CN112528941A - Automatic parameter setting system based on neural network - Google Patents
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 31
- 238000003062 neural network model Methods 0.000 claims abstract description 44
- 230000007246 mechanism Effects 0.000 claims abstract description 36
- 238000012549 training Methods 0.000 claims abstract description 26
- 239000000463 material Substances 0.000 claims abstract description 25
- 235000013305 food Nutrition 0.000 claims abstract description 24
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- 238000000605 extraction Methods 0.000 claims abstract description 10
- 230000000007 visual effect Effects 0.000 claims abstract description 4
- 238000010606 normalization Methods 0.000 claims description 51
- 238000012545 processing Methods 0.000 claims description 27
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- 238000002789 length control Methods 0.000 claims description 2
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
- A47J37/00—Baking; Roasting; Grilling; Frying
- A47J37/06—Roasters; Grills; Sandwich grills
- A47J37/0623—Small-size cooking ovens, i.e. defining an at least partially closed cooking cavity
- A47J37/0629—Small-size cooking ovens, i.e. defining an at least partially closed cooking cavity with electric heating elements
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47J—KITCHEN EQUIPMENT; COFFEE MILLS; SPICE MILLS; APPARATUS FOR MAKING BEVERAGES
- A47J37/00—Baking; Roasting; Grilling; Frying
- A47J37/06—Roasters; Grills; Sandwich grills
- A47J37/0623—Small-size cooking ovens, i.e. defining an at least partially closed cooking cavity
- A47J37/0664—Accessories
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
Abstract
The invention relates to an automatic parameter setting system based on a neural network, which comprises: the first capturing mechanism comprises a plurality of image capturing units and is used for acquiring images corresponding to a plurality of different visual angles; the second capturing mechanism is used for acquiring the baking temperature, the rolling frequency and the baking time length set by a user when the baking operation is executed by the oven each time; and the parameter extraction mechanism is used for taking the plurality of images to be processed as input data of the deep neural network model after training so as to operate the model, and obtaining three output data of the model so as to respectively serve as set baking temperature, set rolling times and set baking duration. The automatic parameter setting system based on the neural network is simple and convenient to operate and wide in application. On the basis of a customized artificial intelligence recognition mode, the food material for placing the oven at present is automatically set for parameters including barbecue temperature, rolling times and baking time, so that the use of an oven user is facilitated.
Description
Technical Field
The invention relates to the field of oven design, in particular to an automatic parameter setting system based on a neural network.
Background
An oven is a sealed electric appliance for baking food or drying products, and is classified into a home electric appliance and an industrial oven. The domestic oven can be used for processing some pasta. An industrial oven is a device used for drying products in industry, and is an electric oven and a gas oven, which are also called as an oven, a drying box and the like.
The electric oven is an electric heating appliance for baking food by utilizing radiation heat emitted by an electric heating element, and can be used for making baked chicken, baked duck, baked bread, cakes and the like. The temperature of the electric oven can be adjusted within the range of 50-250 ℃ according to different needs of the baked food.
The bench oven has the advantages of flexibility, capability of selecting ovens with different configurations according to needs, and price from hundreds of yuan to thousands of yuan due to different qualities and configurations. The other is a built-in oven, which is an upgraded and ultimate version of a small oven. Because the power is larger, the baking speed is high, the sealing performance is good (generally adopting a rubber gasket strip for sealing), the heat insulation performance is good (three layers of toughened glass are used for heat insulation), and the temperature control is accurate, the baking oven is popular with more and more people.
At present, the oven is when using each time, and the user all needs carry out artifical the setting to including barbecue temperature, time and each item parameter when the baking is long according to the experience of oneself, however, because the complexity and the variety of eating the material to and the nonstandard nature of artificial experience, lead to the parameter of setting for each time easily and can't satisfy and eat the material demand, and then need make a round trip parameter adjustment repeatedly, thereby wasted the baking progress, occupied too much cost of labor and time cost.
Disclosure of Invention
In order to solve the technical problems in the related field, the invention provides an automatic parameter setting system based on a neural network, which can automatically set parameters of the roasting temperature, the rolling times and the roasting time of the roasting based on historical experience data after food materials are put into an oven, so that the automatic level of the design of the oven is improved.
