CN112861946B - Neural network training method, cooking inspection method, system and intelligent cooking equipment - Google Patents
Neural network training method, cooking inspection method, system and intelligent cooking equipment Download PDFInfo
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- 238000010411 cooking Methods 0.000 title claims abstract description 349
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 36
- 238000012549 training Methods 0.000 title claims abstract description 27
- 238000007689 inspection Methods 0.000 title claims abstract description 9
- 230000000694 effects Effects 0.000 claims abstract description 78
- 238000004590 computer program Methods 0.000 claims description 11
- 238000012795 verification Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 2
- 235000021186 dishes Nutrition 0.000 description 29
- 244000061456 Solanum tuberosum Species 0.000 description 19
- 235000002595 Solanum tuberosum Nutrition 0.000 description 19
- 241000287828 Gallus gallus Species 0.000 description 18
- 235000012020 french fries Nutrition 0.000 description 15
- FFRBMBIXVSCUFS-UHFFFAOYSA-N 2,4-dinitro-1-naphthol Chemical compound C1=CC=C2C(O)=C([N+]([O-])=O)C=C([N+]([O-])=O)C2=C1 FFRBMBIXVSCUFS-UHFFFAOYSA-N 0.000 description 13
- 235000012015 potatoes Nutrition 0.000 description 12
- 240000005856 Lyophyllum decastes Species 0.000 description 11
- 235000013194 Lyophyllum decastes Nutrition 0.000 description 11
- 238000010438 heat treatment Methods 0.000 description 10
- 235000013305 food Nutrition 0.000 description 3
- 235000013606 potato chips Nutrition 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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Abstract
The invention provides a neural network training method, a cooking inspection method, a system and intelligent cooking equipment, wherein the training method executes the following sample acquisition steps to obtain a plurality of groups of learning samples under the condition that whether the cooking effects of a plurality of intelligent cooking equipment reach standards or not is known: acquiring cooking parameters of the intelligent cooking equipment in the cooking process; shooting the cooked dishes to obtain dish images capable of reflecting the cooking degree of the dishes; identifying the types of dishes according to the dish images; taking cooking parameters, dish images and dish types as input signals and taking whether the cooking effect of the intelligent cooking equipment meets the standard or not as output signals to form a group of learning samples for the artificial neural network to carry out cooking effect checking training; and (3) carrying out cooking effect checking training on the artificial neural network by adopting a plurality of groups of learning samples until the artificial neural network has the capability of checking whether the cooking effect of the intelligent cooking equipment reaches the standard according to cooking parameters, dish images and dish types.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a neural network training method, a cooking inspection system and intelligent cooking equipment.
Background
With the rapid development of society, the catering industry is developed, consumers can order wanted food from the intelligent cooking equipment when not want to cook, and the intelligent cooking equipment can automatically cook corresponding food according to the ordered food, so that the consumers can eat more conveniently.
After the intelligent cooking equipment works for a long time, the cooking effect may not reach the standard due to error accumulation, equipment loss and other reasons, for example, the heating effect of the intelligent cooking equipment is reduced due to equipment loss, so that dishes cooked by the intelligent cooking equipment may be poor in quality due to insufficient heating, and therefore whether the cooking effect of the intelligent cooking equipment reaches the standard or not needs to be checked in time, so that the intelligent cooking equipment which does not reach the standard is used for cooking, and the cooked dishes are poor in quality. However, when the cooking effect of the intelligent cooking device is checked, the intelligent cooking device needs to be made to cook according to the standard cooking parameters (standard cooking temperature, standard cooking duration and the like) of a certain dish to check whether the cooking effect of the intelligent cooking device meets the standard, but the intelligent cooking device can cook various dishes in the actual cooking process, and cooking is not usually performed according to the standard cooking parameters when other dishes are cooked, so that the cooking effect of the intelligent cooking device is inconvenient to check in the actual cooking process.
Disclosure of Invention
The technical problem to be solved by the invention is how to conveniently test the cooking effect of the intelligent cooking equipment.
