CN111752170A - Intelligent cooking method and device - Google Patents
Intelligent cooking method and device Download PDFInfo
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- CN111752170A CN111752170A CN201910243909.2A CN201910243909A CN111752170A CN 111752170 A CN111752170 A CN 111752170A CN 201910243909 A CN201910243909 A CN 201910243909A CN 111752170 A CN111752170 A CN 111752170A
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- 238000010411 cooking Methods 0.000 title claims abstract description 402
- 238000000034 method Methods 0.000 title claims abstract description 53
- 239000000463 material Substances 0.000 claims abstract description 262
- 238000004364 calculation method Methods 0.000 claims abstract description 30
- 238000001514 detection method Methods 0.000 claims description 19
- 230000000737 periodic effect Effects 0.000 claims description 6
- 239000000126 substance Substances 0.000 claims description 2
- 238000005094 computer simulation Methods 0.000 claims 1
- 238000003556 assay Methods 0.000 abstract description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 39
- 229910052760 oxygen Inorganic materials 0.000 description 39
- 239000001301 oxygen Substances 0.000 description 39
- 230000005070 ripening Effects 0.000 description 39
- 239000007789 gas Substances 0.000 description 30
- 238000004891 communication Methods 0.000 description 22
- 230000006870 function Effects 0.000 description 10
- 238000004590 computer program Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 235000019640 taste Nutrition 0.000 description 5
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 4
- 230000032683 aging Effects 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 229910002092 carbon dioxide Inorganic materials 0.000 description 2
- 239000001569 carbon dioxide Substances 0.000 description 2
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
Abstract
The application relates to an intelligent cooking method, which comprises the following steps: obtaining cooking parameters and cooking time of food materials; establishing a calculation model with the cooking state of the food material according to the cooking parameters, the cooking time and the cooking state, and determining a cooking degree judgment parameter; and when the cooking degree judgment parameter is lower than a set threshold value, determining that the cooking state meets the cooking. Through the culinary art parameter and the corresponding culinary art time of acquireing the culinary art in-process and eating the material, and then the maturity condition of assay edible material to the maturity when better assurance food cooks brings better experience for the user. The application also discloses an intelligent cooking device and electronic equipment.
Description
The present application relates to the field of intelligent technologies, and for example, to an intelligent cooking method and apparatus.
Background
At present, along with the popularization of intelligent technologies, various intelligent ovens are provided by various electric enterprises, the types of food materials can be identified, preset cooking programs are automatically matched, and the maturity of the food materials is judged by collecting gas concentration values in the ovens in the cooking process.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art: the concentration of the gas in the oven is influenced by the flowing change of the hot air flow in the oven cavity, and the concentration is unstable, so that the judgment result is possibly unreliable.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
According to an aspect of an embodiment of the present disclosure, there is provided an intelligent cooking method.
In some optional embodiments, the method comprises: obtaining cooking parameters and cooking time of food materials; establishing a calculation model with the cooking state of the food material according to the cooking parameters, the cooking time and the cooking state, and determining a cooking degree judgment parameter; and when the cooking degree judgment parameter is lower than a set threshold value, determining that the cooking state meets the cooking.
According to another aspect of the disclosed embodiments, there is provided an intelligent cooking apparatus.
In some optional embodiments, the apparatus comprises: the acquisition module is configured to acquire cooking parameters and cooking time of food materials; the establishment module is configured to determine a maturity judgment parameter according to the cooking parameters and the cooking time of the food materials and a calculation model of the cooking state of the food materials; a determination module configured to determine that the cooking state satisfies maturity when the doneness determination parameter is below a set threshold.
According to another aspect of an embodiment of the present disclosure, an oven is provided.
In some optional embodiments, the oven comprises the intelligent cooking device described above.
According to another aspect of an embodiment of the present disclosure, an electronic device is provided.
In some optional embodiments, the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, which when executed by the at least one processor, cause the at least one processor to perform the intelligent cooking method described above.
According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided.
In some alternative embodiments, the computer-readable storage medium stores computer-executable instructions configured to perform the intelligent cooking method described above.
According to another aspect of an embodiment of the present disclosure, a computer program product is provided.
In some alternative embodiments, the computer program product comprises a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the intelligent cooking method described above.
Some technical solutions provided by the embodiments of the present disclosure can achieve the following technical effects:
through the culinary art parameter and the corresponding culinary art time of acquireing the culinary art in-process and eating the material, and then the maturity condition of assay edible material to the maturity when guaranteeing food culinary art brings better experience for the user. According to the embodiment of the disclosure, the relevant data of the food material is integrated, and the judgment result is more stable and reliable.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
fig. 1 is a schematic flow chart of an intelligent cooking method provided by an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of another intelligent cooking method provided by the embodiment of the disclosure;
fig. 3 is a schematic view of an intelligent cooking device provided by an embodiment of the present disclosure;
fig. 4 is a schematic view of another intelligent cooking device provided by the embodiment of the present disclosure; and
fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.
