CN111089625A - Binocular vision-simulated river flow real-time monitoring system and method - Google Patents
Binocular vision-simulated river flow real-time monitoring system and method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/66—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
- G01F1/661—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters using light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
- G01F1/002—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow wherein the flow is in an open channel
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Abstract
The invention discloses a binocular vision simulated river flow real-time monitoring system and method. The problem that an existing flood early warning system is inaccurate is solved; the invention comprises a river water surface tracing platform which comprises natural water surface floaters or natural water surface waves; the natural water surface observation platform comprises two high-definition wide-dynamic wide-angle distortion-free camera modules; the image acquisition and transmission platform comprises a plurality of remote routers which communicate through 5G; the flow estimation platform comprises a flow calculation module and a monitoring display module; based on an optical flow method and a 5G technology, an online data real-time transmission platform is established, rapid data flow analysis and online flow monitoring are achieved, and analysis response time reaches a hundred milliseconds level. And the river network is reliably measured and estimated in real time by using computer vision means and computing power, so that the severity of the event is more accurately estimated, and the sudden flood condition is improved.
Description
Technical Field
The invention relates to the field of visual flow measurement, in particular to a binocular vision simulated river flow real-time monitoring system and method.
Background
Floods are a devastating natural disaster on a global scale. It is estimated that 2.5 million people worldwide are affected by floods each year and incur billions of dollars of losses. Flood forecasting may help individuals and authorities better prepare to ensure the safety of people, but many areas currently do not provide accurate forecasting. Moreover, pre-warning systems that do exist may be inaccurate and unable to take action, resulting in too many people not being sufficiently prepared and not fully aware of the situation before a flood occurs.
For example, a "hydrological monitoring system based on internet of things" disclosed in chinese patent literature, whose publication number "CN 109506631A" includes: the hydrologic monitoring equipment is arranged in the monitoring area and is configured to collect hydrologic data of the monitoring area and wirelessly transmit the hydrologic data; the hydrologic monitoring terminal is configured to wirelessly receive hydrologic data transmitted by the hydrologic monitoring equipment, perform statistics and processing, and upload the processed hydrologic data through Ethernet communication; and the monitoring center is configured to communicate with the hydrological monitoring terminal through the Ethernet, so that the hydrological data of the monitoring area can be remotely managed and displayed. The system only monitors hydrology, cannot estimate the flood amount, and cannot improve the sudden flood condition.
Disclosure of Invention
The present invention mainly solves the above problems of the prior art; the system and the method for monitoring the river flow in real time simulate binocular vision are provided, and the river network is reliably measured and estimated in real time by using a computer vision means and computing power, so that the severity of an event is more accurately estimated, and the sudden flood condition is improved.
The technical problem of the invention is mainly solved by the following technical scheme:
a binocular vision-simulating real-time river flow monitoring system comprises
The river water surface tracing platform comprises a river to be detected, a plurality of control points arranged on two banks of the river to be detected, and natural water surface floaters or natural water surface waves;
the natural water surface observation platform comprises two high-definition wide-dynamic wide-angle distortion-free camera modules; the camera module is erected at the high position of the river to be detected and is positioned at a position where the visual field can contain the river to be detected and all control points;
the image acquisition and transmission platform comprises a plurality of remote routers which communicate through 5G; the first router is in communication connection with the camera module, and the second router is in communication connection with the flow estimation platform;
the flow estimation platform comprises a flow calculation module and a monitoring display module; the flow calculation module is in communication connection with the second router, and the monitoring display module is connected with the flow calculation module.
The camera module is used, visual detection is utilized, contact with a tested river is not needed, the system is more stable and safer, and the obtained data is more accurate; the 5G is used for carrying out remote communication, data are transmitted in real time, barriers of information transmission are broken, and the transmission speed is accelerated; the detection is more reliable. The flow value prediction is adjusted through real-time measurement data, so that the hydraulic risk is better evaluated; the measurement of the flood flow is used for improving the sudden flood condition and solving the corresponding hydraulic problem; computer vision means and powerful computing power are utilized to better measure flood flow and thereby more accurately assess the severity of an event.