In order to solve the technical problems, the invention provides a training method for an artificial neural network for checking the cooking effect of intelligent cooking equipment, which comprises the following steps:
p. under the condition that whether the cooking effect of a plurality of intelligent cooking devices respectively meets the standard is known, the plurality of intelligent cooking devices are made to cook various dishes respectively, the plurality of intelligent cooking devices are made to cook the same dish respectively according to different cooking parameters, the following sample acquisition steps are respectively executed in each cooking process, a plurality of groups of learning samples are obtained, and the sample acquisition steps executed in each cooking process comprise the following A, B, C, D:
a, acquiring cooking parameters of the intelligent cooking equipment in a cooking process;
shooting the cooked dishes to obtain dish images reflecting the cooking degree of the dishes;
c, identifying the type of the dishes according to the dish image;
d, taking the cooking parameters, the dish images and the dish varieties as input signals, and taking whether the cooking effect of the intelligent cooking equipment meets the standard or not as output signals to form a group of learning samples for the artificial neural network to carry out cooking effect checking training;
and Q, adopting the plurality of groups of learning samples to perform cooking effect checking training on the artificial neural network until the artificial neural network has the capability of checking whether the cooking effect of the intelligent cooking equipment reaches the standard according to cooking parameters, dish images and dish types.
Preferably, the cooking parameters include cooking temperature, cooking time period and amount of dishes.
Preferably, in the step a, the cooking temperature, the cooking duration and the dish amount are obtained from a cooking start program of the intelligent cooking apparatus.
The invention also provides a cooking effect checking method of the intelligent cooking equipment, which comprises the following steps:
a. acquiring cooking parameters of the intelligent cooking equipment in the cooking process;
b. shooting the cooked dishes to obtain dish images capable of reflecting the cooking degree of the dishes;
c. identifying the types of dishes according to the dish images;
d. inputting the cooking parameters, the dish images and the dish variety types into a trained artificial neural network, and checking whether the cooking effect of the intelligent cooking equipment meets the standard or not by the trained artificial neural network according to the dish types, the cooking parameters and the dish images.
Preferably, the cooking parameters include cooking temperature, cooking time period and amount of dishes.
Preferably, in the step a, the cooking temperature, the cooking duration and the dish amount are obtained from a cooking start program of the intelligent cooking apparatus.
Preferably, the trained artificial neural network is an artificial neural network obtained after the training method is performed.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the training method as described above.
Preferably, the computer program when executed by a processor also implements the steps in the verification method as described above.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps in the verification method as described above.
The invention also provides an intelligent cooking appliance comprising a computer readable storage medium and a processor interconnected, the computer readable storage medium being as described above.
The invention also provides a cooking effect checking system of the intelligent cooking equipment, which comprises the intelligent cooking equipment and a server, wherein the intelligent cooking equipment is in communication connection with the server, and the server comprises a computer readable storage medium and a processor which are mutually connected, and the computer readable storage medium is as described above.
