Underground roadway support monitoring method and system based on video monitoring

Abstract

The invention relates to the technical field of image processing, in particular to an underground roadway support monitoring method and system based on video surveillance, and the method comprises the steps: obtaining the support damage degree in each frame of image according to the internal gray level change of a support change area in each frame of image and the position distribution condition; according to the support damage degree and the time interval between the adjacent expansion image sequences of the support change areas, obtaining an expansion dense factor of each support change area; according to the damage condition of the newly-added support change area, obtaining newly-added support extension damage of each frame of image; according to the newly added support extension damage and the extension damage risk factor, obtaining support extension damage significance of each frame of image; and monitoring the underground roadway support based on the support expansion damage significance. According to the invention, the accuracy of underground roadway support monitoring is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an underground tunnel support monitoring method and system based on video monitoring. Background Art

[0002] Underground tunnel support monitoring systems play a vital role in the safety management of underground projects such as mines and tunnels. Traditional underground tunnel support monitoring methods use video surveillance technology to monitor and record the status of tunnel supports in real time, effectively preventing and eliminating safety hazards. In underground tunnel monitoring systems, video signals typically occupy a large amount of storage space, especially in high-resolution and long-term monitoring situations, where the data volume is huge and real-time performance is critical. This places higher demands on the storage and image processing capabilities of video monitoring systems. To achieve efficient data storage and processing within limited storage resources, video stream compression technology is widely used in underground tunnel support monitoring systems.

[0003] Since underground tunnel support monitoring videos occupy a large amount of storage space, and monitoring systems often use video streams with fixed compression parameters for compressed storage, this method can achieve high compression efficiency when processing unimportant frame images, but often causes large data loss when processing important frame images; for underground tunnel support monitoring videos, if a frame image contains key information, such as deformation, expansion, and damage of the support structure, this information is crucial for judging the safety of the tunnel. Over-compression of the frame image containing this key information may lead to the loss of important information, thereby affecting the effectiveness and accuracy of monitoring; that is, due to the large amount of video data collected in real time, traditional compression may cause greater damage to important video data, reducing the accuracy of tunnel support monitoring. Summary of the Invention

[0004] The present invention provides an underground tunnel support monitoring method and system based on video monitoring to solve the existing problem: due to the large amount of video data collected in real time, traditional compression may cause significant damage to important video data, thereby reducing the accuracy of tunnel support monitoring.

[0005] The underground tunnel support monitoring method and system based on video monitoring of the present invention adopts the following technical solutions: The present invention proposes an underground tunnel support monitoring method based on video monitoring, which includes the following steps: Obtain monitoring video of underground tunnel support; By analyzing the grayscale changes between adjacent frames in the monitoring video, the support change area in each frame is obtained; based on the internal grayscale changes in the support change area, the damage factor of each support change area is obtained; based on the damage factor and position distribution of the support change area, the support damage degree in each frame is obtained; Obtain all sub-expanded image sequences of each support change area; obtain the expansion density factor of each support change area based on the support damage degree and the time interval between adjacent sub-expanded image sequences of the support change area; obtain the expansion damage risk factor of each support change area based on the expansion density factor and the change in the area of ​​the support change area between each expansion image sequence; obtain the newly added support change area in each frame of image, and obtain the newly added support expansion damage of each frame of image based on the damage of the newly added support change area; obtain the support expansion damage significance of each frame of image based on the newly added support expansion damage and the expansion damage risk factor; Monitoring of underground tunnel support based on support extension damage significance.

[0006] Preferably, the specific method of obtaining the support change area in each frame image by analyzing the grayscale changes between adjacent frame images in the monitoring video is: The monitoring video of underground tunnel support Frame image The coordinate position of the pixel point is recorded as the target position; The gray value of the pixel is The absolute value of the difference between the grayscale values ​​of the pixels at the target position in the frame image is recorded as The pixel at the Frame image and The grayscale difference between the frame images; The pixel at the Frame image and Frame image and The normalized value of the mean grayscale difference between frame images is recorded as Frame image Grayscale change value of each pixel; Preset a neighborhood grayscale parameter , if Frame image The grayscale change value of the pixel is greater than or equal to the neighborhood grayscale parameter , will The pixel point is recorded as the changed pixel point; Each closed area formed by all the changed pixels in the frame image is recorded as a changed area; All the changed areas in the frame image are input into the trained neural network to obtain the first All support change areas in the frame image.