Preferably, the natural water surface floater is leaves or an environment-friendly spherical material; the natural water surface floating objects are uniformly scattered on the water surface of the river to be measured. The natural floater is used, so that the pollution to the river to be detected is reduced; the leaves are used as natural floating objects, are distinguished from the surrounding environment of the river to be detected in color, the resolution ratio is improved, the subsequent flow calculation based on vision is facilitated, and the accuracy is improved.
Preferably, the control points are yellow or red fork marks; the number of the control points is not less than 10. The control point is yellow or red, is different from the surrounding environment in color, and is convenient to select an object; and at least 10 control points are selected, so that the accuracy in calculation is improved.
Preferably, the two camera modules carry out binocular vision matching, and the positions of the two eyes of a human are simulated to monitor the river to be detected. By adopting the scheme, the visual field of the camera can be enlarged, the monitoring range is wider, and the calculation result is more reliable.
A binocular vision-simulated river flow real-time monitoring method comprises the following steps:
s1: establishing a detection system, and selecting not less than 10 control points according to the actual terrain environment;
s2: establishing a world coordinate system by using a total station in the visual field range of the camera module, and acquiring coordinate information of a control point as a prior link;
s3: confirming that the river water surface tracing platform is normal; shooting images or video streams of a river to be detected by using a natural water surface observation platform; the shot image or video stream is transmitted to a flow estimation platform through an image acquisition and transmission platform;
s4: a flow calculation module in the flow estimation platform calculates river flow field information by using an optical flow method to obtain pixel point displacement vector information;
s5: selecting two control points positioned at two sides of a river bank to be detected, defining a flow measuring section, and acquiring depth information of the flow measuring section;
s6: and calculating the flow of the section by combining the depth information of the flow measuring section and the river flow field information.
Flood flow is estimated using visual means and computer power to accurately assess the severity of the event. Selecting control points according to the terrain, wherein the control points are selected on the flat terrain without obvious fluctuation, and the space with more than five square meters is used for erecting related equipment; the visual field is wide, so that the shooting visual field of the camera, namely the wide angle of 100 degrees of the camera can cover the position of the whole river to be measured; and (3) selecting at least 10 control points on two banks of the river by using a total station, and selecting more than five points on two sides of the river respectively, establishing a world coordinate system, acquiring world three-dimensional x, y and z coordinate information of each control point, and inputting the coordinate information serving as coordinate information into a flow calculation module. And whether the system is established wrongly or not is confirmed, so that the reliability of the detection data is ensured.
Preferably, the step S3 includes the following steps:
s31: confirming the river water surface tracing platform, namely observing whether the river water surface to be detected has natural floaters or natural water wave surfaces with higher resolution, if so, entering the next step, and if not, returning to the step S1 to reestablish the system;
s32: confirming that the visual fields of the two camera modules in the natural water surface observation platform can contain all control points and the river to be measured, and if so, shooting video streams or images by the camera modules through 25 FPS; if not, returning to the step S1 to reestablish the system;
s33: transmitting the video stream or image to a first router through a communication connection; a remote router in the image acquisition and transmission platform transmits the video stream or the image to a second router through a 5G technology; and the second router transmits the video stream or the image to a flow calculation module in the flow estimation platform through communication connection.
The system can normally operate, achieve the expected function, and ensure the accuracy, real-time performance and reliability of the data obtained by monitoring and the result obtained by calculation.
Preferably, the optical flow method estimates the velocity field information of the river from the video image data; obtaining nulls by different image gray level distributions in the image sequencePixel displacement information of each point in the space; let f (x, y, t) represent the gray scale value which is the brightness of the pixel point x (x, y) on the image coordinate at time t; the speed of the pixel point at the time t is omega (x, y) ═ u, v)T(ii) a Wherein u is the velocity vector of the x-axis in the coordinate system, and v is the velocity vector of the y-axis in the coordinate system. The optical flow method is mainly based on the minimization of an energy function of an illumination conservation assumption and a spatial velocity field smoothing assumption, and the optical flow calculation is to estimate the motion of an object from video image data. The brightness (gray value) displayed by a pixel point in two adjacent frames of images is unchanged, in the space, the motion can be described by a motion field, on an image plane, the motion of an object is often reflected by the difference of the gray distribution of different images in an image sequence, and an optical flow field is a two-dimensional vector field which reflects the change trend of the gray of each point on the image and can be regarded as an instantaneous speed field generated by the motion of the pixel point with the gray on the image plane.
