CN114067533A - Geological disaster photographing monitoring and early warning method - Google Patents
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Abstract
The invention discloses a geological disaster photographing monitoring and early warning method, which comprises the following steps: s1: determining the size of each target according to the distance from the video camera, the maximum displacement value and the magnification of the video camera by taking each target image which can be clearly obtained as a target; s2: adopting a video camera to carry out real-time image acquisition on each target of a plurality of measuring lines and a plurality of measuring points of the monitored landslide body; based on the collected video image, performing high-precision distortion correction on the lens camera by using a panoramic calibration field, filtering noise interference by wavelet transformation, and then restoring the image through image reconstruction; s3: the recovered image data are transmitted to a computer server, and the computer server carries out real-time calculation on the vertical displacement of each target and the displacement in the landslide direction; s4: the computer server outputs the calculation result in a chart or a format designated by a user; s5: and immediately performing acousto-optic early warning by the computer server when the computer server detects that the threshold value is exceeded.
Description
Technical Field
The invention relates to the technical field of geological disaster monitoring, in particular to a displacement deformation photographic monitoring and early warning method for collapse, landslide, ground subsidence and ground settlement.
Background
China is one of the countries with the most serious geological disasters and the most threatened population in the world, and has the defects of multiple disaster hidden dangers, wide distribution and high prevention difficulty, such as collapse, landslide, debris flow, ground collapse, ground cracks, ground subsidence and the like. Particularly, in the slope rock-soil body, due to the inherent conditions such as geological environment, historical deformation and the like, earthquake or human activities are used as inducements, rainfall or irrigation promotes the continuous deformation of the landslide, and under the action of self-gravity, a local structure is damaged, the supporting force is gradually lost, and the whole body slides along a certain direction or a plurality of directions to cause the landslide or collapse. Factors influencing the deformation and damage of the side slope include internal factors such as landform, geological lithology and the like, and also include inducing factors such as rainwater, river water, underground water, earthquake and freezing action, human activities and long-term creep, and due to uncertainty of the factors, the geological disaster monitoring difficulty is high, and a monitoring and early warning method suitable for the factors needs to be researched.
The national and local geological management departments are also continuously trying to monitor the deformation displacement of the side slope by various means, and there are traditional monitoring methods: such as a macroscopic geology experience method, a simple observation method, and also a data measurement method, such as a stay wire displacement meter, a geodetic measurement method, a GPS monitoring method and a laser radar method; and measuring methods such as satellite-borne interference radar and TDR monitoring. Although these methods are effective, they all have the problems of high cost, much manual intervention, poor real-time performance, long cycle, and the like.
Therefore, a distributed real-time visualized side slope geological disaster photographing monitoring and early warning method with high precision and low cost is urgently needed.
Disclosure of Invention
In order to overcome the problems, the invention provides a geological disaster photographing monitoring and early warning method, which improves the monitoring precision, reduces the strength and cost of conventional monitoring, and has the advantages of convenient operation and low use and installation cost.
The technical scheme adopted by the invention is as follows:
a geological disaster photography monitoring and early warning method comprises the following steps:
s1: determining the size of each target according to the distance from the video camera, the maximum displacement value and the magnification of the video camera by taking each target image which can be clearly obtained as a target;
s2: adopting a video camera to carry out real-time image acquisition on each target of a plurality of measuring lines and a plurality of measuring points of the monitored landslide body; based on the collected video image, performing high-precision distortion correction on the lens camera by using a panoramic calibration field, filtering noise interference by wavelet transformation, and then restoring the image through image reconstruction;
s3: the recovered image data are transmitted to a computer server, and the computer server carries out real-time calculation on the vertical displacement of each target and the displacement in the landslide direction;
s4: the computer server outputs the calculation result in a chart or a format designated by a user;
s5: and if the computer server detects that the threshold value is exceeded, immediately performing acousto-optic early warning, and simultaneously sending the scene pictures to the intelligent terminals and calling the emergency contact. The monitoring precision is improved, the strength and the cost of conventional monitoring are reduced, the operation is convenient, and the use and installation cost is low.