[0007] Preferably, the specific method for obtaining the damage factor of each support change region according to the internal grayscale change of the support change region is: Preset a grayscale parameter , will The mean of the grayscale change values ​​of all pixels in the support change area and the grayscale parameter The ratio of The normalized value of the product of the number of all pixels in the support change area is recorded as The damage factor of each support change area.

[0008] Preferably, the specific method for obtaining the support damage degree in each frame of image based on the damage factor and position distribution of the support change area is: The first The number of all support change areas in the frame image is the same as the The product of the mean values ​​of the loss factors of all support change areas in the frame image is recorded as the first product; the All support change regions in the frame image are combined in any two ways to obtain several support change region combinations; The inversely proportional normalized value of the cumulative sum of the Euclidean distances between the centroid positions of two support change regions in all support change region combinations in the frame image is recorded as the support distribution value; The normalized value of the product of the first product and the support distribution value is used as the first The support damage degree in the frame image.

[0009] Preferably, the specific method for obtaining all sub-extended image sequences of each support change area is: Preset a neighborhood parameter , in the underground tunnel support monitoring video, the first Frame image and The nearest frame image The image sequence composed of frame images is The neighborhood range image sequence of the frame image; using the KLT tracking algorithm, obtain the first Frame image The support change area is in The support change area corresponding to each frame image in the neighborhood range image sequence of the frame image; Preset an area change parameter , in In the neighborhood range image sequence of the frame image, traverse in sequence to obtain the first The absolute value of the difference in the number of all pixels in the support change area is calculated until the absolute value of the difference is less than the area change parameter for the first time. When traversing, all the frame images before the last frame image constitute the first extended image sequence; starting from the last frame image traversed, obtain the second extended image sequence according to the method of obtaining the first extended image sequence, and so on, obtain the first extended image sequence. All sub-extended image sequences of the support change area.

[0010] Preferably, the specific method for obtaining the expansion density factor of each support change area according to the support damage degree and the time interval between adjacent sub-expanded image sequences of the support change area is: The first The average value of the support damage degree of all frame images in the image sequence of the neighborhood range of the frame image is The neighborhood range of the frame image in the image sequence The product of the number of all sub-extended image sequences in the support change area is recorded as the second product; In the neighborhood range image sequence of the frame image, the first The first support change area The second extended image sequence and the The time interval between the first frame of the extended image sequence is recorded as The time factor of the second extended image sequence; The inverse proportional normalized value of the cumulative sum of the time factors of all sub-expanded image sequences in the support change area is recorded as The expansion interval density of each support change area; The product of the expansion interval density of the support change area and the second product is used as the first Frame image The expansion density factor of each support change area.

[0011] Preferably, the specific method for obtaining the expansion damage risk factor of each support change region based on the expansion density factor and the area change of the support change region between each expansion image sequence is: In the In the neighborhood range image sequence of the frame image, the first The first support change area The number of frames between the first and last frames in the extended image sequence The union of all pixels in the support change area is recorded as the first set; The first support change area The number of frames between the first and last frames in the extended image sequence The intersection of all pixels in the support change area is recorded as the second set; the difference in the number of elements between the first set and the second set is recorded as the first set. The expansion factor of the second expanded image sequence; The expansion factor of the second expanded image sequence is the same as that of the first The ratio between the number of all extended frame images in the extended image sequence is recorded as The expansion rate factor of the second expanded image sequence; The cumulative sum of the expansion rate factors of all sub-expanded image sequences in the support change area is recorded as The expansion trend value of the support change area; The product of the expansion density factor and the expansion trend value of the support change area is used as the The expansion damage risk factor of each support change area.

[0012] Preferably, the specific method of obtaining the newly added support change area in each frame of image and obtaining the newly added support extension damage of each frame of image according to the damage condition of the newly added support change area is: In the In the neighborhood image sequence of the frame image, the remaining support change areas that are not matched by the tracking matching method are recorded as newly added support change areas; The first The number of all newly added support change areas in the neighborhood range image sequence of the frame image is the same as the number of the first The normalized value of the product of the cumulative sum of the damage factors of all newly added support change areas in the image sequence of the neighborhood range of the frame image is used as the first New support for extended damage resistance of frame images.

