CN112801715B - Intelligent control system of water purifier - Google Patents
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Abstract
The application provides an intelligent control system of a water purifier, which comprises a shooting module, an identity recognition module, a water purifier control module and a communication module, wherein the shooting module is used for shooting the water purifier; the shooting module is used for acquiring a head image of a person using the water purifier; the identity recognition module is used for judging whether the person has the right to use the water purifier or not based on the head image, and if so, the identity recognition module sends a starting instruction to the water purifier control module; the water purifier control module is used for controlling water output of the water purifier after receiving the starting instruction, and transmitting the water output to the communication module after stopping water output; the communication module is used for sending the water yield to a background server, and the background server is used for generating a fee bill based on the water yield. The application effectively solves the problem of inconvenient charge management of the water purifier in the public area.
Description
Technical Field
The application relates to the field of control, in particular to an intelligent control system of a water purifier.
Background
The water purifier is water treatment equipment for carrying out deep filtration and purification treatment on water quality according to the use requirement of the water. At present, a plurality of large office places are provided with water purifiers, and water discharged from the water purifiers can be directly drunk. However, since tea rooms in these sites are often commonly used by a plurality of companies, it is a difficult problem how to charge for the use of the water purifier. The charging is generally carried out by a rechargeable card in the prior art, but the rechargeable card needs to be recharged once at intervals, which is inconvenient.
Disclosure of Invention
In view of the above problems, an object of the present application is to provide an intelligent control system for a water purifier, which includes a photographing module, an identification module, a water purifier control module, and a communication module;
the shooting module is used for acquiring a head image of a person using the water purifier and transmitting the head image to the identity recognition module;
the identity recognition module is used for judging whether the person has the right to use the water purifier or not based on the head image, and if so, the identity recognition module sends a starting instruction to the water purifier control module;
the water purifier control module is used for controlling water output of the water purifier after receiving the starting instruction, and transmitting the water output to the communication module after stopping water output;
the communication module is used for sending the water yield to a background server, and the background server is used for generating a fee bill based on the water yield.
Preferably, the shooting module comprises an image pickup unit, a light unit and a detection unit;
the detection unit is used for detecting whether personnel stay in front of the water purifier, if so, the camera unit is awakened;
the camera unit is used for acquiring the head image of the person after being awakened and transmitting the head image to the identity recognition module;
the light unit is used for providing additional illumination light for the camera unit.
Preferably, the image pickup unit comprises an image pickup sub-unit and a light ray judging sub-unit;
the light sensor is used for detecting the light brightness of the environment where the water dispenser is located and transmitting the light brightness to the camera subunit;
the camera subunit is used for starting the light unit when the light brightness is lower than a preset brightness threshold value, then acquiring the head image of the person, and directly acquiring the head image of the person when the light brightness is higher than or equal to the preset brightness threshold value.
Preferably, the identity recognition module comprises a preprocessing unit, a feature extraction unit and a judgment unit;
the preprocessing unit is used for preprocessing the head image to obtain a preprocessed image;
the feature extraction unit is used for extracting feature information contained in the preprocessed image and transmitting the feature information to the judgment unit;
the judging unit is used for receiving the characteristic information transmitted by the characteristic extracting unit, matching the characteristic information with the characteristic information of the personnel with the right of using the water purifier stored in the characteristic information database, if the matching is successful, sending a starting instruction to the water purifier control module, and transmitting the identity identification ID corresponding to the characteristic information to the communication module.
Preferably, the communication module is configured to send the identification ID and the water yield to a background server.
Preferably, the background server is configured to generate a fee bill based on the identification ID and the water yield.
Compared with the prior art, the application has the advantages that:
the application judges whether the user has the use right or not in a face recognition mode, if the user has the use right, the water purifier is controlled to discharge water, and then the water discharge amount is sent to a background service for charging. Thus, the problem of inconvenient charge management of the water purifier in the public area is solved.