The invention has the following beneficial effects: in the process of training the artificial neural network, the learning sample of the artificial neural network is obtained in the process of cooking various dishes, so that the intelligent cooking equipment has the capability of checking whether the cooking effect of the intelligent cooking equipment meets the standard according to cooking parameters, dish images and dish types, and in the process of cooking by the intelligent cooking equipment, the trained artificial neural network can conveniently check whether the cooking effect of the intelligent cooking equipment meets the standard according to the dish types, the cooking parameters and the dish images as long as the dishes cooked by the intelligent cooking equipment are one of the dishes.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a training method for an artificial neural network for verifying cooking effects of an intelligent cooking appliance;
fig. 2 is a flowchart of a cooking effect checking method of the intelligent cooking appliance.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
Example 1
In this embodiment, the intelligent cooking apparatus may cook various dishes, such as chicken steaks, french fries, and the like. For each dish, the intelligent cooking device is provided with standard cooking parameters such as standard cooking temperature, standard cooking duration, standard dish amount, and the like, for example: for the fried chicken, the standard cooking parameters set by the intelligent cooking equipment are 200g of chicken, the cooking temperature is 200 ℃ and the cooking time is 5min; for french fries, standard cooking parameters set by the intelligent cooking equipment are 200g of potato, the cooking temperature is 180 ℃, and the cooking time is 5min. If the cooking effect of the intelligent cooking equipment reaches the standard, the intelligent cooking equipment fries 200g of chicken for 5min according to the cooking temperature of 200 ℃, golden fried chicken chops will be fried, and the intelligent cooking equipment fries 200g of potatoes for 5min according to the cooking temperature of 180 ℃, golden fried potato chips will be fried; if the cooking effect of the intelligent cooking device is not up to standard, such as the heating effect is too high, the intelligent cooking device fries 200g chicken for 5 minutes according to the cooking temperature of 200 ℃, the intelligent cooking device fries dark brown fried chicken chops, and the intelligent cooking device fries 200g potatoes for 5 minutes according to the cooking temperature of 180 ℃, and the intelligent cooking device fries dark brown fried potato chips. Therefore, whether the cooking effect of the intelligent cooking equipment reaches the standard or not needs to be checked, so that the quality of cooked dishes is poor due to the fact that the intelligent cooking equipment which does not reach the standard is adopted for cooking.
In this embodiment, the intelligent cooking appliance includes a computer readable storage medium and a processor coupled to each other, the computer readable storage medium having a computer program stored thereon. In order to conveniently check whether the cooking effect of the intelligent cooking appliance is up to standard, a computer program on a computer readable storage medium of the intelligent cooking appliance is executed by a processor to implement a training method for an artificial neural network as shown in fig. 1, before cooking, as follows:
under the condition that whether the cooking effects of other intelligent cooking devices reach the standards or not is known, the other intelligent cooking devices are enabled to cook the fried chicken and the fried potato strips respectively, wherein for the same dishes, the other intelligent cooking devices are enabled to execute the cooking starting programs respectively so as to cook according to different cooking parameters, for example: the cooking effect of the first intelligent cooking equipment is known to reach the standard, and the first intelligent cooking equipment is made to fry 200g of chicken and 200g of potatoes for 5min according to the cooking temperature of 200 ℃; the cooking effect of the second intelligent cooking equipment is known to reach the standard, and the second intelligent cooking equipment is made to fry 200g of chicken and 200g of potatoes for 5min according to the cooking temperature of 180 ℃; the cooking effect of the third intelligent cooking device is known to be not up to the standard (the cooking effect is too high), so that the third intelligent cooking device respectively fries 250g of chicken and 250g of potatoes for 5min according to the cooking temperature of 200 ℃; the cooking effect of the fourth intelligent cooking device is known not to reach the standard (the cooking effect is too low), so that the fourth intelligent cooking device can fry 200g of chicken and 200g of potatoes for 8min … … respectively according to the cooking temperature of 200 ℃; the following sample acquisition steps are then performed for each cooking session to obtain a plurality of sets of learning samples, wherein the sample acquisition steps performed for each cooking session are as follows A, B, C, D:
A. after the intelligent cooking equipment executes a cooking starting program for cooking, the cooking temperature, the cooking time and the dish quantity obtained from the cooking starting program are used as cooking parameters in the cooking process;
B. shooting the cooked dishes to obtain dish images capable of reflecting the cooking degree of the dishes, wherein: in the dish image with proper dish curing degree, the dishes are golden yellow; in the dish image with the too high degree of cooking, the dishes are in a scorched black; in the dish image with low dish curing process, the dishes are light white;
C. identifying the type of the dishes according to the dish image; the dish shape of the fried chicken chops is disc-shaped, and the dish shape of the french fries is strip-shaped, so that whether the dish varieties are fried chicken chops or french fries can be identified from the dish shape in the dish image;
D. the cooking parameters, the dish images and the dish variety types are used as input signals, and whether the cooking effect of the intelligent cooking equipment meets the standard or not is used as an output signal, so that a group of learning samples for the artificial neural network to carry out cooking effect checking training are formed.
