CN113155860A - Flow state video monitoring-based method and system for diagnosing structural damage of water-passing building - Google Patents
Flow state video monitoring-based method and system for diagnosing structural damage of water-passing building Download PDFInfo
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- CN113155860A CN113155860A CN202011503647.8A CN202011503647A CN113155860A CN 113155860 A CN113155860 A CN 113155860A CN 202011503647 A CN202011503647 A CN 202011503647A CN 113155860 A CN113155860 A CN 113155860A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
Abstract
The invention relates to a method and a system for diagnosing structural damage of a water-passing building based on flow state video monitoring, wherein the system comprises a high-definition fog-penetrating camera, a base, a special workstation and a computer display; the high-definition fog-penetrating camera is arranged on the base; a cavity is arranged in the base; the network interface and the power interface of the high-definition fog-penetrating camera are arranged in the cavity; the high-definition fog-penetrating camera is provided with an intelligent wiper module; the power interface, the computer display and the special workstation are respectively connected with a power supply through a power line; the network interface is connected with the special workstation through a data transmission line; the special workstation comprises an image analysis module, a background database and an alarm module. The method for diagnosing whether the structure of the water passing structure is damaged or not by adopting the video monitoring of the flow discharge state and the water surface line is innovated, can timely discover the abnormal flow state in the flow discharge process, timely take measures to stop the damage, and can be widely applied to the fields of safety monitoring, teaching and research of flow discharge facilities and the like.
Description
Technical Field
The invention belongs to the technical field of hydraulic and hydroelectric engineering and video monitoring, and particularly relates to a method and a system for diagnosing structural damage of a water-passing structure based on flow state video monitoring.
Background
The water passing building undertakes important tasks of flood discharge and overflow, and particularly when the flood flow in a flood season is large, the water passing building is in a long-time discharge state, and the water flow velocity of the water passing building in large-scale hydropower engineering is often as high as 40-50 m/s. Under the long-term scouring action of high-speed water flow, the probability of damage to the water passing buildings is high, and according to incomplete statistics, 1/3 water passing buildings are damaged to different degrees, and some water passing buildings are quite serious. If the water passing structure is damaged during drainage, the water passing structure cannot find and take measures in time to cause loss due to the fact that personnel do not patrol in time, cannot approach to clear observation under the influence of atomization, water flow is shielded by a structure and the like. With the development of the high-definition camera technology, the remote, fog-penetrating and high-definition camera technology can replace manual real-time monitoring of the drainage process, and a high-speed and high-reliability image processing and analyzing technology is configured, so that abnormal conditions of the drainage flow state and the water surface line can be found in time, and the function of diagnosing structural damage of the water-passing building is further realized. Therefore, the research on the method for diagnosing the structural damage of the water passing structure based on the flow state video monitoring has important theoretical significance and practical value for realizing the real-time monitoring of the running state of the water passing structure, ensuring the structural safety, reducing the economic loss and the labor cost and improving the monitoring level.
The existing method for diagnosing the structural damage of the water-passing building basically adopts a sensor technology and mainly has the following defects: (1) the arrangement of the sensors generally needs to be carried out on the structure body of the water-passing building, the preparation work is more, the flow is complex, the coordination is more, the time consumption is long, and the construction quality defect can become a new increased incentive for the damage of the water-passing building; (2) the sensor equipment is contacted with high-speed water flow for a long time, so that the durability is poor, and the failure rate is high; (3) the sensor monitoring is the observation of 'points', and the integral working state change of the drainage structure is difficult to truly and comprehensively reflect. Therefore, how to overcome the defects of the prior art is a problem which needs to be solved urgently in the technical field at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for diagnosing structural damage of a water-passing building based on flow state video monitoring.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a water-passing building structure damage diagnosis system based on flow state video monitoring comprises a high-definition fog-penetrating camera, a base, a special workstation and a computer display;
the high-definition fog-penetrating camera is arranged on the base; a cavity is arranged in the base; a network interface and a power interface of the high-definition fog-penetrating camera are arranged in the cavity;
the high-definition fog-penetrating camera is provided with an intelligent wiper module; the intelligent wiper module is used for cleaning water drops on the lens of the high-definition fog-penetrating camera in time and keeping the lens clean;
the power interface, the computer display and the special workstation are respectively connected with a power supply through a power line;
the network interface is connected with the special workstation through a data transmission line;
the special workstation comprises an image analysis module, a background database and an alarm module;
the image analysis module is respectively connected with the background database, the alarm module and the computer display;
the image analysis module is used for processing the image shot by the high-definition fog-penetrating camera, analyzing the profile of the water surface line, comparing the characteristic point coordinates of the obtained water surface line profile with the image characteristic point coordinates corresponding to the normal working condition in the background database, and if the comparison result is abnormal, sending a command to the alarm module to alarm;
the background database is also used for storing images before being processed by the image analysis module and data obtained by processing;
the computer display is used for displaying the image before the image analysis module processes and the data obtained by the processing.
Further, it is preferable that a protective cover is further included; the safety cover is arranged outside the base and the high-definition fog-penetrating camera and used for protecting the base and the high-definition fog-penetrating camera.
