CN101520448B - Water quality pollution early warning method - Google Patents
Water quality pollution early warning method Download PDFInfo
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- CN101520448B CN101520448B CN2009100383188A CN200910038318A CN101520448B CN 101520448 B CN101520448 B CN 101520448B CN 2009100383188 A CN2009100383188 A CN 2009100383188A CN 200910038318 A CN200910038318 A CN 200910038318A CN 101520448 B CN101520448 B CN 101520448B
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Abstract
The invention discloses a water quality pollution early warning method, which comprises the following steps: introducing raw water to be monitored into a fish tank, and putting a certain number of small fish into the fish tank; acquiring continuous frames of image sequences in real time, and converting the frames into digital images; smoothly processing the acquired frames of the image sequences; performing threshold segmentation treatment on the frames of the image sequences after smooth processing; performing noise removal treatment on the frames of the image sequences after the threshold segmentation; calculating a kinematical parameter of the small fish according to the frames of the image sequences after the noise removal treatment; setting a preset kinematical parameter, and comparing the calculated kinematical parameter with the preset kinematical parameter; and judging whether the calculated kinematical parameter is greater than the preset kinematical parameter or not, and determining that the water quality to be monitored is polluted when the calculated kinematical parameter is greater than the preset kinematical parameter. The method has the advantages that the method is timely and accurate to detect water quality.
Description
Technical field
The present invention relates to a kind of water quality pollution early warning technology, relating in particular to a kind of is the biological water quality pollution early warning technology of early warning with the fish.
Background technology
At present, the quality problem that China's water supply industry faces mainly contains two aspects, and the one, people improve constantly the requirement of water quality standard; The 2nd, the drinking water source in the current society generally receives industrial pollution.How effectively to increase water quality and ensure that the urban water supply sustainable development has become the modern city problem demanding prompt solution.Therefore domestic technical application about water quality situation prediction also seldom receives when polluting when water quality, only in running water pipe, flows out the water with peculiar smell or has under the situation that the resident declares, and we can find and carry out the water quality treatment pollution.
This information delay property has caused the major pollution incident of drinking water source to take place repeatedly, and drinking water quality safety is faced with serious threat often.This shows that the early detection of water pollution accident and alarm will be to ensure drinking water quality safety, realize the optimal path of safety water supply.
At present water sample is carried out the content that chemical analysis can accurately detect target contaminant; But because pollutant is of a great variety; Thisly water sample is carried out chemico-analytic technology can't confirm main toxicant rapidly; And then can't accurately kill microorganisms in water, finally be difficult to adapt to increasingly high water quality standard for drinking water requirement.
In addition; Existing physical chemistry monitoring instrument detection method have length consuming time, testing cost high, can't realize defective such as real-time continuous monitoring; These also defective cause us can't find the water pollution sign apace, can not differentiate the water pollution degree exactly.
Thereby, be necessary to provide a kind of water quality pollution early warning device, so that overcome the shortcoming and deficiency of prior art.
Summary of the invention
The purpose of this invention is to provide a kind of water quality pollution early warning method, it can be realized in real time, water quality detection accurately, thereby realizes the early warning purpose.
To achieve these goals, the present invention provides a kind of water quality pollution early warning method, comprises step: former water to be monitored is imported in the fish jar, and certain number of small fish is put into fish jar; Gather the continuous images sequence frame in real time, and be converted into digital picture; Image sequence frame to being collected carries out smoothing processing; Image sequence frame after the smoothing processing is carried out Threshold Segmentation to be handled; Image sequence frame to after the Threshold Segmentation carries out denoising; Calculate the kinematic parameter of small fish according to the image sequence frame after the denoising; Set preset kinematical parameter, and kinematic parameter that calculates and predetermined kinetic parameter are compared; And whether judge the kinematic parameter that calculates greater than predetermined kinetic parameter, definite water quality of being monitored is received pollution when greater than predetermined kinetic parameter.
The invention has the advantages that: water quality detection relatively in time, accurately.
To combine accompanying drawing below, describe the present invention in detail through preferred embodiment.
Description of drawings
Fig. 1 is the structured flowchart that is used to carry out the device of water quality pollution early warning method of the present invention.