Since the cooking effect of the first intelligent cooking device is up to standard and the first intelligent cooking device fries 200g chicken and 200g potato for 5 minutes according to a cooking temperature of 200 ℃, the chicken steaks in the dish image should be golden yellow and the french fries should be dark brown (since the actual cooking temperature of french fries is greater than the standard cooking temperature). Therefore, in the input signals of the first group of learning samples, the cooking parameters are 200g of chicken, the cooking temperature is 200 ℃ and the cooking time is 5min, the dish images are golden yellow, the dish types are fried chicken chops, and the output signals are the cooking effect reaching standards; in the input signals of the second group of learning samples, the cooking parameters are 200g of potatoes, the cooking temperature is 200 ℃ and the cooking time is 5min, the dish images are in a dark brown color, the dish varieties are French fries, and the output signals are the cooking effect reaching the standard.
Since the cooking effect of the second intelligent cooking device also meets the standard, and the second intelligent cooking device fries 200g chicken and 200g potato for 5 minutes according to the cooking temperature of 180 ℃, the fries in the dish image should be light white (because the actual cooking temperature of the fries is less than the standard cooking temperature), and the fries should be golden yellow. Therefore, in the input signals of the third group of learning samples, the cooking parameters are 200g of chicken, the cooking temperature is 180 ℃ and the cooking time is 5min, the dish images are light white, the dish varieties are fried chicken chops, and the output signals are the cooking effect reaching standards; in the input signals of the fourth group of learning samples, the cooking parameters are 200g of potatoes, the cooking temperature is 180 ℃ and the cooking time is 5min, the dish images are golden yellow, the dish types are French fries, and the output signals are the cooking effect reaching the standard.
Since the cooking effect of the third intelligent cooking device is not up to standard (the heating effect is too high), and the third intelligent cooking device fries 250g chicken and 250g potato for 5 minutes at a cooking temperature of 200 ℃, respectively, the fries in the dish image may be golden yellow (the chicken amount is larger than the standard dish amount, if the standard of the fries should be light white, but the heating effect of the third intelligent cooking device is too high, i.e. the actual cooking temperature is higher than 200 ℃, and thus may be golden yellow), and the fries may be dark brown (the potato amount is larger than the standard dish amount, and the cooking temperature of the fries is higher than the standard cooking temperature, if the standard of the fries should be golden yellow, but the heating effect of the third intelligent cooking device is too high, i.e. the actual cooking temperature is higher than 200 ℃, and thus may be dark brown). Therefore, in the input signals of the fifth group of learning samples, the cooking parameters are 250g of chicken, the cooking temperature is 200 ℃ and the cooking time is 5min, the dish images are golden yellow, the dish types are fried chicken chops, and the output signals are the cooking effect which does not reach the standard; in the input signals of the sixth group of learning samples, the cooking parameters are 250g of potatoes, the cooking temperature is 200 ℃ and the cooking time is 5min, the dish images are in dark brown, the dish varieties are fried chicken chops, and the output signals are the cooking effect which does not reach the standard.
Since the cooking effect of the fourth intelligent cooking apparatus is also not up to standard (the heating effect is too low), and the fourth intelligent cooking apparatus fries 200g chicken and 200g potato for 8min at a cooking temperature of 200 ℃, respectively, the fries in the dish image may be golden yellow (the actual cooking time period of the fries is longer than the standard cooking time period, if the actual cooking time period of the fries should be dark brown according to the standard of the fries, but the heating effect of the fourth intelligent cooking apparatus is too low, i.e., the actual cooking temperature is lower than 200 ℃, and thus may be golden), and the fries may be dark brown (even though the heating effect of the fourth intelligent cooking apparatus is too low, i.e., the actual cooking temperature of the fries is lower than 200 ℃, but the actual cooking time period of the fries is longer than the standard cooking time period, and thus should be dark brown). Therefore, in the input signals of the seventh group of learning samples, the cooking parameters are 200g of chicken, the cooking temperature is 200 ℃ and the cooking time is 8min, the dish images are golden yellow, the dish types are fried chicken chops, and the output signals are the cooking effect which does not reach the standard; in the input signals of the eighth group of learning samples, the cooking parameters are 200g of potatoes, the cooking temperature is 200 ℃ and the cooking time is 8min, the dish images are in a dark brown color, the dish varieties are French fries, and the output signals are that the cooking effect does not reach the standard.
