CN111752178A - Intelligent cooking method and device - Google Patents
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- 238000010411 cooking Methods 0.000 title claims abstract description 239
- 238000000034 method Methods 0.000 title claims abstract description 61
- 230000005070 ripening Effects 0.000 claims description 36
- 230000008569 process Effects 0.000 claims description 25
- 230000032683 aging Effects 0.000 claims 1
- 238000004643 material aging Methods 0.000 claims 1
- 238000004891 communication Methods 0.000 description 20
- 238000004590 computer program Methods 0.000 description 8
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- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 2
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Abstract
The application relates to an intelligent cooking method, which comprises the steps of obtaining a cooking parameter of a food material, and representing the degree of the food material during cooking; acquiring cooking parameters of the food material in cooking, and representing the degree of the food material in cooking; and determining the cooking degree of the food material according to the cooking parameters and the cooking parameters, so that the cooking degree of the food during cooking is ensured, and better experience is brought to a user. The application also discloses intelligent cooking device, oven and electronic equipment.
Description
Technical Field
The application relates to the technical field of intelligent kitchen appliances, for example to an intelligent cooking method and device.
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 intelligent cooking method comprises: acquiring a cooking parameter of a food material, and representing the degree of the food material during cooking; acquiring cooking parameters of the food material in cooking, and representing the cooking degree of the food material; and determining the cooking degree of the food material according to the cooking parameters and the cooking parameters.
According to another aspect of the disclosed embodiments, there is provided an intelligent cooking apparatus.
In some optional embodiments, the intelligent cooking apparatus comprises: the first acquisition module is used for acquiring cooking parameters of food materials; the second acquisition module is used for acquiring cooking parameters of the food materials in cooking; and the judging module is used for determining the maturity of the food material according to the cooking parameters and the maturity parameters.
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.
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 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:
by acquiring the cooking parameters of the food materials and the cooking parameters in the cooking process, the cooking degree condition of the food materials is analyzed, so that the cooking degree of the food during cooking is ensured, and better experience is brought to a user. According to the embodiment of the disclosure, the relevant data is acquired based on the food material, 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 diagram of an intelligent cooking method provided by an embodiment of the present disclosure;
fig. 2 is a schematic flow chart diagram 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: a first acquisition module; 302: a second acquisition module; 303: a determination unit;
401: a first acquisition unit; 402: a second acquisition unit; 403: a first recognition unit; 404: a third acquisition unit; 405: a first determination unit;
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, acquiring a cooking parameter of the food material, and representing the degree of the food material during cooking;
s102, acquiring cooking parameters of the food materials in cooking, and representing the degree of the food materials in the cooking;
s103, determining the cooking degree of the food material according to the cooking parameters and the cooking parameters;
the above steps will be described separately below.
In step S101, acquiring a ripening parameter of a food material, wherein the ripening parameter is used for representing a degree of the food material when it is ripe, such as a ripening 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.
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.
In step S102, a cooking parameter of the food material during a cooking process is obtained to characterize a degree of the food material during the cooking, for example, an appearance chromaticity C of the food material during the cooking1. For example, in the cooking process, the appearance chromaticity C of the food material is obtained1。
In step S103, determining the maturity of the food material according to the cooking parameter and the maturity parameter, for example, when a difference between the cooking parameter and the maturity parameter satisfies a set threshold, determining that the food material satisfies a maturity requirement, and stopping cooking; and when the difference value between the cooking parameter and the cooking parameter does not meet the set threshold value, determining that the food material does not meet the cooking requirement, and continuing to cook.
According to the embodiment of the food cooking method and the food cooking system, the cooking parameters of the food materials and the cooking parameters in the cooking process are obtained, and then the cooking degree condition of the food materials is analyzed, so that the cooking degree of the food during cooking is guaranteed, and better experience is brought to a user.
The embodiment of the present disclosure further includes an intelligent cooking method, as shown in fig. 2, including:
s201, acquiring cooking parameters of food materials;
s202, acquiring image information of the food materials in the cooking process;
s203, acquiring cooking parameters of the food materials based on the image information;
s204, acquiring a similarity parameter between the cooking parameter and the ripening parameter;
s205, determining the maturity of the food materials according to the similarity parameters;
the above steps will be described separately below.
In step S201, a ripening parameter of the food material is acquired, for example, by acquiring image information of the food material.
For example, image information of the food material is captured by a camera and uploaded to a server, and the server performs recognition based on the image information to obtain the ripening parameter of the food material. The food material is identified, for example by means of an image recognition algorithm.
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, the ripening parameter is, for example, 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.
