region;

[0008] Calculate the first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid region; determine whether the first difference exceeds the first set threshold. If so, calculate the second difference between the current average infrared temperature and the initial average infrared temperature of each grid region, and determine whether the second difference exceeds the second set threshold. If it exceeds, it indicates that the current grid region is a rupture risk region. If it does not exceed, it indicates that the current grid region is a water inrush risk region;

[0009] During the tunnel construction process, the fracture risk areas and water inrush risk areas of different headings are recorded to obtain the heading fracture risk area sequence and the heading water inrush risk area sequence. By performing spatial surface fitting on the two sequences respectively, the fracture risk surface and the water inrush risk surface are obtained. According to the intersection situation between the fracture risk surface and the unexcavated heading, the fracture risk area of the unexcavated heading is confirmed. According to the intersection situation between the water inrush risk surface and the unexcavated heading, the water inrush risk area of the unexcavated heading is confirmed.

[0010] On the other hand, an intelligent early warning system for heading risks based on infrared temperature variance is provided, including:

[0011] A preprocessing module, which is configured to: obtain the infrared image of the heading; preprocess the infrared image of the heading; perform grid processing on the preprocessed heading image to obtain a number of grid areas; calculate the average infrared temperature and the infrared temperature variance of each grid area;

[0012] A calculation module, which is configured to: calculate the first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid area; determine whether the first difference exceeds the first set threshold. If so, calculate the second difference between the current average infrared temperature and the initial average infrared temperature of each grid area, and determine whether the second difference exceeds the second set threshold. If it exceeds, it means that the current grid area is a fracture risk area. If it does not exceed, it means that the current grid area is a water inrush risk area;

[0013] An output module, which is configured to: record the fracture risk areas and water inrush risk areas of different headings during the tunnel construction process to obtain the heading fracture risk area sequence and the heading water inrush risk area sequence; perform spatial surface fitting on the two sequences respectively to obtain the fracture risk surface and the water inrush risk surface; confirm the fracture risk area of the unexcavated heading according to the intersection situation between the fracture risk surface and the unexcavated heading; confirm the water inrush risk area of the unexcavated heading according to the intersection situation between the water inrush risk surface and the unexcavated heading.

[0014] The above technical solution has the following advantages or beneficial effects:

[0015] During the rock fracture process, energy is accumulated and released. Especially when the rock is broken, a large amount of energy is released outward, resulting in an increase in the temperature of the front rock mass. And before the water inrush occurs at the heading, the temperature of the rock mass in front of the water source will decrease. The infrared temperature information on the surface of the heading is captured by infrared thermal imaging technology, and the abnormal change of the surface temperature of the heading is represented by the infrared temperature variance, so as to analyze the possible risk situations of the heading. At the same time, record the risk situations that occur on the excavated headings, and predict the risk situations on the unexcavated headings through three-dimensional fitting. Description of the Drawings

[0016] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and do not unduly limit the invention.

[0017] Figure 1 It is a flowchart of the method for the first embodiment.

[0018] Figure 2 It is a schematic diagram of dividing the tunnel face into several regions of the same size for the first embodiment.

[0019] Figure 3 It is a schematic diagram of the fitting result of the fracture risk surface for the first embodiment.

[0020] Figure 4 It is a schematic diagram of the fracture risk area of the unexcavated tunnel face for the first embodiment.

[0021] Figure 5 It is a schematic diagram of the fitting result of the water inrush risk surface for the first embodiment.

[0022] Figure 6 It is a schematic diagram of the water inrush risk area of the unexcavated tunnel face for the first embodiment. Detailed implementation manners

[0023] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanations of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0024] The first embodiment, as Figure 1 shown, this embodiment provides an intelligent early warning method for the tunnel face risk based on the infrared temperature variance, including:

[0025] S101: Obtain the infrared image of the tunnel face; preprocess the infrared image of the tunnel face; perform grid processing on the preprocessed tunnel face image to obtain a number of grid regions; calculate the average infrared temperature and infrared temperature variance of each grid region;

[0026] S102: Calculate the first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid region; determine whether the first difference exceeds the first set threshold. If so, calculate the second difference between the current average infrared temperature and the initial average infrared temperature of each grid region, and determine whether the second difference exceeds the second set threshold. If it exceeds, it indicates that the current grid region is a fracture risk area. If it does not exceed, it indicates that the current grid region is a water inrush risk area;