After each cooking process is executed with the sample obtaining step to obtain a plurality of groups of learning samples, the plurality of groups of learning samples are adopted to carry out cooking effect checking training on the artificial neural network until the artificial neural network has the capability of checking whether the cooking effect of the intelligent cooking equipment reaches the standard according to the cooking parameters, the dish images and the dish types, so that the cooking effect checking method of the intelligent cooking equipment can be realized by utilizing the artificial neural network, and the cooking effect checking method is described in detail as follows a, b, c, d:
a. acquiring cooking parameters of the intelligent cooking equipment in a cooking process, wherein the cooking parameters are that after the intelligent cooking equipment executes a cooking starting program for cooking, the cooking temperature acquired from the cooking starting program is 200 ℃, the cooking duration is 5min and the quantity of dishes is 200g;
b. shooting the cooked dishes to obtain dish images capable of reflecting the cooking degree of the dishes, and recognizing that the dishes are golden yellow from the dish images;
c. identifying the dish shape as long according to the dish image, thereby identifying the dish type as french fries;
d. inputting the cooking parameters, the dish images and the dish variety types into a trained artificial neural network, wherein the artificial neural network is obtained after the training method is executed, and then checking whether the cooking effect of the intelligent cooking equipment reaches the standard or not according to the dish variety, the cooking parameters and the dish images by the trained artificial neural network. The standard cooking parameters of the French fries are 180 ℃ and the cooking time is 5min and 200g of potatoes, and in the actual cooking process, the cooking parameters are 200 ℃ and the cooking time is 5min and 200g of potatoes, so that the deep-black French fries are obtained according to the standard of the French fries, but the finally obtained French fries are golden yellow in practice, namely, cooking according to the too high cooking temperature cannot cause the French fries to become deep black, which means that the heating effect of the intelligent cooking device is too low, and therefore, the cooking effect of the intelligent cooking device is verified to be not up to standard.
Example 2
This embodiment is substantially the same as embodiment 1 except that: the intelligent cooking appliance is part of a cooking effect inspection system that further includes a server in communicative connection with the intelligent cooking appliance, the server including a computer-readable storage medium and a processor that are interconnected, the computer-readable storage medium having a computer program stored thereon that is executed by the processor to implement a training method for an artificial neural network as shown in fig. 1 and/or a cooking effect inspection method as shown in fig. 2, the training method for the artificial neural network being the same as embodiment 1 and the cooking effect inspection method being the same as embodiment 1, and further description thereof is omitted.
The above described embodiments of the intelligent cooking appliance and cooking effect verification system are illustrative only, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the intelligent cooking device and the cooking effect checking system disclosed by the embodiment of the invention are disclosed as the preferred embodiment of the invention, and are only used for illustrating the technical scheme of the invention, but are not limited to the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (11)
1. The training method for the artificial neural network for checking the cooking effect of the intelligent cooking equipment is characterized by comprising the following steps of:
p. under the condition that whether the cooking effect of a plurality of intelligent cooking devices respectively meets the standard is known, the plurality of intelligent cooking devices are made to cook various dishes respectively, the plurality of intelligent cooking devices are made to cook the same dish respectively according to different cooking parameters, the following sample acquisition steps are respectively executed in each cooking process, a plurality of groups of learning samples are obtained, and the sample acquisition steps executed in each cooking process comprise the following A, B, C, D:
a, acquiring cooking parameters of the intelligent cooking equipment in a cooking process;
shooting the cooked dishes to obtain dish images reflecting the cooking degree of the dishes;
c, identifying the type of the dishes according to the dish image;
d, taking the cooking parameters, the dish images and the dish varieties as input signals, and taking whether the cooking effect of the intelligent cooking equipment meets the standard or not as output signals to form a group of learning samples for the artificial neural network to carry out cooking effect checking training;
and Q, adopting the plurality of groups of learning samples to perform cooking effect checking training on the artificial neural network until the artificial neural network has the capability of checking whether the cooking effect of the intelligent cooking equipment reaches the standard according to cooking parameters, dish images and dish types.