[0013] Preferably, the specific method for obtaining the support extension damage significance of each frame image based on the newly added support extension damage and extension failure risk factor is: The first The normalized value of the cumulative sum of the expansion damage risk factors of all support change areas in the frame image is used as the first The initial support extension damage of the frame image; The initial support extension damage of the frame image and the The normalized value of the product of the newly added support extension damage of the frame image is used as the first Support extension damage significance of frame images.

[0014] The present invention also proposes an underground tunnel support monitoring system based on video monitoring, which includes a memory and a processor. The processor executes a computer program stored in the memory to implement the steps of the above-mentioned underground tunnel support monitoring method based on video monitoring.

[0015] The beneficial effects of the technical solution of the present invention are: the present invention obtains the degree of support damage in each frame of image based on the internal grayscale changes and position distribution of the support change area in each frame of image; obtains the expansion density factor of each support change area based on the degree of support damage and the time interval between adjacent sub-expanded image sequences of the support change area; obtains the newly added support expansion damage of each frame of image based on the damage situation of the newly added support change area; obtains the support expansion damage significance of each frame of image based on the newly added support expansion damage and the expansion destruction risk factor; monitors underground tunnel support based on the support expansion damage significance; and through differentiated compression storage of underground tunnel support monitoring videos, it can not only ensure image processing efficiency and storage savings, but also not lose important monitoring information, thereby improving the accuracy of underground tunnel support monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 Flowchart of the steps of the underground tunnel support monitoring method based on video monitoring of the present invention; Figure 2 It is a characteristic relationship flow chart of the underground tunnel support monitoring method based on video monitoring of the present invention. DETAILED DESCRIPTION

[0018] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of the underground tunnel support and monitoring method and system based on video monitoring proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the underground tunnel support monitoring method and system based on video monitoring provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flowchart of a method for monitoring underground tunnel support based on video monitoring provided by one embodiment of the present invention, the method comprising the following steps: Step S001: Obtain monitoring video of underground tunnel support.

[0022] In a specific implementation of an embodiment of the present invention, a specific method for obtaining a monitoring video of an underground tunnel support is as follows: Install underground monitoring cameras to collect real-time data of underground tunnels and obtain monitoring videos of underground tunnel support.

[0023] At this point, the monitoring video of the underground tunnel support is obtained through the above method.

[0024] Step S002: Obtain the support change area in each frame image by analyzing the grayscale changes between adjacent frame images in the monitoring video; obtain the damage factor of each support change area based on the internal grayscale changes of the support change area; obtain the support damage degree in each frame image based on the damage factor of the support change area and its position distribution.

[0025] It should be noted that in videos or continuous frames, the scenes are usually static, and only when certain objects or structures change will there be obvious grayscale changes. For example, when the support structure is deformed, damaged, or people move, the grayscale value of the relevant area will change. Therefore, the grayscale value changes of pixels between adjacent frames are analyzed to identify the pixels with large grayscale changes, and the support change areas are identified from these change areas through a neural network. Finally, the degree of support damage in each frame of the image is evaluated based on the change size, area, number and distribution of these support change areas.

[0026] Specifically, the monitoring video of the underground tunnel support is divided into several separate frame images using OpenCV technology; wherein, OpenCV technology is an existing technology and will not be described in detail in this embodiment.

[0027] Preferably, in some implementations of the embodiments of the present invention, by analyzing the grayscale changes between adjacent frames in the surveillance video, a specific method for obtaining the support change area in each frame is: The monitoring video of underground tunnel support Frame image The coordinate position of the pixel point is recorded as the target position; The gray value of the pixel is The absolute value of the difference between the grayscale values ​​of the pixels at the target position in the frame image is recorded as The pixel at the Frame image and The grayscale difference between the frame images; The pixel at the Frame image and Frame image and The normalized value of the mean grayscale difference between frame images is recorded as Frame image Grayscale change value of each pixel; Among them, for the last frame of the monitoring video of the underground tunnel support, The normalized value of the grayscale value of the pixel in the last frame is recorded as The grayscale change value of each pixel.