[0027] S103: Record the rupture risk areas and water inrush risk areas of different tunnel faces during tunnel construction to obtain a tunnel face rupture risk area sequence and a tunnel face water inrush risk area sequence; obtain a rupture risk surface and a water inrush risk surface by performing spatial surface fitting on the two sequences respectively; confirm the rupture risk area of ​​the unexcavated tunnel face according to the intersection of the rupture risk surface and the unexcavated tunnel face; confirm the water inrush risk area of ​​the unexcavated tunnel face according to the intersection of the water inrush risk surface and the unexcavated tunnel face.

[0028] Furthermore, the step S101: acquiring an infrared image of the tunnel face includes:

[0029] An infrared camera is installed at a set distance from the tunnel face, and the tunnel face is monitored using the infrared camera, with an infrared image of the tunnel face collected once at a set interval.

[0030] For example, an infrared camera is fixed at a certain distance from the tunnel face to continuously monitor the tunnel face, storing a thermal imaging picture every 0.1 seconds.

[0031] Furthermore, the step S101: preprocessing the infrared image of the tunnel face includes:

[0032] For the infrared image of the tunnel face, the top area, bottom area, and both sides of the tunnel in the image are deleted, and only the arch area of ​​the tunnel face is retained.

[0033] For example, the tunnel face area is delineated according to the engineering design and field measurement results, the top, bottom and both sides of the tunnel in the image are eliminated, and only the arched area of ​​the tunnel face is retained.

[0034] Furthermore, the S101: gridding the preprocessed tunnel face image to obtain a plurality of grid areas; the size of each grid area is 0.5 m×0.5 m.

[0035] At the same time, a rectangular coordinate system is established on the preprocessed palm face image, with the bottom edge of the palm face as the X-axis, the center point of the bottom edge of the palm face as the origin, and the line perpendicular to the X-axis as the Y-axis.

[0036] For example, Figure 2 As shown in the figure, the infrared image is standardized and the tunnel face is divided into several regions of the same size. The region size is 0.5m×0.5m. The edge of the tunnel face that only intersects with part of the region is regarded as one region. The midpoint of the bottom edge of the tunnel face is taken as the origin, and each region is represented by coordinates, such as region A(1,1).

[0037] Furthermore, the step S101: calculating the average infrared temperature and the infrared temperature variance of each grid area includes:

[0038] When starting infrared monitoring, set the obtained average infrared temperature and infrared temperature variance as the initial values;

[0039] The temperature of area A(1, 1) is composed of the pixel points within its range and the temperature values on the pixel points, and can be represented by the temperature matrix of the following formula:

[0040]

[0041] Among them, T(L y ,L x ) represents the temperature value of the pixel point located at the coordinate (L x , L y ).

[0042] Calculate the average infrared temperature AT (Average Temperature) of the grid area A(1, 1):

[0043]

[0044] Obtain the infrared temperature variance TV (Temperature Variance) of the grid area A(1, 1):

[0045]

[0046] Furthermore, the S102: calculating the first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid area includes:

[0047] Set an infrared temperature variance threshold. When the increase amplitude ΔTV of the infrared temperature variance of a certain area exceeds the threshold θ compared with the initial variance value, give an early warning;

[0048] ΔTV = TV t - TV 0 ;

[0049] Among them, TV t is the infrared temperature contrast of this area at time t, and TV 0 is the initial infrared temperature variance at the start of monitoring.

[0050] Furthermore, the S102: calculating the second difference between the current average infrared temperature and the initial average infrared temperature of each grid area, and judging whether the second difference exceeds the second set threshold. If it exceeds, it means that the current grid area is a rupture risk area. If it does not exceed, it means that the current grid area is a water inrush risk area, includes:

[0051] When the infrared temperature variance of a certain grid area exceeds the threshold θ, it indicates that the temperature in this area has changed significantly. Further, compare whether the current average infrared temperature in the current grid area has increased or decreased compared to the initial average infrared temperature, so as to judge whether the current grid area is a rupture risk area or a water inrush risk area, thereby realizing the real-time monitoring of abnormal conditions of the current tunnel face. When ΔAT is greater than 0, mark this area as having a rupture risk. When ΔAT is less than 0, mark this area as having a water inrush risk;

[0052] ΔAT = AT t - AT 0 ;

[0053] Among them, AT t is the average infrared temperature of this area at time t, and AT 0 is the initial average infrared temperature variance at the start of monitoring.

