CN112833435A - Artificial intelligence-based range hood air volume control method, system and equipment - Google Patents
Artificial intelligence-based range hood air volume control method, system and equipment Download PDFInfo
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- CN112833435A CN112833435A CN202110088965.0A CN202110088965A CN112833435A CN 112833435 A CN112833435 A CN 112833435A CN 202110088965 A CN202110088965 A CN 202110088965A CN 112833435 A CN112833435 A CN 112833435A
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- cooking
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- temperature change
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- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 32
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000010411 cooking Methods 0.000 claims abstract description 209
- 230000006399 behavior Effects 0.000 claims abstract description 101
- 239000000779 smoke Substances 0.000 claims abstract description 99
- 238000001514 detection method Methods 0.000 claims abstract description 36
- 238000002372 labelling Methods 0.000 claims abstract description 23
- 238000013145 classification model Methods 0.000 claims abstract description 14
- 238000009835 boiling Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 6
- 239000006233 lamp black Substances 0.000 claims description 6
- 239000006096 absorbing agent Substances 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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Classifications
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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
- F24C15/2021—Arrangement or mounting of control or safety systems
-
- 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
- F24C15/2042—Devices for removing cooking fumes structurally associated with a cooking range e.g. downdraft
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Ventilation (AREA)
Abstract
The invention discloses an artificial intelligence-based range hood air volume control method, which comprises the following steps of: carrying out cooking behavior classification and labeling on the collected cooking images to obtain a cooking behavior data set, and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set; inputting a cooking behavior data set into an artificial intelligence model to train so as to obtain a classification model, and inputting an oil smoke detection data set into the artificial intelligence model to train so as to obtain a detection model; classifying the real-time cooking behaviors through a classification model to obtain a cooking behavior result a; then, identifying the oil smoke during cooking by using a detection model to obtain the real-time oil smoke amount; judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking, and determining an actual cooking behavior c according to the relation between a and b; and determining the air intake of the range hood according to the actual cooking behavior c and the real-time oil smoke amount.
Description
Technical Field
The invention relates to the field of range hoods, in particular to a range hood air volume control method based on artificial intelligence.
Background
In the use process of the traditional range hood, a user needs to manually adjust the air volume of the range hood according to the size of oil smoke, but the user inhales excessive oil smoke due to untimely manual adjustment, and the health of the user is affected in the past.
In the prior art, the intelligent lampblack machine calculates cooking behaviors according to the amplitude change of a cooking temperature curve, captures the moment of temperature sudden change of a cooking range area, and realizes lampblack prejudgment, so that the air inlet quantity of the range hood is adjusted to a proper gear in advance, and lampblack is gathered and sucked in advance; the range hood adopting the oil smoke identification model is also provided, and is used for acquiring and detecting oil smoke images in real time, judging the size of oil smoke and controlling the air volume of the range hood according to the size of the oil smoke; however, the cooking behavior is calculated according to the cooking temperature curve, and the technical scheme for predicting the air intake of the range hood is not mature enough, so that the prediction is not accurate, and the air volume control based on oil smoke recognition can be detected only after oil smoke appears, so that a certain time delay exists between the rise of the oil smoke and the adjustment of the air volume to a proper size.
Disclosure of Invention
In order to solve the technical problems, the invention provides an artificial intelligence-based range hood air volume control method.
In order to solve the technical problems, the invention adopts the following technical scheme:
an artificial intelligence-based range hood air volume control method comprises the following steps:
the method comprises the following steps: carrying out cooking behavior classification and labeling on the collected cooking images to obtain a cooking behavior data set, and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set;
step two: inputting a cooking behavior data set into an artificial intelligence model to train so as to obtain a classification model, and inputting an oil smoke detection data set into the artificial intelligence model to train so as to obtain a detection model;
step three: classifying the real-time cooking behaviors through a classification model to obtain a cooking behavior result a; then, identifying the oil smoke during cooking by using a detection model to obtain the real-time oil smoke amount;
step four: judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking, and determining an actual cooking behavior c according to the relation between a and b;
step five: and determining the air intake of the range hood according to the actual cooking behavior c, the real-time oil smoke amount and the temperature change of the cooking pot body.