Reference numerals:
301: an acquisition module; 302: establishing a module; 303: a decision module;
401: a first acquisition unit; 402: a second acquisition unit; 403: a first establishing unit; 404: a second establishing unit; 405: a third establishing unit; 406: a decision module;
500: a processor; 501: a memory; 502: a communication interface; 503: a bus;
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
The embodiment of the present disclosure provides an intelligent cooking method, as shown in fig. 1, including:
s101, obtaining cooking parameters and cooking time of food materials;
s102, establishing a calculation model with the cooking state of the food material according to the cooking parameters and the cooking time, and determining a cooking degree judgment parameter;
s103, when the cooking degree judgment parameter is lower than a set threshold value, determining that the cooking state meets the requirement of cooking;
the above steps will be described separately below.
In step S101, a cooking parameter of a food material is obtained, where the cooking parameter is used to represent a degree of the food material in cooking, such as an appearance chromaticity of the food material; for example, in the cooking process, the appearance chromaticity C of the food material is obtained1；
Obtaining cooking time T of food material in cooking1The cooking time is used for representing the cooking time of the food material.
In step S102, the food material ripening status is used for representing the degree of the food material under the ripening condition, such as the ripening chromaticity of the food material and the ripening time; for example, according to the food materials, the server searches for the corresponding cooking menu to obtain the maturity color C and the maturity time T of the food materials.
The ripening chromaticity C may be the chromaticity of the food material in a ripened state; or the corresponding chromaticity when the food material reaches the edible maturity; or the color corresponding to the maturity of the food material when the food material is eaten preferably; or the chroma corresponding to the user-defined maturity. The cooking time T may be a time required for cooking when the food material can be eaten or a time required for cooking when the food material satisfies a cooking condition; or a cooking time corresponding to the ripening chromaticity C.
Optionally, the cooking state of the food material may be obtained before cooking starts, or may be obtained during cooking.
The calculation model is, for example, a maturity degree determination model established according to the relationship between the maturity state and the cooking parameters and the cooking time;
for example, according to the maturity C and appearance C1Color difference level of (1), the time of agingTime T and the cooking time T1And carrying out weighted calculation to determine a maturity judging parameter.
In step S103, the doneness determination parameter is used to represent a difference between the doneness level during cooking of the food material and the cooking condition represented by the cooking state of the food material. And when the cooking degree judgment parameter is lower than a set threshold value, determining that the cooking state meets the cooking.
This disclosed embodiment, through the culinary art parameter and the corresponding culinary art time of acquireing the in-process edible material, and then the maturity condition of assay edible material to the maturity when guaranteeing food culinary art brings better experience for the user. According to the embodiment of the disclosure, the relevant data of the food materials are integrated, and the judgment result is more stable and reliable.
The embodiment of the present disclosure further provides an intelligent cooking method, as shown in fig. 2, including:
s201, acquiring cooking parameters of the food materials according to the cooking states of the food materials;
s202, obtaining cooking time corresponding to the cooking state of the food material;
s203, acquiring a first difference value between the cooking parameter and a parameter corresponding to the cooking state of the food material;
s204, acquiring a second difference value between the cooking time and the time corresponding to the cooking state of the food material;
s205, establishing a calculation model related to the first difference and the second difference, and determining a maturity judging parameter;
s206, when the cooking degree judgment parameter is lower than a set threshold value, determining that the cooking state meets the requirement of cooking;
the above steps will be described separately below.
In step S201, obtaining a cooking parameter of the food material according to a cooking state of the food material in cooking; the obtaining of the cooking state of the food material in the cooking includes obtaining image information of the food material, where the image information includes cooking initial image information; and detecting periodic image information in the cooking of the food material. The image information of the detection period in the cooking of the food material is, for example, 5 seconds, and the image information of the food material is acquired every 5 seconds in the cooking. In the above, the image information of the detection period in the cooking of the food material is obtained, and when it is determined in step S206 that the food material is ripe, the obtaining of the image information of the detection period in the cooking of the food material is stopped.
The image information is obtained by, for example, capturing an image of the food material with a camera. Optionally, the camera may be a camera disposed on the cooking device, or another device with a shooting function, and is connected to the cooking device through wireless communication or wired communication.