[0054] Furthermore, the step S103: Record the rupture risk areas and water inrush risk areas of different tunnel faces during the tunnel construction process, and obtain the tunnel face rupture risk area sequence and the tunnel face water inrush risk area sequence, including:

[0055] Record the area coordinates (x, y) where different tunnel faces rupture and water out during the forward progress of the tunnel, and form the tunnel face rupture risk area sequence (x i , y i , S i ) and the water inrush risk area sequence (x j , y j , S j ).

[0056] Furthermore, the step S103: By performing spatial surface fitting on the two sequences respectively, obtain the rupture risk surface and the water inrush risk surface. The fitting process of the rupture risk surface is as follows:

[0057] The three-dimensional space surface of the rupture risk surface can be expressed by the following formula:

[0058] S = ax 2 + by 2 + cxy + dx + ey + f;

[0059] Among them, a, b, c, d, e, and f are undetermined coefficients;

[0060] For several known rupture risk points (x i , y i , S i ) on the tunnel face, the error function is:

[0061]

[0062] To obtain the coefficients a, b, c, d, e, and f that minimize the value of the error function, take the partial derivatives of each coefficient and set them to 0, resulting in a system of linear equations:

[0063]

[0064] Input a series of rupture risk points (x i , y i , S i ) on the known heading face to obtain the undetermined coefficients a, b, c, d, e, and f, thereby forming a rupture risk surface.

[0065] As shown in Figure 3 and Figure 5 , the fitting process of the rupture risk surface is the same as that of the water inrush risk surface, except that the input sequences are different; the input sequence for the fitting process of the water inrush risk surface is the water inrush risk area sequence.

[0066] Further, the S103: According to the intersection situation between the rupture risk surface and the unexcavated heading face, confirm the rupture risk area of the unexcavated heading face; according to the intersection situation between the water inrush risk surface and the unexcavated heading face, confirm the water inrush risk area of the unexcavated heading face, including:

[0067] Take the intersection area between the rupture risk surface and the unexcavated heading face as the rupture risk area of the unexcavated heading face; the rupture risk area of the unexcavated heading face is as shown in Figure 4 ;

[0068] Take the intersection area between the water inrush risk surface and the unexcavated heading face as the water inrush risk area of the unexcavated heading face. The water inrush risk area of the unexcavated heading face is as shown in Figure 6 .

[0069] Based on the intersection situation between the unexcavated heading face and the rupture risk surface and the water inrush risk surface, the rupture and water inrush situations in this heading face can be predicted.

[0070] Fit the rupture risk surface and the water inrush risk surface based on the rupture and water inrush risks of the first six excavated heading faces. According to the intersection situation between the two fitted surfaces and the unexcavated heading face, the rupture and water inrush risk areas on the seventh unexcavated heading face can be predicted.

[0071] Based on the rupture and water inrush situations after the excavation of the seventh heading face, the data of the rupture risk area sequence and the water inrush risk area sequence can be supplemented, and the rupture and water inrush risk areas of the next heading face to be excavated can be further predicted.

[0072] By deeply integrating infrared thermal imaging technology and spatial fitting methods, it is not only possible to achieve real-time early warning of the risks of the current tunnel face, but also to intelligently predict the possible risk situations of the next tunnel face based on historical data. As a non-contact monitoring technology, infrared thermal imaging technology has good penetration and the ability to conduct large-scale real-time monitoring. Through an infrared camera, real-time, continuous, and high-resolution infrared temperature information of the tunnel face can be collected; through the infrared temperature variance, the abnormal temperature changes in each area of the tunnel face can be keenly captured, and then the types of risks can be judged by comparing the rise and fall of the temperature; the three-dimensional spatial fitting algorithm is used to achieve intelligent prediction of the tunnel face risks, making the historical risk early warning information of the tunnel face three-dimensional and visually displaying the risk changes of the tunnel face, so as to accurately identify the risk location, scale, and type, and jointly complete the intelligent early warning of the current and future risks of the tunnel face.