Preferably, in step S2, the process of correcting the lens distortion with high precision by using the panoramic field specifically includes:
s21: adjusting the focal length to be farthest, nearest and middle, and respectively detecting and correcting three groups of distortion parameters;
s22: at least more than 20 groups of pictures are taken on each focal section, and distortion parameters on the corresponding focal section are calculated from the 20 groups of pictures;
s23: resolving distortion quantities of different field focusing positions by a high-order polynomial, and performing primary distortion correction in the first step;
s24: on the basis of primary distortion correction of the first step, high-precision feature recognition is carried out on the sequence image, wavelet change is utilized to decompose the image, the influence of various noise interference factors on image recognition is filtered, and then the image is restored through image reconstruction.
Preferably, the relative position adopted by the video cameras is mutually calibrated, and the specific process comprises the following steps: the focal lengths of the video camera lenses are set to be different, the focal length is long, and the farthest target is amplified and is clearer; the focal length is short, the visual field range is wider, and each measuring point target of each measuring line can cover; when the video camera is aligned with the target of the monitored slope landslide body, the same target is shot due to different focal lengths, and small changes in the shot interior are reflected on the image through the cameras with different focal sections and are reflected by different pixel point positions; and calculating the relative variation of the camera through the calibrated distortion parameters, the optical collinear equation and the focal length resolution, and further correcting the imaging error caused by the small structural change of the main camera.
Preferably, the optical lens distortion solution and correction algorithm is as follows: checking and correcting a model of a common optical lens: wherein, the image space coordinate system is as follows:
△x＝(x–x0)(k1r2+k2r4)+p1[r2+2(x–x0)2]+2p2(x–x0)(y–y0)+α(x–x0)+β(y–y0)
△y＝(y–y0)(k1r2+k2r4)+p2[r2+2(y–y0)2]+2p1(x–x0)(y–y0)；
wherein: delta x and delta y are image point correction values; x and y are coordinates of image points in an image space coordinate system; x0, y0 are principle-like points,
preferably, the computer servers work in a direct network connection or a remote distributed mode through a network.
Preferably, before step S1, the method further comprises determining a monitoring area, fixing the position of the video camera, drawing a plurality of measuring lines, and marking a plurality of measuring points.
Preferably, the target is painted at intervals of black and white and is firmly installed at the measuring point.
Preferably, the influence of each target sequence frame is acquired by using a video camera installed in the field, and the video camera is locally stored for not less than 7 days.
The invention has the beneficial effects that:
the invention discloses a geological disaster photographing monitoring and early warning method, which comprises the following steps: s1: determining the size of each target according to the distance from the video camera, the maximum displacement value and the magnification of the video camera by taking each target image which can be clearly obtained as a target; s2: adopting a video camera to carry out real-time image acquisition on each target of a plurality of measuring lines and a plurality of measuring points of the monitored landslide body; based on the collected video image, performing high-precision distortion correction on the lens camera by using a panoramic calibration field, filtering noise interference by wavelet transformation, and then restoring the image through image reconstruction; s3: the recovered image data are transmitted to a computer server, and the computer server carries out real-time calculation on the vertical displacement of each target and the displacement in the landslide direction; s4: the computer server outputs the calculation result in a chart or a format designated by a user; s5: and if the computer server detects that the threshold value is exceeded, immediately performing acousto-optic early warning, and simultaneously sending the scene pictures to the intelligent terminals and calling the emergency contact. The monitoring precision is improved, the strength and the cost of conventional monitoring are reduced, the operation is convenient, and the use and installation cost is low.
Drawings
FIG. 1 is a radial distortion block diagram of the present invention;
FIG. 2 is a diagram of the tangential distortion structure of the present invention;
FIG. 3 is a diagram of the steps of the method of the present invention.
