CN109654559B - Healthy cooking system capable of identifying water vapor and oil smoke - Google Patents

Healthy cooking system capable of identifying water vapor and oil smoke Download PDF

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CN109654559B
CN109654559B CN201811630728.7A CN201811630728A CN109654559B CN 109654559 B CN109654559 B CN 109654559B CN 201811630728 A CN201811630728 A CN 201811630728A CN 109654559 B CN109654559 B CN 109654559B
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oil smoke
concentration
temperature
smoke
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CN109654559A (en
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陈小平
司徒伟贤
林勇进
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Foshan Viomi Electrical Technology Co Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24C—DOMESTIC STOVES OR RANGES ; DETAILS OF DOMESTIC STOVES OR RANGES, OF GENERAL APPLICATION
    • F24C15/00—Details
    • F24C15/20—Removing cooking fumes
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Abstract

The utility model provides a can discern healthy culinary art system of steam and oil smoke, is provided with and is used for obtaining the image acquisition module who produces smog size to the regional oil smoke image analysis of culinary art and in real time, is used for detecting the temperature sensing module of pan temperature, is used for detecting harmful substance detection device and the lampblack absorber of the harmful substance concentration in the culinary art region, and image acquisition module is connected with temperature sensing module, harmful substance detection device and lampblack absorber electricity respectively. This healthy culinary art system can judge whether smog in the current culinary art environment is steam or oil smoke. When the smoke is the water vapor, the range hood is started to exhaust the water vapor when the smoke exceeds the smoke threshold, and when the smoke is the harmful substance detection device, the harmful substance detection device detects the concentration of the harmful substances and carries out health grade division. The range hood and the kitchen range work according to the health grade, so that the concentration of harmful substances in the current environment is quickened to be reduced, and the body health of a user is guaranteed.

Description

Healthy cooking system capable of identifying water vapor and oil smoke
Technical Field
The invention relates to the field of cooking equipment, in particular to a healthy cooking system capable of identifying water vapor and oil smoke.
Background
In modern life, many households produce a great deal of smoke during cooking. The smoke is composed of water vapor and oil smoke. Researches show that the cooking oil fume has complex components, certain inhalation toxicity, immunotoxicity and mutagenicity and certain harm to human health. The fume gas contains various harmful substances, such as polycyclic aromatic hydrocarbons, particulate matters and volatile organic substances. The range hood in the prior art cannot automatically identify water vapor and oil smoke in the current cooking environment and cannot identify the concentration of harmful substances in the oil smoke, so that the intelligent development of the range hood is greatly limited.
Therefore, aiming at the defects of the prior art, the healthy cooking system capable of identifying water vapor and oil smoke is necessary to solve the defects of the prior art.
Disclosure of Invention
The invention aims to avoid the defects of the prior art and provides a healthy cooking system capable of identifying water vapor and oil smoke. This healthy culinary art system that can discern steam and oil smoke can distinguish steam and the oil smoke in the preceding culinary art environment to can detect the harmful substance concentration in the oil smoke.
The above object of the present invention is achieved by the following technical measures:
the utility model provides a healthy culinary art system of ability discernment steam and oil smoke is provided with and is used for obtaining the image acquisition module who produces smog size to the regional oil smoke image analysis of culinary art and in real time, is used for detecting the temperature sensing module of pan temperature, is used for detecting harmful substance detection device and the lampblack absorber of the harmful substance concentration in the culinary art region, and image acquisition module is connected with temperature sensing module, harmful substance detection device and lampblack absorber electricity respectively.
The temperature sensing module senses the temperature of the cookware to obtain a temperature signal, the temperature signal is transmitted to the image acquisition module as a temperature output signal, the image acquisition module acquires oil smoke images of a cooking area and analyzes the oil smoke images to obtain the size of generated smoke in real time to obtain an oil smoke output signal, and the image acquisition module judges the oil smoke output signal and the temperature output signal according to water vapor and oil smoke.
And when the oil smoke output signal is greater than or equal to the oil smoke threshold value and the temperature output signal is less than or equal to the temperature threshold value, determining the oil smoke as water vapor, obtaining a processing signal by the image acquisition module and sending the processing signal to the range hood, and receiving the processing signal by the range hood and carrying out wind power adjustment.
When the oil smoke output signal is greater than or equal to the oil smoke threshold value and the temperature output signal is greater than the temperature threshold value, the oil smoke is judged, the temperature output signal and the oil smoke output signal are sent to the harmful substance detection device by the image acquisition module, and the harmful substance detection device receives the temperature output signal and the oil smoke output signal and detects harmful substances.
And when the oil smoke output signal is smaller than the oil smoke threshold value and the temperature output signal is smaller than or equal to the temperature threshold value, the image acquisition module does not process the oil smoke output signal.
And when the oil smoke output signal is smaller than the oil smoke threshold value and the temperature output signal is larger than the temperature threshold value, the image acquisition module does not process the oil smoke output signal.
Preferably, the oil smoke threshold value is 35 to 45.
Preferably, the temperature threshold is 98 ℃ to 102 ℃.
More preferably, the soot threshold value is 40.
More preferably, the temperature threshold is 100 ℃.
The output data in the oil smoke output signal of the image acquisition module is defined as lambda 'and lambda' is more than or equal to 0, and the output data in the temperature output signal of the temperature sensing module is defined as kappa.
And when the current season is spring, performing oil smoke correction on the lambda 'to obtain a correction value lambda, wherein the lambda is lambda', and taking the corrected lambda as the output data of the new image acquisition module.
And when the current season is autumn, performing oil smoke correction on the lambda 'to obtain a correction value lambda, wherein the lambda is equal to lambda', and taking the corrected lambda as output data of a new image acquisition module.
When the current season is summer, the oil smoke correction is carried out on the lambda' to obtain a correction value lambda, and
Figure 518095DEST_PATH_IMAGE001
and taking the corrected lambda as the output data of the new image acquisition module.
When the current season is winter, the oil smoke correction is carried out on the lambda' to obtain a correction value lambda, and
Figure 764269DEST_PATH_IMAGE002
and taking the corrected lambda as the output data of the new image acquisition module.
Preferably, above-mentioned harmful substance detection device is provided with the particulate matter detection subassembly that is used for detecting the regional particle concentration of culinary art, is used for detecting the VOC sensor of volatile organic compounds and is used for calculating the calculation module of the regional polycyclic aromatic hydrocarbon concentration of current culinary art, and calculation module is connected with image acquisition module, particulate matter detection subassembly, VOC sensor and lampblack absorber electricity respectively.
Particulate matter sensor subassembly detects particulate matter concentration in the regional oil smoke of culinary art and obtains particulate matter concentration signal and sends to calculation module, and the VOC sensor gathers the volatile organic compounds concentration in current culinary art region and obtains VOC concentration signal and transmits to calculation module, and calculation module receives particulate matter concentration signal, VOC concentration signal, oil smoke output signal and temperature output signal respectively then handles the polycyclic aromatic hydrocarbon concentration that obtains current culinary art region in real time.
Preferably, the calculation module is a calculation module which is constructed by mathematical modeling to obtain a mathematical relationship between the temperature, the smoke size, the particulate matter concentration and the concentration of the volatile organic compounds and the concentration of the polycyclic aromatic hydrocarbon.
Preferably, the calculation module is a linear calculation module or a nonlinear calculation module.
When the calculating module is a nonlinear calculating module, the nonlinear calculating module is an exponential calculating module, a power calculating module, a logarithmic calculating module, a neural network calculating module or a machine learning calculating module.
Preferably, the calculation formula of the calculation module is formula (I),
Figure 337332DEST_PATH_IMAGE003
wherein
Figure 40977DEST_PATH_IMAGE004
Is the total concentration of polycyclic aromatic hydrocarbon gas in the cooking area, C is the output data of the particle sensing component,
Figure 103611DEST_PATH_IMAGE005
is the output data of the VOC sensor.