Further, preferably, the background database further includes a normal flow state database and an abnormal flow state database; when the image analysis module compares the profile of the water surface line of the image shot by the high-definition fog-penetrating camera with the normal flow state data in the background database, and the comparison result is abnormal, storing the image before being processed by the image analysis module and the data obtained by processing in the abnormal flow state database; and otherwise, storing in a normal flow state database.
The water line profile analysis comprises the following specific steps:
a. scanning the image row by row and column by column, acquiring RGB color values of each pixel point, and respectively storing the RGB color values as Ri,j、Gi,j、Bi,jWherein i is the pixel row number and j is the pixel column number;
b. setting RGB similarity threshold a of adjacent pixel points of each line0Setting a discontinuous contrast threshold n0Setting a continuous contrast threshold value m0；
c. Calculating the similarity value of adjacent pixel points from left to right:
if ai,j≥a0Discontinuous pairs of side wall areasRatio n Edge0; if ai,j<a0Then n isEdge＝n Edge+ 1; when n isEdge＝n0Then, recording pixel coordinate value x of clear boundary of ith row left side walli, left wall；
d. From xi, left wallContinue to compare to the right if ai,j≥a0Continuous contrast number m of water surface areaWater (W) ＝mWater (W)+ 1; if ai,j<a0Then m isWater (W)0; when m isWater (W)＝m0Recording the pixel coordinate value x of the left clear boundary of the ith row of water surfacei, left waterTaking xi, left wallAnd xi, left waterIs taken as the coordinate x of the left boundary of the water linei, left；
e. From xi, left waterContinue to compare to the right if ai,j≥a0Discrete contrast number n of water surface area Water (W)0; if ai,j<a0Then n isWater (W)＝nWater (W)+ 1; when n isWater (W)＝n0Recording the pixel coordinate value x of the ith row water surface right clear boundaryi, right water；
f. From xi, right waterContinue to compare to the right if ai,j≥a0Continuous contrast number m of side wall areaEdge ＝mEdge+ 1; if ai,j<a0Then m isEdge0; when m isEdge＝m0Recording pixel coordinate value x of clear boundary of the i-th row water surface right walli, right wallTaking xi, right waterAnd xi, right wallIs taken as the coordinate x of the left boundary of the water linei, right；
g. Calculating line by line according to the steps c to f, and respectively connecting xi, leftAnd xi, rightAs the left and right contours of the waterline.
Further, it is preferable that a00.85 to 0.95; n is01 to 5; m is0Is 2 to 5.
Further, it is preferable that the water line profile feature point coordinates H are set to be the same as the water line profile feature point coordinates Hi(X, Y) and image feature point coordinates H 'corresponding to normal working conditions in background database'i(X, Y) pairsWhen | Hi (X,Y)-H′i(X,Y)∣/H′i(X,Y)>When 10 percent of the flow is regarded as abnormal flow, an alarm is started, and data are stored in an abnormal flow database; when | Hi(X,Y)-H′i(X,Y)∣/H′i (X,Y)<And when 10 percent of the flow rate is determined as a normal flow rate, storing the flow rate in a normal flow rate database.
The invention also provides a method for diagnosing the structural damage of the water-passing building based on the flow state video monitoring, and the system for diagnosing the structural damage of the water-passing building based on the flow state video monitoring comprises the following steps:
step (1), aiming at the characteristics of the flow state of the leakage flow, a plurality of characteristic points are arranged on the site to enable the characteristic points to cover the whole shooting range as much as possible, scour-resistant and easily-recognized marks are placed at the characteristic points, the coordinates of the marks are measured, and the marks are input into a background database;
step (2), storing the discharge flow state, water surface line and characteristic point data of the water-passing building under the normal working condition and prototype observation data obtained in real time on site into a normal flow state database; storing the drainage flow state, water surface line and characteristic point data of the water passing structure under the abnormal working condition and the original abnormal data actually generated on site into an abnormal flow state database;
step (3), starting a high-definition fog-penetrating camera, aiming at the drainage flow state of the water passing building, and adjusting the shooting height until the shot image covers the whole water surface and the side walls on the two sides of the water passing building; starting the intelligent wiper module, adjusting an aperture, a focal length, exposure parameters and a shutter speed, and ensuring that a shot image is clear;
step (4), formally shooting images with resolution meeting the requirements, shooting a high-definition image every 10 minutes, and performing image processing analysis to determine the outline of the water surface line;
step (5), calculating the water surface line contour feature point coordinate H obtained by the imagei(X, Y) and image feature point coordinates H 'corresponding to normal working conditions in background database'i(X, Y) is compared, when | -Hi(X,Y)-H′i(X,Y)∣/H′i(X,Y)>When the flow rate is 10 percent, the abnormal flow state is considered, an alarm is started, and data are stored in an abnormal flow state database; when |)Hi(X,Y)-H′i(X,Y)∣/H′iWhen the (X, Y) is less than or equal to 10 percent, the flow state is regarded as a normal flow state and is stored in a normal flow state database;
and (6) after the flow leakage is finished, closing the high-definition fog-penetrating camera, and storing all images in a background database.
When the system is used, the water surface line profile is compared with the situation of manual observation, and if the situation is not good, the a can be modified0、n0、m0And repeating the steps c to f until the requirement of the analysis precision is met.
In the invention, the principle of selecting the characteristic points is that the whole water surface line profile is uniformly covered as much as possible and a certain number of fixed points with known coordinates are included.