For example, the server searches a cooking menu to obtain the ripeness degree C of the food material in the ripeness state.
The maturity chroma C is constructed by a standard Lab color model:
C=(a,b) (1)
wherein a is the numerical value of a channel in Lab space of the maturation chromaticity C;
b is the value of b channel in Lab space for the maturity C.
In step S202, the acquiring image information of the food material in the cooking process includes: acquiring the food cooking initial image information; and acquiring image information of a preset time interval in the cooking of the food material. The image information of the food material is obtained at a preset time interval during the cooking of the food material, for example, the image information of the food material is obtained every 5 seconds during the cooking.
In step S205, when it is determined that the food material has met the cooking requirement, the obtaining of the image information of the preset time interval in the cooking of the food material is stopped.
In step S203, a cooking parameter of the food material, such as an initial chromaticity C of the food material, is obtained based on the image information0And the apparent color C during cooking1. Initial chroma C0After the food material cooking initial image information is obtained in step S202, the image information is processed to calculate the initial chromaticity C of the food material0(ii) a The apparent chroma C1In step S202, after the image information of the preset time interval in the cooking of the food material is obtained, the image information is processed to calculate the appearance chromaticity C of the food material1。
For example, in step S203, 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, aiThe value of a channel of a pixel i in the food material area in Lab space is obtained;
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 cooking processing image information of the food materials; appearance chroma C1The method for obtaining the average chromaticity C 'is the same as that of the average chromaticity C' by processing the image information of the preset time interval in the cooking of the food material.
In step S204, a similarity parameter between the cooking parameter and the ripening parameter is obtained; the similarity parameter is, for example, an absolute value obtained by calculating the similarity between the cooking parameter and the ripening parameter; or calculating the absolute numerical value of each dimension characteristic based on the cooking parameter and the ripening parameter.
For example, the similarity parameter is based on the ripeness C of the food material and the initial chromaticity C of the food material0Appearance chroma C1And performing similarity calculation to obtain a cooking color difference △ C.
Optionally, 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 larger the value of the cooking color difference Δ C, the larger the color difference.
In step S205, determining a maturity degree of the food material according to the similarity parameter; for example, when the similarity parameter is lower than a set threshold, it is determined that the food material has met maturity, and an instruction is sent to stop cooking; and when the similarity parameter is not lower than a set threshold value, judging that the food materials are not ripe, sending an instruction, continuing to cook, continuously acquiring image information of the food materials after a preset time interval, acquiring cooking parameters, continuously acquiring the similarity parameter based on the image information, and judging the ripe food materials until the food materials are 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.
Optionally, in step S201, the cooking parameter of the food material may further include a cooking time of the food material. The cooking time can be the time required for cooking the food material to eat or the time required for cooking the food material to meet the cooking standard; or a cooking time corresponding to the ripening chromaticity C. In step S202, when the cooking time arrives, acquisition of image information of a preset time interval is started.
For example, in step S201, the maturity color C of the food material and the cooking time T of the food material are obtainedmin(ii) a The ripening chromaticity C is the chromaticity corresponding to the food material when the food material reaches the edible ripening degree; the cooking time T of the food materialminThe cooking time when the food material reaches an edible maturity. In step S202, acquiring cooking initial image information of the food material; continuously cooking until the food material cooking time TminAnd then, acquiring the image information of the food material at the preset time interval. For example, when the cooking time reaches TminAnd then, acquiring image information once at each preset time interval, uploading the image information to a server, and entering the step S204 to acquire cooking parameters. When it is determined in step S205 that the food material has satisfied the cooking requirement, the acquisition of the image information of the preset time interval in the cooking of the food material is stopped.
Thus, the cooking time T of the food materialminAfter the food is obtained, the image information of the food is acquired at intervals, so that the information acquisition amount and the parameter calculation amount are reduced, and the redundancy degree is reduced. The cooking time T of the food materialminThe timing of (2) can be obtained by a timer built in the cooking device or other timing devices.
According to the embodiment of the disclosure, the cooking degree condition of the food material is analyzed by acquiring the cooking parameters of the food material and the cooking parameters in the cooking process, and when the food material theoretically needs to be cooked for a long time and the food does not meet the cooking 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.
The embodiment of the present disclosure also provides an intelligent cooking device, as shown in fig. 3, including:
a first obtaining module 301 configured to obtain cooking parameters of food materials;
a second obtaining module 302 configured to obtain cooking parameters of the food materials in cooking;
a determining module 303 configured to determine a maturity degree of the food material according to the cooking parameter and the maturity parameter;
the above modules will be described separately below.