Fig. 2 is the stereographic map of the fish jar of water quality pollution early warning device shown in Figure 1.
Fig. 3 is the method flow diagram of water quality pollution early warning of the present invention.
Embodiment
With reference now to accompanying drawing, present invention is described.
The inventor's principle is: some hydrobiont is very high to the sensitivity of change of water quality, can rapidly, accurately judge water quality whether the situation that poisons pollution is arranged through this hydrobiological behavior of some is analyzed.Thus; The inventor thinks: based on the water quality pollution early warning technology of analyzing the bio-toxicity reaction can be promptly and accurately discovery, early warning and water quality treatment contamination accident; Effectively guarantee water quality; And then avoid the generation of drinking water pollution accident, also can accurate water quality data be provided simultaneously for system's control of water technology.
Such as; Fish just react very sensitive to the variation of the chemical constitution in the water, and fish also are and the immediate vertebra class of mankind animal simultaneously, therefore through the variation of its movement locus is followed the tracks of and analyzed; Can reach the requirement of remote monitoring water quality situation; In case unusual condition appears in water quality, corresponding variation will take place in the motion conditions of fish, thereby has realized finding the morning of water pollution, early report to the police and control early.
Describe water quality pollution early warning method of the present invention below in detail.
Referring to Fig. 1 and Fig. 2, at first introduce water quality pollution early warning device according to the present invention comprise fish jar 10, be used for to the inside of fish jar 10 carry out the video camera 20 of live image collection, be connected with said video camera 20 so as the analog image that video camera 20 is collected convert into digital picture analog to digital converter 30, be connected and be used to handle image and control video camera 20 and the microprocessor 40 of analog to digital converter 30 and the storer 50 that is used for storing image data with said analog to digital converter 30.
Fig. 2 has showed the detailed spatial structure of fish jar 10.As shown in the figure, fish jar 10 is as general as cubic shaped, and it comprises diapire 13, extended pair of sidewalls 12 reaches antetheca 11 and the rear wall 14 that extends from the both sides, front and back of said diapire 13 from the left and right sides of said diapire 13.Said diapire 13, antetheca 11, rear wall 14 and pair of sidewalls 12 have defined the inner space of fish jar 10 jointly.On the said sidewall 12 water delivering orifice 122, subsequent use intake-outlet 124 and freeing port 126 are installed; Then be equipped with on another sidewall 12 and overflow water out 128 and water inlet 121.
In the said inner space near the position of rear wall 14 be vertically arranged with LED-backlit plate 127 and with said LED-backlit plate 127 milky backlight 129 separated by a distance.In addition, inherence, said inner space is vertically arranged with support 125 near the position of antetheca 11.Support 125 has near the side plate of said pair of sidewalls 12 (not label), is formed for regulating the diversion trench of water velocity in the fish jar 10 on this side plate.
Said video camera 20 is arranged on the diapire 13 of said fish jar 10 position near antetheca 11, and towards said inner space.In addition, said video camera 20 can be supported by camera mount 123.
The following water quality early-warning method that realizes by above-mentioned water quality pollution early warning device of describing with reference to figure 1-3.
At first, former water to be monitored is imported in the said fish jar 10, will put into said fish jar 10 (step 100 of Fig. 3) such as zebra fish (height is about 20*10mm, and color is for darker) the vertebrate of the some of former water sensitive to be monitored.Former water to be monitored is being imported in the process of said fish jar 10, and water delivering orifice 122, subsequent use intake-outlet 124, freeing port 126 that can be through said fish jar 10, the parameters such as capacity of overflowing water in water out 128 and 121 pairs of water velocities of water inlet, the fish jar 10 are regulated.
Secondly; Start video camera 20; Thereby cause video camera 20 to gather the sequence of image frames of fish jar 10 inner spaces in real time, and convert digital picture (step 200 among Fig. 3) under the control of microprocessor 40 by the analog image that analog to digital converter 30 collects said video camera 20.Preferably, said video camera 20 is taken the inner space of fish jar 10 with the speed of the 1-8 width of cloth/second, simultaneously with data transmission in image pick-up card, and every inner space of taking a time at a distance from 5 seconds.