In the invention, the abnormal flow state database is mainly used for accumulating data for analyzing the correlation between the damage characteristic and the leakage flow state later, and can be used for rechecking and comparing the data of frequently-occurring abnormal working conditions when the data amount is large.
The structural damage diagnosed by the invention means that the damage degree of the structure, including erosion pits, abrasion pits, air erosion pits and the like, has influence on the flow state of water flow, which exceeds the tolerance range, namely 10%.
According to the invention, the high-definition fog-penetrating camera has the functions of rain prevention, fog penetration and vibration resistance, and adopts a higher shutter speed, so that the shooting effect of high-speed dynamic water flow of 40-50 m/s is met, and the clear shooting function in a high-speed water flow atomization area is realized.
The base should determine the installation position according to the image size, the focal length of the lens, the size of the target object and the actual conditions in the field. The base has stability and anti-scouring characteristic, lays network interface and power source in the base, can charge for the fog camera is passed through to the high definition through power source, transmits the image of shooing to special workstation in real time.
The special workstation has strong image processing capability, large capacity, high speed and high reliability.
In the invention, the characteristic points are the characteristic points which are clear in target, easy to identify and unchanged in position and are extracted from the image for improving the image registration precision. And after the image analysis, the water surface line difference is larger than a specified threshold value, and then an alarm function is started.
The background database stores the drainage flow state picture, the water surface line data and the characteristic point data of the water-passing building, which are obtained by the methods of prototype observation, numerical simulation, physical model test and the like and meet the requirements by the verified measurement precision, and comprises a normal working state database and an abnormal working state database of the water-passing building.
The high-definition fog-penetrating camera and the base are provided with a protective cover for preventing severe weather conditions or artificial damage during field work.
The image processing of the image analysis module comprises water surface line analysis, characteristic point extraction and characteristic point abnormity judgment; the background database stores flow charts, water surface lines and characteristic point data of the water passing building under various working conditions.
The installation operation of the system of the invention is as follows: determining a shooting position at one side perpendicular to the water flow direction of the water passing structure according to the image size, the lens focal length, the size of a target object and actual field conditions; installing a base at a shooting position; the high-definition fog-penetrating camera is arranged on the base and fixed well, and is connected with the power interface and the network interface; according to the transmission distance requirement and the field network condition, a special workstation is installed at a corresponding position, is connected with the high-definition fog-penetrating camera network interface and is used for receiving image data transmitted by the high-definition fog-penetrating camera in real time.
The high-definition fog-penetrating camera has the high-definition required resolution ratio not lower than 2560 x 1440.
Compared with the prior art, the invention has the beneficial effects that:
(1) the non-destructive non-contact observation of the health state of the structure of the water passing building is realized. The invention adopts the remote, fog-penetrating and high-definition camera technology to shoot the flow discharge state, carries out structural health diagnosis by using the cause and effect relationship that the structural damage can cause certain influence on the flow state of the water flow, does not need to carry out destructive construction on the structure of the water-passing building, and avoids the defect of forming a new defect inducement due to the installation of a sensor on the structure.
(2) The health state of the whole drainage structure can be comprehensively and truly reflected. The invention does not observe certain points, but observes the whole leakage flow state, and because the defects of any point and the defects with certain threats can inevitably cause the change of the leakage flow state, the structure can be judged whether to be damaged or not by observing the leakage flow state.
(3) Is economical, convenient and quick. The invention does not need to coordinate all parties for examination, approval, scheduling and the like, has small preparation workload, simple flow, less required personnel, recyclable equipment and low labor cost and equipment cost.
(4) High reliability and high durability. The high-definition fog-penetrating camera is not in direct contact with high-speed water flow, the problems of failure and the like caused by the fact that the sensor is washed by the high-speed water flow are solved, the high-definition fog-penetrating camera is started when shooting is carried out and is protected when not in use, and the high-definition fog-penetrating camera is high in reliability and strong in durability.
In conclusion, the method for diagnosing the structural damage of the water-passing building based on the flow state video monitoring has the advantages of economy, simplicity, convenience, quickness, high reliability and strong durability, can comprehensively and truly reflect the health state of the drainage structure, can carry out nondestructive non-contact real-time observation on the health state of the structure of the water-passing building, and is widely applied to the fields of safety monitoring, high-speed water flow state monitoring, scientific research, education and the like of the water-passing building in hydropower engineering and other industries.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic structural diagram of a water-passing structure damage diagnosis system based on flow state video monitoring according to the present invention;
FIG. 2 is a diagram of the hardware and software system architecture of the present invention;
FIG. 3 is a schematic flow chart of the flow state video monitoring-based damage identification and diagnosis process of the structure of the water-passing building;
FIG. 4 is a schematic flow chart of a water line profile analysis;
FIG. 5 is a real shot of the flow state of the water-passing building;
FIG. 6 is a diagram showing the results of the contour analysis processing of the water line;
fig. 7 is a water surface and its contour plot.
In the figure, 1-high definition fog-penetrating camera; 2-an intelligent wiper module; 3-a base; 4-a network interface; 5-a power interface; 6-power line; 7-a data transmission line; 8-computer display; 9-a dedicated workstation; 10-a protective cover; 11-a power supply; 12-an image analysis module; 13-background database; 14-alarm module.
Detailed Description
The present invention will be described in further detail with reference to examples.