The first obtaining module 301 is configured to obtain a ripening parameter of the food material, for example, a ripening chromaticity C of the food material. For example, the first obtaining module 301 searches for a corresponding cooking menu through a server according to the food material, and obtains the maturity degree C of the food material.
A second obtaining module 302 configured to obtain a cooking parameter of the food material during a cooking process, for example, an appearance chromaticity C of the food material during the cooking process1. For example, the second obtaining module 302 obtains the appearance chromaticity C of the food material during the cooking process1。
A determining module 303, configured to determine the maturity of the food material according to the maturity parameter and the cooking parameter, for example, when a difference between the maturity parameter and the cooking parameter satisfies a set threshold, the determining module 303 determines that the food material is mature, and the cooking is stopped; when the difference between the cooking parameter and the cooking parameter does not satisfy the set threshold, the determining module 303 determines that the food material is not cooked, and continues to cook the food material.
According to the embodiment of the present disclosure, the first obtaining module 301 obtains the doneness parameter of the food material, the second obtaining module 302 obtains the cooking parameter in the cooking process, and then the doneness condition of the food material is analyzed by the determining module 303, so that the doneness of the food during cooking is ensured, and better experience is brought to the user.
The embodiment of the present disclosure also provides another intelligent cooking apparatus, as shown in fig. 4, including:
a first obtaining unit 401, configured to obtain a ripening parameter of a food material according to image information of the food material;
a second obtaining unit 402 configured to obtain image information of the food material during cooking;
a first identification unit 403 configured to obtain cooking parameters of the food material based on the image information.
A third obtaining unit 404 configured to obtain a similarity parameter between the cooking parameter and the ripening parameter;
a first determining unit 405 configured to determine the maturity of the food material according to the instruction parameter;
the above-described units will be described separately below.
A first obtaining unit 401, configured to obtain a ripening parameter of the food material according to the image information of the food material. For example, the first obtaining unit 401 obtains image information of the food material and uploads the image information to the server, and the server performs recognition based on the image information to obtain the ripening parameter of the food material. The food material is identified, for example by means of an image recognition algorithm.
The acquisition of the image information can be obtained by shooting through a camera, the camera can be a camera arranged in the cooking device and connected to the first acquisition unit 401 through wireless communication or wired communication, or other devices with shooting functions are connected to the cooking device through wireless communication or wired communication and acquired by the first acquisition unit 401.
The ripening parameter is, for example, 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.
For example, the first obtaining module 401 searches a cooking menu through a server according to the food material, and obtains the maturity degree C of the food material in the mature state.
A second obtaining unit 402, configured to obtain image information of the food material during cooking, including: acquiring the food cooking initial image information; and acquiring image information of a preset time interval in the cooking of the food material. The obtaining of the image information of the food material at the preset time interval in the cooking process is, for example, the second obtaining unit obtains the image information of the food material every 5 seconds in the cooking process.
The image information can be obtained by shooting through a camera, which may be a camera disposed in the cooking device and connected to the second obtaining unit 402 through wireless communication or wired communication, or other devices with shooting function and connected to the cooking device through wireless communication or wired communication and obtained by the first obtaining unit 402.
A first identification unit 403 configured to obtain cooking parameters of the food material based on the image information; the cooking parameters are, for example, inAfter the second acquiring unit 402 acquires the initial image information of the cooking of the food material, the first identifying unit 403 processes the image information and calculates the initial chromaticity C of the food material0(ii) a For example, after the second acquiring unit 402 acquires the image information of the food material at the preset time interval in the cooking process, the first identifying unit 403 processes the image information to calculate the appearance chromaticity C of the food material1。
A third obtaining unit 404 configured to obtain a similarity parameter between the cooking parameter and the ripening parameter; the similarity parameter is, for example, an absolute value obtained by calculating the similarity between the cooking parameter and the ripening parameter; or calculating the absolute numerical value of each dimension characteristic based on the cooking parameter and the ripening parameter.
For example, the third obtaining unit 404 obtains the initial chromaticity C obtained by the first identifying unit 403 and the maturity chromaticity C obtained by the first obtaining unit 4010Appearance chroma C1And performing similarity calculation to obtain a cooking color difference △ C.
A first determining unit 405 configured to determine the maturity of the food material according to the instruction parameter; for example, when the similarity parameter obtained by the third obtaining unit 404 is smaller than the set threshold, the first determining unit 405 determines that the food material is ripe, sends an instruction, and stops the operation of the cooking apparatus; when the similarity parameter obtained by the third obtaining unit 404 is not less than the set threshold, the first determining unit 405 determines that the food material is not ripe enough, sends an instruction, continues to cook, continues to obtain the image information of the food material through the second obtaining unit 402 after a preset time interval, obtains the cooking parameter by the first identifying unit 403, obtains the similarity parameter by the third obtaining unit 404, and uploads the similarity parameter to the first determining unit 405 to perform food material ripening determination until the food material is ripe enough.