Specifically, in the fourth step, when the actual cooking action c is determined by the relationship between the cooking action result a and the cooking action result b,
wherein maxTempChgAnd (a, b) selecting one of the cooking behavior result a and the cooking behavior result b with larger temperature change of the cooking pot, wherein the temperature change of the frying or frying cooking pot is larger than that of stewing or cooking, and the temperature change of the stewing or cooking pot is larger than that of frying.
Specifically, in the first step, when the collected cooking image is marked, the cooking behaviors include frying, stir-frying, boiling and stewing; when the collected cooking image is labeled with the oil smoke amount, the image is analyzed manually, samples without oil smoke, small smoke, medium smoke and large smoke are divided, and labeling is carried out according to the samples when labeling is carried out subsequently.
Specifically, when the cooking behavior result b is judged according to the temperature change of the cooking pot body during cooking in the fourth step, the first threshold value of the temperature change of the cooking pot body is tempthresh1The temperature change value of the cooking pot body within 2 minutes is tempdeltaThe ratio tempratio＝tempdelta/tempthresh1(ii) a If 0 < tempratioIf the cooking behavior result is less than 1, the result b is frying or deep-frying; if 1 < tempratioIf the cooking behavior result is less than 2.5, the cooking behavior result b is cooking or stewing; if temp. is detectedratioIf the cooking behavior result is more than 2.5, the result b is stir-frying.
Specifically, step five isWhen the air inlet amount of the range hood is determined according to the actual cooking behavior, the real-time oil smoke amount and the temperature change of the cooking pot body, if the actual cooking behavior is frying, the temperature change amount of the cooking pot body is larger than temp in the oil smoke detection processthresh2When the real-time oil smoke amount is small smoke, the air intake amount is adjusted to the maximum gear; wherein tempthresh2Is the second threshold value of the temperature change of the cooking pot body.
Specifically, when the air inlet amount of the range hood is determined according to the actual cooking behavior, the real-time oil smoke amount and the temperature change of the cooking pot body in the fifth step, if the actual cooking behavior is stewing or boiling, and the temperature change amount of the cooking pot body is larger than temp in the oil smoke detection processthresh2When the actual oil smoke amount is large smoke, the air intake amount is adjusted to a middle gear; wherein tempthresh2Is the second threshold value of the temperature change of the cooking pot body.
The utility model provides a lampblack absorber air volume control system based on artificial intelligence, includes:
the labeling module is used for carrying out cooking behavior classification labeling on the collected cooking images to obtain a cooking behavior data set and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set;
the model generation module is used for inputting the cooking behavior data set into the artificial intelligence model to train so as to obtain a classification model, and inputting the oil smoke detection data set into the artificial intelligence model to train so as to obtain a detection model;
the recognition module classifies the real-time cooking behaviors through the classification model to obtain a cooking behavior result a; then, identifying the oil smoke during cooking by using a detection model to obtain the real-time oil smoke amount;
the cooking behavior determining module is used for judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking and determining an actual cooking behavior c according to the relation between a and b;
and the air intake adjusting module determines the air intake of the range hood according to the actual cooking behavior c, the real-time oil smoke amount and the temperature change of the cooking pot body.
A computer device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the air volume control method of the range hood is realized when the processor executes the computer program.
Compared with the prior art, the invention has the beneficial technical effects that:
the invention combines two data sources of temperature and image, reduces misjudgment and delay judgment caused by a single data source, thereby reasonably adjusting the air quantity of the smoke machine and absorbing oil smoke.
Drawings
Fig. 1 is a flowchart of an air volume control method according to the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a method for controlling the air volume of a range hood based on artificial intelligence comprises the following steps:
s1: and carrying out cooking behavior classification and labeling on the collected cooking images to obtain a cooking behavior data set, and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set.
Specifically, in the first step, when the collected cooking image is marked, the cooking behaviors include frying, stir-frying, boiling and stewing; when the collected cooking image is labeled with the oil smoke amount, the image is analyzed manually, samples without oil smoke, small smoke, medium smoke and large smoke are divided, and labeling is carried out according to the samples when labeling is carried out subsequently.
The cooking image is collected, the corresponding cooking behavior in the cooking image is labeled, the oil smoke amount corresponding to the cooking image is labeled, so that the artificial intelligent model can detect the cooking process in real time, and a corresponding cooking behavior result and the real-time oil smoke amount are output.
S2: the cooking behavior data set is input into the artificial intelligence model to be trained to obtain a classification model, and the oil smoke detection data set is input into the artificial intelligence model to be trained to obtain a detection model.