2. The training method of claim 1, wherein the cooking parameters include a cooking temperature, a cooking time period, and a quantity of dishes.
3. The training method according to claim 2, wherein in the step a, the cooking temperature, the cooking time period, and the dish amount are acquired from a cooking start program of the intelligent cooking apparatus.
4. The cooking effect checking method of the intelligent cooking equipment is characterized by comprising the following steps of:
a. acquiring cooking parameters of the intelligent cooking equipment in the cooking process;
b. shooting the cooked dishes to obtain dish images capable of reflecting the cooking degree of the dishes;
c. identifying the types of dishes according to the dish images;
d. inputting the cooking parameters, the dish images and the dish variety types into a trained artificial neural network, and checking whether the cooking effect of the intelligent cooking device meets the standard or not by the trained artificial neural network according to the dish variety, the cooking parameters and the dish images, wherein the trained artificial neural network is obtained by executing the training method of any one of claims 1 to 3.
5. The method of claim 4, wherein the cooking parameters include cooking temperature, cooking duration, and amount of dishes.
6. The inspection method of claim 5, wherein in step a, the cooking temperature, the cooking duration, and the quantity of dishes are obtained from a cooking start program of the intelligent cooking device.
7. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the training method according to any of claims 1 to 3.
8. The computer-readable storage medium according to claim 7, wherein the computer program, when executed by a processor, further implements the steps in the inspection method according to any one of claims 4 to 6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps in the verification method according to any one of claims 4 to 6.
10. An intelligent cooking appliance comprising a computer readable storage medium and a processor, interconnected, wherein the computer readable storage medium is as claimed in any one of claims 7 to 9.
11. A cooking effect verification system for an intelligent cooking appliance comprising an intelligent cooking appliance and a server, the intelligent cooking appliance in communication with the server, the server comprising a computer readable storage medium and a processor interconnected, wherein the computer readable storage medium is as claimed in any one of claims 7 to 9.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JPH09257711A (en) * | 1996-03-21 | 1997-10-03 | Matsushita Electric Ind Co Ltd | Cooking evaluation apparatus and surface evaluation apparatus |
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| JPH09257711A (en) * | 1996-03-21 | 1997-10-03 | Matsushita Electric Ind Co Ltd | Cooking evaluation apparatus and surface evaluation apparatus |
| CN110555579A (en) * | 2018-06-01 | 2019-12-10 | 佛山市顺德区美的电热电器制造有限公司 | Cooking grading method, intelligent cooking equipment, server and storage medium |
| CN112084825A (en) * | 2019-06-14 | 2020-12-15 | 佛山市顺德区美的电热电器制造有限公司 | Cooking evaluation method, cooking recommendation method, computer device and storage medium |
| CN112183914A (en) * | 2019-07-05 | 2021-01-05 | 杭州海康威视系统技术有限公司 | Dish item evaluation method and device |
| CN110824942A (en) * | 2019-11-20 | 2020-02-21 | 广东美的厨房电器制造有限公司 | Cooking apparatus, control method thereof, control system thereof, and computer-readable storage medium |
| CN111594881A (en) * | 2020-06-01 | 2020-08-28 | 朱永凤 | Intelligent gas stove detection and control system based on big data |
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