Detailed Description
The present invention will be described with reference to the accompanying drawings and embodiments.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. The following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance. Furthermore, the terms "horizontal", "vertical", "suspended", and the like do not imply that the components are required to be absolutely horizontal or suspended, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Distortion correction of optical lens: in photogrammetry, imaging by using an optical lens has some deformation, particularly the deformation near the periphery of an image is larger, the deformation is divided into radial distortion and tangential distortion, a strict mathematical calculation formula is required to eliminate the distortion, and particularly in high-precision slope close-range monitoring application, a change model of the radial distortion and the tangential distortion is shown in fig. 1 and fig. 2. And (3) calibrating the pose of multi-lens imaging: the internal structure of the monitoring equipment is a metal structure, so that the monitoring equipment is relatively stable in general, but in high-precision slope monitoring application, small changes of the internal structure can cause large errors at a long distance, so that in high-precision application, the small changes of the internal structure must be solved, and real-time calculation and correction of a close-range main lens and a close-range auxiliary lens are required by a real-time self-correction technology.
The application of the current close-range image video monitoring in geological disaster monitoring and early warning, such as Licailin, Zhang Jianqing and Guobaozu, a novel landslide monitoring method using a close-range photogrammetry technology [ J ] computer engineering and application, 2011,47(03):6-8. Huyuhai. application of the close-range photogrammetry technology in landslide monitoring [ D ]. Chengdu university, 2012. Lemna. application of the close-range photogrammetry technology in landslide monitoring [ J ] science and technology wind, 2014(07): 57) is to perform superposition analysis by using a two-stage DEM model; chenchu, Jiangxing Yu, Zhang scholars, Wanlin, Wu Zhengpeng, application research of close-range photogrammetry in landslide monitoring [ J ] urban survey, 2015(01): 105-. The application of the Shexingdong, Yangdhong, the close-range photography technology in the deformation monitoring of the side slope of the open mine [ J ]. value engineering, 2016,35(21): 123-: through data comparison, the deviation of the close-range photogrammetry and the coordinate measured by the total station is within a range of 3 mm.
Example 1: referring to fig. 1 to 3, a geological disaster photography monitoring and early warning method of the present embodiment includes the following steps: s1: determining the size of each target according to the distance from the video camera, the maximum displacement value and the magnification of the video camera by taking each target image which can be clearly obtained as a target; s2: adopting a video camera to carry out real-time image acquisition on each target of a plurality of measuring lines and a plurality of measuring points of the monitored landslide body; based on the collected video image, performing high-precision distortion correction on the lens camera by using a panoramic calibration field, filtering noise interference by wavelet transformation, and then restoring the image through image reconstruction; s3: the recovered image data are transmitted to a computer server, and the computer server carries out real-time calculation on the vertical displacement of each target and the displacement in the landslide direction; s4: the computer server outputs the calculation result in a chart or a format designated by a user; s5: and if the computer server detects that the threshold value is exceeded, immediately performing acousto-optic early warning, and simultaneously sending the scene pictures to the intelligent terminals and calling the emergency contact. The monitoring precision is improved, the strength and the cost of conventional monitoring are reduced, the operation is convenient, and the use and installation cost is low.
The method adopts a non-contact close-range image monitoring scheme. Settle equipment in more stable place, then carry out equidistant video and image acquisition to the side slope, utilize the APP software of the integrated embedding of front end to carry out preliminary management to the mode and the data of gathering, accomplish the primary treatment of data, then carry out rear end high accuracy landslide displacement volume through remote transmission to the server and solve. The precision displacement of 0.1-1mm can be achieved. The technology is not three-dimensional measurement, can only monitor the displacement of an image in the longitudinal and transverse directions, and cannot monitor the displacement of a camera in the sight line direction.