In step S201, a cooking parameter of the food material is obtained, for example, the initial chromaticity C of the food material is obtained0And the apparent color C during cooking1. Initial chroma C0After the initial image information of the cooking of the food material is obtained, the image information is processed, and the initial chromaticity C of the food material is calculated0(ii) a The apparent chroma C1After the image information of the detection period in the cooking of the food material is obtained, the image information is processed, and the appearance chromaticity C of the food material is calculated1。
For example, a food material area in the food material image information is obtained through an image segmentation algorithm; calculating the average color C' of the appearance of the food material in the food material area;
the above average chroma C' is constructed by a standard Lab color model:
C'＝(a',b') (2)
wherein a 'is the value of the channel a in Lab space of the average chroma C';
b 'is the value of b channel in Lab space of the maturity C'.
Optionally, the value a 'of the a channel of the average chromaticity C' in the Lab space is:
wherein, aiFor a channel of pixel i in Lab space in the food material areaA numerical value;
n is the total number of pixels in the food material area.
Optionally, the value b 'of the b channel of the average chromaticity C' in the Lab space is:
wherein, biThe value of a channel b of a pixel i in a Lab space in the food material area is shown;
n is the total number of pixels in the food material area.
Initial chroma C0The method for obtaining the average chromaticity C 'is the same as that of the average chromaticity C' by processing the information of the food cooking initial image; appearance chroma C1The calculation method of (3) is the same as the average chromaticity C' by processing the image information of the detection period in the cooking of the food material.
In step S202, a cooking time corresponding to the cooking state of the food material is obtained, for example, the cooking time T when the image information is obtained1。
The cooking time T of the food material1The time can be acquired by a timer built in the cooking device or other timing devices connected with the cooking device.
In step S203, obtaining a first difference between the cooking parameter and a parameter corresponding to a cooking state of the food material; the cooking state of the food material corresponds to a parameter, such as a set of parameters that meets the cooking requirement, corresponding to the cooking parameter. For example, when the cooking parameter is the chromaticity of the appearance of the food material, the parameter corresponding to the cooking state of the food material is the chromaticity of the cooking of the food material.
For example, in step S202, the cooking parameter of the food material, the initial chromaticity C, is obtained0Appearance chroma C1(ii) a Acquiring parameters corresponding to the cooking state of the food materials, including the cooking chromaticity C of the food materials; for example, according to the food material, the server searches for the corresponding cooking menu to obtain the maturity degree C of the food material.
The ripening chromaticity C may be the chromaticity of the food material in a ripened state; or the corresponding chromaticity when the food material reaches the edible maturity; or the color corresponding to the maturity of the food material when the food material is eaten preferably; or the chroma corresponding to the user-defined maturity.
The first difference is obtained by calculating the similarity between the cooking parameter and the ripening parameter; for example, the food material is obtained according to the ripeness color C of the food material and the initial color C of the food material0Appearance chroma C1And performing similarity calculation to obtain a cooking color difference △ C, which is a first difference.
For example, the cooking color difference Δ C is:
wherein C is the ripening chromaticity of the food material standard;
C0the initial chromaticity of the food material in the cooking process;
C1the appearance chromaticity of the food material in the cooking process.
As can be seen from the formulae (1) to (4),
obtaining a cooking color difference Delta C; the value range of the value is [0,1 ]; the larger the value of the cooking color difference Δ C, the larger the color difference.
In step S204, a second difference between the cooking time and a time corresponding to a cooking state of the food material is obtained; the cooking state of the food material corresponds to time, which is used for representing the cooking time required by the food material to meet the cooking condition, for example, the cooking time required by the food material to be eaten or the cooking time required by the food material to meet the cooking condition; or a cooking time corresponding to the maturity level C in step S203.
For example, the second difference is a difference obtained by calculating the cooking time and the ripening time; for example, according to the cooking time T of the food material1And calculating the cooking time T of the food material to obtain a time difference, namely a first difference.
The cooking time T may be a time required for cooking when the food material can be eaten or a time required for cooking when the food material satisfies a cooking condition; or a cooking time corresponding to the ripening chromaticity C.
For example, the cooking time T is acquired in step S2021Acquiring the time T corresponding to the cooking state of the food material according to the cooking chromaticity C in the step S204, and acquiring the cooking time T according to the cooking time T1And the time T corresponding to the aging state is calculated, and the obtained time difference △ T is a second difference value.
For example, the time difference Δ T is:
wherein, T1Is the cooking time of the food material;
t is the corresponding time of the drawing state of the food material;
accordingly, a second difference Δ T is obtained, which has a value range of [0,1 ].
In step S205, a calculation model related to the first difference and the second difference is established, and a maturity determination parameter is determined; the calculation model is, for example, a maturity determination model obtained by weighting the first difference and the second difference.
For example, a doneness determination model M ═ f (Δ C, Δ T) is created based on the first difference Δ C obtained in step S203 and the second difference Δ T obtained in step S204, and the obtained M value is a doneness determination parameter.