Preferably, the step 5 comprises the following steps:
s51: respectively selecting two control points positioned at two sides of a river bank, enabling a connecting line of the two control points to be perpendicular to a river to be measured, and defining a section formed by the connecting lines of the two control points as a final flow measuring section;
s52: and acquiring the depth information of the flow measurement section by using the conventional measurement means.
The depth information of the flow measuring section, namely the height information of the river bed at each point of the flow measuring section is obtained by using the existing measuring means, such as a handheld point measuring instrument and acoustic radar equipment, and the depth information is used for constructing the topography of the river bed.
Preferably, the flow measurement section is vertically divided into n small blocks, and the flow of the section is calculated by the following formula:
qi＝ViAi
wherein A isiIs the cross-sectional area of the ith section;
lithe section vertical line length of the i-th part, namely the depth of the river section of the section;
Viis the average flow velocity of the section of the i-th section;
qipartial flow being the cross-section of the i-th section;
and Q is the final river section flow.
The water flow particle of the river to be measured is displaced at any moment, and a flow calculation model is constructed by combining the acquired depth information of the river flow field and the flow measurement section based on the idea of differentiation. The river section is vertically divided into a plurality of small blocks, each small block represents local flow, the flow is obtained by multiplying the average velocity of the vertical line and the area between the vertical lines, and finally all the small flows are summed up to obtain the final river section flow.
The invention has the beneficial effects that: the computer vision means and the computing power are used for carrying out real-time and reliable flow measurement and estimation on the river network, the system does not need to be in contact with a measured river, the system is more stable and safer, and the obtained data is more accurate; the 5G is used for carrying out remote communication, data are transmitted in real time, barriers of information transmission are broken, the transmission speed is higher, and the detection is more reliable; thereby more accurately assessing the severity of the event.
Drawings
Fig. 1 is a block diagram of a river discharge real-time monitoring system according to the present invention.
Fig. 2 is a flow chart of a real-time river discharge monitoring method according to the present invention.
In the figure, 1, a river water surface tracing platform, 11, a natural water surface floater, 12, a control point, 2, a natural water surface observation platform, 21, a camera module, 3, an image acquisition and transmission platform, 31, a router, 4, a flow estimation platform, 41, a flow calculation module, 42 and a monitoring display module are arranged.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b):
a binocular vision-simulated river flow real-time monitoring system is shown in figure 1 and comprises a river water surface tracing platform 1, a natural water surface observation platform 2, an image acquisition and transmission platform 3 and a flow estimation platform 4. The natural water surface observation platform 2 is arranged near the river water surface tracing platform 1, the natural water surface observation platform 2 is in communication connection with the image acquisition and transmission platform 3, and the image acquisition and transmission platform 3 is in communication connection with the flow estimation platform 4.
The river surface tracing platform 1 comprises a river to be detected, a plurality of control points 13 arranged on two banks of the river to be detected, and natural water surface floaters 11 or natural water surface waves.
The number of the control points 12 is not less than 10, and the control points 12 are yellow or red fork marks. The control point 12 is yellow or red, and the color is greatly different from that of the river to be detected, so that the control point is striking. The object is conveniently selected in the subsequent calculation, the number of the control points 12 is not less than 10, and the calculated data amount is increased, so that the calculated data are more reliable.
The natural water surface floating objects 11 are leaves or environment-friendly spherical materials, and the natural water surface floating objects 11 are uniformly scattered on the water surface of the river to be detected. The leaves, the environment-friendly spherical material and the river to be measured have obvious difference in color, so that the leaves, the environment-friendly spherical material and the river to be measured can be distinguished quickly, selection of objects during calculation after calculation is facilitated, and the accuracy of calculation results is improved.