Preferably, the calculation formula of the calculation module is formula (II),
Figure 509185DEST_PATH_IMAGE006
wherein
Figure 531280DEST_PATH_IMAGE004
For total concentration of polycyclic aromatic hydrocarbon gas in cooking zoneAnd C is the output data of the particle sensing component,
Figure 276382DEST_PATH_IMAGE005
is the output data of the VOC sensor.
Preferably, the particle detection assembly is configured as at least one of a PM2.5 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 2.5 micrometers in the oil smoke in the current cooking area, a PM10 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 10 micrometers in the oil smoke in the current cooking area, a PM1.0 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 1.0 micrometers in the oil smoke in the current cooking area, a PM0.1 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 0.1 micrometers in the oil smoke in the current cooking area, and a PMA sensor for detecting the concentration of particles with equivalent diameter less than or equal to 0.05 micrometers in the oil smoke in the current cooking area.
The healthy cooking system capable of identifying water vapor and oil smoke is further provided with a stove, and the stove is electrically connected with the calculation module.
Preferably, the calculation module is a calculation module capable of performing health grade division according to the concentration of the particulate matters, the concentration of the volatile organic compounds and the concentration of the polycyclic aromatic hydrocarbons.
The calculation module carries out health grade division according to particulate matter concentration, volatile organic compounds concentration and polycyclic aromatic hydrocarbon concentration in the current environment and obtains health grade signal, and the calculation module sends health grade signal to lampblack absorber and stove, and the lampblack absorber receives health grade signal and carries out wind-force regulation, and the stove receives health grade signal and carries out firepower regulation.
Preferably, the temperature sensing module is a temperature sensor.
When the range hood, the stove, the cooking bench and a pot used in cooking work normally, the degree of the vertical height from the temperature sensor to the ground is Ht, and Ht is more than or equal to 0 and less than or equal to 3 m. The center of the range hood is defined as X0. The horizontal distance between the temperature sensor and X0 is defined as Xt, and Xt is more than or equal to 0 and less than or equal to 2 m.
Preferably, the temperature sensor is an invasive temperature sensor.
The temperature sensor is assembled on an external cooker used in cooking; or
The temperature sensor is a cooker frame assembled on the stove; or
The temperature sensor is assembled on an external cooking bench.
Preferably, the temperature sensor is a non-invasive temperature sensor far away from a cooking bench, a cooker and a stove.
The visual angle of the temperature sensor is defined as theta, theta is more than 0 degree and less than 360 degrees, the included angle between the central axis of the temperature sensor and the horizontal direction is defined as β, and the included angle is more than 0 degree and less than β and less than or equal to 90 degrees.
The coincidence plane of the projection plane of the temperature sensor and the cooking top is defined as P, the point with the largest distance from X0 in the range of P is defined as Pf, the point with the smallest distance from X0 in the range of P is defined as Pn, the distance between Pf and Pn is defined as Lp, and Lp is greater than 0.
A projected surface of a pot used in cooking on the top along a detection direction of the temperature sensor is defined as P ', and P' is included inside P.
When β is 90 °, the value of Ht is obtained by formula (iii),
Figure 837813DEST_PATH_IMAGE007
formula (III);
when β ≠ 90 °, the value of Ht is obtained by the formula (IV),
Figure 481415DEST_PATH_IMAGE008
formula (IV).
The invention discloses a healthy cooking system capable of identifying water vapor and oil smoke, which is provided with an image acquisition module, a temperature sensing module, a harmful substance detection device and a range hood, wherein the image acquisition module is used for analyzing oil smoke images in a cooking area and obtaining the size of generated smoke in real time, the temperature sensing module is used for detecting the temperature of a cookware, and the harmful substance detection device and the range hood are used for detecting the concentration of harmful substances in the cooking area. This healthy culinary art system can judge whether smog in the current culinary art environment is steam or oil smoke. When the smoke is the water vapor, the range hood is started to exhaust the water vapor when the smoke exceeds the smoke threshold, and when the smoke is the harmful substance detection device, the harmful substance detection device detects the concentration of the harmful substances and carries out health grade division. The range hood and the kitchen range work according to the health grade, so that the concentration of harmful substances in the current environment is quickened to be reduced, and the body health of a user is guaranteed.
Drawings
The invention is further illustrated by means of the attached drawings, the content of which is not in any way limiting.
Fig. 1 is a schematic flowchart of a working process of a healthy cooking system capable of identifying moisture and oil smoke in embodiment 1.
Fig. 2 is a schematic flowchart of a working process of the healthy cooking system capable of identifying moisture and soot in embodiment 5.
Fig. 3 is an assembly schematic diagram of the range hood with the temperature sensor of the embodiment 6 and a pot, a stove and a cooking bench used in cooking.
Fig. 4 is a schematic view showing a relationship between a superposed plane P of a projected plane of the temperature sensor and the top of the kitchen range and a projected plane P' of a pot used in cooking along a detection direction of the temperature sensor on the top of the kitchen range.
Fig. 5 is a view angle θ diagram of the temperature sensor.
Fig. 6 is a schematic diagram of an angle β between the central axis of the temperature sensor and the horizontal direction.
In fig. 1 to 6, the following components are included:
the cooking range hood comprises a range hood 1, a temperature sensor 2, a cooker 3 used in cooking, a stove 4 and a cooking bench 5.
Detailed Description
The technical solution of the present invention is further illustrated by the following examples.
Example 1.
The utility model provides a healthy culinary art system of ability discernment steam and oil smoke, as shown in figure 1, be provided with be used for obtaining the image acquisition module that produces smog size to the regional oil smoke image analysis of culinary art in real time, be used for detecting the temperature sensing module of pan temperature, be used for detecting harmful substance detection device and the lampblack absorber 1 of the harmful substance concentration in the culinary art, image acquisition module is connected with temperature sensing module, harmful substance detection device and lampblack absorber 1 electricity respectively.
The temperature sensing module senses the temperature of the cookware to obtain a temperature signal, the temperature signal is transmitted to the image acquisition module as a temperature output signal, the image acquisition module acquires oil smoke images of a cooking area and analyzes the oil smoke images to obtain the size of generated smoke in real time to obtain an oil smoke output signal, and the image acquisition module judges the oil smoke output signal and the temperature output signal according to water vapor and oil smoke.
When the oil smoke output signal is greater than or equal to the oil smoke threshold value and the temperature output signal is less than or equal to the temperature threshold value, the oil smoke is judged to be water vapor, the image acquisition module obtains a processing signal and sends the processing signal to the range hood 1, and the range hood 1 receives the processing signal and adjusts wind power.
When the oil smoke output signal is greater than or equal to the oil smoke threshold value and the temperature output signal is greater than the temperature threshold value, the oil smoke is judged, the temperature output signal and the oil smoke output signal are sent to the harmful substance detection device by the image acquisition module, and the harmful substance detection device receives the temperature output signal and the oil smoke output signal and detects harmful substances.
It should be noted that, in the present invention, the situation that the oil smoke output signal is smaller than the oil smoke threshold and the temperature output signal is smaller than or equal to the temperature threshold, or the oil smoke output signal is smaller than the oil smoke threshold and the temperature output signal is greater than the temperature threshold is not processed.
The purpose of distinguishing the water vapor and the oil smoke in advance is that the harmful substance detection device can detect the concentration of the harmful substance when the water vapor is used, so that the workload of the harmful substance detection device is reduced.
The oil fume threshold value is 35-45, and the temperature threshold value is 98-102 ℃. The oil smoke threshold of the present embodiment is preferably 40, and the temperature threshold is preferably 100 ℃.