Drawings
The application will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the application, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a diagram illustrating an exemplary embodiment of an intelligent control system for a water purifier according to the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
As shown in the embodiment of fig. 1, the present application provides an intelligent control system for a water purifier, which includes a photographing module, an identification module, a water purifier control module, and a communication module;
the shooting module is used for acquiring a head image of a person using the water purifier and transmitting the head image to the identity recognition module;
the identity recognition module is used for judging whether the person has the right to use the water purifier or not based on the head image, and if so, the identity recognition module sends a starting instruction to the water purifier control module;
the water purifier control module is used for controlling water output of the water purifier after receiving the starting instruction, and transmitting the water output to the communication module after stopping water output;
the communication module is used for sending the water yield to a background server, and the background server is used for generating a fee bill based on the water yield.
The face recognition starts the water purifier, so that the using steps of the water purifier are simplified, and the use of the water purifier is facilitated.
Preferably, the water purifier control module comprises a stop button, and the stop button is used for controlling the water purifier to stop water outlet.
Preferably, the water yield is the flow rate of a water outlet of the water purifier in a period from starting water outlet to stopping water outlet of the water purifier.
Preferably, the shooting module comprises an image pickup unit, a light unit and a detection unit;
the detection unit is used for detecting whether personnel stay in front of the water purifier, if so, the camera unit is awakened;
the camera unit is used for acquiring the head image of the person after being awakened and transmitting the head image to the identity recognition module;
the light unit is used for providing additional illumination light for the camera unit.
Through setting up detecting element, can avoid the camera to be in running state always, be favorable to practicing thrift the electric energy. The light unit is favorable for avoiding the speed of the face recognition caused by insufficient illumination.
Preferably, the image pickup unit comprises an image pickup sub-unit and a light ray judging sub-unit;
the light sensor is used for detecting the light brightness of the environment where the water dispenser is located and transmitting the light brightness to the camera subunit;
the camera subunit is used for starting the light unit when the light brightness is lower than a preset brightness threshold value, then acquiring the head image of the person, and directly acquiring the head image of the person when the light brightness is higher than or equal to the preset brightness threshold value.
Preferably, the identity recognition module comprises a preprocessing unit, a feature extraction unit and a judgment unit;
the preprocessing unit is used for preprocessing the head image to obtain a preprocessed image;
the feature extraction unit is used for extracting feature information contained in the preprocessed image and transmitting the feature information to the judgment unit;
the judging unit is used for receiving the characteristic information transmitted by the characteristic extracting unit, matching the characteristic information with the characteristic information of the personnel with the right of using the water purifier stored in the characteristic information database, if the matching is successful, sending a starting instruction to the water purifier control module, and transmitting the identity identification ID corresponding to the characteristic information to the communication module.
Preferably, the preprocessing the head image to obtain a preprocessed image includes:
graying treatment is carried out on the head image, so that a first image is obtained;
carrying out noise reduction treatment on the first image to obtain a second image;
and performing image enhancement processing on the second image to obtain a preprocessed image.
Preferably, the subjecting the head image to graying processing to obtain a first image includes:
the following judgment is respectively carried out on the pixel points in the head image, and a face pixel point set is obtained:
for the pixel point a in the head image, the three components in the YCbCr color model are respectively marked as Y (a), cb (a) and Cr (a), if 31+beta 1 ≤Y(a)≤239+β 1 And 89+beta 2 ≤Cb(a)≤117+β 2 And 139+beta 3 ≤Cr(a)≤169+β 3 Storing the pixel point a into a face pixel point set;
β 1 、β 2 、β 3 the correction parameters for the Y component, cb component, and Cr component are respectively indicated,
β 1 ＝(1-sm(Y))×Y st ×fh(Y)
where sm (Y) represents the degree of distinction between the Y component of the head image and the Y component of the standard image ST,nstx and nsty represent the number of rows and columns, respectively, in ST (i, j) represent the value of the Y component of the pixel of coordinates (i, j) in ST, he (x+i, y+j) represent the value of the Y component of the pixel of coordinates (x+i, y+j) in the head image, and (x, Y) represent the coordinates of the initial comparison pixel in the head image, Y st Representing a preset reference value, Y st ∈[1,3]And Y is st Is an integer, k represents a control parameter for preventing the correction factor from causing 239+beta 1 Beyond the range of the value, fh (Y) represents a sign judgment function, if the average value of the Y components of all the pixels in the head image is greater than the Y components of all the pixels in STAverage value of the quantity, then fh (Y) is 1, otherwise fh (Y) is-1;
β 2 、β 3 calculation mode and beta of (2) 1 The calculation modes of (a) are consistent, and are not repeated here; calculation of beta 2 Only the Y component is correspondingly replaced by the Cb component, and beta is calculated 3 When the Y component is changed into the Cr component correspondingly, the Y component is only needed.