Next, the image sequence frame that is collected is carried out smoothing processing (step 300 among Fig. 3).Specifically, microprocessor 40 carries out Corner Detection such as the Harris Corner Detection Algorithm to the sequence of image frames of being gathered through carrying out appropriate algorithm, thus upper left a, left side of confirming the aqua region in the fish jar 10 successively down, the position of bottom right, upper right four angle points.Come out according to the area identification of four angle points confirming, thereby accomplish the selected of observation area the water in the fish jar 10.Confirm the observation area of fish jar, can reduce the size of the image of handling, thereby the raising system is such as the processing speed of microprocessor 40.
Picture smooth treatment can be carried out like this: the pixel value in the certain field in the observed image that in the observation area, collects carries out Filtering Processing according to its range; Thereby make the MARG of observed image comparatively complete, simultaneously picture noise is dropped to minimum.
Then, the image sequence frame after the smoothing processing is carried out Threshold Segmentation and handle (step 400 among Fig. 3).Carrying out image threshold segmentation can be carried out like this: the adaptive threshold extraction algorithm by object edge gradient characteristic is realized the differentiation to required observed image.The binary-state threshold of promptly setting according to the user comes in the accurate surveying image Tracking Recognition scope to small fish.
Subsequently, the image sequence frame after the Threshold Segmentation is carried out denoising (step 500 among Fig. 3), thereby further eliminate the less image of area, promptly less than the impurity of 0.01mm (like excreta of bubble, fish etc.).
Calculate the kinematic parameter (step 600 among Fig. 3) of small fish according to the image sequence frame after the denoising.Particularly; Setting up the background frame number is 300 frames; Be about to this 300 two field picture data as a reference; Through in these data the small fish in the selected observation area being identified,, thereby realize identification to small fish in the observation area mainly by the demarcation of small fish position coordinates in the image and confirming of small fish direction of motion.Each bar small fish in the observation area can be irised out through different colours in image shows, and every small fish in the observation area also on the mark corresponding sequence number, these data messages are kept in the said storer 50.
Further, calculate the speed of moving about, average height and the average turning number of times of small fish through the movement locus of the small fish after the sign.To every small fish, said microprocessor 40 starts one respectively to be followed the tracks of and the identification thread, calculates move about speed, average height and the average turning number of times of small fish in this 300 two field picture.Specifically, 1 second to be the movement velocity size that unit calculates fish, calculate the size of small fish average velocity then according to the setting (the background frame number is made as 300 frames) of background frame number; Calculate the average height of small fish in the fish jar coordinate system, confirm the average height of small fish in the background frame number that has been provided with; The speed of adjacent two frame fishes is calculated in the calculating of average turning number of times earlier, and the point of movement velocity generation break-in for turning according to the position that changes 40 frames before and after the direction, further judged, thereby calculates the turning number of times of small fish.
In addition, said microprocessor 40 carries out the Kalman prediction through the tracking to small fish sequence target with each sequence target, and the position after will predicting passes to the location identification of carrying out small fish sequence target in the MeanShit algorithm.Realized the prediction of small fish movement locus.Help improving the monitoring analysis speed of Kaitian's source potable water toxicity monitoring system.
Subsequently, set preset kinematical parameter, and kinematic parameter that calculates and predetermined kinetic parameter are compared (step 700 among Fig. 3).
Such as, setting preset kinematical parameter can comprise: set the fish jar width, set up the maximal value of background frame number, context update speed, binary-state threshold, target sizes, the minimum value of target sizes etc.At this, elect the fish jar width as 400mm.When carrying out target detection, utilize the image that has target to set up the background frame number simultaneously.The initial background effect that frame number is set up more at most is good more.The frame number of system default is 300.Context update speed is the influence of present frame to background image, and this coefficient is big more, and the speed of setting up background image is fast more, but the remnants of target are also big more to the influence of background.Drawing context update speed according to test is 0.005 o'clock, and the background of foundation can obtain reasonable effect, so system default is 0.005.