The value of the soot threshold value in the present invention may be 40, or may be any value within 35 to 45. The temperature threshold may be 100 ℃, or any other temperature within the range of 98 ℃ to 102 ℃, and the specific value is determined according to the actual situation.
The output data in the oil smoke output signal of the image acquisition module is defined as lambda 'and lambda' is more than or equal to 0, and the output data in the temperature output signal of the temperature sensing module is defined as kappa.
And when the current season is spring, performing oil smoke correction on the lambda 'to obtain a correction value lambda, wherein the lambda is lambda', and taking the corrected lambda as the output data of the new image acquisition module.
And when the current season is autumn, performing oil smoke correction on the lambda 'to obtain a correction value lambda, wherein the lambda is equal to lambda', and taking the corrected lambda as output data of a new image acquisition module.
When the current season is summer, the oil smoke correction is carried out on the lambda' to obtain a correction value lambda, and
Figure 396281DEST_PATH_IMAGE001
and taking the corrected lambda as the output data of the new image acquisition module.
When the current season is winter, the oil smoke correction is carried out on the lambda' to obtain a correction value lambda, and
Figure 527049DEST_PATH_IMAGE002
and taking the corrected lambda as the output data of the new image acquisition module.
The reason why the present invention corrects the smoke in winter and summer is because the same amount of smoke is generated in the same cooking but visually represents small smoke in summer and visually represents large smoke in winter. So the method can be more accurate after modifying in winter and summer.
For example, in spring, the output data of the oil smoke output signal of the image acquisition module is 100, the output data of the temperature sensing module is 100 ℃, namely λ' is 100, and κ is 100 ℃, namely, the oil smoke is judged to be water vapor. The image acquisition module sends the temperature output signal and the oil smoke output signal to the harmful substance detection device, and the harmful substance detection device receives the temperature output signal and the oil smoke output signal and detects harmful substances.
If the current summer is, the output data of the oil smoke output signal of the image acquisition module is 35, the output data of the temperature sensing module is 110 ℃, lambda' needs to be corrected, and the corrected lambda is 46, namely the oil smoke is judged. The image acquisition module sends the temperature output signal and the oil smoke output signal to the harmful substance detection device, and the harmful substance detection device receives the temperature output signal and the oil smoke output signal and detects harmful substances.
If the current time is autumn, the output data of the oil smoke output signal of the image acquisition module is 45, the output data of the temperature sensing module is 90 ℃, namely the corrected lambda of the lambda' is 45, and the water vapor is judged. The image acquisition module obtains a processing signal and sends the processing signal to the range hood 1, and the range hood 1 receives the processing signal and adjusts wind power.
Harmful substance detection device is provided with the particulate matter detection subassembly that is used for detecting regional particle concentration of culinary art, is used for detecting the VOC sensor of volatile organic compounds and is used for calculating the calculation module of the regional polycyclic aromatic hydrocarbon concentration of current culinary art, and calculation module is connected with image acquisition module, particulate matter detection subassembly, VOC sensor and lampblack absorber 1 electricity respectively.
Particulate matter sensor subassembly detects particulate matter concentration in the regional oil smoke of culinary art and obtains particulate matter concentration signal and sends to calculation module, and the VOC sensor gathers the volatile organic compounds concentration in current culinary art region and obtains VOC concentration signal and transmits to calculation module, and calculation module receives particulate matter concentration signal, VOC concentration signal, oil smoke output signal and temperature output signal respectively then handles the polycyclic aromatic hydrocarbon concentration that obtains current culinary art region in real time.
The calculation module is a calculation module which is constructed by mathematical modeling and obtains mathematical relations of temperature, smoke size, particulate matter concentration, volatile organic matter concentration and polycyclic aromatic hydrocarbon concentration.
The calculation module is obtained through mathematical modeling, and the mathematical modeling is to collect mathematical relations between different temperatures, smoke sizes, particle concentrations, volatile organic compound concentrations and harmful gas polycyclic aromatic hydrocarbon concentrations through experiments. Sampling detection is carried out according to different experimental conditions to obtain different types of polycyclic aromatic hydrocarbon concentrations, analysis and classification are carried out to obtain a mathematical model, and therefore the calculation module can judge the current different types of polycyclic aromatic hydrocarbon concentrations according to the detection conditions of temperature, smoke size, particulate matter concentration and volatile organic matter concentration.
The calculation module of the invention is one of a linear calculation module, a nonlinear calculation module, an exponential calculation module, a power calculation module, a logarithmic calculation module, a neural network calculation module, a machine learning calculation module or a deep learning calculation module.
The image acquisition module acquires the condition of oil smoke generated in the cooking process in real time, specifically acquires pictures of corresponding areas in real time, processes the current kitchen oil smoke concentration, and transmits data to the calculation module.
The processing method of the image acquisition module comprises the following steps:
the image acquisition module is used for processing on the basis of an initial image acquired by the imaging equipment, the initial image is a gray scale image, the acquired initial images are serialized and sequentially processed through the initial image of a subsequent frame and the initial image of a previous frame, and the current kitchen oil smoke concentration of each subsequent frame at the moment of the initial image is obtained.
The step process of obtaining the current kitchen oil smoke concentration at the moment of the initial image of the next frame by processing the initial image of the next frame and the initial image of the previous frame each time is as follows:
(1) performing frame difference processing on the initial image of the next frame and the initial image of the previous frame to obtain a frame difference image;
(2) denoising the frame difference image in an open operation mode to obtain a denoised image;
(3) carrying out edge detection on the denoised image, and marking a motion area as an initial region of interest;
(4) carrying out gray average value calculation and area smoothness calculation on the initial region of interest, taking the region which meets the requirements of gray average value and smoothness as the next region of interest, and taking other regions as interference elimination;
(5) and (4) respectively counting the interested areas extracted in the step (4), and obtaining oil smoke concentration assignment according to the counting result.
In the step (1), the frame difference operation on the acquired initial image to obtain a frame difference image specifically comprises:
and the image acquisition module performs subtraction on the next frame of image and the previous frame of image according to the sequence of the received initial images to obtain a frame difference image with a highlighted dynamic area.
The denoising method comprises the following steps of (2) denoising a frame difference image by using an open operation to obtain a denoised image, and specifically comprises the following steps: firstly, carrying out corrosion operation on the frame difference image to eliminate noise points and tiny spikes in the image and break narrow connection; and performing expansion operation on the corroded image to recover the smoke characteristics in the original frame difference image.
The step (3) of performing edge detection on the denoised image, and marking a motion region as an initial region of interest specifically comprises the following steps: and detecting the edge of the highlight area of the frame difference image, marking the highlight area, and taking the marked area as an initial region of interest.
Specifically, the gray mean value and the area smoothness of each initial region of interest are calculated to obtain the gray mean value and the gray smoothness corresponding to each initial region of interest, the initial regions of interest which simultaneously meet the condition that the calculated gray mean value is smaller than a gray threshold and the gray smoothness is smaller than the gray smoothness threshold are used as regions of interest, and other initial regions of interest are determined as interference regions.
Specifically, in the step (5), aiming at the interested areas extracted in the step (4), the gray levels of all pixels in each interested area image are summed to obtain the gray level of each interested area image, and then the gray level of each interested area image is summed to obtain the oil smoke concentration assignment.
The target area acquired by the imaging device is represented by an area S, and any one frame of initial image is the imaging of the corresponding area S.