It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples do not specify particular techniques or conditions, and are performed according to the techniques or conditions described in the literature in the art or according to the product specifications. The materials or equipment used are not indicated by manufacturers, and all are conventional products available by purchase.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. Further, "connected" as used herein may include wirelessly connected.
In the description of the present invention, "a plurality" means two or more unless otherwise specified. The terms "inner," "upper," "lower," and the like, refer to an orientation or a state relationship illustrated in the drawings for convenience in describing the present invention and simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated in a particular manner, and thus should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "provided" are to be construed broadly, e.g., as being fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. To those of ordinary skill in the art, the specific meanings of the above terms in the present invention are understood according to specific situations.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including 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. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As shown in fig. 1, a system for diagnosing structural damage of a water passing building based on flow state video monitoring comprises a high-definition fog-penetrating camera 1, a base 3, a special workstation 9 and a computer display 8;
the high-definition fog-penetrating camera 1 is arranged on the base 3; a cavity is arranged in the base 3; the network interface 4 and the power interface 5 of the high-definition fog-transparent camera 1 are arranged in the cavity;
the high-definition fog-penetrating camera 1 is provided with an intelligent wiper module 2; the intelligent wiper module 2 is used for cleaning water drops on the lens of the high-definition fog-penetrating camera 1 in time and keeping the lens clean;
the power interface 5, the computer display 8 and the special workstation 9 are respectively connected with a power supply 11 through a power line 6;
the network interface 4 is connected with a special workstation 9 through a data transmission line 7;
the special workstation 9 comprises an image analysis module 12, a background database 13 and an alarm module 14;
the image analysis module 12 is respectively connected with the background database 13, the alarm module 14 and the computer display 8;
the image analysis module 12 is used for processing the image shot by the high-definition fog-penetrating camera 1, analyzing the water surface line profile, comparing the feature point coordinates of the obtained water surface line profile with the image feature point coordinates corresponding to the normal working condition in the background database 13, and if the comparison result is abnormal, sending an instruction to the alarm module 14 for alarming;
the background database 13 is also used for storing images before being processed by the image analysis module 12 and data obtained by processing;
the computer display 8 is used for displaying the image before processing by the image analysis module 12 and the data obtained by processing.
Preferably, the protective cover 10 is also included; the protective cover 10 is arranged outside the base 3 and the high-definition fog-penetrating camera 1 and is used for protecting the base 3 and the high-definition fog-penetrating camera 1.
Preferably, the background database 13 further includes a normal flow state database and an abnormal flow state database; when the image analysis module 12 compares the water surface line profile of the image shot by the high-definition fog-penetrating camera 1 with the normal flow state data in the background database, and the comparison result is abnormal, storing the image before processing by the image analysis module 12 and the data obtained by processing in the abnormal flow state database; and otherwise, storing in a normal flow state database.
The water line profile analysis comprises the following specific steps:
a. scanning the image row by row and column by column, acquiring RGB color values of each pixel point, and respectively storing the RGB color values as Ri,j、Gi,j、Bi,jWherein i is the pixel row number and j is the pixel column number;
b. setting RGB similarity threshold a of adjacent pixel points of each line0Setting a discontinuous contrast threshold n0Setting a continuous contrast threshold value m0；
c. Calculating the similarity value of adjacent pixel points from left to right:
if ai,j≥a0Discrete contrast number n of side wall area Edge0; if ai,j<a0Then n isEdge＝n Edge+ 1; when n isEdge＝n0Then, recording pixel coordinate value x of clear boundary of ith row left side walli, left wall；
d. From xi, left wallContinue to compare to the right if ai,j≥a0Continuous contrast number m of water surface areaWater (W) ＝mWater (W)+ 1; if ai,j<a0Then m isWater (W)0; when m isWater (W)＝m0Recording the pixel coordinate value x of the left clear boundary of the ith row of water surfacei, left waterTaking xi, left wallAnd xi, left waterIs taken as the coordinate x of the left boundary of the water linei, left；
e. From xi, left waterContinue to compare to the right if ai,j≥a0Discrete contrast number n of water surface area Water (W)0; if ai,j<a0Then n isWater (W)＝nWater (W)+ 1; when n isWater (W)＝n0Recording the pixel coordinate value x of the ith row water surface right clear boundaryi, right water；
f. From xi, right waterContinue to compare to the right if ai,j≥a0Continuous contrast number m of side wall areaEdge ＝mEdge+ 1; if ai,j<a0Then m isEdge0; when m isEdge＝m0Recording pixel coordinate value x of clear boundary of the i-th row water surface right walli, right wallTaking xi, right waterAnd xi, right wallIs taken as the coordinate x of the left boundary of the water linei, right；
g. Calculating line by line according to the steps c to f, and respectively connecting xi, leftAnd xi, rightAs the left and right contours of the waterline.
Preferably, a00.85 to 0.95; n is01 to 5; m is0Is 2 to 5.
Preferentially, the coordinates H of the characteristic points of the water surface line profile are calculatedi(X, Y) and image feature point coordinates H 'corresponding to normal working conditions in background database 13'i(X, Y) is compared, when | -Hi(X,Y)-H′i (X,Y)∣/H′i(X,Y)>When 10 percent of the flow is regarded as abnormal flow, an alarm is started, and data are stored in an abnormal flow database; when | Hi(X,Y)-H′i(X,Y)∣/H′i(X,Y)<And when 10 percent of the flow rate is determined as a normal flow rate, storing the flow rate in a normal flow rate database.