Example 2: in step S2 of this embodiment, the process of performing high-precision distortion correction on the lens by using the panoramic calibration field specifically includes: s21: adjusting the focal length to be farthest, nearest and middle, and respectively detecting and correcting three groups of distortion parameters; s22: at least more than 20 groups of pictures are taken on each focal section, and distortion parameters on the corresponding focal section are calculated from the 20 groups of pictures; s23: resolving distortion quantities of different field focusing positions by a high-order polynomial, and performing primary distortion correction in the first step; s24: on the basis of primary distortion correction of the first step, high-precision feature recognition is carried out on the sequence image, wavelet change is utilized to decompose the image, the influence of various noise interference factors on image recognition is filtered, and then the image is restored through image reconstruction.
Example 3: the relative position adopted by the video camera of the embodiment is mutually calibrated, and the specific process comprises the following steps: the focal lengths of the video camera lenses are set to be different, the focal length is long, and the farthest target is amplified and is clearer; the focal length is short, the visual field range is wider, and each measuring point target of each measuring line can cover; when the video camera is aligned with the target of the monitored slope landslide body, the same target is shot due to different focal lengths, and small changes in the shot interior are reflected on the image through the cameras with different focal sections and are reflected by different pixel point positions; and calculating the relative variation of the camera through the calibrated distortion parameters, the optical collinear equation and the focal length resolution, and further correcting the imaging error caused by the small structural change of the main camera.
Example 4: the optical lens distortion solution and correction algorithm of the present embodiment is as follows: checking and correcting a model of a common optical lens: wherein, the image space coordinate system is as follows: Δ x ═ x0)(k1r2+k2r4)+p1[r2+2(x–x0)2]+2p2(x–x0)(y–y0)+α(x–x0)+β(y–y0)；△y＝(y–y0)(k1r2+k2r4)+p2[r2+2(y–y0)2]+2p1(x–x0) (y-y 0); wherein: delta x and delta y are image point correction values; x and y are coordinates of image points in an image space coordinate system; x0, y0 are principle-like points,
by shooting the calibration field pictures by a plurality of cameras, corresponding 11 distortion parameters are calculated, and then the acquired images are subjected to inverse correction, so that the image distortion caused by distortion can be corrected.
Example 5: the computer server of the embodiment adopts direct network connection or remote multi-station distributed work through networking. The method of this embodiment further includes determining the monitoring area, fixing the position of the video camera, drawing a plurality of measuring lines, and marking a plurality of measuring points before step S1. The target of this embodiment is painted at intervals of black and white and is firmly installed at the measuring point. In the embodiment, the influence of each target sequence frame is obtained by using the video camera installed in the field, and the video camera is locally stored for not less than 7 days.
Structural design: the iron is still hard, and the stability of the monitoring equipment is required as high-precision slope monitoring equipment. Therefore, the whole structure of the equipment is fixed by adopting a metal material, and comprises lens fixation, camera sensor fixation and circuit board fixation. And the place which is easy to loose is cut by a whole piece of aluminum material. The invention adopts the technology of remotely adjusting the angle to aim at the monitored target, remotely adjusting the aperture to a proper position, automatically adjusting the photographing time and automatically balancing the white balance. In a construction site, the equipment shell and the universal joint are screwed by 4 screws, and the universal joint is fixed at a stable point by 6 expansion screws, wherein the site needs to be selected according to the environment of the site and the position of a monitored target. Then, a plastic target is manufactured, the installation position of the target is determined according to the key position of the landslide, and the target is generally fixed on the key point by using a solid material and a method and can be seen by the monitoring equipment. And data are processed and arranged at a server end in real time, and sequence images transmitted by 4G or a network cable are utilized to perform real-time image analysis and calculation, so that the deformation condition of key points or survey lines of the landslide body is calculated. According to the technical scheme provided by the invention, a whole set of complete work flow and technical scheme are formed in the links of hardware design and processing of the slope landslide monitoring equipment, front-end data processing, remote control acquisition mode, data analysis and processing of a rear-end server and the like. The monitoring precision is improved, the strength and the cost of manual conventional monitoring are reduced, the whole structure is simple, the operation is convenient, the use and installation cost is low, and the device is particularly suitable for popularization and application.