The initial image is made up of m x n pixels,
the gray values of the pixels of the subsequent frame initial image a are represented by a matrix AH,
Figure 524960DEST_PATH_IMAGE009
，
Figure 154394DEST_PATH_IMAGE010
representing the gray corresponding to the ith row and jth column pixels in the initial image A of the next frameThe value i is the row where the pixel is located, j is the column where the pixel is located, i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n; the sub-region where the ith row and jth column pixels in the initial image A of the later frame are located is
Figure 872951DEST_PATH_IMAGE011
。
The grey values of the pixels of the initial image B of the previous frame are represented by a matrix BH,
Figure 717279DEST_PATH_IMAGE012
，
Figure 495879DEST_PATH_IMAGE013
representing the gray values corresponding to the ith row and jth column pixels in the initial image B of the previous frame, wherein the sub-regions where the ith row and jth column pixels in the initial image B of the previous frame are located are
Figure 114074DEST_PATH_IMAGE014
。
The gray values of the pixels of the frame difference image D are represented by a matrix DH,
Figure 26535DEST_PATH_IMAGE015
，
Figure 335156DEST_PATH_IMAGE016
representing the gray values corresponding to the ith row and jth column of pixels in the frame difference image D, and the sub-regions where the ith row and jth column of pixels are located in the frame difference image D are
Figure 815816DEST_PATH_IMAGE017
。
In the frame difference image, it is,
Figure 154263DEST_PATH_IMAGE018
the area (2) is black;
Figure 870415DEST_PATH_IMAGE019
is highlighted.
Wherein, the step (2) of carrying out corrosion operation on the frame difference image specifically comprises the following steps:
2-11, arbitrarily defining a convolution kernel theta;
2-12, performing convolution on the convolution kernel theta and the frame difference image; when the convolution kernel theta traverses the frame difference image, extracting a pixel gray minimum value p of a convolution result in the area covered by the convolution kernel and a pixel point C coincident with the center of the convolution kernel;
passing the gray level of the pixel C
Figure 33543DEST_PATH_IMAGE020
Indicating that k and q are the row sequence number and the column sequence number of the pixel point C,k⊂i，q⊂j。
obtaining a minimum pixel point matrix P of a convolution result obtained in the process of traversing a frame difference image by a convolution kernel theta, wherein the gray level of the minimum pixel point matrix P passes through the matrix
Figure 232574DEST_PATH_IMAGE021
Represents;
2-13 correspondingly endowing the gray level of the pixel point matrix P to a pixel point C to obtain a corrosion image;
the expansion operation is carried out on the corrosion image in the step (2), and the method specifically comprises the following steps:
2-21, arbitrarily defining a convolution kernel β;
2-22, convolving the convolution kernel β with the corrosion image, and extracting the pixel gray maximum value o of the convolution result in the area covered by the convolution kernel and the pixel point R coincident with the center of the convolution kernel when the convolution kernel β traverses the corrosion image;
the gray level of the pixel point R passes through the matrix
Figure 871366DEST_PATH_IMAGE022
Indicating that l and v are the row serial number and the column serial number of the pixel point R,l⊂I,，v⊂j。
obtaining a convolution result maximum value pixel point matrix O obtained in the process of traversing the corrosion image by the convolution kernel β, wherein the gray scale of the maximum value pixel point matrix O passes through the matrix
Figure 996DEST_PATH_IMAGE023
Represents;
2-13, correspondingly endowing the gray level of the maximum pixel point matrix O to the pixel point R to obtain an expanded image, wherein the obtained expanded image is the de-noised image.
Wherein the step (3) is carried out by the following steps:
3-1, defining a filter Y, wherein the filter is a t x t matrix, and t is an odd number;
3-2, traversing the filter Y through the denoised image, and calculating the position of the central pixel point of the filter at each position
The gray value of the noise image and the gray values of other pixels in the neighborhood of the central pixel are calculated according to the formula (I), the edge detection value Xz of the central pixel at each position of the filter is calculated, z is a mark when the filter Y traverses the noise-removed image,
Figure 392532DEST_PATH_IMAGE024
f. g is the matrix serial number of the pixel points, f is more than or equal to 1 and less than or equal to t, g is more than or equal to 1 and less than or equal to t, e is the gray value of the denoised image of the pixel point at each position of the filter, α is a weight coefficient and corresponds to the position of the filter;
3-3, subtracting the gray value of other pixel points in the neighborhood of the central pixel point from the edge detection value Xz of the central pixel point at each position of the filter, and judging whether the absolute value of the difference is greater than a threshold delta or not;
counting the number greater than the threshold value, if the number exceeds the threshold value
Figure 277311DEST_PATH_IMAGE025
Judging the pixel point position of the de-noised image corresponding to the central pixel point of the filter position as an edge point, and marking;
and 3-4, traversing the whole de-noised image by using the filter to obtain all marked edge points and obtain a preliminary region of interest.
t is 3.
It should be noted that the processing method of the image capturing module is only one of the processing methods, and as for the processing methods of other image capturing modules, the method that only the output data of the image capturing module of the cooking area can be obtained can be applied to the healthy cooking system capable of identifying water vapor and oil smoke, and all the methods are within the protection scope of the present invention.
It should be noted that the image capturing module of the present invention uses a camera to detect the smoke size in the cooking area, and can be used as the image capturing module of the present invention as long as the above functions of the present invention can be realized. The calculation module of the present invention calculates the concentration of the polycyclic aromatic hydrocarbon in the current cooking area according to the temperature, the smoke size, the concentration of the particulate matter, and the concentration of the volatile organic compound, and the calculation module is a calculator or a module with a calculation function, which can be used as the calculation module of the present invention.
This healthy culinary art system of ability discernment steam and oil smoke is provided with and is used for obtaining the image acquisition module who produces smog size to the regional oil smoke image analysis of culinary art and in real time, is used for detecting the temperature sensing module of pan temperature, is used for detecting harmful substance detection device and the lampblack absorber 1 of the harmful substance concentration in the culinary art region, and image acquisition module is connected with temperature sensing module, harmful substance detection device and lampblack absorber 1 electricity respectively. This healthy culinary art system can judge whether smog in the current culinary art environment is steam or oil smoke. When the smoke is water vapor, the range hood 1 is started to exhaust the water vapor when the smoke exceeds the smoke threshold, and when the smoke is harmful substance detection device, the harmful substance detection device detects the concentration of the harmful substance.
Example 2.
A healthy cooking system capable of identifying water vapor and oil smoke has the same other characteristics as embodiment 1, and is different in that: the calculation formula of the calculation module is formula (I),
Figure 544344DEST_PATH_IMAGE026
wherein
Figure 212086DEST_PATH_IMAGE027
Is the total concentration of polycyclic aromatic hydrocarbon gas in the cooking area, C is the output data of the particle sensing component,
Figure 225173DEST_PATH_IMAGE028
is the output data of the VOC sensor.
When kappa ∈ (0 ℃, 200 ℃),
Figure 280853DEST_PATH_IMAGE029
，λ∈(0,300)，
Figure 238445DEST_PATH_IMAGE030
when the temperature of the water is higher than the set temperature,
Figure 366936DEST_PATH_IMAGE031
,
Figure 359163DEST_PATH_IMAGE032
,
Figure 320166DEST_PATH_IMAGE033
。
when kappa.epsilon (200 ℃, 240 ℃),
Figure 905999DEST_PATH_IMAGE034
，λ∈(300,500)，
Figure 181123DEST_PATH_IMAGE035
，
Figure 355752DEST_PATH_IMAGE036
。
wherein
Figure 799241DEST_PATH_IMAGE037
Is the concentration of bicyclic polycyclic aromatic hydrocarbons and tricyclic polycyclic aromatic hydrocarbons,
Figure 262583DEST_PATH_IMAGE038
is the concentration of the tetracyclic polycyclic aromatic hydrocarbon,
Figure 341398DEST_PATH_IMAGE039
is the concentration of pentacyclic polycyclic aromatic hydrocarbon and hexacyclic polycyclic aromatic hydrocarbon.