Specifically, in the second step, the artificial intelligence model is any one of fast RCNN, YOLO v3, and SSD.
Fast RCNN, YOLO v3, SSD are common artificial intelligence models that can be trained to obtain a classification detection model.
S3: classifying the real-time cooking behaviors through a classification model to obtain a cooking behavior result a; and then, identifying the oil smoke during cooking by using the detection model to obtain the real-time oil smoke amount.
S4: and (4) judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking, and determining an actual cooking behavior c according to the relation between a and b.
Specifically, in the fourth step, when the actual cooking action c is determined by the relationship between the cooking action result a and the cooking action result b,
wherein maxTempChgAnd (a, b) selecting one of the cooking behavior result a and the cooking behavior result b with larger temperature change of the cooking pot, wherein the temperature change of the frying or frying cooking pot is larger than that of stewing or cooking, and the temperature change of the stewing or cooking pot is larger than that of frying.
The cooking behavior result is confirmed through two data sources, wherein the two data sources are an image data source and a temperature data source respectively; even when no oil smoke is generated, the air intake of the range hood can be judged in advance according to the temperature data, and the condition that the body of a user is damaged due to delayed starting of the range hood is avoided.
Specifically, when the cooking behavior result b is judged according to the temperature change of the cooking pot body during cooking in the fourth step, the first threshold value of the temperature change of the cooking pot body is tempthresh1The temperature change value of the cooking pot body within 2 minutes is tempdeltaThe ratio tempratio＝tempdelta/tempthresh1(ii) a If it is not0＜tempratioIf the cooking behavior result is less than 1, the result b is frying or deep-frying; if 1 < tempratioIf the cooking behavior result is less than 2.5, the cooking behavior result b is cooking or stewing; if temp. is detectedratioIf the cooking behavior result is more than 2.5, the result b is stir-frying.
S5: and determining the air intake of the range hood according to the actual cooking behavior c, the real-time oil smoke amount and the temperature change of the cooking pot body.
Specifically, when the air inlet amount of the range hood is determined according to the actual cooking behavior, the real-time oil smoke amount and the temperature change of the cooking pot body in the fifth step, if the actual cooking behavior is frying, the temperature change amount of the cooking pot body is larger than temp in the oil smoke detection processthresh2When the real-time oil smoke amount is small smoke, the air intake amount is adjusted to the maximum gear; wherein tempthresh2Is the second threshold value of the temperature change of the cooking pot body.
Specifically, when the air inlet amount of the range hood is determined according to the actual cooking behavior, the real-time oil smoke amount and the temperature change of the cooking pot body in the fifth step, if the actual cooking behavior is stewing or boiling, and the temperature change amount of the cooking pot body is larger than temp in the oil smoke detection processthresh2When the actual oil smoke amount is large smoke, the air intake amount is adjusted to a middle gear; wherein tempthresh2Is the second threshold value of the temperature change of the cooking pot body.
In the invention, the detection model detects the oil smoke amount at equal time intervals, and measures the temperature variation of the cooking pot body during the period when detecting the oil smoke.
If the actual cooking behavior is frying, the actual amount of the oil smoke is small smoke, and the air intake is adjusted to the lowest gear.
The maximum gear, the middle gear and the minimum gear can be divided according to needs, experiments or experience, or divided equally.
The utility model provides a lampblack absorber air volume control system based on artificial intelligence, includes:
the labeling module is used for carrying out cooking behavior classification labeling on the collected cooking images to obtain a cooking behavior data set and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set;
the model generation module is used for inputting the cooking behavior data set into the artificial intelligence model to train so as to obtain a classification model, and inputting the oil smoke detection data set into the artificial intelligence model to train so as to obtain a detection model;
the recognition module classifies the real-time cooking behaviors through the classification model to obtain a cooking behavior result a; then, identifying the oil smoke during cooking by using a detection model to obtain the real-time oil smoke amount;
the cooking behavior determining module is used for judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking and determining an actual cooking behavior c according to the relation between a and b;
and the air intake adjusting module determines the air intake of the range hood according to the actual cooking behavior c, the real-time oil smoke amount and the temperature change of the cooking pot body.