[0028] Preset a neighborhood grayscale parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; Jordi Frame image The grayscale change value of the pixel is greater than or equal to the neighborhood grayscale parameter , will The pixel point is recorded as the changed pixel point; Each closed area formed by all the changed pixels in the frame image is recorded as a changed area; The first All the changed areas in the frame image are input into the trained neural network to obtain the first All support change areas in the frame image; The support change area includes: the support fracture, collapse or deformation area; the non-support change area includes: the movement of staff and equipment; the neural network used in this embodiment is YOLOv3, and the method for obtaining the data set for training the neural network is: A large number of underground tunnel support monitoring images are collected, and the support change area in each underground tunnel support monitoring image is manually marked with a bounding box, and this marking result is recorded as the label of each underground tunnel support monitoring image; a large number of underground tunnel support monitoring images and their corresponding labels are collected to form a data set; the neural network is trained using the data set, and the loss function used in the training process is the mean square error loss function; the specific training process is well known in neural networks, and the specific training process will not be repeated in this embodiment.

[0029] It should be noted that if the grayscale change value of the pixel point in the support change area is larger and the area is larger, it means that the support change area changes more obviously between adjacent frames, reflecting the structural problem of deformation or damage of the support structure; the larger the area of ​​the support change area, the wider the impact range, and the possible damage or change degree is also more serious. A support change area with a larger area usually means that the change in the support structure is not local, but large-scale, and there is a greater risk of damage; and a large number of support change areas means that the support structure has undergone extensive changes, which may lead to damage in a larger range; therefore, when the distribution of the support change area is more concentrated, it means that the damage occurs in the same location or adjacent areas, which may pose a greater threat to the integrity of the support structure.

[0030] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining the support damage degree in each frame of image is as follows: According to the internal grayscale changes of the support change area, the damage factor of each support change area is obtained; Preset a grayscale parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; The first The mean of the grayscale change values ​​of all pixels in the support change area and the grayscale parameter The ratio of The normalized value of the product of the number of all pixels in the support change area is recorded as Damage factor of each support change area; The specific formula is: Where, The damage factor representing the support change area; Represents the mean value of the grayscale change values ​​of all pixels in the support change area; Indicates the number of all pixels in the support change area; represents the linear normalization function.

[0031] The first The number of all support change areas in the frame image is the same as the The product of the mean values ​​of the loss factors of all support change areas in the frame image is recorded as the first product; The first All support change regions in the frame image are combined in any two ways to obtain several support change region combinations; The inversely proportional normalized value of the cumulative sum of the Euclidean distances between the centroid positions of two support change regions in all support change region combinations in the frame image is recorded as the support distribution value; The normalized value of the product of the first product and the support distribution value is used as the first The degree of support damage in the frame image; The specific formula is: Where, Indicates the The degree of support damage in the frame image; Indicates the The number of all support change areas in the frame image; Indicates the The mean of the loss factors of all support change areas in the frame image; Indicates the The number of all support change area combinations in the frame image; Indicates the Frame image The Euclidean distance between the centroid positions of two support change areas within a support change area combination; represents the linear normalization function; Represents an exponential function with a natural constant as the base, and the embodiment adopts Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can choose the inverse proportional function and normalization function according to the actual situation.

[0032] So far, the support damage degree in each frame of image is obtained through the above method.

[0033] Step S003: Obtain all sub-extended image sequences of each support change area; obtain the expansion density factor of each support change area according to the support damage degree and the time interval between adjacent sub-extended image sequences of the support change area; obtain the expansion destruction risk factor of each support change area according to the expansion density factor and the area change of the support change area between each extended image sequence; obtain the newly added support change area in each frame of image, and obtain the newly added support expansion damage of each frame of image according to the damage of the newly added support change area; obtain the support expansion damage significance of each frame of image according to the newly added support expansion damage and the expansion destruction risk factor.

[0034] It should be noted that if the support change area in the image may expand intermittently in the future, or new support change areas may be added, these all indicate that the impact of the image on the subsequent stage of tunnel support monitoring becomes particularly critical, because it reflects the major changes that may occur in the support structure in certain key areas; the larger the expansion area of ​​the support change area and the faster the rate, the stronger the support damage expansibility and the more serious the structural damage to the support, that is, large-scale changes and expansions in a short period of time are dangerous signals, indicating that the support damage is rapidly expanding and will have a serious impact on the support structure; therefore, by combining the area changes and time intervals of different types of support change areas between consecutive frames, the support damage expansibility of each frame image is obtained.