The natural water surface observation platform 2 comprises two USB high-definition wide-dynamic wide-angle distortion-free camera modules 21.
The camera module 21 is erected at a high position of the river to be measured and is located at a position where the visual field can contain all the control points 12 and the river to be measured. Two camera modules 21 carry out binocular vision matching, and the position of anthropomorphic dummy binocular carries out the monitoring to the river that awaits measuring.
The image acquisition and transmission platform 3 comprises a remote router 31 which communicates through 5G.
In this embodiment, the router includes two routers, one of the routers 31 is connected to the camera module 21, and the other router 31 is connected to the traffic estimation platform 4.
The 5G technology comprehensively improves the data transmission speed, realizes simultaneous high-speed file transmission and data reading and writing of multiple devices, and breaks the information transmission barrier.
The flow estimation platform 4 comprises a flow calculation module 41 and a monitoring display module 42.
The flow calculation module 41 is in communication connection with the router 31, and the monitoring display module 42 is connected with the flow calculation module 41. The flow calculation module 41 has an independently developed optical flow program for rapidly processing important information of images and videos and calculating a real-time flow field for river flow analysis and calculation. The monitoring display module 42 can watch the river video monitoring in real time, realize the cooperative monitoring, and achieve the rapid and stable river information sharing.
A binocular vision-simulated river flow real-time monitoring method is shown in figure 2 and comprises the following steps:
s1: and establishing a detection system, and selecting at least 10 control points for flow measurement according to the actual terrain environment.
The control points are selected on the flat terrain without obvious fluctuation, and the space with more than five square meters is used for erecting related equipment; and the visual field is wide, so that the shooting visual field of the camera, namely the wide angle of 100 degrees of the camera can cover the whole river measuring position.
S2: and in the visual field range of the camera module, establishing a world coordinate system by using a total station to acquire coordinate information as a prior link.
And (2) selecting at least 10 control points on two banks of the river by using a total station, and respectively selecting more than 5 control points on two sides of the river to establish a world coordinate system, and acquiring world three-dimensional x, y and z coordinate information of each ground control point as coordinate information to input the coordinate information into the flow calculation module.
S3: confirming that the river water surface tracing platform is normal; shooting a river to be measured by using a natural water surface observation platform; and the flow rate is transmitted to the flow rate estimation platform through the image acquisition and transmission platform.
S31: and confirming the river water surface tracing platform, namely observing whether the river water surface to be detected has natural floaters or natural water wave surfaces with higher resolution, if so, entering the next step, otherwise, returning to the step S1 to reestablish the system.
In principle, the flow measurement accuracy of the river speed measurement method based on vision depends on the visibility of a water flow tracer in the field of view of a camera, the fluid flow following performance and the distribution condition of the tracer on the surface of a river to a great extent.
S32: confirming that the visual fields of the two camera modules in the natural water surface observation platform can contain all control points and the river to be measured, and if so, shooting video streams or images by the camera modules through 25 FPS; if not, the process returns to step S1 to reestablish the system.
The shot visual field can cover all tracers in the river to be detected, video streams or pictures can be shot clearly, and accuracy in subsequent calculation is guaranteed.
S33: transmitting the video stream or image to a first router through a communication connection; two different-place routers in the image acquisition and transmission platform transmit video streams or images to a second router through a 5G technology; and the second router transmits the video stream or the image to a flow calculation module in the flow estimation platform through communication connection.
The method has the advantages that the different-place landfills are established through different-place routes, based on the 5G technology, the data transmission speed is comprehensively improved, multiple devices can simultaneously transmit files at high speed and read and write data, information transmission barriers are broken, data intercommunication in different-place local area networks is realized, important image and video information is rapidly processed, and river video monitoring is assisted to be watched in real time; the cooperative office is realized, the rapidness and the stability are achieved, and the information sharing of a plurality of branch institutions is completed; the method realizes the information sharing of the real-time image video stream, quickly transmits and processes data information, and achieves the purpose of monitoring the river flow on line.