For this reason, the implementation of the present invention requires the following several important points:
(1) taking data of multiple normalized pictures of the food materials shot before the food materials are baked by the oven in each historical time as input data of a deep neural network model, taking the baking temperature, the rolling times and the baking duration set by a user in each historical baking of the oven as output data of the deep neural network model, and carrying out successive training on the deep neural network model;
(2) the method comprises the steps of intelligently identifying the roasting temperature, the rolling times and the roasting duration of the roasting based on normalized data of a plurality of pictures of various angles of food materials shot before roasting by an oven by adopting a gradually trained deep neural network model, and automatically setting the data respectively, so that the situation that a user is involved in complicated and repeated parameter selection operation is avoided.
According to an aspect of the present invention, there is provided a neural network-based automated parameter setting system, the system including:
a first capturing mechanism, including a plurality of image capturing units arranged at different corners in the oven, for performing a plurality of image capturing operations at different viewing angles on food materials before the food materials are placed in the oven and roasted to obtain a corresponding plurality of captured images at different viewing angles;
the first capturing mechanism acquires a plurality of sub-view captured images acquired when the oven performs baking operation each time;
the second capturing mechanism is used for acquiring the baking temperature, the rolling frequency and the baking time length set by a user when the baking operation is executed by the oven each time;
the content conversion equipment is connected with the first capturing mechanism and is used for performing parameter normalization processing on a plurality of captured images with different view angles acquired each time baking operation is performed so as to obtain a plurality of converted images with different view angles;
the model analysis device is respectively connected with the content conversion device and the second capturing mechanism and used for taking a plurality of view-dividing conversion images acquired during each baking operation as input data of the deep neural network model, taking the baking temperature, the rolling times and the baking duration set by a user during each baking operation as three output data of the deep neural network model and training the deep neural network model;
the first capturing mechanism and the content conversion equipment acquire a plurality of corresponding sub-view conversion images of food materials placed in an oven at the current moment and before the food materials are roasted to serve as a plurality of images to be processed;
the parameter extraction mechanism is respectively connected with the content conversion equipment and the model analysis equipment and is used for taking a plurality of images to be processed as input data of the deep neural network model after training to operate the deep neural network model after training, and obtaining three output data of the deep neural network model after training to be respectively used as set baking temperature, set rolling times and set baking duration;
wherein, a plurality of minutes visual angle conversion images that acquire when carrying out the baking operation each time are regarded as the input data of degree of depth neural network model, and the baking temperature, the number of times of rolling and the baking duration that will user set for when carrying out the baking operation each time are regarded as three output data of degree of depth neural network model, and training including the degree of depth neural network model: performing roasting operation once, training the deep neural network model once, and after the deep neural network model training corresponding to the historical roasting operation times is finished successively, taking the obtained deep neural network model as a deep neural network model after the training is finished;
wherein the performing of the parameter normalization process on the plurality of captured images at the divisional angles acquired each time the baking operation is performed to obtain the plurality of converted images at the divisional angles includes: and sequentially performing resolution normalization processing, contrast normalization processing and signal-to-noise ratio normalization processing on the multiple sub-view captured images acquired during each baking operation to obtain multiple sub-view converted images.
The automatic parameter setting system based on the neural network is simple and convenient to operate and wide in application. On the basis of a customized artificial intelligence recognition mode, the food material for placing the oven at present is automatically set for parameters including barbecue temperature, rolling times and baking time, so that the use of an oven user is facilitated.
Detailed Description
An embodiment of the neural network-based automated parameter setting system of the present invention will be described in detail below.
A good electric oven should be well sealed so that heat loss is reduced. The oven is usually opened from top to bottom, so the degree of lubrication of the oven door needs to be carefully tested. The box door cannot be too tight, otherwise people are easily scalded when the box door is opened by force; it can not loosen too much, and can prevent from falling off carelessly in use. The more the barbecue trays and barbecue racks in the electric oven are, the better.
The electric oven is an electric appliance with suddenly increased temperature, so the material of the oven is required to be thick and safe. The product with high oven material quality needs to adopt two layers of glass and the high-standard 0.5mm thick cold-rolled plate or stainless steel panel material in the industry. The middle-high grade product should have at least 3 baking tray positions, which can be respectively close to the upper fire, close to the lower fire and positioned in the middle. In addition, whether the inside of the oven is easy to clean is also an important point of investigation.
The temperature of the oven can not be automatically adjusted generally, and the temperature adjuster of the oven is an automatic switch, and the oven stops working when reaching the set temperature and continues heating when the temperature is lower than the set temperature. If the temperature is controlled, the switch can be manually turned off every few minutes.