The method for diagnosing the structural damage of the water-passing building based on the flow state video monitoring uses the system for diagnosing the structural damage of the water-passing building based on the flow state video monitoring, and comprises the following steps:
step (1), aiming at the characteristics of the flow state of the leakage flow, a plurality of characteristic points are arranged on the site to enable the characteristic points to cover the whole shooting range as much as possible, scour-resistant and easily-recognized marks are placed at the characteristic points, the coordinates of the marks are measured, and the marks are input into a background database;
step (2), storing the discharge flow state, water surface line and characteristic point data of the water-passing building under the normal working condition and prototype observation data obtained in real time on site into a normal flow state database; storing the drainage flow state, water surface line and characteristic point data of the water passing structure under the abnormal working condition and the original abnormal data actually generated on site into an abnormal flow state database;
step (3), starting a high-definition fog-penetrating camera, aiming at the drainage flow state of the water passing building, and adjusting the shooting height until the shot image covers the whole water surface and the side walls on the two sides of the water passing building; starting the intelligent wiper module, adjusting an aperture, a focal length, exposure parameters and a shutter speed, and ensuring that a shot image is clear;
step (4), formally shooting images with resolution meeting the requirements, shooting a high-definition image every 10 minutes, and performing image processing analysis to determine the outline of the water surface line;
step (5), calculating the water surface line contour feature point coordinate H obtained by the imagei(X, Y) and image feature point coordinates H 'corresponding to normal working conditions in background database'i(X, Y) is compared, when | -Hi(X,Y)-H′i(X,Y)∣/H′i(X,Y)>When the flow rate is 10 percent, the abnormal flow state is considered, an alarm is started, and data are stored in an abnormal flow state database; when | Hi(X,Y)-H′i(X,Y)∣/H′iWhen the (X, Y) is less than or equal to 10 percent, the flow state is regarded as a normal flow state and is stored in a normal flow state database;
and (6) after the flow leakage is finished, closing the high-definition fog-penetrating camera, and storing all images in a background database.
Application example 1
As shown in fig. 1, the water-passing building structure damage diagnosis device based on flow state video monitoring comprises a high-definition fog-penetrating camera 1 embedded with an intelligent wiper module 2, a base 3 for installing the high-definition fog-penetrating camera 1, a network interface 4 and a power interface 5 installed in a cavity of the base 3, a data transmission line 7 for connecting the high-definition fog-penetrating camera 1 and a special workstation 9, a power line 6 for connecting the high-definition fog-penetrating camera 1 and a power supply 11, the special workstation 9 for installing image analysis software 12, a computer display 8 for displaying an analysis result connected with the special workstation 9, and a protective cover 10 for covering the high-definition fog-penetrating camera 1 and the base 3.
The installation operation is as follows: embedding the intelligent wiper module 2 into the high-definition fog-penetrating camera 1, and determining the installation position of the high-definition fog-penetrating camera 1 according to the image size, the lens focal length, the target object size and the actual field condition on one side of the water flow direction perpendicular to the water flow of the water passing structure; a base 3 is installed at a shooting position, a cavity is arranged below the base 3, and a network interface 4 and a power interface 5 are arranged in the cavity; the high-definition fog-penetrating camera 1 is arranged on the base 3 and fixed, and is connected with the power interface 5 and the network interface 4; according to the transmission distance requirement and the field network condition, a special workstation 9 is installed at a corresponding position, is connected with the network interface 4 of the high-definition fog-penetrating camera 1 and is used for receiving image data transmitted by the high-definition fog-penetrating camera 1 in real time; the special workstation 9 is provided with an image analysis processing software system 12 for carrying out water surface line analysis, feature point extraction and feature point abnormity judgment; a background database 13 is constructed on the special workstation 9 and used for storing flow charts, water surface lines and characteristic point data of the water passing building under various working conditions; after the drainage is finished, the high-definition fog-penetrating camera 1 is detached and placed in a special box, and the protective cover 10 is covered on the base 3.
The protective cover 10 is not covered in the shooting process, if the camera is not used for a long time after shooting, the camera is disassembled and placed in a special box, if shooting is only temporarily interrupted, such as waiting for working condition adjustment and the like, the protective cover is covered on the base 3 and the high-definition fog-penetrating camera 1.
A method for identifying and diagnosing structural damage of a water passing building based on flow state video monitoring uses the device and comprises the following steps:
(1) connecting a power line 6 and a data transmission line 7, starting the high-definition fog-penetrating camera 1, aligning the drainage flow state of the water passing structure, adjusting the shooting height and the shooting angle, trying to shoot a plurality of images, wherein the shot images should cover the whole water surface and the side walls on the two sides of the water passing structure;
(2) starting the intelligent wiper module 2, adjusting an aperture, a focal length, exposure parameters and a shutter speed, and trying to shoot a plurality of pictures to ensure that the shot images are clear;
(3) shooting a high-definition image every 10 minutes according to the adjusted shooting height, angle and shooting parameters, transmitting the high-definition image to a special workstation 9, and operating image processing and analyzing software 12 to perform image processing and analysis;
(4) observing the analysis result of the image processing and analyzing software 12 on the computer display 8 in real time, and processing early warning abnormal information in time;
(5) after the image shooting is finished, the intelligent windscreen wiper module 2 and the high-definition fog-penetrating camera 1 are closed, and the high-definition fog-penetrating camera 1 is detached and placed in a special box; covering the base 3 with the protective cover 10;
(6) the water surface line profile analysis parameters are determined by the following steps, the site of the flow state of the water discharge of the water passing building is photographed as shown in figure 5, and the water surface line analysis is carried out by taking the photograph as an example.