S4: and a flow calculation module in the flow estimation platform calculates river flow field information by using an optical flow method to obtain pixel point displacement vector information.
The optical flow method is mainly based on the minimization of an energy function of an illumination conservation assumption and a spatial velocity field smoothing assumption, and the optical flow calculation is to estimate the motion of an object from video image data.
The brightness (gray value) displayed by one pixel point in two adjacent frames of images is unchanged; let f (x, y, t) denote the brightness (gray value) of a pixel point x (x, y) at the time t, and the velocity of the pixel point at the time is ω (x, y) (u, v)T. Wherein u is the velocity vector of the x-axis in the coordinate system, and v is the velocity vector of the y-axis in the coordinate system.
On one image plane, the motion of the object is represented by the difference of the gray-scale distribution of the different images in the image sequence. The optical flow field is a two-dimensional vector field which reflects the change trend of the gray scale of each point on the image and can be regarded as an instantaneous velocity field generated by the movement of a pixel point with the gray scale on an image plane.
When the image is used as input, the pixel displacement information of each point on the space can be obtained through optical flow method calculation, and then the velocity field is calculated.
The obtained river flow field information refers to the velocity field information of the river.
S5: selecting two control points positioned at two sides of the river bank to be detected, defining a flow measuring section and acquiring flow measuring section information.
S51: and respectively selecting two control points positioned at two sides of the river bank, enabling the connecting line of the two control points to be perpendicular to the river to be measured, and defining a section formed by the connecting lines of the two control points as a final flow measuring section.
S52: and acquiring the depth information of the cross section of the river to be measured by using the conventional measuring means.
The depth information of the flow measuring section, namely the height information of the river bed at each point of the flow measuring section is obtained by using the existing measuring means, such as a handheld point measuring instrument and acoustic radar equipment, and the depth information is used for constructing the topography of the river bed.
S6: and calculating the section flow by combining the section information and the river flow field information.
The water flow particle of the river to be measured is displaced at any moment, and a flow calculation model is constructed by combining the acquired depth information of the river flow field and the flow measurement section based on the idea of differentiation.
And vertically cutting the flow measuring section into n small blocks, wherein each small block represents local flow, the flow is obtained by multiplying the average velocity of the vertical line and the area between the vertical lines, and finally, summing all the small flows to obtain the final flow of the river section. The calculation formula is as follows:
qi＝ViAi
wherein A isiIs the cross-sectional area of the ith section;
lithe section vertical line length of the i-th part is the depth of a river section below the section;
Viis the average flow velocity of the section of the i-th section;
qipartial flow being the cross-section of the i-th section;
and Q is the final river section flow.
The invention uses computer vision means and computing power to carry out real-time reliable flow measurement and estimation on the river network, does not need to be in contact with the river to be measured, and has more stable and safer system and more accurate obtained data; the 5G is used for carrying out remote communication, data are transmitted in real time, barriers of information transmission are broken, the transmission speed is higher, and the detection is more reliable; thereby more accurately assessing the severity of the event.
Claims (9)
1. A binocular vision-simulating real-time river flow monitoring system is characterized by comprising
The river surface tracing platform (1) comprises a river to be detected, a plurality of control points (12) arranged on two banks of the river to be detected, and natural water surface floaters (11) or natural water surface waves;
the natural water surface observation platform (2) comprises two high-definition wide-dynamic wide-angle distortion-free camera modules (21); the camera module (21) is erected at the high position of the river to be detected and is positioned at the position where the visual field can contain the river to be detected and all the control points (12);
the image acquisition and transmission platform (3) comprises a plurality of remote routers (31) which communicate through 5G; the first router is in communication connection with the camera module (21), and the second router is in communication connection with the flow estimation platform (4);
the flow estimation platform (4) comprises a flow calculation module (41) and a monitoring display module (42); the flow calculation module (41) is in communication connection with the second router, and the monitoring display module (42) is connected with the flow calculation module (41).