[0035] Preferably, in some implementations of the embodiments of the present invention, the specific method for obtaining all sub-extended image sequences of each support change area is: Preset a neighborhood parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; In the underground tunnel support monitoring video, Frame image and The nearest frame image The image sequence composed of frame images is The neighborhood range image sequence of the frame image; use the KLT (Kanade-Lucas-Tomasi) tracking algorithm to track according to time until it can no longer be tracked, and obtain the first Frame image The support change area is in The support change area corresponding to each frame image in the neighborhood range image sequence of the frame image; Among them, if All the frames after the frame image do not meet the When Frame image and All frames after the frame image constitute the first A neighborhood range image sequence of a frame image; the KLT tracking algorithm is a prior art and will not be described in detail in this embodiment; Preset an area change parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; In the In the neighborhood range image sequence of the frame image, traverse in sequence to obtain the first The absolute value of the difference in the number of all pixels in the support change area is calculated until the absolute value of the difference is less than the area change parameter for the first time. When traversing, all the frame images before the last frame image constitute the first extended image sequence; starting from the last frame image traversed, obtain the second extended image sequence according to the method of obtaining the first extended image sequence, and so on, obtain the first extended image sequence. All sub-extended image sequences of the support change area; The specific method is: In the In the neighborhood image sequence of the frame image, if the The number of all pixels in the support change area is Frame image and The absolute value of the difference between the frame images is greater than or equal to the area change parameter , will The frame image is recorded as An extended frame image of the support change area; if the The number of all pixels in the support change area is Frame image and The absolute value of the difference between the frame images is greater than or equal to the area change parameter , will The frame image is recorded as The extended frame image of the support change area; and so on, until the The absolute value of the difference between the number of all pixels in the support change area between the latest adjacent frame images is less than the area change parameter , and obtain the All the extended frame images of the support change area, and the sequence composed of all the extended frame images is recorded as the first The first extended image sequence of the support change area; according to the acquisition method of the first extended image sequence, the first All sub-extended image sequences of the support change area.

[0036] Preferably, in some implementations of the embodiments of the present invention, if the time intervals between the extended image sequences of the support change region are shorter and greater in number, and the degree of support damage in these frame images is greater, it indicates that the support change region is undergoing multiple and intensive expansions in the neighborhood range image sequence, that is, the support structure may be undergoing relatively rapid deformation or damage, and such changes have a significant impact on the safety and stability of the support structure; therefore, according to the degree of support damage and the time intervals between adjacent sub-extended image sequences of the support change region, the specific method for obtaining the expansion density factor of each support change region is as follows: The first The average value of the support damage degree of all frame images in the image sequence of the neighborhood range of the frame image is The neighborhood range of the frame image in the image sequence The product of the number of all sub-extended image sequences in the support change area is recorded as the second product; In the neighborhood range image sequence of the frame image, the first The first support change area The second extended image sequence and the The time interval between the first frame of the extended image sequence is recorded as The time factor of the second extended image sequence; The inverse proportional normalized value of the cumulative sum of the time factors of all sub-expanded image sequences in the support change area is recorded as The expansion interval density of each support change area; The product of the expansion interval density of the support change area and the second product is used as the first Frame image The expansion density factor of each support change area; The specific formula is: Where, Indicates the Frame image The expansion density factor of each support change area; Indicates the The average value of the support damage degree of all frame images in the image sequence within the neighborhood range of the frame image; Indicates the The neighborhood range of the frame image in the image sequence The number of all sub-extended image sequences in the support change area; Indicates the The neighborhood range of the frame image in the image sequence The first support change area The second extended image sequence and the The time interval between the first frames of the sub-extended image sequences; Represents an exponential function with a natural constant as the base, and the embodiment adopts Model to present inverse proportional relationship and normalization processing, As the input of the model, the implementer can choose the inverse proportional function and normalization function according to the actual situation.