Example 6: the experimental process comprises the following steps: 1. distortion parameter calculation is carried out on a multi-lens camera by utilizing a laboratory panoramic calibration field, and three groups of distortion parameters (x) are respectively calibrated by adjusting the focal length to the farthest, the nearest and the middle0,y0,f,k1,k2,p1,p2And a, p), at least 10 groups of photos are taken on each focal section, distortion parameters on the corresponding focal section are calculated by the 10 groups of photos, and then distortion quantities of different positions of field focusing are fitted by a high-order polynomial so as to correct field primary lens distortion. In the step, a plurality of technical distortion parameters of the position are preferably selected within the adjustable range of the focal length of the visual lens, and the field fitting is favorably improved.
2. On the basis of the primary distortion correction of the first step, high-precision feature recognition is carried out on the sequence image, the interpretability of the image is improved by utilizing a wavelet transform algorithm, and the accuracy and precision of the feature recognition are improved. Various noise interference factors are filtered to influence image recognition. Generally, the higher resolution waveforms have lower signal-to-noise ratios, especially at levels of detail, with information volumes less than 1bit (individual samples even appear negative). If these signal components are used in the image matching operation, an error matching, i.e., a gross error, is generated, which causes a serious trouble in the subsequent processing. The method comprises the steps of firstly carrying out wavelet change in the horizontal direction and the vertical direction corresponding to a high-resolution image, decomposing high frequency and low frequency, eliminating noise to improve the information proportion, restoring the image after 1-level wavelet change, and then effectively filtering various noises by reconstructing the image.
3. Homography mutual calibration of multiple cameras: the focus that many cameras used is different, the focus of main lens is longer a little more, the short visual field scope of other camera lens focuses is wider, and the orientation is different, because these camera lenses all shoot except that the target has other ground targets, and because the inside small change of slope monitoring facilities reflects on the formation of image through the camera of different focal length, many camera sequence images have certain overlap, distortion parameter through checking out the school like this, optics collineation equation and focal length resolution ratio can calculate the relative change volume of many cameras, correct the measured value of main function camera, and the precision is improved.
The invention has the advantages that:
1) compared with the traditional slope monitoring equipment, the slope monitoring equipment is lower in cost;
2) the invention has high automation degree, works automatically in the whole process, does not need manual intervention, and only manually intervenes when the image acquisition frequency is adjusted;
3) the monitoring of the invention adopts a multi-camera lens mutual calibration technology in the internal structure, and simultaneously, the high-precision distortion correction is carried out on the main lens, so the precision is equal to or higher than that of the traditional geological disaster close-range photography monitoring equipment.
Most of the existing other similar close-range monitoring technologies adopt on-site manual photographing observation or three-dimensional calculation in a camera or dual-camera stereo mode, and have the prospect of being not widely applicable and popularized in terms of precision and cost.
Claims (8)
1. A geological disaster photography monitoring and early warning method is characterized by comprising the following steps:
s1: determining the size of each target according to the distance from the video camera, the maximum displacement value and the magnification of the video camera by taking each target image which can be clearly obtained as a target;
s2: adopting a video camera to carry out real-time image acquisition on each target of a plurality of measuring lines and a plurality of measuring points of the monitored landslide body; based on the collected video image, performing high-precision distortion correction on the lens camera by using a panoramic calibration field, filtering noise interference by wavelet transformation, and then restoring the image through image reconstruction;
s3: the recovered image data are transmitted to a computer server, and the computer server carries out real-time calculation on the vertical displacement of each target and the displacement in the landslide direction;
s4: the computer server outputs the calculation result in a chart or a format designated by a user;
s5: and if the computer server detects that the threshold value is exceeded, immediately performing acousto-optic early warning, and simultaneously sending the scene pictures to the intelligent terminals and calling the emergency contact.