Generating a preliminary face image according to the pixels in the face pixel set;
performing corrosion treatment and expansion treatment on the preliminary face image to obtain a final face image;
and carrying out graying treatment on the final face image to obtain a first image.
According to the embodiment of the application, the face pixel points are identified before the grey processing of the head image, so that the number of the pixel points participating in subsequent calculation is greatly reduced, and the calculation speed is improved. After all face pixel points are identified, a preliminary face image is generated by the face pixel points, and the illumination adjustment of the face cannot be completely consistent, so that a face area in the preliminary face image has a plurality of holes, and the holes are processed through corrosion and expansion processing, so that a complete face area is obtained. The amount of information contained in the first image is effectively increased. The method is favorable for accurate identification in the follow-up process. In the process of judging the pixel points in the head image, the application designs the self-adaptive change interval range instead of the appointed interval range, mainly because the shooting conditions of different images are different, if the appointed interval mode is adopted, the identification accuracy is not high enough, and the erroneous judgment is easy to occur. In the calculation of the correction parameters, the difference between the pixel points in the head image and the standard image in space and the component average value under the Y component is fully considered, so that the accurate correction parameters can be obtained, and if only the component average value is considered and the difference in space is not considered, the obtained difference is obviously inaccurate, because the distribution of the pixel points can be completely different.
Preferably, the noise reduction processing is performed on the first image to obtain a second image, including:
determining a noise detection threshold thr;
and carrying out noise reduction processing on the first image in an iterative mode:
first iteration:
setting the detection threshold to th 1 ，All pixel values in the first image are in the interval th 1 ，255]The pixels in the pixel array are stored in a noise pixel set noU 1 Wherein t is a preset interval parameter, t is [5,10 ]],
For noU, the following procedure is adopted 1 Noise reduction processing is carried out on noise pixel points in the pixel array:
for noU 1 Pixel point b in (a) 1 Will b 1 Pixels in a neighborhood of h×h size are stored in a set Ub 1 Removing Ub 1 Maximum value and minimum value of pixel values in (b), and obtaining Ub 1 The standard deviation of the pixel values of the rest pixel points in the (b) is adopted in the following way if the standard deviation is larger than the preset standard deviation threshold value 1 The noise reduction treatment is carried out so as to reduce the noise,
wherein an (b) 1 ) Representation pair b 1 As a result of noise reduction, n (c) represents Ub 1 Pixel value of pixel point c in (a), km (b 1 C) represents pixel point c and pixel point b 1 Straight line distance between them, gcb represents Ub 1 All pixel points and b 1 Standard deviation of straight line distance between f (b) 1 ) Representation b 1 Tf (c) represents the gradient magnitude of c,nUb 1 representing Ub 1 The total number of elements in (a);
if the standard deviation is smaller than or equal to a preset standard deviation threshold value, adopting a median filtering mode to perform the filtering on b 1 Performing noise reduction treatment;
pair noU 1 After noise reduction processing is carried out on all noise pixel points in the image, a noise reduction image jn of the first iteration is obtained 1 ；
Nth iteration:
setting the detection threshold to th n ，All pixel values in the first image are in the interval th n ，th n-1 ) The pixels in the pixel array are stored in a noise pixel set noU n Wherein t is a preset interval parameter, t is [5,10 ]],
At jn n-1 Is to noU in the following way n Noise reduction processing is carried out on noise pixel points in the pixel array:
for noU n Pixel point b in (a) n Will b n Pixels in a neighborhood of h×h size are stored in a set Ub n In removing Ub n Maximum value and minimum value of pixel values in (b), and obtaining Ub n The standard deviation of the pixel values of the rest pixel points in the (b) is adopted in the following way if the standard deviation is larger than the preset standard deviation threshold value n The noise reduction treatment is carried out so as to reduce the noise,
wherein an (b) n ) Representation pair b n As a result of noise reduction, n (c) n ) Representing Ub n Pixel point c of (3) n Pixel value of km (b) n ,c n ) Representing pixel point c n And pixel point b n Straight line distance between gcb n Representing Ub n All pixel points and b n Standard deviation of straight line distance between them, tf (b) n ) Representation b n Gradient amplitude, tf (c) n ) Representation c n Is set to be a gradient magnitude of (1),nUb n representing Ub n The total number of elements in (a);
if the standard deviation is smaller than or equal to a preset standard deviation threshold value, adopting a median filtering mode to perform the filtering on b n Performing noise reduction treatment;
pair noU n Noise reduction is carried out on all noise pixel points in the image to obtain a noise reduction image jn of the nth iteration n ；
The condition for ending the iteration is th n Less than thr.