For example, when kappa is 100 ℃, C is
Figure 432850DEST_PATH_IMAGE040
The number of lambda is 100,
Figure 486388DEST_PATH_IMAGE041
when k, C, K, C, K, C,
Figure 171447DEST_PATH_IMAGE042
directly substituting the data value of the sum lambda into a formula to obtain
Figure 913007DEST_PATH_IMAGE043
Is 1106.6 and
Figure 734333DEST_PATH_IMAGE043
has the unit of
Figure 457307DEST_PATH_IMAGE044
I.e. concentration of polycyclic aromatic hydrocarbons in the current environment of
Figure 754296DEST_PATH_IMAGE045
。
Figure 440492DEST_PATH_IMAGE046
Has a concentration of 774.62pg/m3，
Figure 460532DEST_PATH_IMAGE047
Has a concentration of 221.32pg/m3，
Figure 777244DEST_PATH_IMAGE048
Has a concentration of 110.66pg/m3。
The healthy cooking system of this embodiment can calculate through measuring temperature, smog size, particulate matter concentration and volatile organic compounds concentration and obtain the polycyclic aromatic hydrocarbon concentration of current culinary art region, can calculate the concentration of bicyclic polycyclic aromatic hydrocarbon, tricyclic polycyclic aromatic hydrocarbon, four ring polycyclic aromatic hydrocarbon, five ring polycyclic aromatic hydrocarbon and six ring polycyclic aromatic hydrocarbon in the current environment.
Example 3.
A healthy cooking system capable of identifying water vapor and oil smoke has the same other characteristics as embodiment 1, and is different in that: the calculation formula of the calculation module is formula (II),
Figure DEST_PATH_IMAGE049
wherein
Figure 30371DEST_PATH_IMAGE050
Is the total concentration of polycyclic aromatic hydrocarbon gas in the cooking area, C is the output data of the particle sensing component,
Figure DEST_PATH_IMAGE051
is the output data of the VOC sensor.
When kappa. epsilon. (0 ℃, 200 ℃), C. epsilon. (0. mu.g/m)3，3000μg/m3)，λ∈(0,300)，
Figure 894159DEST_PATH_IMAGE051
∈(0mg/m3，5mg/m3) When the temperature of the water is higher than the set temperature,
Figure 955656DEST_PATH_IMAGE052
。
when the temperature is in the range of kappa E (200 ℃, 240 ℃), in the range of C E (3000. mu.g/m)3，5000μg/m3)，λ∈(300，500)，
Figure DEST_PATH_IMAGE053
∈(5mg/m3，10mg/m3) When the temperature of the water is higher than the set temperature,
Figure 963976DEST_PATH_IMAGE054
,
Figure DEST_PATH_IMAGE055
。
wherein
Figure 907661DEST_PATH_IMAGE056
Is the concentration of bicyclic polycyclic aromatic hydrocarbons and tricyclic polycyclic aromatic hydrocarbons,
Figure DEST_PATH_IMAGE057
is the concentration of the tetracyclic polycyclic aromatic hydrocarbon,
Figure 14288DEST_PATH_IMAGE058
is the concentration of pentacyclic polycyclic aromatic hydrocarbon and hexacyclic polycyclic aromatic hydrocarbon.
For example, when kappa is 100 ℃, C is 1000. mu.g/m3The number of lambda is 100,
Figure DEST_PATH_IMAGE059
is 1mg/m3When k, C, K, C, K, C,
Figure 461450DEST_PATH_IMAGE059
directly substituting the data value of the sum lambda into a formula to obtain
Figure 759445DEST_PATH_IMAGE060
Is 1106.6 and
Figure 393689DEST_PATH_IMAGE060
in units of pg/m3I.e. the concentration of polycyclic aromatic hydrocarbons in the current environment is 1106.6pg/m3。
Figure DEST_PATH_IMAGE061
Has a concentration of 774.62pg/m3，
Figure 100745DEST_PATH_IMAGE062
Has a concentration of 221.32pg/m3，
Figure DEST_PATH_IMAGE063
Has a concentration of 110.66pg/m3。
The healthy cooking system of this embodiment can calculate through measuring temperature, smog size, particulate matter concentration and volatile organic compounds concentration and obtain the polycyclic aromatic hydrocarbon concentration of current culinary art region, can calculate the concentration of bicyclic polycyclic aromatic hydrocarbon, tricyclic polycyclic aromatic hydrocarbon, four ring polycyclic aromatic hydrocarbon, five ring polycyclic aromatic hydrocarbon and six ring polycyclic aromatic hydrocarbon in the current environment.
Example 4.
A healthy cooking system capable of identifying water vapor and oil smoke has the same other characteristics as embodiment 2 or embodiment 3, except that: the particle detection component is set as at least one of a PM2.5 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 2.5 microns in the oil smoke of the current cooking area, a PM10 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 10 microns in the oil smoke of the current cooking area, a PM1.0 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 1.0 microns in the oil smoke of the current cooking area, a PM0.1 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 0.1 microns in the oil smoke of the current cooking area and a PMA sensor for detecting the concentration of particles with equivalent diameter less than or equal to 0.05 microns in the oil smoke of the current cooking area.
The specific particulate matter detecting assembly of this embodiment includes a PM2.5 sensor, a PM10 sensor, a PM1.0 sensor, a PM0.1 sensor, and a PMA sensor. The PM2.5 sensor, the PM10 sensor, the PM1.0 sensor, the PM0.1 sensor, and the PMA sensor are all electrically connected to the computing module.
And the output data C of the particulate matter sensing assembly is the sum of the output data of the PM2.5 sensor, the PM10 sensor, the PM1.0 sensor, the PM0.1 sensor and the PMA sensor. That is to say the sum of the particulate matter concentrations of PM2.5, PM10, PM1.0, PM0.1 and PMA.
It should be noted that the particulate matter detecting component of the present invention can be a PM2.5 sensor, a PM10 sensor, a PM1.0 sensor, a PM0.1 sensor, and a PMA sensor, or any one or more combinations of the above sensors, and the specific embodiment is determined according to the actual situation.
In contrast to embodiment 2 or 3, the particulate matter detecting assembly of the present embodiment can detect the particulate matter concentrations of PM2.5, PM10, PM1.0, PM0.1, and PMA of the current cooking zone.
Example 5.
A healthy cooking system capable of identifying moisture and oil smoke, as shown in fig. 2, the other features are the same as those of embodiment 4, except that: the healthy cooking system is also provided with a stove 4, and the stove 4 is electrically connected with the calculation module.
The calculation module is a calculation module capable of performing health grade division according to the concentration of the particulate matters, the concentration of the volatile organic compounds and the concentration of the polycyclic aromatic hydrocarbons.
The calculation module carries out health grade according to particulate matter concentration, volatile organic compounds concentration and polycyclic aromatic hydrocarbon concentration in the current environment and divides and obtain the health grade signal, and the calculation module sends health grade signal to lampblack absorber 1 and stove 4, and lampblack absorber 1 receives health grade signal and carries out wind-force regulation, and stove 4 receives health grade signal and carries out the firepower regulation.
The health grade dividing method comprises the following steps:
in the embodiment, the health grade is classified according to the GBT18883-2 indoor air quality standard, and the invention can also be classified according to other quality standards, such as GB3059-2012 and WTO environmental quality Standard. The present invention may also be partitioned according to other predetermined environmental quality values.
In this example, polycyclic aromatic hydrocarbons were graded by dividing polycyclic aromatic hydrocarbon concentration by the average limiting concentration of benzo [ a ] pyrene specified by national standards, as follows:
Figure 871255DEST_PATH_IMAGE064
and when the epsilon is more than or equal to 0 and less than or equal to 0.5, judging the polycyclic aromatic hydrocarbon grade as healthy.
When the epsilon is more than 0.5 and less than or equal to 1, the polycyclic aromatic hydrocarbon grade is judged to be good.
When the epsilon is more than 1 and less than or equal to 5, the polycyclic aromatic hydrocarbon grade is judged to be medium.