At present, the oven is when using each time, and the user all needs carry out artifical the setting to including barbecue temperature, time and each item parameter when the baking is long according to the experience of oneself, however, because the complexity and the variety of eating the material to and the nonstandard nature of artificial experience, lead to the parameter of setting for each time easily and can't satisfy and eat the material demand, and then need make a round trip parameter adjustment repeatedly, thereby wasted the baking progress, occupied too much cost of labor and time cost.
In order to overcome the defects, the invention builds an automatic parameter setting system based on the neural network, and can effectively solve the corresponding technical problem.
The automatic parameter setting system based on the neural network according to the embodiment of the invention comprises:
a first capturing mechanism, including a plurality of image capturing units arranged at different corners in the oven, for performing a plurality of image capturing operations at different viewing angles on food materials before the food materials are placed in the oven and roasted to obtain a corresponding plurality of captured images at different viewing angles;
the first capturing mechanism acquires a plurality of sub-view captured images acquired when the oven performs baking operation each time;
the second capturing mechanism is used for acquiring the baking temperature, the rolling frequency and the baking time length set by a user when the baking operation is executed by the oven each time;
the content conversion equipment is connected with the first capturing mechanism and is used for performing parameter normalization processing on a plurality of captured images with different view angles acquired each time baking operation is performed so as to obtain a plurality of converted images with different view angles;
the model analysis device is respectively connected with the content conversion device and the second capturing mechanism and used for taking a plurality of view-dividing conversion images acquired during each baking operation as input data of the deep neural network model, taking the baking temperature, the rolling times and the baking duration set by a user during each baking operation as three output data of the deep neural network model and training the deep neural network model;
the first capturing mechanism and the content conversion equipment acquire a plurality of corresponding sub-view conversion images of food materials placed in an oven at the current moment and before the food materials are roasted to serve as a plurality of images to be processed;
the parameter extraction mechanism is respectively connected with the content conversion equipment and the model analysis equipment and is used for taking a plurality of images to be processed as input data of the deep neural network model after training to operate the deep neural network model after training, and obtaining three output data of the deep neural network model after training to be respectively used as set baking temperature, set rolling times and set baking duration;
wherein, a plurality of minutes visual angle conversion images that acquire when carrying out the baking operation each time are regarded as the input data of degree of depth neural network model, and the baking temperature, the number of times of rolling and the baking duration that will user set for when carrying out the baking operation each time are regarded as three output data of degree of depth neural network model, and training including the degree of depth neural network model: performing roasting operation once, training the deep neural network model once, and after the deep neural network model training corresponding to the historical roasting operation times is finished successively, taking the obtained deep neural network model as a deep neural network model after the training is finished;
wherein the performing of the parameter normalization process on the plurality of captured images at the divisional angles acquired each time the baking operation is performed to obtain the plurality of converted images at the divisional angles includes: and sequentially performing resolution normalization processing, contrast normalization processing and signal-to-noise ratio normalization processing on the multiple sub-view captured images acquired during each baking operation to obtain multiple sub-view converted images.
Next, the detailed configuration of the neural network-based automatic parameter setting system of the present invention will be further described.
In the automatic parameter setting system based on the neural network:
sequentially performing resolution normalization, contrast normalization and signal-to-noise normalization on a plurality of sub-view captured images acquired each time a baking operation is performed to obtain a plurality of sub-view converted images, comprising: performing a size scaling operation on each of the sub-perspective captured images to complete a resolution normalization process on the plurality of sub-perspective captured images.
In the automatic parameter setting system based on the neural network:
performing a size scaling operation on each of the sub-perspective captured images to complete a resolution normalization process on the plurality of sub-perspective captured images comprises: the resolutions of the plurality of images after the resolution normalization processing are equal.
In the automatic parameter setting system based on the neural network:
sequentially performing resolution normalization, contrast normalization and signal-to-noise normalization on a plurality of sub-view captured images acquired each time a baking operation is performed to obtain a plurality of sub-view converted images, comprising: performing a contrast lifting operation on one or more of the plurality of sub-perspective captured images to complete contrast normalization processing of the plurality of sub-perspective captured images.
In the automatic parameter setting system based on the neural network:
performing a contrast lifting operation on one or more of the plurality of perspective captured images to complete contrast normalization processing of the plurality of perspective captured images comprises: the contrast of the plurality of images after the contrast normalization process is equal.