Optionally, the first obtaining unit 401 may be configured to obtain the shortest time T required for cooking the food materialminAnd a ripening parameter C. The minimum time required for cooking may be the minimum time required for cooking in which the food material can be eaten, or the minimum time required for cooking in which the food material satisfies a ripening standard.
For example, the first obtaining unit 401 is configured to obtain the cooking time and the cooking chromaticity of the food material. The cooking time can be the time required for cooking the food material to eat or the time required for cooking the food material to meet the cooking standard; or a cooking time corresponding to the ripening chromaticity C. The second obtaining module 402 is configured to, when the cooking time is reached, start obtaining the image information at the preset time interval until the food material is cooked.
Therefore, after the cooking time is up, the image information of the food materials is acquired at intervals, the information acquisition amount is reduced, the parameter calculation amount is reduced, and the redundancy is reduced. The cooking time can be measured by a timer disposed in the cooking device, and the timer is connected to the first obtaining unit 401 and the second obtaining unit 402 through wired communication or wireless communication; other devices with timing devices can also be connected with the cooking device through wired communication or wireless communication, and the cooking device is connected with the first acquisition unit 401 and the second acquisition unit 402.
According to the embodiment of the disclosure, the cooking degree condition of the food material is analyzed by acquiring the cooking parameters of the food material and the cooking parameters in the cooking process, and when the food material theoretically needs to be cooked for a long time and the food does not meet the cooking 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.
The embodiment of the disclosure also provides an oven, which comprises the intelligent cooking device.
The disclosed embodiments also provide 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 that, when executed by a computer, cause the computer to perform the 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 the functional application and data processing by executing the software program, instructions and modules stored in the memory 501, so as to implement the intelligent cooking method in the above method embodiment.
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:
acquiring a cooking parameter of a food material, and representing the degree of the food material during cooking;
acquiring cooking parameters of the food material in cooking, and representing the cooking degree of the food material;
and determining the cooking degree of the food material according to the cooking parameters and the cooking parameters.
2. The method of claim 1, wherein the obtaining of the cooking parameters of the food material in the cooking comprises:
and acquiring the cooking parameters of the food materials according to the image information of the food materials in the cooking process.
3. The method of claim 2, wherein the graphical information of the food material in the cooking comprises
The food cooking initial image information;
and image information of a preset time interval in the cooking of the food material.
4. The method of any of claims 1 to 3, wherein said determining the maturity of the food material based on the cooking parameter and the maturity parameter comprises:
acquiring similarity parameters of the cooking parameters and the ripening parameters;
and determining the maturity of the food material according to the similarity parameter.
5. The method of claim 4, wherein the determining the maturity of the food material according to the similarity parameter comprises:
when the similarity parameter is lower than a set threshold value, judging that the food materials meet the maturity;
and when the similarity parameter is not lower than a set threshold value, judging that the food materials are not fully cooked.
6. An intelligent cooking device, comprising:
the first acquisition module is used for acquiring cooking parameters of food materials;
the second acquisition module is used for acquiring cooking parameters of the food materials in cooking;
and the judging module is used for determining the maturity of the food material according to the cooking parameters and the maturity parameters.
7. The apparatus of claim 6, wherein the first obtaining module comprises:
the food material aging control device comprises a first obtaining unit, a second obtaining unit and a control unit, wherein the first obtaining unit is used for obtaining the aging parameters of food materials according to the image information of the food materials.
8. The apparatus of claim 6, wherein the second obtaining module comprises:
the second acquisition unit is used for acquiring the image information of the food material in the cooking process;
and the first identification unit is used for acquiring the cooking parameters of the food materials based on the image information.
9. The apparatus of claim 8, wherein the second obtaining unit is further configured to: acquiring the food cooking initial image information; and acquiring image information of a preset time interval in the cooking of the food material.
10. The apparatus of any of claims 6 to 9, wherein the determining module comprises:
a third obtaining unit, configured to obtain a similarity parameter between the cooking parameter and the ripening parameter;
a first judging unit for determining the maturity of the food material according to the similarity parameter;
when the similarity parameter is lower than a set threshold value, the first judging unit judges that the food material is ripe; when the similarity parameter is not lower than a set threshold, the first judging unit judges that the food material is not ripe.
11. An oven, characterized in that it comprises a device according to 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 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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