2. The binocular vision simulated real-time river flow monitoring system according to claim 1, wherein the natural water surface floating objects (11) are leaves or environment-friendly spherical materials; the natural water surface floating objects (11) are uniformly scattered on the water surface of the river to be measured.
3. The binocular vision simulated real-time river discharge monitoring system and method as recited in claim 1, wherein the control points (12) are yellow or red forked markers; the number of the control points (12) is not less than 10.
4. The binocular vision simulated real-time river flow monitoring system as claimed in claim 1, 2 or 3, wherein the two camera modules (21) perform binocular vision matching to simulate binocular positions of a human to monitor a river to be monitored.
5. A binocular vision simulated river flow real-time monitoring method is characterized in that the binocular vision simulated river flow real-time monitoring system is adopted, and the method comprises the following steps:
s1: establishing a detection system, and selecting not less than 10 control points according to the actual terrain environment;
s2: in the visual field range of the camera module (21), a world coordinate system is established by using a total station, and coordinate information of the control point (12) is acquired as a prior link;
s3: confirming that the river water surface tracing platform (1) is normal; shooting images or video streams of a river to be detected by using a natural water surface observation platform (2); the shot images or video streams are transmitted to a flow estimation platform (4) through an image acquisition and transmission platform (3);
s4: a flow calculation module (41) in the flow estimation platform (4) calculates river flow field information by using an optical flow method to obtain pixel point displacement vector information;
s5: selecting two control points (12) positioned at two sides of a river bank to be detected, defining a flow measuring section and acquiring depth information of the flow measuring section;
s6: and calculating the flow of the section by combining the depth information of the flow measuring section and the river flow field information.
6. The binocular vision simulated real-time river discharge monitoring system and method as claimed in claim 5, wherein the step S3 comprises the steps of:
s31: confirming the river water surface tracing platform (1) by observing whether the river water surface to be detected has natural floaters (11) or natural water wave surfaces with higher resolution, if so, entering the next step, and if not, returning to the step S1 to reestablish the system;
s32: confirming that the visual fields of two camera modules (21) in the natural water surface observation platform (2) can contain all control points (12) and rivers to be measured, and if so, shooting video streams or images by the camera modules (21) by using 25 FPS; if not, returning to the step S1 to reestablish the system;
s33: transmitting the video stream or image to a first router through a communication connection; a remote router (31) in the image acquisition and transmission platform (3) transmits the video stream or image to a second router through a first router by a 5G technology; the second router transmits the video stream or the image to a flow calculation module (41) in the flow estimation platform (4) through a communication connection.
7. The binocular vision simulated real-time river discharge monitoring system and method as claimed in claim 5 or 6, wherein the optical flow method estimates river velocity field information from video image data; obtaining pixel displacement information of each point in space through different image gray level distribution in an image sequence; let f (x, y, t) represent the gray scale value which is the brightness of the pixel point x (x, y) on the image coordinate at time t; the speed of the pixel point at the time t is omega (x, y) ═ u, v)T(ii) a Wherein u is the velocity vector of the x-axis in the coordinate system, and v is the velocity vector of the y-axis in the coordinate system.
8. The binocular vision simulated real-time river discharge monitoring system and method as claimed in claim 7, wherein the step 5 comprises the steps of:
s51: respectively selecting two control points (12) positioned at two sides of a river bank, enabling a connecting line of the two control points (12) to be perpendicular to a river to be measured, and defining a section formed by the connecting lines of the two control points (12) as a final flow measuring section;
s52: and acquiring the depth information of the cross section of the river by using the conventional measuring means.
9. The binocular vision simulated real-time river discharge monitoring system and method according to claim 8, wherein a flow measuring cross section is vertically cut into n small blocks, and the cross section discharge is calculated by the following formula:
qi＝ViAi
wherein A isiIs the cross-sectional area of the ith section;
lithe section vertical line length of the i-th part, namely the depth of the river section of the section;
Viis the average flow velocity of the section of the i-th section;
qipartial flow being the cross-section of the i-th section;
and Q is the final river section flow.
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