[0037] Preferably, in some implementations of the embodiments of the present invention, if the expansion density factor of the support change region and the number of expanded frame images in each expansion image sequence of the support change region are smaller, it means that each expansion of the support change region occurs faster; and the rapid change in the area of ​​the support change region within a short time interval indicates that the expansion rate of the support change region is high, and the risk of damage to the support structure is greater; therefore, based on the expansion density factor and the change in the area of ​​the support change region between each expansion image sequence, the specific method for obtaining the expansion damage risk factor of each support change region is: In the In the neighborhood range image sequence of the frame image, the first The first support change area The number of frames between the first and last frames in the extended image sequence The union of all pixels in the support change area is recorded as the first set; The first support change area The number of frames between the first and last frames in the extended image sequence The intersection of all pixels in the support change area is recorded as the second set; the difference in the number of elements between the first set and the second set is recorded as the first set. The expansion factor of the second expanded image sequence; The expansion factor of the second expanded image sequence is the same as that of the first The ratio between the number of all extended frame images in the extended image sequence is recorded as The expansion rate factor of the second expanded image sequence; The cumulative sum of the expansion rate factors of all sub-expanded image sequences in the support change area is recorded as The expansion trend value of the support change area; The product of the expansion density factor and the expansion trend value of the support change area is used as the The expansion damage risk factor of each support change area; The specific formula is: Where, Indicates the Frame image The expansion damage risk factor of each support change area; Indicates the Frame image The expansion density factor of each support change area; Indicates the The neighborhood range of the frame image in the image sequence The number of all sub-extended image sequences in the support change area; 、 Respectively represent The neighborhood range of the frame image in the image sequence The first support change area The number of frames between the first and last frames in the extended image sequence The number of elements in the union and intersection of all pixels in the support change area; Indicates the The neighborhood range of the frame image in the image sequence The first support change area The number of extended frame images in the secondary extended image sequence; Indicates taking the absolute value; in, Indicates the The support change area is in The expansion factor for the second expansion, Indicates the The area of ​​the support change zone is The expansion rate factor for the second expansion.

[0038] Preferably, in In the neighborhood image sequence of the frame image, the remaining support change areas that are not matched by the tracking matching method are recorded as newly added support change areas; Preferably, in some implementations of the embodiments of the present invention, if There is a new change area between the frame image and any frame image, and the change area belongs to the support change area, the larger the damage factor is; In the future stage of the frame image, the expansion of the existing support change area is newly added on the basis of the original one, which means that the damage expansion of the support in the future stage will continue or intensify. Therefore, based on the loss factor of the newly added support change area, the specific method for obtaining the damage of the newly added support expansion in each frame image is as follows: The first The number of all newly added support change areas in the neighborhood range image sequence of the frame image is the same as the number of the first The normalized value of the product of the cumulative sum of the damage factors of all newly added support change areas in the image sequence of the neighborhood range of the frame image is used as the first New support extension damage resistance of frame images; The specific formula is: Where, Indicates the New support extension damage resistance of frame images; Indicates the The number of all newly added support change areas in the image sequence within the neighborhood range of the frame image; Indicates the The neighborhood range of the frame image in the image sequence The damage factor of the newly added support change area; represents the linear normalization function.

[0039] Preferably, in some implementations of the embodiments of the present invention, if The more the frame image is damaged, the more The larger the expansion damage risk factor of the support change area in the frame image, the greater the In the future stage of the frame image, there is not only the damage extension in the existing support change area, but also the damage extension in the newly added support change area, which indicates that the first The impact of frame images on subsequent tunnel support monitoring becomes particularly critical because they reflect significant changes in the support structure's extended damage that may occur in certain key frame images. Therefore, based on the newly added support extended damage and extended failure risk factors, the specific method for obtaining the support extended damage significance of each frame image is as follows: The first The normalized value of the cumulative sum of the expansion damage risk factors of all support change areas in the frame image is used as the first The initial support extension damage of the frame image; The initial support extension damage of the frame image and the The normalized value of the product of the newly added support extension damage of the frame image is used as the first Support extension damage significance of frame images; The specific formula is: Where, Indicates the Support extension damage significance of frame images; Indicates the New support extension damage resistance of frame images; Indicates the Initial support extension damage of frame images; represents the linear normalization function.

[0040] So far, the support extension damage significance of each frame image is obtained through the above method.

[0041] Step S004: Monitoring underground tunnel support based on support extension damage significance.

[0042] It should be noted that for any frame image in the underground tunnel support monitoring video, if the support extension damage significance of the frame image is greater, it means that the support damage extension in the frame image is rapid and serious, and a lower compression weight should be assigned to retain a greater degree of details.