A computer device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the air volume control method of the range hood is realized when the processor executes the computer program.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (8)
1. An artificial intelligence-based range hood air volume control method comprises the following steps:
the method comprises the following steps: carrying out cooking behavior classification and labeling on the collected cooking images to obtain a cooking behavior data set, and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set;
step two: inputting a cooking behavior data set into an artificial intelligence model to train so as to obtain a classification model, and inputting an oil smoke detection data set into the artificial intelligence model to train so as to obtain a detection model;
step three: classifying the real-time cooking behaviors through a classification model to obtain a cooking behavior result a; then, identifying the oil smoke during cooking by using a detection model to obtain the real-time oil smoke amount;
step four: judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking, and determining an actual cooking behavior c according to the relation between a and b;
step five: and determining the air intake of the range hood according to the actual cooking behavior c, the real-time oil smoke amount and the temperature change of the cooking pot body.
2. The artificial intelligence-based range hood air volume control method according to claim 1, characterized in that: in the fourth step, when the actual cooking behavior c is determined according to the relationship between the cooking behavior result a and the cooking behavior result b,
wherein maxTempChgAnd (a, b) selecting one of the cooking behavior result a and the cooking behavior result b with larger temperature change of the cooking pot, wherein the temperature change of the frying or frying cooking pot is larger than that of stewing or cooking, and the temperature change of the stewing or cooking pot is larger than that of frying.
3. The artificial intelligence-based range hood air volume control method according to claim 1, characterized in that: in the first step, when the collected cooking images are marked, the cooking behaviors comprise frying, stir-frying, boiling and stewing; when the collected cooking image is labeled with the oil smoke amount, the image is analyzed manually, samples without oil smoke, small smoke, medium smoke and large smoke are divided, and labeling is carried out according to the samples when labeling is carried out subsequently.
4. The artificial intelligence-based range hood air volume control method according to claim 3, characterized in that: when the cooking behavior result b is judged according to the temperature change of the cooking pot body during cooking in the fourth step, the first threshold value of the temperature change of the cooking pot body is tempthresh1The temperature change value of the cooking pot body within 2 minutes is tempdeltaThe ratio tempratio＝tempdelta/tempthresh1(ii) a If 0<tempratio<1, the cooking action result b is frying or deep-frying; if 1 is<tempratio<2.5, the cooking behavior result b is cooking or stewing; if temp. is detectedratio>2.5, the cooking action result b is stir-frying.
5. The artificial intelligence based range hood air volume control method according to claim 3, wherein when the air volume of the range hood is determined according to the actual cooking behavior, the real-time oil smoke volume and the temperature change of the cooking pan body in the fifth step, if the actual cooking behavior is stir-frying, the temperature change of the cooking pan body is greater than temp during the oil smoke detection processthresh2When the real-time oil smoke amount is small smoke, the air intake amount is adjusted to the maximum gear; wherein tempthresh2Is the second threshold value of the temperature change of the cooking pot body.
6. The artificial intelligence based range hood air volume control method according to claim 3, wherein when the air volume of the range hood is determined according to the actual cooking behavior, the real-time oil smoke volume and the temperature change of the cooking pan body in the fifth step, if the actual cooking behavior is stewing or boiling, and the temperature change of the cooking pan body is larger than temp during the oil smoke detection processthresh2Actual oil smokeWhen the smoke is heavy smoke, the air intake is adjusted to a middle gear; wherein tempthresh2Is the second threshold value of the temperature change of the cooking pot body.
7. The utility model provides a lampblack absorber air volume control system based on artificial intelligence which characterized in that includes:
the labeling module is used for carrying out cooking behavior classification labeling on the collected cooking images to obtain a cooking behavior data set and carrying out oil smoke amount labeling on the collected cooking images to obtain an oil smoke detection data set;
the model generation module is used for inputting the cooking behavior data set into the artificial intelligence model to train so as to obtain a classification model, and inputting the oil smoke detection data set into the artificial intelligence model to train so as to obtain a detection model;
the recognition module classifies the real-time cooking behaviors through the classification model to obtain a cooking behavior result a; then, identifying the oil smoke during cooking by using a detection model to obtain the real-time oil smoke amount;
the cooking behavior determining module is used for judging a cooking behavior result b according to the temperature change of the cooking pot body during cooking and determining an actual cooking behavior c according to the relation between a and b;
and the air intake adjusting module determines the air intake of the range hood according to the actual cooking behavior c, the real-time oil smoke amount and the temperature change of the cooking pot body.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the range hood air volume control method according to any one of claims 1 to 6 when executing the computer program.
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Application publication date: 20210525 |