When the epsilon is more than 5 and less than or equal to 10, the grade of the polycyclic aromatic hydrocarbon is judged to be poor.
When 10 < ε, the polycyclic aromatic hydrocarbon rating is judged to be severe.
Wherein the C benzo [ a ] pyrene is the average limited concentration of the benzo [ a ] pyrene specified by the national standard, and the C benzo [ a ] pyrene is 1ng/m 3.
It should be noted that the polycyclic aromatic hydrocarbon grade of the present invention can be classified according to other values of epsilon, and this example only provides an implementation scheme, and other polycyclic aromatic hydrocarbon grade classification methods according to polycyclic aromatic hydrocarbon concentration also fall within the protection scope of the present invention.
Carrying out health definite value division on the polycyclic aromatic hydrocarbon grade to obtain the polycyclic aromatic hydrocarbon grade
Figure DEST_PATH_IMAGE065
。
When the polycyclic aromatic hydrocarbon grade is healthy, then
Figure 136889DEST_PATH_IMAGE065
Is 1.
When the polycyclic aromatic hydrocarbon grade is good, then
Figure 727270DEST_PATH_IMAGE065
Is 2.
When the polycyclic aromatic hydrocarbon grade is medium, then
Figure 956126DEST_PATH_IMAGE065
Is 3.
When the polycyclic aromatic hydrocarbon grade is poor, then
Figure 581142DEST_PATH_IMAGE065
Is 4.
When the polycyclic aromatic hydrocarbon grade is severe, then
Figure 925667DEST_PATH_IMAGE065
Is 5.
The polycyclic aromatic hydrocarbon grade of the present invention may be as described above, or may be different according to the actual condition
Figure 393558DEST_PATH_IMAGE065
The present example shows only one possibility, corresponding to a wide range of polycyclic aromatic hydrocarbon grades
Figure 770312DEST_PATH_IMAGE065
All values fallFall within the scope of the invention.
The calculation module of the present invention performs an air quality index assessment based on the output data of the PM10 sensor, the PM2.5 sensor, the PM1.0 sensor, the PM0.1 sensor, or the PMA sensor and a selected air quality criteria, as follows;
Figure 826999DEST_PATH_IMAGE066
where M is the current air quality index.
Figure DEST_PATH_IMAGE067
The selected air quality standard is the high value corresponding to the particulate matter concentration limit value of C.
Figure 122851DEST_PATH_IMAGE068
The lower value of the particulate matter concentration limit value corresponding to C in the selected air quality standard is selected.
Figure DEST_PATH_IMAGE069
Neutralizing for selected air quality criteria
Figure 359928DEST_PATH_IMAGE070
Corresponding air mass fraction index.
Figure DEST_PATH_IMAGE071
Neutralizing for selected air quality criteria
Figure 642837DEST_PATH_IMAGE072
Corresponding air mass fraction index.
This example illustrates the invention according to table 1, as follows:
1. air quality index and PM10 and PM2.5 project concentration limits
Figure DEST_PATH_IMAGE073
For example, if the current CPM 2.5 actually measured is 425 μm/m3, and the high and low values of the PM2.5 concentration limit are found, the method can be used
Figure 570342DEST_PATH_IMAGE074
。
Figure DEST_PATH_IMAGE075
A value of (a) corresponds to an air quality index (IAQI) of 500, i.e.
Figure 787827DEST_PATH_IMAGE076
。
Figure DEST_PATH_IMAGE077
The value of (A) corresponds to an air quality index (IAQI) of 400, i.e.
Figure 371255DEST_PATH_IMAGE078
. Then respectively connect
Figure DEST_PATH_IMAGE079
And CPM 2.5 into the following formula.
Figure 463714DEST_PATH_IMAGE080
M is 475.
It should be noted that, in this embodiment, only one air quality standard and CPM 2.5 corresponding number are selected, but different air quality standards and CPMs 10, CPM 2.5, CPM 1.0, CPM 0.1 and CPMA are also within the scope of the present invention.
Carrying out healthy definite value division on the air quality index to obtain the grade of the particulate matter
Figure DEST_PATH_IMAGE081
；
When M is more than or equal to 0 and less than or equal to 400, then
Figure 589933DEST_PATH_IMAGE081
＝1；
When 400 <When M is less than or equal to 600, then
Figure 634112DEST_PATH_IMAGE081
＝2；
When M is more than 600 and less than or equal to 700, then
Figure 439257DEST_PATH_IMAGE081
＝3；
When M is more than 700 and less than or equal to 800, then
Figure 554981DEST_PATH_IMAGE081
＝4；
When 900 < M, then
Figure 971925DEST_PATH_IMAGE081
＝5。
It should be noted that the air quality index of the present invention can be divided according to other values of M. The present embodiment is merely to provide an implementation, and other methods of classifying particulate matter according to the output data of the PM10 sensor, the PM2.5 sensor, the PM1.0 sensor, the PM0.1 sensor, and the PMA sensor also fall within the scope of the present invention.
It is noted that the air quality index of the present invention corresponds to the particle level
Figure 187005DEST_PATH_IMAGE081
As shown in the above, the air quality indexes may be different according to different actual conditions
Figure 338501DEST_PATH_IMAGE081
The present example shows only one possibility, corresponding to various air quality indices
Figure 477489DEST_PATH_IMAGE081
Values fall within the scope of the invention.
The calculation module of the invention also carries out health fixed value division on the output data of the VOC sensor to obtain the grade of the volatile organic compounds
Figure 906197DEST_PATH_IMAGE082
。
Volatile organic grades of the invention
Figure 416813DEST_PATH_IMAGE082
Particularly, the output data of the VOC sensor is divided into different ranges, and corresponding health fixed values are given to the corresponding ranges.
For example when
Figure DEST_PATH_IMAGE083
When it is, then
Figure 242555DEST_PATH_IMAGE082
＝1；
When in use
Figure 106606DEST_PATH_IMAGE084
When it is, then
Figure 780033DEST_PATH_IMAGE082
＝2；
When in use
Figure DEST_PATH_IMAGE085
When it is, then
Figure 884386DEST_PATH_IMAGE082
＝3；
When in use
Figure 151419DEST_PATH_IMAGE086
When it is, then
Figure 147057DEST_PATH_IMAGE082
＝4；
When in use
Figure DEST_PATH_IMAGE087
When it is, then
Figure 398959DEST_PATH_IMAGE082
＝5。
Volatile organic Compounds and the like of the present exampleStage
Figure 985798DEST_PATH_IMAGE082
Is based on GBT18883-2002 indoor air quality standard
Figure 287597DEST_PATH_IMAGE088
Is 0.6 mg/m 3. The scope of the present invention based on other air quality criteria or other divisions is intended to fall within the scope of the present invention.
It should be noted that the output data of the VOC sensor of the present invention can be divided into health-fixed values according to the data
Figure DEST_PATH_IMAGE089
Other values of (a) are divided. This example merely provides an implementation of the volatile organic compound rating based on the output data of other VOC sensors
Figure 618085DEST_PATH_IMAGE090
Also fall within the scope of the invention.