Compared with the traditional noise reduction mode, the application adopts an iterative noise reduction mode, and can effectively improve the noise reduction accuracy. Specifically, at the time of the first iteration, the noise reduction processing is performed in the first image, and by the time of the nth iteration, the noise reduction processing is performed on the noise reduction image jn obtained at the time of the (n-1) th iteration n-1 The setting mode enables the noise reduction result of the last time to influence the noise reduction result of the next time. Specifically, at the beginning, the detection threshold is very large, the accuracy is very high, so that the noise reduction result of the noise point is very accurate, and the noise reduction processing of the noise point is performed on a very accurate noise reduction image at the next iteration noise reduction, so that the accuracy of the noise reduction processing can be improved. The iteration is performed so that the initial noise reduction result can always influence the final noise reduction result, and therefore the accuracy of the overall noise reduction result is improved.
When the noise is reduced for a specific noise point, the self-adaptive noise reduction method is adopted, if the pixel point difference of the pixel points which are remained after the maximum pixel value and the minimum pixel value are removed in the neighborhood of the noise point is smaller, namely the standard deviation is smaller, the noise point is likely to be an isolated noise point, so that the noise point is processed in a median filtering mode, and if the standard deviation is larger, the noise point is subjected to noise reduction processing in a weighted summation mode according to the difference of the noise point and the neighborhood pixel point, so that the noise point can be well smoothed, the gradient amplitude can well reflect the structural condition of the pixel point, and the noise reduction accuracy is improved.
Preferably, performing image enhancement processing on the second image to obtain a preprocessed image, including:
calculating the second image by using a sobel algorithm to obtain a first intermediate image;
performing edge connection processing on the first intermediate image to obtain a second intermediate image;
fusing the second intermediate image and the second image to obtain a preprocessed image:
yc(s)＝d 1 ×fmp(s)+d 2 ×sep(s)
wherein fmp(s) represents the pixel value of the pixel point s in the second intermediate image, sep(s) represents the pixel value of the pixel point s in the second image, yc(s) represents the pixel value of the pixel point s in the pre-processed image, d 1 And d 2 Is a preset weight parameter.
In the second intermediate image, edge pixel points in the second image are screened out and then weighted fusion of the second intermediate image and the second image is utilized, so that the edge pixel points in the obtained preprocessed image are enhanced, images with higher information content are provided for subsequent recognition, and the accuracy of the subsequent recognition can be effectively improved.
Preferably d 1 ∈(0,1)，d 2 ∈(0,1)。
Preferably, the communication module is configured to send the identification ID and the water yield to a background server.
Preferably, the background server is configured to generate a fee bill based on the identification ID and the water yield.
The expense bill comprises information such as the use date, the identity ID, the water yield, the expense and the like, and can be pushed to the user side in a regular pushing mode for paying according to the expense bill. The application is certainly more convenient than the traditional way of swiping cards, and the cost does not need to be settled separately each time, and can be settled periodically, for example, once a month.