2. The geologic hazard photographic monitoring and early warning method as claimed in claim 1, wherein in step S2, the process of performing high-precision distortion correction on the shot by using the panoramic calibration field specifically comprises:
s21: adjusting the focal length to be farthest, nearest and middle, and respectively detecting and correcting three groups of distortion parameters;
s22: at least more than 20 groups of pictures are taken on each focal section, and distortion parameters on the corresponding focal section are calculated from the 20 groups of pictures;
s23: resolving distortion quantities of different field focusing positions by a high-order polynomial, and performing primary distortion correction in the first step;
s24: on the basis of primary distortion correction of the first step, high-precision feature recognition is carried out on the sequence image, wavelet change is utilized to decompose the image, the influence of various noise interference factors on image recognition is filtered, and then the image is restored through image reconstruction.
3. The geological disaster photography monitoring and early warning method as claimed in claim 1, wherein the adopted relative positions of the video cameras are mutually calibrated, and the specific process comprises: the focal lengths of the video camera lenses are set to be different, the focal length is long, and the farthest target is amplified and is clearer; the focal length is short, the visual field range is wider, and each measuring point target of each measuring line can cover; when the video camera is aligned with the target of the monitored slope landslide body, the same target is shot due to different focal lengths, and small changes in the shot interior are reflected on the image through the cameras with different focal sections and are reflected by different pixel point positions; and calculating the relative variation of the camera through the calibrated distortion parameters, the optical collinear equation and the focal length resolution, and further correcting the imaging error caused by the small structural change of the main camera.
4. The geological disaster photography monitoring and early warning method as claimed in claim 1, wherein the optical lens distortion solution and correction algorithm is as follows: checking and correcting a model of a common optical lens: wherein, the image space coordinate system is as follows:
△x＝(x–x0)(k1r2+k2r4)+p1[r2+2(x–x0)2]+2p2(x–x0)(y–y0)+α(x–x0)+β(y–y0)
△y＝(y–y0)(k1r2+k2r4)+p2[r2+2(y–y0)2]+2p1(x–x0)(y–y0)；
wherein: delta x and delta y are image point correction values; x and y are coordinates of image points in an image space coordinate system; x0, y0 are principle-like points,
5. the geological disaster photography monitoring and early warning method as claimed in claim 1, wherein the computer server adopts direct network connection or remote distributed work of multiple computers through networking.
6. The geologic hazard photographic monitoring and pre-warning method of claim 1, wherein prior to step S1, the method further comprises determining a monitoring area, fixing the position of the video camera, drawing a plurality of lines, and marking a plurality of measurement points.
7. The geological disaster photographic monitoring and early warning method as claimed in claim 1, wherein the target is painted at intervals of black and white and is firmly installed at the measuring point.
8. The geological disaster photography monitoring and early warning method as claimed in claim 1, wherein the influence of each target sequence frame is obtained by using a video camera installed in the field, and the video camera is stored locally for not less than 7 days.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115376283A (en) * | 2022-08-23 | 2022-11-22 | 江西理工大学 | Monitoring and early warning method and system based on multivariate data fusion |
| CN117558106A (en) * | 2023-11-24 | 2024-02-13 | 中国地质科学院探矿工艺研究所 | Non-contact type surface deformation quantitative monitoring and early warning method and monitoring system |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010074527A (en) * | 2008-09-18 | 2010-04-02 | Oki Electric Ind Co Ltd | Monitor control apparatus, and monitor system |