The calculation model is, for example:
wherein Δ C is a first difference between the cooking parameter and a parameter corresponding to a cooking state of the food material;
the delta T is a second difference value between the cooking time and the time corresponding to the cooking state of the food material;
is a very small constant that prevents the denominator from being 0.
In step S206, when the doneness determination parameter is lower than a set threshold, it is determined that the cooking state satisfies maturity; further comprising determining that the cooking state does not satisfy maturity when the similarity metric is not below a set threshold.
Determining the maturity of the food material according to the maturity judging parameter; for example, when the doneness determination parameter is lower than a set threshold, it is determined that the food material has met the doneness, and an instruction is sent to stop cooking; and when the maturity judging parameter is not lower than the set threshold, judging that the food material is not ripe, sending an instruction, continuing to cook, after the detection period, continuing to acquire the cooking state of the food material, acquiring the cooking parameter, acquiring the cooking time, continuing to acquire the maturity judging parameter based on the cooking state, and judging the maturity of the food material until the food material is judged to be ripe.
Alternatively, the set threshold may be in a range of 0.03 to 0.07, and specifically may be 0.03, 0.04, 0.05, 0.06, or 0.07. Different values can be set according to different tastes, and the smaller the value is set, the closer the taste is to the set standard of the ripening parameter.
According to the embodiment of the disclosure, the calculation relationship is established by acquiring the cooking parameters, the cooking time and the ripening state of the corresponding food material in the cooking process, and further the ripening condition of the food material is analyzed, so that when the food material theoretically reaches the required cooking time but the food does not meet the ripening requirement, the cooking can be continued; when the food reaches the target mature state, the cooking is automatically stopped, and the phenomenon that the food is over cooked is prevented; thereby guaranteeing the cooking degree of food during cooking and bringing better experience to users. On the other hand, the parameters of the food materials are obtained for judgment, the influence of environmental changes is small, the stability degree is high, and the judgment result is reliable.
Optionally, in step S201, the cooking state of the food material further includes gas concentration information of a space where the food material is located, where the gas concentration information is, for example, a measured oxygen concentration or a measured carbon dioxide concentration. The gas concentration comprises gas concentration information of initial cooking of a space where the food material is located; and detecting the gas concentration information of the period in the space cooking where the food material is located.
The gas concentration information can be obtained by detecting through a gas sensor. Alternatively, the gas sensor may be a gas sensor disposed on the cooking device, or other device having a gas detection function, and is connected to the cooking device through wireless communication or wired communication.
In step S201, acquiring cooking parameters of the food material, where the cooking parameters further include oxygen concentration in a space where the food material exits; for example, the initial oxygen concentration τ of the space where the food material is located0Detecting the periodic oxygen concentration tau in space cooking of the food material1. The initial oxygen concentration τ0Processing the obtained gas concentration information after obtaining the gas concentration information of the space where the food material is initially cooked; detecting the oxygen concentration tau of the cycle during the cooking1And processing the obtained gas concentration information after the gas concentration information of the detection period in the cooking of the food material is obtained.
Correspondingly, in step S202, the cooking parameters of the food material are obtained and include an initial chromaticity C0Appearance chroma C1And initial oxygen concentration τ0Oxygen concentration of the detection period τ1Acquiring parameters corresponding to the cooking state of the food material, wherein the parameters comprise the cooking chromaticity C of the food material and the cooking oxygen concentration tau of the food material, and a first difference value is a difference value obtained by calculating the similarity between the cooking parameter and the cooking parameter, and comprises a cooking color difference △ C and an oxygen concentration difference △ tau.
The ripening oxygen concentration τ is the oxygen concentration of the cooking space in the state of ripening of the food; or the oxygen concentration of the corresponding cooking space when the food material reaches the edible maturity; or the oxygen concentration of the cooking space when the food material is preferably eaten; and the oxygen concentration of the cooking space corresponding to the user-defined maturity.
For example, the difference Δ τ in oxygen concentration is:
wherein tau is the ripening chromaticity of the food material standard;
τ0the initial oxygen concentration of the space where the food material is located;
τ1detecting the oxygen concentration of the space where the food material is located in a period;
obtaining the oxygen concentration difference delta tau; the value range of the value is [0,1 ]; the larger the value of the difference Δ τ in oxygen concentration, the larger the difference in oxygen concentration, and the further the difference is from the aging standard.
The cooking color difference Δ C can be obtained from equation (5).
Correspondingly, in step S205, a calculation model related to the first difference and the second difference is established, and a maturity determination parameter is determined; the calculation model is, for example, a maturity determination model obtained by weighting the first difference and the second difference.