The invention calculates the health grade U as the following method:
1. calculation module will polycyclic aromatic hydrocarbons grade
Figure DEST_PATH_IMAGE091
Grade of particulate matter
Figure 656317DEST_PATH_IMAGE092
And volatile organic grade
Figure DEST_PATH_IMAGE093
Comparing, selecting the maximum value as the health grade U of the current cooking area as follows,
Figure 679636DEST_PATH_IMAGE094
。
2. calculation module will polycyclic aromatic hydrocarbons grade
Figure 390103DEST_PATH_IMAGE091
Grade of particulate matter
Figure 478276DEST_PATH_IMAGE092
And volatile organic grade
Figure 715223DEST_PATH_IMAGE093
And the health grade U of the current cooking area is obtained by adding, as shown in the following formula,
Figure DEST_PATH_IMAGE095
。
3. calculation module will polycyclic aromatic hydrocarbons grade
Figure 830815DEST_PATH_IMAGE091
Multiplying by polycyclic aromatic hydrocarbon weight factor
Figure 153212DEST_PATH_IMAGE096
Grade of particulate matter
Figure 497606DEST_PATH_IMAGE092
Multiplying by a particulate matter weight factor
Figure DEST_PATH_IMAGE097
And volatile organic grade
Figure 746315DEST_PATH_IMAGE093
Multiplying by a volatile organic weight factor
Figure 111438DEST_PATH_IMAGE098
And comparing, selecting the maximum value as the health grade U of the current cooking area as follows,
Figure DEST_PATH_IMAGE099
。
4. calculation module will polycyclic aromatic hydrocarbons grade
Figure 639240DEST_PATH_IMAGE091
Multiple ringsAromatic weighting factor
Figure 318483DEST_PATH_IMAGE100
Grade of particulate matter
Figure 405388DEST_PATH_IMAGE092
Multiplying by a particulate matter weight factor
Figure DEST_PATH_IMAGE101
And volatile organic grade
Figure 488881DEST_PATH_IMAGE093
Multiplying by a volatile organic weight factor
Figure 395657DEST_PATH_IMAGE102
And the health grade U of the current cooking area is obtained by adding, as shown in the following formula,
Figure DEST_PATH_IMAGE103
。
the health level U calculation of the present embodiment is specifically the first one. For example when
Figure 207754DEST_PATH_IMAGE091
＝2，
Figure 883586DEST_PATH_IMAGE104
、
Figure 793773DEST_PATH_IMAGE093
When 4, the health level U of the current cooking area is 4. A smaller value for U indicates healthier, and a larger value for U indicates unhealthy.
It should be noted that the 4 methods of the present invention may be selected as the first method, or may be selected as the other three methods according to the actual situation, and the specific implementation manner is determined according to the actual situation. For the third method of the invention
Figure DEST_PATH_IMAGE105
May be other values, specific embodimentsAccording to the actual situation. For the fourth process of the invention
Figure 797632DEST_PATH_IMAGE106
The content of the organic acid is 0.2,
Figure DEST_PATH_IMAGE107
other values are possible, and the specific embodiment is determined according to actual conditions.
Compared with the embodiment 4, the healthy cooking system of the embodiment can perform healthy grade division on the polycyclic aromatic hydrocarbon concentration, the particulate matter concentration and the VOC concentration of the current environment, and meanwhile, the healthy cooking system can adjust and automatically adjust the wind speed of the range hood 1 and the firepower of the stove 4 according to the healthy grade, so that the polycyclic aromatic hydrocarbon, the oil smoke, the particulate matter concentration and the VOC concentration of the current environment are all reduced.
Example 6.
A healthy cooking system capable of identifying moisture and oil smoke, as shown in fig. 3 to 6, the other features are the same as those of embodiment 5, except that: the temperature sensing module of the present invention is a temperature sensor 2.
When the range hood 1, the stove 4, the cooking bench 5 and the cookware 3 used in cooking work normally, the vertical height from the temperature sensor 2 to the ground is Ht, Ht is more than or equal to 0 and less than or equal to 3m, the center of the range hood 1 is defined as X0, the horizontal distance between the temperature sensor 2 and X0 is defined as Xt, and Xt is more than or equal to 0 and less than or equal to 2 m.
The temperature sensor 2 is a non-invasive temperature sensor 2 remote from a cooktop 5, a pot or a stove 4.
It should be noted that the non-invasive temperature sensor 2 of the present invention may be mounted on the range hood 1, or may be mounted on a wall, or may be another object far away from the cooking bench 5, the pot, and the stove 4, and the specific implementation is determined according to the actual situation. It is within the scope of the present invention that the non-invasive temperature sensor 2 of the present invention is not provided on the cooktop 5, cookware, and stove 4. The non-invasive temperature sensor 2 of the present embodiment is mounted to the range hood 1.
The visual angle of the temperature sensor 2 is defined as theta, 0 degrees & lt theta & lt 360 degrees, the included angle between the central axis of the temperature sensor 2 and the horizontal direction is defined as β degrees, and 0 degrees & lt β & lt 90 degrees.
The coincidence plane of the projection plane of the temperature sensor 2 and the cooktop 5 is defined as P, the point at the maximum distance from X0 in the P range is defined as Pf, the point at the minimum distance from X0 in the P range is defined as Pn, the distances between Pf and Pn are defined as Lp, and Lp > 0.
A projected surface of the pot 3 used in cooking on the cooktop 5 in the detection direction of the temperature sensor 2 is defined as P ', and P' is contained inside P.
The installation height of the temperature sensor 2 of the present invention can be calculated by the formulas (iii) and (iv).
When β is 90 °, the value of Ht is obtained by formula (iii),
Figure 661421DEST_PATH_IMAGE108
formula (III).
When β ≠ 90 °, the value of Ht is obtained by the formula (IV),
Figure DEST_PATH_IMAGE109
。
for example, when β is 90 °, Lp is 1m, and θ is 90 °, then Ht calculated by formula (iii) is 0.5 m.
When β is 15 °, Lp is 1m, and θ is 90 °, then Ht by formula (iv) is 0.87 m.
It should be noted that Lp, θ and β in the present invention may be values of the above two examples, or may be other values, for example, θ may be 160 °, 60 ° or 100 °, Lp may be 0.8 m, 1.5 m or 2m, β may be 80 °, 60 ° or 45 °, and the specific values of Lp, θ and β are determined according to actual conditions, and may be calculated according to the formulas (iii) and (iv) of the present invention according to actual values of Lp, θ and β, and therefore, the height of the temperature sensor 2 of the range hood 1 obtained for Lp, θ and β is not described herein.
Example 7.
A healthy cooking system capable of identifying water vapor and oil smoke has the same other characteristics as embodiment 6, except that: the temperature sensor 2 of the present invention is an invasive temperature sensor 2.
The invasive temperature sensor 2 of the present invention can be mounted to an external pot used in cooking, a pot rack of an external stove 4, and an external cooking bench 5, and the specific mounting position is determined according to the actual situation. The invasive temperature sensor 2 of the present embodiment is mounted to an external pot used in cooking.
Compared with embodiment 1, the distance between the invasive temperature sensor 2 of the embodiment and the cookware to be detected is closer, so that the obtained detection data are more accurate.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (15)

1. The utility model provides a healthy culinary art system of ability discernment steam and oil smoke which characterized in that: the cooking range hood is provided with an image acquisition module, a temperature sensing module, a harmful substance detection device and a range hood, wherein the image acquisition module is used for analyzing oil smoke images in a cooking area and obtaining the size of generated smoke in real time;
the temperature sensing module senses the temperature of the cooker to obtain a temperature signal and transmits the temperature signal as a temperature output signal to the image acquisition module, the image acquisition module acquires oil smoke images of a cooking area for analysis and obtains the size of generated smoke in real time to obtain an oil smoke output signal, the image acquisition module judges the oil smoke output signal and the temperature output signal according to water vapor and oil smoke,
when the oil smoke output signal is greater than or equal to the oil smoke threshold value and the temperature output signal is less than or equal to the temperature threshold value, the oil smoke is judged to be water vapor, the image acquisition module obtains a processing signal and sends the processing signal to the range hood, the range hood receives the processing signal and carries out wind power adjustment,
when the oil smoke output signal is greater than or equal to the oil smoke threshold value and the temperature output signal is greater than the temperature threshold value, the oil smoke is judged, the temperature output signal and the oil smoke output signal are sent to the harmful substance detection device by the image acquisition module, the harmful substance detection device receives the temperature output signal and the oil smoke output signal and detects harmful substances,
when the oil smoke output signal is smaller than the oil smoke threshold value and the temperature output signal is smaller than or equal to the temperature threshold value, the image acquisition module does not process the oil smoke output signal;
and when the oil smoke output signal is smaller than the oil smoke threshold value and the temperature output signal is larger than the temperature threshold value, the image acquisition module does not process the oil smoke output signal.