a. Scanning the image row by row and column by column, acquiring RGB color values of each pixel point, and respectively storing the RGB color values as Ri,j、Gi,j、Bi,jWherein i is the pixel line number (i ═ 1 &)2101) J is the column number of the pixel (j 1-1931);
b. in the embodiment, the horizontal line of FIG. 5 is top-bottom type, so it is necessary to compare the pixel values of the image row by row, and set the RGB similarity threshold a for the adjacent pixel points in each row00.95, a discontinuous contrast threshold n is set0Setting a continuous contrast threshold m as 10＝3；
c. Calculating the similarity value of adjacent pixels from top to bottom, taking the pixel comparison of the 1 st column, the 1 st line and the 2 nd line as an example, R1,1＝88、G1,1＝116、B1,1＝156，R2,1＝86、G2,1＝114、 B2,1154, then
a1,1≥a0Discrete contrast number n of side wall areaEdgeContinue the downward comparison at 0, when j is 713, a713,1＝0.94<a0At this time nEdge＝1＝n0Recording the pixel coordinate value x of the clear boundary on the 1 st columnUpper boundary, 1＝713；
d. From i to x1, upper boundaryContinue the downward comparison, a714,1＝0.91<a0Then the water surface area is continuously compared by a number mWater (W)＝0，a715,1＝0.86<a0Then m isWater (W)＝0，a716,1＝0.93<a0Then m isWater (W)＝0， a717,1＝0.98≥a0Then m isWater (W)＝1，a718,1＝0.99≥a0Then m isWater (W)＝2，a719,1＝0.99≥ a0At this time mWater (W)＝3＝m0Recording the pixel coordinate value x of the clear boundary on the water surface of the 1 st columnFeeding water,1719, take xUpper boundary, 1And xFeeding water, 1Mean value x ofTo 1 from716 as the coordinate of the upper end of the water line;
e. from i to xFeeding water, 1The downward comparison is continued until j is 1019, a1019,1＝0.94<a0At this time nWater (W)＝1＝n0While recording the image of the clear boundary under the water surface of column 1Pixel coordinate value xLaunching device,1＝1019；
f. From i to xLaunching, 1Continue the downward comparison, a1020,1＝0.94<a0Then the water surface area is continuously compared by a number mWater (W)＝0，a1021,1＝0.95≥a0Then m isWater (W)＝1，a1022,1＝0.96≥a0Then m isWater (W) ＝2，a1023,1＝0.98≥a0Then m isWater (W)When m is equal to 3Water (W)＝m0Recording the pixel coordinate value x of the clear boundary under the 1 st columnLower boundary, 11023, take xLower boundary, 1And xLaunching, 1Mean value x ofLower, 11021 is used as the coordinate of the lower end of the water line;
g. calculating column by column according to the steps c to f, connecting xUpper, j(j 1-1931) connecting x as the contour on the water surface lineLower, j(j 1-1931) as the lower profile of the water surface line, as shown by the upper and lower black lines in fig. 6, the identified profile of the water surface line better envelops the white highly aerated water body in the figure through artificial observation and comparison, and the identification precision meets the analysis requirement.
(7) Taking the contour line on the water surface line shown in fig. 6 as an example, extracting feature points, selecting 10 feature points along the x direction, and uniformly covering the whole contour line, wherein the coordinates of the extracted feature points are (1, 716), (211, 622), (421, 549), (631, 556), (841, 557), (1051, 588), (1261, 647), (1471, 740), (1681, 811), (1891, 951) in sequence.
(8) And comparing the extracted feature point coordinates with coordinates in a normal state in a background database, wherein X coordinates are consistent during comparison, and the result is shown in table 1, so that the difference between the extracted feature point Y coordinates and the coordinates Y' in the normal state in the background database is within 10%, and therefore, the feature point Y coordinates belong to the normal state, an alarm is not started, and the coordinate data are stored in a normal flow state database.
TABLE 1
Application example 2
Application example 2 differs from application example 1 in that the last two steps are different and the rest are the same.
(7) Taking the right contour line of the water surface line shown in fig. 7 as an example, extracting feature points, selecting 10 feature points along the y direction, uniformly covering the whole contour line, and sequentially setting the coordinates of the extracted feature points as (207, 5), (209, 45), (210, 85), (212, 125), (221, 165), (260, 205), (314, 245), (294, 285), (299, 325), (366, 365), (392, 405).
(8) And comparing the extracted feature point coordinates with coordinates in a normal state in a background database, and selecting Y coordinates to be consistent when comparing, wherein the result is shown in Table 2, and the difference between the extracted feature point X coordinates and the coordinates X' in the normal state in the background database exceeds 10% from Y205, even reaches 20-44%, which indicates that the structure is seriously damaged at the moment, and starting an alarm and storing the data in an abnormal flow state database.