[0043] Preferably, in some implementations of the embodiments of the present invention, the specific method for monitoring underground tunnel support based on support extension damage significance is: For any frame of image in the monitoring video of underground tunnel support, the product of the inverse of the support damage significance of the any frame of image and the initial quantization parameter in the H.264 video compression algorithm is used as the modified quantization parameter of the any frame of image; Inputting the corrected quantization parameters of each frame of image into the H.264 video compression algorithm, compressing the underground tunnel support monitoring video to obtain a compressed underground tunnel support monitoring video; transmitting the compressed underground tunnel support monitoring video to a ground control center or monitoring platform via an underground wireless network or wired network and decompressing it to obtain a number of underground tunnel support monitoring images; Preset a quality threshold parameter , wherein this embodiment is based on This example is described as an example, and this embodiment is not specifically limited. Depends on the specific implementation situation; The underground tunnel support monitoring image is input into the SLAM technology to perform 3D modeling of the underground tunnel support and generate detailed 3D point cloud data or grid model; based on the generated 3D model, the structural quality assessment value of the support is obtained; if the structural quality assessment value of the support is less than the quality threshold parameter When the problem occurs, it indicates that there is a risk in the underground tunnel support and the staff needs to be notified in time to arrange maintenance.

[0044] Among them, SLAM technology and H.264 video compression algorithm are both existing technologies, and are not described in detail in this embodiment.

[0045] See also Figure 2 , which shows a characteristic relationship flow chart of an underground tunnel support monitoring method based on video monitoring; Through the above steps, the underground tunnel support monitoring method based on video monitoring is completed.

[0046] Another embodiment of the present invention provides an underground tunnel support monitoring system based on video monitoring, the system including a memory and a processor, and when the processor executes the computer program stored in the memory, it performs the above method steps S001 to S004.

[0047] 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 principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The underground tunnel support monitoring method based on video monitoring is characterized by: The method comprises the following steps: Obtain monitoring video of underground tunnel support; By analyzing the grayscale changes between adjacent frames in the monitoring video, the support change area in each frame is obtained; based on the internal grayscale changes in the support change area, the damage factor of each support change area is obtained; based on the damage factor and position distribution of the support change area, the support damage degree in each frame is obtained; Obtain all sub-expanded image sequences of each support change area; obtain the expansion density factor of each support change area based on the support damage degree and the time interval between adjacent sub-expanded image sequences of the support change area; obtain the expansion damage risk factor of each support change area based on the expansion density factor and the change in the area of ​​the support change area between each expansion image sequence; obtain the newly added support change area in each frame of image, and obtain the newly added support expansion damage of each frame of image based on the damage of the newly added support change area; obtain the support expansion damage significance of each frame of image based on the newly added support expansion damage and the expansion damage risk factor; Monitoring of underground tunnel support based on support extension damage significance.

2. The underground tunnel support monitoring method based on video monitoring according to claim 1 is characterized in that: The specific method of obtaining the support change area in each frame image by analyzing the grayscale changes between adjacent frame images in the monitoring video is: The monitoring video of underground tunnel support Frame image The coordinate position of the pixel point is recorded as the target position; The gray value of the pixel is The absolute value of the difference between the grayscale values ​​of the pixels at the target position in the frame image is recorded as The pixel at the Frame image and The grayscale difference between the frame images; The pixel at the Frame image and Frame image and The normalized value of the mean grayscale difference between frame images is recorded as Frame image Grayscale change value of each pixel; Preset a neighborhood grayscale parameter , if Frame image The grayscale change value of the pixel is greater than or equal to the neighborhood grayscale parameter , will The pixel point is recorded as the changed pixel point; Each closed area formed by all the changed pixels in the frame image is recorded as a changed area; All the changed areas in the frame image are input into the trained neural network to obtain the first All support change areas in the frame image.

3. The underground tunnel support monitoring method based on video monitoring according to claim 2 is characterized in that: The specific method for obtaining the damage factor of each support change area according to the internal grayscale change of the support change area is: Preset a grayscale parameter , will The mean of the grayscale change values ​​of all pixels in the support change area and the grayscale parameter The ratio of is recorded as the first ratio; The first ratio and the The normalized value of the product of the number of all pixels in the support change area is recorded as The damage factor of each support change area.

4. The underground tunnel support monitoring method based on video monitoring according to claim 1 is characterized in that: The specific method for obtaining the support damage degree in each frame image based on the damage factor and position distribution of the support change area is: The first The number of all support change areas in the frame image is the same as the The product of the mean values ​​of the loss factors of all support change areas in the frame image is recorded as the first product; the All support change regions in the frame image are combined in any two ways to obtain several support change region combinations; The inversely proportional normalized value of the cumulative sum of the Euclidean distances between the centroid positions of two support change regions in all support change region combinations in the frame image is recorded as the support distribution value; The normalized value of the product of the first product and the support distribution value is used as the first The support damage degree in the frame image.