The calculation model, for example, establishes a maturity determination model M ═ f (Δ C, Δ τ, Δ T) based on the step first difference Δ C, Δ τ and the second difference Δ T, and the obtained M value is a maturity determination parameter.
The calculation model is, for example:
wherein Δ C is a cooking color difference in the first difference value, and can be obtained by formula (5);
Δ τ is an oxygen concentration difference in the first difference value, and can be obtained by formula (8);
Δ T is a time difference in the second difference value, and can be obtained by equation (6);
alpha is used for adjusting the weight of the influence of the cooking color difference delta C on the doneness judgment;
beta is used for adjusting the influence weight of the oxygen concentration difference delta tau on maturity judgment;
is a very small constant that prevents the denominator from being 0.
Wherein, the larger the alpha + beta is equal to 1, the smaller the color difference between the appearance chromaticity and the cooking chromaticity of the food is when the cooking condition is satisfied, and the closer the chromaticity of the food is to the ideal target; the larger the beta, the closer the mouthfeel of the food is to the ideal target when the ripening conditions are met. These two parameters are used to adapt to the user's preference for the appearance and mouthfeel of the food, respectively, for being settable according to preference and taste. In the disclosed embodiment, α is 0.8 and β is 0.2. The color of the food meeting the ripening standard is closer to the color of the ripening.
By combining parameters of the food, the gas concentration of the space in which the food is placed and the cooking time in the cooking state of the food, performing multi-factor comprehensive judgment and further analyzing the cooking degree condition of the food, when the food theoretically needs the cooking time and the food does not meet the cooking requirement, the food can be continuously cooked; when the food reaches the target mature state, the cooking is automatically stopped, and the phenomenon that the food is over cooked is prevented; thereby guaranteeing the cooking degree of food during cooking and bringing better experience to users.
The embodiment of the present disclosure also provides an intelligent cooking device, as shown in fig. 3, including:
an obtaining module 301 configured to obtain cooking parameters and cooking time of food materials;
an establishing module 302 configured to determine a doneness determination parameter according to the cooking parameter and the cooking time of the food material and a calculation model of the cooking state of the food material;
a determination module 303 configured to determine that the cooking state satisfies maturity when the doneness determination parameter is lower than a set threshold;
the above modules are described below.
In the obtaining module 301, a cooking parameter of a food material is obtained, where the cooking parameter is used to characterize a degree of the food material in cooking, for example, appearance chromaticity of the food material; for example, in the cooking process, the obtaining module 301 obtains the appearance chromaticity of the food materialC1；
Obtaining cooking time T of food material in cooking1The cooking time is used for representing the cooking time of the food material.
In the establishing module 302, the food material ripening status is used for representing the degree of the food material under the ripening condition, such as the ripening color and the ripening time of the food material; for example, according to the food materials, the establishing module 302 searches for a corresponding cooking menu through a server, and obtains the ripening chromaticity C and the ripening time T of the food materials.
The ripening chromaticity C may be the chromaticity of the food material in a ripened state; or the corresponding chromaticity when the food material reaches the edible maturity; or the color corresponding to the maturity of the food material when the food material is eaten preferably; or the chroma corresponding to the user-defined maturity. The cooking time T may be a time required for cooking when the food material can be eaten or a time required for cooking when the food material satisfies a cooking condition; or a cooking time corresponding to the ripening chromaticity C.
The calculation model for establishing is, for example, a maturity degree determination model established by the establishing module 302 according to the relationship between the maturity state and the cooking parameter and the cooking time;
for example, the establishing module 302 is configured to determine the maturity C and the appearance C according to the maturity C and the appearance C1The color difference level of (a), the ripening time T and the cooking time T1And carrying out weighted calculation to determine a maturity judging parameter.
In the determining module 303, the doneness determining parameter is used to represent a difference between the doneness degree in the cooking of the food material and the cooking condition represented by the cooking state of the food material. When the doneness determination parameter is lower than a set threshold, the determination module 303 determines that the cooking state satisfies the doneness.
This disclosed embodiment, through the culinary art parameter and the corresponding culinary art time of acquireing the in-process edible material, and then the maturity condition of assay edible material to the maturity when guaranteeing food culinary art brings better experience for the user. According to the embodiment of the disclosure, the relevant data of the food materials are integrated, and the judgment result is more stable and reliable.
The embodiment of the present disclosure also provides an intelligent cooking apparatus, as shown in fig. 4, including:
a first obtaining unit 401 configured to obtain a cooking parameter of the food material according to a cooking state of the food material;
a second obtaining unit 402 configured to obtain a cooking time corresponding to a cooking state of the food material;
a first establishing unit 403, configured to obtain a first difference between the cooking parameter and a parameter corresponding to a cooking state of the food material;
a second establishing unit 404 configured to obtain a second difference between the cooking time and a time corresponding to a cooking state of the food material;
a third establishing unit 405 configured to establish a calculation model related to the first difference and the second difference, and obtain a maturity determination parameter;
a determination module 406, configured to determine that the cooking state satisfies maturity when the maturity determination parameter is lower than a set threshold;
the above-described apparatus is described below.