TABLE 2
The existing method is basically based on visual identification, the visual identification has the defects of untimely and inaccurate visual identification, on-site patrol personnel cannot observe the information near and clearly due to various reasons such as atomization influence, structural object shielding and the like, cannot observe the information continuously for 24 hours due to the influence of body tolerance, and often cannot find abnormal phenomena in time after the end of leakage current and then go to the on-site observation. The high-definition fog-penetrating camera is provided with the intelligent wiper module, can solve the influence of atomized water drops on definition, can be arranged at a position which is not shielded by a structural object, obtains the best observation visual field, and can perform 24-hour uninterrupted observation.
The foregoing shows and describes the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A water-passing building structure damage diagnosis system based on flow state video monitoring is characterized by comprising a high-definition fog-penetrating camera (1), a base (3), a special workstation (9) and a computer display (8);
the high-definition fog-penetrating camera (1) is arranged on the base (3); a cavity is arranged in the base (3); the network interface (4) and the power interface (5) of the high-definition fog-penetrating camera (1) are arranged in the cavity;
the high-definition fog-penetrating camera (1) is provided with an intelligent wiper module (2); the intelligent wiper module (2) is used for cleaning water drops on the lens of the high-definition fog-penetrating camera (1) in time and keeping the lens clean;
the power interface (5), the computer display (8) and the special workstation (9) are respectively connected with a power supply (11) through a power line (6);
the network interface (4) is connected with a special workstation (9) through a data transmission line (7);
the special workstation (9) comprises an image analysis module (12), a background database (13) and an alarm module (14);
the image analysis module (12) is respectively connected with the background database (13), the alarm module (14) and the computer display (8);
the image analysis module (12) is used for processing the image shot by the high-definition fog-penetrating camera (1), analyzing the profile of the water surface line, comparing the characteristic point coordinates of the obtained water surface line profile with the image characteristic point coordinates corresponding to the normal working condition in the background database (13), and if the comparison result is abnormal, sending an instruction to the alarm module (14) for alarming;
the background database (13) is also used for storing images before being processed by the image analysis module (12) and data obtained by processing;
the computer display (8) is used for displaying the images before being processed by the image analysis module (12) and the data obtained by processing.
2. A flow-state video monitoring-based water passing building structure damage diagnosis system according to claim 1, further comprising a protective cover (10); the protective cover (10) is arranged outside the base (3) and the high-definition fog-penetrating camera (1) and used for protecting the base (3) and the high-definition fog-penetrating camera (1).
3. A flow state video monitoring-based water passing building structure damage diagnosis system as claimed in claim 1, wherein the background database (13) further comprises a normal flow state database and an abnormal flow state database; when the image analysis module (12) compares the profile of the water surface line of the image shot by the high-definition fog-penetrating camera (1) with the normal flow state data in the background database, and the comparison result is abnormal, storing the image before being processed by the image analysis module (12) and the data obtained by processing in the abnormal flow state database; otherwise, the data is stored in the normal flow state database.
4. A flow state video monitoring based damage diagnosis system for a water passing building structure according to claim 1, wherein the water line profile analysis comprises the following steps:
a. scanning the image row by row and column by column, acquiring RGB color values of each pixel point, and respectively storing the RGB color values as Ri,j、Gi,j、Bi,jWherein i is the pixel row number and j is the pixel column number;
b. setting RGB similarity threshold a of adjacent pixel points of each line0Setting a discontinuous contrast threshold n0Setting a continuous contrast threshold value m0；
c. Calculating the similarity value of adjacent pixel points from left to right:
if ai,j≥a0Discrete contrast number n of side wall areaEdge0; if ai,j<a0Then n isEdge＝nEdge+ 1; when n isEdge＝n0Then, recording pixel coordinate value x of clear boundary of ith row left side walli, left wall；
d. From xi, left wallContinue to compare to the right if ai,j≥a0Continuous contrast number m of water surface areaWater (W)＝mWater (W)+ 1; if ai,j<a0Then m isWater (W)0; when m isWater (W)＝m0Then, recording pixel coordinate value x of the left clear boundary of the ith row water surfacei,Left waterTaking xi, left wallAnd xi, left waterIs taken as the coordinate x of the left boundary of the water linei, left；
e. From xi, left waterContinue to compare to the right if ai,j≥a0Discrete contrast number n of water surface areaWater (W)0; if ai,j<a0Then n isWater (W)＝nWater (W)+ 1; when n isWater (W)＝n0Then, recording pixel coordinate value x of the ith row water surface right clear boundaryi,Right water；
f. From xi, right waterContinue to compare to the right if ai,j≥a0Continuous contrast number m of side wall areaEdge＝mEdge+ 1; if ai,j<a0Then m isEdge0; when m isEdge＝m0Recording pixel coordinate value x of clear boundary of ith row water surface right side walli, right wallTaking xi, right waterAnd xi, right wallIs taken as the coordinate x of the left boundary of the water linei, right；
g. Calculating line by line according to the steps c to f, and respectively connecting xi, leftAnd xi, rightAs the left and right contours of the waterline.
5. A water-passing building structure damage diagnosis system based on flow state video monitoring as claimed in claim 4, wherein a00.85 to 0.95; n is01 to 5; m is0Is 2 to 5.