5. The underground tunnel support monitoring method based on video monitoring according to claim 1 is characterized in that: The specific method for obtaining all sub-extended image sequences of each support change area is: Preset a neighborhood parameter , in the underground tunnel support monitoring video, the first Frame image and The nearest frame image The image sequence composed of frame images is The neighborhood range image sequence of the frame image; using the KLT tracking algorithm, obtain the first Frame image The support change area is in The support change area corresponding to each frame image in the neighborhood range image sequence of the frame image; Preset an area change parameter , in In the neighborhood range image sequence of the frame image, traverse in sequence to obtain the first The absolute value of the difference in the number of all pixels in the support change area is calculated until the absolute value of the difference is less than the area change parameter for the first time. When traversing, all the frame images before the last frame image constitute the first extended image sequence; starting from the last frame image traversed, obtain the second extended image sequence according to the method of obtaining the first extended image sequence, and so on, obtain the first extended image sequence. All sub-extended image sequences of the support change area.

6. The underground tunnel support monitoring method based on video monitoring according to claim 5 is characterized in that: The specific method for obtaining the expansion density factor of each support change area according to the support damage degree and the time interval between adjacent sub-expanded image sequences of the support change area is: The first The average value of the support damage degree of all frame images in the image sequence of the neighborhood range of the frame image is The neighborhood range of the frame image in the image sequence The product of the number of all sub-extended image sequences in the support change area is recorded as the second product; In the neighborhood range image sequence of the frame image, the first The first support change area The second extended image sequence and the The time interval between the first frame of the extended image sequence is recorded as The time factor of the second extended image sequence; The inverse proportional normalized value of the cumulative sum of the time factors of all sub-expanded image sequences in the support change area is recorded as The expansion interval density of each support change area; The product of the expansion interval density of the support change area and the second product is used as the first Frame image The expansion density factor of each support change area.

7. The underground tunnel support monitoring method based on video monitoring according to claim 5 is characterized in that: The specific method for obtaining the expansion damage risk factor of each support change area based on the expansion density factor and the area change of the support change area between each expansion image sequence is: In the In the neighborhood range image sequence of the frame image, the first The first support change area The number of frames between the first and last frames in the extended image sequence The union of all pixels in the support change area is recorded as the first set; The first support change area The number of frames between the first and last frames in the extended image sequence The intersection of all pixels in the support change area is recorded as the second set; the difference in the number of elements between the first set and the second set is recorded as the first set. The expansion factor of the second expanded image sequence; The expansion factor of the second expanded image sequence is the same as that of the first The ratio between the number of all extended frame images in the extended image sequence is recorded as The expansion rate factor of the second expanded image sequence; The cumulative sum of the expansion rate factors of all sub-expanded image sequences in the support change area is recorded as The expansion trend value of each support change area; The first The product of the expansion density factor and the expansion trend value of the support change area is used as the The expansion damage risk factor of each support change area.

8. The underground tunnel support monitoring method based on video monitoring according to claim 5 is characterized in that: The specific method for obtaining the newly added support change area in each frame of image and obtaining the newly added support extension damage of each frame of image according to the damage condition of the newly added support change area is: In the In the neighborhood image sequence of the frame image, the remaining support change areas that are not matched by the tracking matching method are recorded as newly added support change areas; The first The number of all newly added support change areas in the neighborhood range image sequence of the frame image is the same as the number of the first The normalized value of the product of the cumulative sum of the damage factors of all newly added support change areas in the image sequence of the neighborhood range of the frame image is used as the first New support for extended damage resistance of frame images.

9. The underground tunnel support monitoring method based on video monitoring according to claim 1 is characterized in that: The specific method for obtaining the support extension damage significance of each frame image based on the newly added support extension damage and extension failure risk factors is: The first The normalized value of the cumulative sum of the expansion damage risk factors of all support change areas in the frame image is used as the first Initial support extension damage of frame images; The first The initial support extension damage of the frame image and the The normalized value of the product of the newly added support extension damage of the frame image is used as the first Support extension damage significance of frame images.

10. An underground tunnel support monitoring system based on video monitoring, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the underground tunnel support monitoring method based on video monitoring as described in any one of claims 1 to 9 are implemented.

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