2. The healthy cooking system capable of identifying moisture and soot according to claim 1, wherein: the oil smoke threshold value is 35-45;
the temperature threshold is 98-102 ℃.
3. The healthy cooking system capable of identifying moisture and soot according to claim 2, wherein: the oil smoke threshold value is 40;
the temperature threshold is 100 ℃.
4. The healthy cooking system capable of identifying moisture and smoke according to claim 3, wherein: collecting images
The output data in the oil smoke output signal of the module is defined as lambda 'and lambda' is more than or equal to 0, the output data in the temperature output signal of the temperature sensing module is defined as kappa,
when the current season is spring, performing oil smoke correction on the lambda 'to obtain a correction value lambda, wherein lambda is lambda', and taking the corrected lambda as output data of a new image acquisition module;
when the current season is autumn, performing oil smoke correction on lambda 'to obtain a correction value lambda, wherein lambda is lambda', and taking the corrected lambda as output data of a new image acquisition module;
when the season is at presentIn summer, the oil smoke correction is carried out on the lambda' to obtain a corrected value lambda, and
Figure FDA0002354449150000021
taking the corrected lambda as the output data of a new image acquisition module;
when the current season is winter, the oil smoke correction is carried out on the lambda' to obtain a correction value lambda, and
Figure FDA0002354449150000022
and taking the corrected lambda as the output data of the new image acquisition module.
5. The healthy cooking system capable of identifying moisture and smoke according to claim 4, wherein: the harmful substance detection device is provided with a particulate matter detection assembly for detecting the particle concentration of a cooking area, a VOC sensor for detecting volatile organic compounds and a calculation module for calculating the polycyclic aromatic hydrocarbon concentration of the current cooking area, wherein the calculation module is respectively and electrically connected with the image acquisition module, the particulate matter detection assembly, the VOC sensor and the range hood;
particulate matter sensor subassembly detects particulate matter concentration in the regional oil smoke of culinary art and obtains particulate matter concentration signal and sends to calculation module, and the VOC sensor gathers the volatile organic compounds concentration in current culinary art region and obtains VOC concentration signal and transmits to calculation module, and calculation module receives particulate matter concentration signal, VOC concentration signal, oil smoke output signal and temperature output signal respectively then handles the polycyclic aromatic hydrocarbon concentration that obtains current culinary art region in real time.
6. The healthy cooking system capable of identifying moisture and smoke according to claim 5, wherein: the calculation module is a calculation module which is constructed by mathematical modeling and obtains mathematical relations of temperature, smoke size, particulate matter concentration and volatile organic matter concentration and polycyclic aromatic hydrocarbon concentration.
7. The healthy cooking system capable of identifying moisture and smoke according to claim 6, wherein: the calculation module is a linear calculation module or a nonlinear calculation module;
when the calculation module is a nonlinear calculation module, the nonlinear calculation module is an exponential calculation module, a power calculation module, a logarithmic calculation module, a neural network calculation module or a machine learning calculation module.
8. The healthy cooking system capable of identifying moisture and smoke according to claim 7, wherein: the calculation formula of the calculation module is formula (I),
Cpolycyclic aromatic hydrocarbons＝0.05κ+0.01λ+0.0005C+25CVOC+6*10-5Kappa lambda C +475.1 formula (I),
wherein C isPolycyclic aromatic hydrocarbonsIs the total concentration of polycyclic aromatic hydrocarbon gas in the cooking area, C is the output data of the particle sensing component, CVOCIs the output data of the VOC sensor.
9. The healthy cooking system capable of identifying moisture and smoke according to claim 7, wherein: the calculation formula of the calculation module is formula (II),
Cpolycyclic aromatic hydrocarbons＝0.05κ0.98+0.01λ1.05+0.0005C1.05+25CVOC 1.05+6*10-5Kappa lambda C +469.5 formula (II),
wherein C isPolycyclic aromatic hydrocarbonsIs the total concentration of polycyclic aromatic hydrocarbon gas in the cooking area, C is the output data of the particle sensing component, CVOCIs the output data of the VOC sensor.
10. The healthy cooking system capable of recognizing moisture and smoke according to claim 8 or 9, wherein: the particle detection component is set to be at least one of a PM2.5 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 2.5 micrometers in the oil smoke of the current cooking area, a PM10 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 10 micrometers in the oil smoke of the current cooking area, a PM1.0 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 1.0 micrometers in the oil smoke of the current cooking area, a PM0.1 sensor for detecting the concentration of particles with equivalent diameter less than or equal to 0.1 micrometers in the oil smoke of the current cooking area and a PMA sensor for detecting the concentration of particles with equivalent diameter less than or equal to 0.05 micrometers in the oil smoke of the current cooking.
11. The healthy cooking system capable of recognizing moisture and smoke according to claim 10, wherein: a stove is also arranged and electrically connected with the computing module;
the calculation module is a calculation module capable of performing health grade division according to the concentration of the particulate matters, the concentration of the volatile organic compounds and the concentration of the polycyclic aromatic hydrocarbon;
the calculation module carries out health grade division according to particulate matter concentration, volatile organic compounds concentration and polycyclic aromatic hydrocarbon concentration in the current environment and obtains health grade signal, and the calculation module sends health grade signal to lampblack absorber and stove, and the lampblack absorber receives health grade signal and carries out wind-force regulation, and the stove receives health grade signal and carries out firepower regulation.
12. The healthy cooking system capable of recognizing moisture and smoke according to claim 11, wherein: the temperature sensing module is a temperature sensor;
when the range hood, the stove, the cooking bench and the pot used in cooking work normally, the degree of the vertical height from the temperature sensor to the ground is Ht, Ht is more than or equal to 0 and less than or equal to 3m,
the center of the range hood is defined as X0,
the horizontal distance between the temperature sensor and X0 is defined as Xt, and Xt is more than or equal to 0 and less than or equal to 2 m.
13. The healthy cooking system capable of recognizing moisture and smoke according to claim 12, wherein: the temperature sensor is an invasive temperature sensor;
the temperature sensor is assembled on an external cooker used in cooking; or
The temperature sensor is a cooker frame assembled on the stove; or
The temperature sensor is assembled on an external cooking bench.
14. The healthy cooking system capable of recognizing moisture and smoke according to claim 12, wherein: the temperature sensor is a non-invasive temperature sensor far away from a cooking bench, a cooker and a stove.
15. The healthy cooking system capable of identifying water vapor and oil smoke according to claim 14, wherein the viewing angle of the temperature sensor is defined as θ, and 0 ° < θ < 360 °, the included angle between the central axis of the temperature sensor and the horizontal direction is defined as β, and 0 ° < β ° or more and 90 ° or less;
defining the coincidence plane of the projection plane of the temperature sensor and the cooking bench as P, defining the point with the maximum distance from X0 in the range of P as Pf, defining the point with the minimum distance from X0 in the range of P as Pn, defining the distance between Pf and Pn as Lp, and defining Lp to be more than 0;
defining a projection plane of a pot used in cooking on a cooking bench along the detection direction of the temperature sensor as P ', and P' is contained in P;
when β is 90 °, the value of Ht is obtained by formula (iii), where Ht is Lp/[2 × tan (θ/2) ] formula (iii);
when β ≠ 90 °, the value of Ht obtained from formula (iv) is Lp/[2 × tan (90 ° - β — θ/2) ] formula (iv).
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