[0073] Embodiment 2

[0074] This embodiment provides an intelligent early warning system for tunnel face risks based on infrared temperature variance, including:

[0075] A preprocessing module, which is configured to: obtain an infrared image of the tunnel face; preprocess the infrared image of the tunnel face; perform grid processing on the preprocessed tunnel face image to obtain a number of grid areas; calculate the average infrared temperature and infrared temperature variance of each grid area;

[0076] A calculation module, which is configured to: calculate the first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid area; determine whether the first difference exceeds the first set threshold. If so, calculate the second difference between the current average infrared temperature and the initial average infrared temperature of each grid area, and determine whether the second difference exceeds the second set threshold. If it exceeds, it means that the current grid area is a rupture risk area. If it does not exceed, it means that the current grid area is a water inrush risk area;

[0077] An output module, which is configured to: record the rupture risk areas and water inrush risk areas of different tunnel faces during the tunnel construction process to obtain a tunnel face rupture risk area sequence and a tunnel face water inrush risk area sequence; through spatial surface fitting of the two sequences respectively, obtain a rupture risk surface and a water inrush risk surface; confirm the rupture risk area of the unexcavated tunnel face according to the intersection situation between the rupture risk surface and the unexcavated tunnel face; confirm the water inrush risk area of the unexcavated tunnel face according to the intersection situation between the water inrush risk surface and the unexcavated tunnel face.

[0078] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent early warning method for tunnel face risk based on infrared temperature variance is characterized by: include: Acquire infrared images of the tunnel face; Preprocess the infrared image of the tunnel face; Performing grid processing on the preprocessed tunnel face image to obtain a number of grid areas; Calculate the average infrared temperature and infrared temperature variance of each grid area; Calculate a first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid area; determine whether the first difference exceeds a first set threshold value, if so, calculate a second difference between the current average infrared temperature and the initial average infrared temperature of each grid area, and determine whether the second difference exceeds a second set threshold value, if exceeded, it indicates that the current grid area is a rupture risk area, if not exceeded, it indicates that the current grid area is a water inrush risk area; During the tunnel construction process, the rupture risk areas and water inrush risk areas of different tunnel faces were recorded to obtain the tunnel face rupture risk area sequence and the tunnel face water inrush risk area sequence; the rupture risk surface and the water inrush risk surface were obtained by performing spatial surface fitting on the two sequences respectively; the rupture risk area of ​​the unexcavated tunnel face was confirmed according to the intersection of the rupture risk surface and the unexcavated tunnel face; According to the intersection of the water inrush risk surface and the unexcavated tunnel face, the water inrush risk area of ​​the unexcavated tunnel face is confirmed.

2. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 1 is characterized in that: Acquire infrared images of the tunnel face, including: An infrared camera is installed at a set distance from the tunnel face, and the tunnel face is monitored by the infrared camera, and an infrared image of the tunnel face is collected once at a set interval; Preprocess the infrared image of the tunnel face, including: For the infrared image of the tunnel face, the top area, bottom area, and both sides of the tunnel in the image are deleted, and only the arch area of ​​the tunnel face is retained.

3. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 1 is characterized in that: Calculate the mean infrared temperature and infrared temperature variance for each grid area, including: When infrared monitoring is started, the average infrared temperature and infrared temperature variance obtained are set as initial values; the temperature of area A (1, 1) is composed of the pixels within its range and the temperature values ​​on the pixels, which is represented by the temperature matrix of the following formula: Among them, T(L y ,L x ) indicates the coordinate (L x , L y ) at the pixel temperature; Calculate the average infrared temperature AT of the grid area A(1,1): Obtain the infrared temperature variance TV of the grid area A(1,1):

4. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 3 is characterized in that: Calculating the first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid area includes: Set the infrared temperature variance threshold. When the increase in the infrared temperature variance of a certain area by ΔTV compared to the initial variance value exceeds the threshold θ, an early warning is issued. ΔTV=TV t -TV0; Among them, TV t is the infrared temperature contrast of the area at time t, and TV0 is the initial infrared temperature variance when monitoring begins.

5. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 3 is characterized in that: Calculate the second difference between the current average infrared temperature and the initial average infrared temperature of each grid area, and determine whether the second difference exceeds the second set threshold. If it exceeds, it indicates that the current grid area is a rupture risk area. If it does not exceed, it indicates that the current grid area is a water inrush risk area, including: When ΔAT is greater than 0, the area is marked as having a risk of rupture, and when ΔAT is less than 0, the area is marked as having a risk of water inrush; ΔAT=AT t -AT0; Among them, AT t is the average infrared temperature of the area at time t, and AT0 is the initial average infrared temperature variance when monitoring begins.

6. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 1 is characterized in that: During the tunnel construction process, the rupture risk areas and water inrush risk areas of different tunnel faces are recorded to obtain the tunnel face rupture risk area sequence and the tunnel face water inrush risk area sequence, including: The coordinates (x, y) of the areas where fractures and water discharges occur at different faces during tunnel advancement are recorded to form a sequence of face fracture risk areas (x i ,y i ,S i ) and water inrush risk area sequence (x j ,y j ,S j ).

7. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 6 is characterized in that: By fitting the two sequences separately, the rupture risk surface and the water inrush risk surface are obtained. The fitting process of the rupture risk surface is as follows: The three-dimensional space surface of the rupture risk surface can be expressed as follows: S=ax 2 +by 2 +cxy+dx+ey+f; Among them, a, b, c, d, e and f are unknown coefficients; For several fracture risk points (x i ,y i ,S i ), the error function is: In order to find the coefficients a, b, c, d, e and f that minimize the error function value, take the partial derivative of each coefficient and make it equal to 0, and we can get the linear equation system: A series of fracture risk points (x i ,y i ,S i ) input, and the unknown coefficients a, b, c, d, e and f are obtained, thus forming the rupture risk surface.

8. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 7 is characterized in that: According to the intersection of the fracture risk surface and the unexcavated tunnel face, the fracture risk area of ​​the unexcavated tunnel face is confirmed; According to the intersection of the water inrush risk surface and the unexcavated tunnel face, the water inrush risk area of ​​the unexcavated tunnel face is confirmed, including: The intersection area between the fracture risk surface and the unexcavated tunnel face is regarded as the fracture risk area of ​​the unexcavated tunnel face; The intersection area between the water inrush risk surface and the unexcavated tunnel face is taken as the water inrush risk area of ​​the unexcavated tunnel face.

9. The intelligent early warning method for tunnel face risk based on infrared temperature variance according to claim 8 is characterized in that: The preprocessed face image is gridded to obtain several grid areas. At the same time, a rectangular coordinate system is established on the preprocessed face image, with the bottom edge of the face as the X-axis, the center point of the bottom edge of the face as the origin, and the line perpendicular to the X-axis as the Y-axis.

10. The intelligent early warning system for tunnel face risk based on infrared temperature variance is characterized by: include: A preprocessing module is configured to: acquire an infrared image of the tunnel face; preprocess the infrared image of the tunnel face; Performing grid processing on the preprocessed tunnel face image to obtain a number of grid areas; Calculate the average infrared temperature and infrared temperature variance of each grid area; A calculation module, which is configured to: calculate a first difference between the current infrared temperature variance and the initial infrared temperature variance of each grid area; determine whether the first difference exceeds a first set threshold, if so, calculate a second difference between the current average infrared temperature of each grid area and the initial average infrared temperature, and determine whether the second difference exceeds a second set threshold, if exceeded, it indicates that the current grid area is a rupture risk area, if not exceeded, it indicates that the current grid area is a water inrush risk area; The output module is configured to: record the rupture risk areas and water inrush risk areas of different tunnel faces during tunnel construction to obtain a tunnel face rupture risk area sequence and a tunnel face water inrush risk area sequence; obtain a rupture risk surface and a water inrush risk surface by performing spatial surface fitting on the two sequences respectively; confirm the rupture risk area of ​​the unexcavated tunnel face according to the intersection of the rupture risk surface and the unexcavated tunnel face; According to the intersection of the water inrush risk surface and the unexcavated tunnel face, the water inrush risk area of ​​the unexcavated tunnel face is confirmed.

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