6. A flow state video monitoring based water-passing building structure damage diagnosis system as claimed in claim 1, wherein the water surface line contour feature point coordinate H is seti(X, Y) and image feature point coordinates H 'corresponding to normal working conditions in background database (13)'i(X, Y) is compared, when | -Hi(X,Y)-H′i(X,Y)∣/H′i(X,Y)>When 10 percent of the flow is regarded as abnormal flow, an alarm is started, and data are stored in an abnormal flow database; when | Hi(X,Y)-H′i(X,Y)∣/H′i(X,Y)<And when 10 percent of the flow rate is determined as a normal flow rate, storing the flow rate in a normal flow rate database.
7. A method for diagnosing structural damage of a water passing building based on flow state video monitoring, which uses the system for diagnosing structural damage of a water passing building based on flow state video monitoring as claimed in any one of claims 1 to 6, and is characterized by comprising the following steps:
step (1), aiming at the characteristics of the flow state of the leakage flow, a plurality of characteristic points are arranged on the site to enable the characteristic points to cover the whole shooting range as much as possible, scour-resistant and easily-recognized marks are placed at the characteristic points, the coordinates of the marks are measured, and the marks are input into a background database;
step (2), storing the discharge flow state, water surface line and characteristic point data of the water passing building under the normal working condition and prototype observation data obtained in real time on site into a normal flow state database; storing the drainage flow state, water surface line and characteristic point data of the water passing structure under the abnormal working condition and the original abnormal data actually generated on site into an abnormal flow state database;
step (3), starting a high-definition fog-penetrating camera, aiming at the drainage flow state of the water passing building, and adjusting the shooting height until the shot image covers the whole water surface and the side walls at the two sides of the water passing building; starting the intelligent wiper module, and adjusting an aperture, a focal length, exposure parameters and a shutter speed to ensure that a shot image is clear;
step (4), formally shooting after obtaining the image with the resolution meeting the requirement, shooting a high-definition image every 10 minutes, and performing image processing analysis to determine the water surface line profile;
step (5), calculating the water surface line contour feature point coordinate H obtained by the imagei(X, Y) and image feature point coordinates H 'corresponding to normal working conditions in background database'i(X, Y) is compared, when | -Hi(X,Y)-H′i(X,Y)∣/H′i(X,Y)>When 10 percent of the flow is regarded as abnormal flow, an alarm is started, and data are stored in an abnormal flow database; when | Hi(X,Y)-H′i(X,Y)∣/H′iWhen the (X, Y) is less than or equal to 10 percent, the flow state is regarded as a normal flow state and is stored in a normal flow state database;
and (6) after the flow leakage is finished, closing the high-definition fog-penetrating camera, and storing all images in a background database.
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| PCT/CN2021/136749 WO2022127683A1 (en) | 2020-12-17 | 2021-12-09 | Method and system for diagnosing structural damage of overwater building on basis of flow-state video monitoring |
| NO20230279A NO20230279A1 (en) | 2020-12-17 | 2021-12-09 | Method and system for diagnosing structural damage of overwater building on basis of flow-state video monitoring |
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| WO2022127683A1 (en) * | 2020-12-17 | 2022-06-23 | 华能澜沧江水电股份有限公司 | Method and system for diagnosing structural damage of overwater building on basis of flow-state video monitoring |
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| CN116528036B (en) * | 2023-07-04 | 2023-09-08 | 李丽 | Scenic spot safety real-time monitoring system and method based on intelligent travel |
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| CN201993108U (en) * | 2010-12-24 | 2011-09-28 | 成都飞辉虹科技有限责任公司 | On-line intelligent monitoring system of tailing pond |
| CN103325216A (en) * | 2012-03-23 | 2013-09-25 | 罗普特（厦门）科技集团有限公司 | Water conservancy flood prevention monitoring early warning method and system based on video monitoring |
| CN106123785B (en) * | 2016-07-25 | 2019-06-21 | 海南省水利水电勘测设计研究院 | A kind of arch dam monitoring system for hydraulic and hydroelectric engineering |
| CN109302692A (en) * | 2018-10-26 | 2019-02-01 | 广州益牛科技有限公司 | Large-scale retaining dam structural damage intelligent real-time monitoring system |
| CN109612527B (en) * | 2018-12-20 | 2021-01-29 | 联誉信息股份有限公司 | Ecological leakage flow monitoring system of hydropower station |
| CN110646434A (en) * | 2019-08-21 | 2020-01-03 | 南京理工大学 | Device and method for automatically monitoring dam damage deformation in real time through video |
| CN113155860A (en) * | 2020-12-17 | 2021-07-23 | 华能澜沧江水电股份有限公司 | Flow state video monitoring-based method and system for diagnosing structural damage of water-passing building |
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| WO2022127683A1 (en) * | 2020-12-17 | 2022-06-23 | 华能澜沧江水电股份有限公司 | Method and system for diagnosing structural damage of overwater building on basis of flow-state video monitoring |
| CN114202894A (en) * | 2021-11-04 | 2022-03-18 | 浙江省水利河口研究院(浙江省海洋规划设计研究院) | Multi-target joint monitoring and early warning device for bridge pier, method and pile foundation |
| CN114202894B (en) * | 2021-11-04 | 2024-01-30 | 浙江省水利河口研究院(浙江省海洋规划设计研究院) | Multi-target joint monitoring and early warning device for bridge pier, method thereof and pile foundation |
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