Binary-state threshold is when being used for target detection and extracting, and present image and background image difference result make greater than this value that pixel value is 255 (being target), are 0 (being background) less than this value.Can adjust according to actual conditions.When small fish and background contrasts are high, suitably improve this value and can reduce noise effect.But if contrast is low, this threshold value will be lost some target informations again than higher.Contrast at this system default is 60.In the test, the setting of binary-state threshold parameter is about at 39 o'clock, and the surveillance map picture is better.The minimum and maximum value coefficient of target size is the coefficient of relative image size.If the wide and higher primary school of target just thinks that in figure image width and size high and the minimum value coefficient this target is a noise.Equally, if target is wide and tall and big in figure image width and size high and the maximal value coefficient, just think that this target also is a noise.System default is that minimum value is 0.002, and maximal value is 0.3.
Preferably, the predetermined kinetic parameter of said setting comprises average velocity, average height and the average turning number of times of small fish.And predetermined average velocity is 20mm/s-80mm/s, and average height is 70mm-250mm, and the number of times of on average turning is 4-12 time.
Kinematic parameter that calculates and predetermined kinetic parameter relatively can be carried out like this: when the average velocity of small fish greater than 20mm/s-80mm/s; Average height is greater than 70mm-250mm; When on average the turning number of times is greater than 4-12 time; The motion that can be considered small fish shows that current water quality is unusual (step 800 among Fig. 3), that is to say that monitored water quality is polluted in improper scope.
Briefly say; In technical scheme of the present invention: adopt 24 hours real-time continuous of digital camera equipment to gather the live image of some fishes in the former water water body; Realization is to the high precision tracking of fish; Analyze the speed of moving about of fish, distance, fish between the fish at basic parameters such as the number of times of unit interval inside turn, live fish quantity by image processing system, judge the motor behavior and the health status of fish, and then whether prediction water quality received polluting make corresponding analysis by the water quality early-warning appearance.
The size of some the fishes of being adopted is 20*10mm, and color is dark, adopts zebra fish usually, and it is small, and the video camera identification is good.
Beneficial effect of the present invention is: small fish is very responsive to water intoxication property material and pollutant; Unusual action through the monitoring fish; It is unusual to monitor out water quality in early days, early confirms the water pollution incident, in time sends early warning signal; The water body objectionable impurities is removed fast, guaranteed drinking water quality safety.
In addition, the present invention installs simply, compact conformation, and easy operating, maintenance and expansion, system's reliability with strong points is high, stable, is fit to multiple on-the-spot service condition.
The above disclosed the preferred embodiments of the present invention that are merely can not limit the present invention's interest field certainly with this, so according to the equivalent variations that claim of the present invention is done, still belong to the scope that the present invention is contained.
Claims (6)
1. a water quality pollution early warning method is characterized in that comprising the steps:
Former water to be monitored is imported in the fish jar, and certain number of small fish is put into fish jar;
Gather the continuous images sequence frame in real time, and be converted into digital picture;
Image sequence frame to being collected carries out smoothing processing;
Image sequence frame after the smoothing processing is carried out Threshold Segmentation to be handled;
Image sequence frame to after the Threshold Segmentation carries out denoising;
Calculate the kinematic parameter of small fish according to the image sequence frame after the denoising;
Set preset kinematical parameter, and kinematic parameter that calculates and predetermined kinetic parameter are compared; And
Whether judge the kinematic parameter that calculates greater than predetermined kinetic parameter, confirm that when greater than predetermined kinetic parameter the water quality of being monitored is polluted;
The step of the image sequence frame that is collected being carried out smoothing processing comprises:
1) carries out the Harris Corner Detection Algorithm by microprocessor; Sequence of image frames to being gathered carries out Corner Detection; Thereby upper left a, left side of confirming the aqua region in the fish jar successively down, the position of bottom right, upper right four angle points, identify out according to the observation area of four angle points confirming with the water in the fish jar; With
2) pixel value in the certain field in the observed image that in the observation area, collects is carried out Filtering Processing according to its range, thereby make the MARG of observed image comparatively complete, simultaneously picture noise is dropped to minimum;
Saidly image sequence frame after the smoothing processing is carried out the Threshold Segmentation processed steps comprise by the adaptive threshold extraction algorithm of object edge gradient characteristic and realize differentiation required observed image;
The step of the image sequence frame after the Threshold Segmentation being carried out denoising comprises the impurity of elimination area less than 0.01mm;
The step that calculates the kinematic parameter of small fish according to the image sequence frame after the denoising comprises:
1) setting up the background frame number is 300 frames; With this 300 two field picture data as a reference; Through in these data the small fish in the selected observation area being identified, thereby realize identification, the image of each the bar small fish in the said observation area is indicated various colors small fish in the observation area; And with on every in the observation area small fish also mark corresponding sequence number, at last these data messages are kept in the storer;
2) calculate the speed of moving about, average height and the average turning number of times of small fish through the movement locus of the small fish after the sign; To every small fish, said microprocessor starts one respectively to be followed the tracks of and the identification thread, is the movement velocity size that unit calculates fish with 1 second; Calculate the size of small fish average velocity then according to being provided with of background frame number; Calculate the average height of small fish in the fish jar coordinate system, confirm the average height of small fish in the background frame number that has been provided with, calculate the speed of adjacent two frame fishes; Possible turning point is confirmed as in the break-in of movement velocity, at the turning number of times that further calculates small fish according to the position that changes direction front and back 40 frames; With
3) microprocessor carries out the Kalman prediction through the tracking to small fish sequence target with each sequence target, and the position after will predicting passes to the location identification of carrying out small fish sequence target in the MeanShit algorithm.