A first obtaining unit 401, configured to obtain a cooking parameter of the food material according to a cooking state of the food material during cooking; the obtaining of the cooking state of the food material in the cooking includes obtaining image information of the food material, where the image information includes cooking initial image information; and detecting periodic image information in the cooking of the food material. The image information of the detection period in the cooking of the food material is, for example, 5 seconds, and the image information of the food material is acquired every 5 seconds in the cooking. In the above, the image information of the detection period in the cooking of the food material is obtained, and when it is determined in step S206 that the food material is ripe, the obtaining of the image information of the detection period in the cooking of the food material is stopped.
The image information is obtained by, for example, capturing an image of the food material with a camera. Optionally, the camera may be a camera disposed on the cooking device, and is connected to the first obtaining unit 401 through wireless communication or wired communication; or other devices with shooting function, connected to the cooking device through wireless communication or wired communication, and the first obtaining unit 401 obtains information.
Obtaining a cooking parameter of the food material, for example, an initial chromaticity C of the food material0And the apparent color C during cooking1. Initial chroma C0After acquiring the initial image information of the cooking of the food material, the first acquiring unit 401 processes the image information and calculates the initial chromaticity C of the food material0(ii) a The apparent chroma C1After acquiring the image information of the detection cycle in the cooking of the food material, the first acquiring unit 401 processes the image information and calculates the appearance chromaticity C of the food material1。
Optionally, the first obtaining unit 401 obtains the cooking state of the food material during cooking, and further obtains the gas concentration information of the space where the food material is located. The gas concentration information is, for example, a measured oxygen concentration or a carbon dioxide concentration. The gas concentration comprises gas concentration information of initial cooking of a space where the food material is located; and detecting the gas concentration information of the period in the space cooking where the food material is located.
The gas concentration information can be obtained by detecting through a gas sensor. Alternatively, the gas sensor may be a gas sensor disposed on the cooking device, and connected to the first obtaining unit 401 through wired communication or wireless communication, or another device having a gas detection function, connected to the cooking device through wireless communication or wired communication, and used by the first obtaining unit 401 to obtain information.
The cooking parameters of the food materials are obtained, and the cooking parameters also comprise the oxygen concentration of a space where the food materials are discharged; for example, the initial oxygen concentration τ of the space where the food material is located0Detecting the periodic oxygen concentration tau in space cooking of the food material1. The initial oxygen concentration τ0For this purpose, the first obtaining unit 401 obtains the gas concentration information of the space where the food material is initially cooked, and then processes the information; detecting the oxygen concentration tau of the cycle during the cooking1To obtain the first acquisition orderThe unit 401 obtains the gas concentration information of the detection period in the cooking of the food material, and then processes the information to obtain the gas concentration information.
A second obtaining unit 402 configured to obtain a cooking time corresponding to a cooking state of the food material;
obtaining a cooking time corresponding to the cooking state of the food material, for example, the cooking time T when the image information is obtained1. The cooking time T of the food material1The cooking device may be connected to the second obtaining unit 402 through a built-in timer of the cooking device by wired communication or wireless communication, or other devices with timing function may be connected to the cooking device by wireless communication or wired communication, and the second obtaining unit 402 obtains information.
A first establishing unit 403, configured to obtain a first difference between the cooking parameter and a parameter corresponding to a cooking state of the food material; the cooking state corresponding parameter of the food material is, for example, a set of parameters that satisfy a cooking requirement corresponding to the cooking parameter acquired by the first acquiring unit. For example, when the cooking parameter is the chromaticity of the appearance of the food material, the first establishing unit 403 obtains the parameter corresponding to the cooking state of the food material as the chromaticity of the cooking of the food material.
Optionally, the first difference includes a cooking color difference △ C in the first obtaining unit 401, a cooking parameter of the food material, an initial chromaticity C is obtained0Appearance chroma C1(ii) a The first establishing unit 403 firstly obtains parameters corresponding to the cooking state of the food material, including the cooking chromaticity C of the food material; for example, according to the food material, the server searches for the corresponding cooking menu to obtain the maturity degree C of the food material.
Optionally, the food material may be confirmed by identifying the image information acquired by the first acquiring unit 401; the first establishing unit can identify food materials through a food material identification algorithm; and searching a corresponding cooking menu through a server according to the food material to obtain the ripening chromaticity C of the food material.