While embodiments of the application have been shown and described, it will be understood by those skilled in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. The intelligent control system of the water purifier is characterized by comprising a shooting module, an identity recognition module, a water purifier control module and a communication module;
the shooting module is used for acquiring a head image of a person using the water purifier and transmitting the head image to the identity recognition module;
the identity recognition module is used for judging whether the person has the right to use the water purifier or not based on the head image, and if so, the identity recognition module sends a starting instruction to the water purifier control module;
the water purifier control module is used for controlling water output of the water purifier after receiving the starting instruction, and transmitting the water output to the communication module after stopping water output;
the communication module is used for sending the water yield to a background server, and the background server is used for generating a fee bill based on the water yield;
the identity recognition module comprises a preprocessing unit, a feature extraction unit and a judgment unit;
the preprocessing unit is used for preprocessing the head image to obtain a preprocessed image;
the feature extraction unit is used for extracting feature information contained in the preprocessed image and transmitting the feature information to the judgment unit;
the judging unit is used for receiving the characteristic information transmitted by the characteristic extracting unit, matching the characteristic information with the characteristic information of the personnel with the right of using the water purifier stored in the characteristic information database, if the matching is successful, sending a starting instruction to the water purifier control module, and transmitting an identification ID corresponding to the characteristic information to the communication module;
the preprocessing the head image to obtain a preprocessed image comprises the following steps:
graying treatment is carried out on the head image, so that a first image is obtained;
carrying out noise reduction treatment on the first image to obtain a second image;
performing image enhancement processing on the second image to obtain a preprocessed image;
the step of performing graying processing on the head image to obtain a first image includes:
the following judgment is respectively carried out on the pixel points in the head image, and a face pixel point set is obtained:
for the pixel point a in the head image, the three components in the YCbCr color model are respectively marked as Y (a), cb (a) and Cr (a), if 31+beta 1 ≤Y(a)≤239+β 1 And 89+beta 2 ≤Cb(a)≤117+β 2 And 139+beta 3 ≤Cr(a)≤169+β 3 Storing the pixel point a into a face pixel point set;
β 1 、β 2 、β 3 the correction parameters for the Y component, cb component, and Cr component are respectively indicated,
β 1 ＝(1-sm(Y))×Y st ×fh(Y)
where sm (Y) represents the degree of distinction between the Y component of the head image and the Y component of the standard image ST,nstx and nsty represent the number of rows and columns, respectively, in ST (i, j) represent the value of the Y component of the pixel of coordinates (i, j) in ST, he (x+i, y+j) represent the value of the Y component of the pixel of coordinates (x+i, y+j) in the head image, and (x, Y) represent the coordinates of the initial comparison pixel in the head image, Y st Representing a preset reference value, Y st ∈[1,3]And Y is st Is an integer, k represents a control parameter for preventing the correction factor from causing 239+beta 1 Beyond the range of the value, fh (Y) represents the sign judgment function if all the pixels in the head imageThe average value of Y components of (C) is larger than the average value of Y components of all pixel points in ST, the value of fh (Y) is 1, otherwise, the value of fh (Y) is-1;
generating a preliminary face image according to the pixels in the face pixel set;
performing corrosion treatment and expansion treatment on the preliminary face image to obtain a final face image;
and carrying out graying treatment on the final face image to obtain a first image.
2. The intelligent control system of a water purifier according to claim 1, wherein the shooting module comprises a camera unit, a light unit and a detection unit;
the detection unit is used for detecting whether personnel stay in front of the water purifier, if so, the camera unit is awakened;
the camera unit is used for acquiring the head image of the person after being awakened and transmitting the head image to the identity recognition module;
the light unit is used for providing additional illumination light for the camera unit.
3. The intelligent control system of a water purifier according to claim 2, wherein the camera unit comprises a camera sub-unit and a light ray judging sub-unit;
the light judging subunit is used for detecting the light brightness of the environment where the water dispenser is located and transmitting the light brightness to the camera shooting subunit;
the camera shooting subunit is used for starting the light unit when the light brightness is lower than a preset brightness threshold value, then acquiring the head image of the person, and directly acquiring the head image of the person when the light brightness is higher than or equal to the preset brightness threshold value.
4. The intelligent control system of claim 1, wherein the communication module is configured to send the identification ID and the water yield to a background server.
5. The intelligent control system according to claim 4, wherein the background server is configured to generate a fee bill based on the identification ID and the water yield.
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