2. method according to claim 1; It is characterized in that: said in the former water importing fish jar to be monitored; And the step that certain number of small fish is put into fish jar comprised former water to be monitored is imported said fish jar; To be 20mm to the some and the width of former water sensitive to be monitored, length be that the zebra fish of 10mm is put into said fish jar.
3. method according to claim 2; It is characterized in that: gather the continuous images sequence frame in real time; And the step that is converted into digital picture comprises the startup video camera; Thereby cause video camera to gather the sequence of image frames of fish jar inner space in real time, and by analog to digital converter under the control of microprocessor with said camera acquisition to analog image convert digital picture into.
4. method according to claim 3 is characterized in that: said video camera is taken the inner space of fish jar with the speed of the 1-8 width of cloth/second, and whenever takes once at a distance from 5 seconds.
5. method according to claim 1 is characterized in that: predetermined kinetic parameter comprises average velocity, average height and the average turning number of times of small fish; And said predetermined average velocity is 20mm/s-80mm/s, and average height is 70mm-250mm, and the number of times of on average turning is 4-12 time.
6. method according to claim 5; It is characterized in that: the step of kinematic parameter that calculates and predetermined kinetic parameter comparison is comprised that the average velocity of working as small fish is greater than 20mm/s-80mm/s; Average height is greater than 70mm-250mm; The motion of confirming small fish when on average the turning number of times is greater than 4-12 time is in improper scope, thereby it is unusual to judge water quality.
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| CN111476765A (en) * | 2020-03-30 | 2020-07-31 | 深圳市水务(集团)有限公司 | Water quality judging method and device |
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| CN112881640B (en) * | 2021-03-19 | 2023-08-08 | 西安工业大学 | Water quality early warning and testing method and system based on zebra fish stress reaction |
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Inventor after: Liu Lijun Inventor after: Zhang Jinsong Inventor after: Wan Ruyi Inventor after: Luo Jiahong Inventor after: Yuan Yichu Inventor after: Huang Kaining Inventor after: Zhao Yan Inventor after: Wang Chun Inventor before: Wan Ruyi Inventor before: Luo Jiahong Inventor before: Yuan Yichu Inventor before: Liu Lijun Inventor before: Li Xiaoru Inventor before: Lu Hongjia Inventor before: Wu Jiang Inventor before: Huang Kaining |
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Free format text: CORRECT: INVENTOR; FROM: WAN RUYI LUO JIAHONG YUAN YICHU LIU LIJUN LI XIAORU LU HONGJIA WU JIANG HUANG KAINING TO: LIU LIJUN ZHANG JINSONG WAN RUYI LUO JIAHONG YUAN YICHU HUANG KAINING ZHAO YAN WANG CHUN |
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Address after: 518030 No. 2001 courtyard, Binhe Road, Guangdong, Shenzhen, 3 Patentee after: Shenzhen Water Technology Co., Ltd. Address before: 518030 No. 2001 courtyard, Binhe Road, Guangdong, Shenzhen, 3 Patentee before: Shenzhen Kaitianyuan Automation Engineering Co., Ltd. |