The ripening chromaticity C may be the chromaticity of the food material in a ripened state; or the corresponding chromaticity when the food material reaches the edible maturity; or the color corresponding to the maturity of the food material when the food material is eaten preferably; or the chroma corresponding to the user-defined maturity.
The first difference is a difference obtained by calculating the similarity between the cooking parameter and the ripening parameter by the first establishing unit 403; for example, the food material is obtained according to the ripeness color C of the food material and the initial color C of the food material0Appearance chroma C1And performing similarity calculation to obtain a cooking color difference △ C, which is a first difference.
Optionally, the first difference further includes an oxygen concentration difference △ τ in the first obtaining unit 401, obtaining the cooking parameter of the food material, and further includes an initial oxygen concentration τ on the basis of the above-mentioned disclosed embodiment0Oxygen concentration of the detection period τ1The parameter corresponding to the cooking state of the food material obtained by the first establishing unit 403 further includes the cooking oxygen concentration τ of the food material on the basis of the disclosed embodiment, the first difference is the difference obtained by the similarity calculation between the cooking parameter and the cooking parameter performed by the first establishing unit 403, and the first difference includes the cooking color difference △ C and the oxygen concentration difference △ τ.
The ripening oxygen concentration τ is the oxygen concentration of the cooking space in the state of ripening of the food; or the oxygen concentration of the corresponding cooking space when the food material reaches the edible maturity; or the oxygen concentration of the cooking space when the food material is preferably eaten; and the oxygen concentration of the cooking space corresponding to the user-defined maturity.
A second establishing unit 404 configured to obtain a second difference between the cooking time and a time corresponding to a cooking state of the food material; the cooking state of the food material corresponds to time, which is used for representing the cooking time required by the food material to meet the cooking condition, for example, the cooking time required by the food material to be eaten or the cooking time required by the food material to meet the cooking condition; or a cooking time corresponding to the maturity level C in the first establishing unit 403.
For example, the second difference is a difference obtained by calculating the cooking time and the ripening time by the second establishing unit 404; for exampleIf yes, the second establishing unit 404 obtains the cooking time T of the food material according to the second obtaining unit 4021And calculating the cooking time T of the food material to obtain a time difference, namely a first difference. The cooking time T may be a time required for cooking when the food material can be eaten or a time required for cooking when the food material satisfies a cooking condition; or a cooking time corresponding to the ripening chromaticity C.
For example, the cooking time T is acquired at the second acquisition unit 4021Acquiring a time T corresponding to the cooking state of the food material according to the cooking chromaticity C in the first establishing unit 403, and acquiring the cooking time T according to the cooking time T1And the time T corresponding to the aging state, the second establishing unit 403 performs calculation processing, and the obtained time difference △ T is a second difference.
A third establishing unit 405 configured to establish a calculation model related to the first difference and the second difference, and determine a maturity determination parameter; the calculation model is, for example, a maturity determination model obtained by weighting the first difference obtained by the first establishing unit 403 and the second difference obtained by the second establishing unit 404 by the third establishing unit 405.
For example, the third establishing unit 405 establishes the doneness determination model M ═ f (Δ C, Δ T) based on the first difference Δ C obtained by the first establishing unit 403 and the second difference Δ T obtained by the second establishing unit 404, and the obtained M value is the doneness determination parameter.
For example, the third establishing unit 405 establishes a doneness determination model M ═ f (Δ C,. DELTA.τ,. DELTA.T) based on the step first difference Δ C,. DELTA.τ, and the second difference Δ T, and obtains M values, that is, doneness determination parameters.
A first determination unit 406 configured to determine that the cooking state satisfies maturity when the doneness determination parameter is lower than a set threshold; further comprising, when the doneness degree determination parameter is not lower than the set threshold, determining that the cooking state is not sufficient for cooking, continuing to obtain the cooking state through the first obtaining unit 401, and obtaining the cooking time through the second obtaining unit 402 until the doneness degree determination parameter is lower than the set threshold, wherein the cooking state is sufficient for cooking.
The first determining unit 406 determines the maturity of the food material according to the maturity determination parameter obtained by the third establishing unit 405; for example, when the doneness determination parameter is lower than a set threshold, it is determined that the food material has met the doneness, and an instruction is sent to stop cooking; when the doneness determination parameter is not lower than the set threshold, it is determined that the food material is not ripe, an instruction is sent, cooking is continued, after the detection period, the first obtaining unit 401 continues to obtain the cooking state of the food material, obtain the cooking parameter, the second obtaining unit 402 obtains the cooking time, and based on the cooking state, the third establishing unit 405 continues to obtain the doneness determination parameter, and food material ripening determination is performed until the first determining unit 406 determines that the food material is ripe.