| JP2013064680A (en) * | 2011-09-20 | 2013-04-11 | Wakayama Univ | Alarm system for displacement detection |
| CN103994724A (en) * | 2014-05-13 | 2014-08-20 | 滕军 | Method for monitoring two-dimensional displacement and strain of structure based on digital image processing technology |
| CN105203030A (en) * | 2015-08-28 | 2015-12-30 | 同创智慧空间（北京）科技有限公司 | Monitoring method of micro displacement at engineering site |
| CN106092059A (en) * | 2016-06-27 | 2016-11-09 | 重庆交通大学 | A kind of works Horizontal Displacement Monitoring Method based on multi-point fitting |
| CN107843204A (en) * | 2017-10-27 | 2018-03-27 | 王文柏 | Side slope three-dimensional deformation monitoring method and system based on monitoring level video camera |
| CN110108984A (en) * | 2019-05-24 | 2019-08-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The spatial relationship synchronous method of power-line patrolling laser radar system multisensor |
| CN112284263A (en) * | 2020-10-16 | 2021-01-29 | 上海同禾工程科技股份有限公司 | Mountain disaster early warning monitoring device and early warning monitoring method based on machine vision |
| CN112330557A (en) * | 2020-11-03 | 2021-02-05 | 中国测绘科学研究院 | Bridge monitoring method of non-contact close-range image |
| CN213342432U (en) * | 2020-11-03 | 2021-06-01 | 中国测绘科学研究院 | High slope close-range monitoring facilities |
| CN214199982U (en) * | 2020-07-21 | 2021-09-14 | 上海同禾工程科技股份有限公司 | Structure displacement measuring system |
| CN113465525A (en) * | 2021-06-21 | 2021-10-01 | 贵州铭源新能源科技发展有限公司 | Geological disaster hidden danger point deformation characterization method based on stereoscopic vision |
-
2021
- 2021-11-27 CN CN202111428524.7A patent/CN114067533A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010074527A (en) * | 2008-09-18 | 2010-04-02 | Oki Electric Ind Co Ltd | Monitor control apparatus, and monitor system |
| JP2013064680A (en) * | 2011-09-20 | 2013-04-11 | Wakayama Univ | Alarm system for displacement detection |
| CN103994724A (en) * | 2014-05-13 | 2014-08-20 | 滕军 | Method for monitoring two-dimensional displacement and strain of structure based on digital image processing technology |
| CN105203030A (en) * | 2015-08-28 | 2015-12-30 | 同创智慧空间（北京）科技有限公司 | Monitoring method of micro displacement at engineering site |
| CN106092059A (en) * | 2016-06-27 | 2016-11-09 | 重庆交通大学 | A kind of works Horizontal Displacement Monitoring Method based on multi-point fitting |
| CN107843204A (en) * | 2017-10-27 | 2018-03-27 | 王文柏 | Side slope three-dimensional deformation monitoring method and system based on monitoring level video camera |
| CN110108984A (en) * | 2019-05-24 | 2019-08-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The spatial relationship synchronous method of power-line patrolling laser radar system multisensor |
| CN214199982U (en) * | 2020-07-21 | 2021-09-14 | 上海同禾工程科技股份有限公司 | Structure displacement measuring system |
| CN112284263A (en) * | 2020-10-16 | 2021-01-29 | 上海同禾工程科技股份有限公司 | Mountain disaster early warning monitoring device and early warning monitoring method based on machine vision |
| CN112330557A (en) * | 2020-11-03 | 2021-02-05 | 中国测绘科学研究院 | Bridge monitoring method of non-contact close-range image |
| CN213342432U (en) * | 2020-11-03 | 2021-06-01 | 中国测绘科学研究院 | High slope close-range monitoring facilities |
| CN113465525A (en) * | 2021-06-21 | 2021-10-01 | 贵州铭源新能源科技发展有限公司 | Geological disaster hidden danger point deformation characterization method based on stereoscopic vision |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115376283A (en) * | 2022-08-23 | 2022-11-22 | 江西理工大学 | Monitoring and early warning method and system based on multivariate data fusion |
| CN115376283B (en) * | 2022-08-23 | 2023-11-28 | 江西理工大学 | Monitoring and early warning method and system based on multivariate data fusion |
| CN117558106A (en) * | 2023-11-24 | 2024-02-13 | 中国地质科学院探矿工艺研究所 | Non-contact type surface deformation quantitative monitoring and early warning method and monitoring system |
| CN117558106B (en) * | 2023-11-24 | 2024-05-03 | 中国地质科学院探矿工艺研究所 | Non-contact type surface deformation quantitative monitoring and early warning method and monitoring system |
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