Optionally, in the first determination module 406, the threshold is set to be in a range of 0.03 to 0.07, and specifically may be 0.03, 0.04, 0.05, 0.06, or 0.07. Different values can be set according to different tastes, and the smaller the value is set, the closer the taste is to the set standard of the ripening parameter.
The disclosed embodiment also provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-mentioned intelligent cooking method.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the above-mentioned intelligent cooking method.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
An embodiment of the present disclosure further provides an electronic device, a structure of which is shown in fig. 5, where the electronic device includes:
at least one processor (processor)500, such as processor 500 in FIG. 5; and a memory (memory)501, and may further include a Communication Interface 502 and a bus 503. The processor 500, the communication interface 502, and the memory 501 may communicate with each other via a bus 503. Communication interface 502 may be used for information transfer. The processor 500 may call the logic instructions in the memory 501 to perform the intelligent cooking method of the above-described embodiment.
In addition, the logic instructions in the memory 501 may be implemented in the form of software functional units and may be stored in a computer readable storage medium when the logic instructions are sold or used as independent products.
The memory 501 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 500 executes functional applications and data processing by executing software programs, instructions and modules stored in the memory 501, that is, implements the intelligent cooking method in the above-described method embodiments.
The memory 501 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 501 may include a high-speed random access memory and may also include a nonvolatile memory.
The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the disclosed embodiments includes the full ambit of the claims, as well as all available equivalents of the claims. As used in this application, although the terms "first," "second," etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, unless the meaning of the description changes, so long as all occurrences of the "first element" are renamed consistently and all occurrences of the "second element" are renamed consistently. The first and second elements are both elements, but may not be the same element. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (12)
1. An intelligent cooking method, comprising:
obtaining cooking parameters and cooking time of food materials;
establishing a calculation model with the cooking state of the food material according to the cooking parameters, the cooking time and the cooking state, and determining a cooking degree judgment parameter;
and when the cooking degree judgment parameter is lower than a set threshold value, determining that the cooking state meets the cooking.
2. The method of claim 1, wherein the obtaining of the cooking parameters and the cooking time of the food material comprises:
acquiring cooking parameters of the food materials according to the cooking states of the food materials;
and acquiring the cooking time corresponding to the cooking state of the food material.
3. The method of claim 2, wherein the cooking state comprises graphical information of the food material; the image information includes:
the food cooking initial image information;
and detecting periodic image information in the cooking of the food material.
4. The method of claim 3, wherein the cooking state further comprises gas concentration information of a space in which the food material is located; the gas concentration information includes:
initial gas concentration information of the space where the food material is located during cooking;
and detecting periodic gas concentration information in space cooking of the food material.
5. The method according to any one of claims 1 to 4, wherein said establishing a calculation model with the cooking state of the food material according to the cooking parameters and the cooking time comprises:
acquiring a first difference value between the cooking parameter and a parameter corresponding to the cooking state of the food material;
acquiring a second difference value between the cooking time and the time corresponding to the cooking state of the food material;
establishing a computational model related to the first difference and the second difference.
6. An intelligent cooking device, comprising:
the acquisition module is used for acquiring cooking parameters and cooking time of food materials;
the establishment module is used for determining a maturity judgment parameter according to the cooking parameters and the cooking time of the food materials and a calculation model of the cooking state of the food materials;
and the judging module is used for determining that the cooking state meets maturity when the maturity judging parameter is lower than a set threshold.
7. The apparatus of claim 6, wherein the obtaining module further comprises:
the first obtaining unit is used for obtaining cooking parameters of the food materials according to the cooking states of the food materials;
and the second acquisition unit is used for acquiring the cooking time corresponding to the cooking state of the food material.
8. The apparatus according to claim 7, wherein the first obtaining module is configured to obtain image information of the cooking state; the first obtaining module is specifically configured to:
acquiring the food cooking initial image information;
and acquiring image information of the detection period in the cooking of the food material.
9. The apparatus of claim 8, wherein the first obtaining module is further configured to obtain gas concentration information of the cooking state; the first obtaining module is specifically configured to:
acquiring initial gas concentration information of the space where the food material is located during cooking;
and acquiring gas concentration information of a detection period in space cooking of the food material.
10. The apparatus according to any one of claims 6 to 9, wherein the establishing module comprises:
the first establishing unit is used for acquiring a first difference value between the cooking parameter and a parameter corresponding to the cooking state of the food material;
a second establishing unit, configured to obtain a second difference between the cooking time and a time corresponding to a cooking state of the food material;
and the third establishing unit is used for establishing a calculation model related to the first difference and the second difference to obtain a maturity judging parameter.
11. An oven comprising an apparatus as claimed in any one of claims 6 to 10.
12. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the at least one processor to perform the method of any one of claims 1-5.
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