In the automatic parameter setting system based on the neural network:
sequentially performing resolution normalization, contrast normalization and signal-to-noise normalization on a plurality of sub-view captured images acquired each time a baking operation is performed to obtain a plurality of sub-view converted images, comprising: performing a filtering operation on one or more of the plurality of view angle captured images to complete signal to noise ratio normalization processing of the plurality of view angle captured images.
In the automatic parameter setting system based on the neural network:
performing a filtering operation on one or more of the plurality of view angle captured images to complete signal to noise ratio normalization processing of the plurality of view angle captured images comprises: and the signal-to-noise ratios of the plurality of images after the signal-to-noise ratio normalization processing are equal.
The automatic parameter setting system based on the neural network can further comprise:
and the temperature control mechanism is arranged on the front panel of the oven, is connected with the parameter extraction mechanism and is used for setting the baking temperature of the baking to be the received set baking temperature.
The automatic parameter setting system based on the neural network can further comprise:
and the frequency control mechanism is arranged on the front panel of the oven, connected with the parameter extraction mechanism and used for setting the rolling frequency of the baking to be the received set rolling frequency.
The automatic parameter setting system based on the neural network can further comprise:
and the time length control mechanism is arranged on the front panel of the oven, connected with the parameter extraction mechanism and used for setting the baking time length of the baking to be the received set baking time length.
In addition, the oven needs to be preheated to a specified temperature before any food is baked, so that the baking time on the recipe can be met. The preheating of the oven requires about time, if the preheating of the oven is insufficient, the temperature may not reach the designated temperature, and if the preheating of the oven is carried out for too long, the service life of the oven may be affected.
The small oven is basically used for baking food, but the medium oven usually comprises an upper layer, a middle layer and a lower layer, the heights of the upper layer, the middle layer and the lower layer can be selected, and the baking tray is arranged on the middle layer as long as the temperatures of the upper fire and the lower fire are not particularly indicated on the recipe; if the upper fire temperature is high and the lower fire temperature is low, the upper fire temperature and the lower fire temperature of the oven can be independently adjusted, but the upper fire temperature and the lower fire temperature are generally added and divided by two, then the baking tray is placed on the upper layer, and whether the surface is over-burnt or not needs to be noticed at any time.
The small oven is easy to be burnt, and at the moment, a layer of aluminum foil paper can be covered on food, or the oven door is slightly opened for heat dissipation; the medium-sized oven has enough space and can control the temperature, and the condition of scorching is less likely to happen unless the oven temperature is too high, too close to the upper fire or too long-time baking is carried out. Although the medium-sized processing oven can control the temperature when the oven temperature is uneven, the medium-sized processing oven still can not be comparable with a professional large oven. Taking the preparation of Chinese and western snacks as an example, because the oven temperature of a medium-sized oven is not as stable as that of a professional large oven, the change of the oven temperature must be carefully noticed during baking, and the snacks must be turned around or cooled down at proper time to avoid the situations that the expansion height of two sides of cakes or bread is uneven, and the biscuits are too scorched and are not cooked, and the like.
It will be apparent to those skilled in the art that various modifications can be made to the above-described embodiments of the present invention, as well as other embodiments, without departing from the scope of the invention. It is understood, therefore, that this invention is not limited to the particular embodiments disclosed, but covers any changes, modifications or alterations within the scope of the present invention as defined by the appended claims.
Claims (10)
1. An automated parameter setting system based on a neural network, comprising:
a first capturing mechanism, including a plurality of image capturing units arranged at different corners in the oven, for performing a plurality of image capturing operations at different viewing angles on food materials before the food materials are placed in the oven and roasted to obtain a corresponding plurality of captured images at different viewing angles;
the first capturing mechanism acquires a plurality of sub-view captured images acquired when the oven performs baking operation each time;
the second capturing mechanism is used for acquiring the baking temperature, the rolling frequency and the baking time length set by a user when the baking operation is executed by the oven each time;
the content conversion equipment is connected with the first capturing mechanism and is used for performing parameter normalization processing on a plurality of captured images with different view angles acquired each time baking operation is performed so as to obtain a plurality of converted images with different view angles;
the model analysis device is respectively connected with the content conversion device and the second capturing mechanism and used for taking a plurality of view-dividing conversion images acquired during each baking operation as input data of the deep neural network model, taking the baking temperature, the rolling times and the baking duration set by a user during each baking operation as three output data of the deep neural network model and training the deep neural network model;
the first capturing mechanism and the content conversion equipment acquire a plurality of corresponding sub-view conversion images of food materials placed in an oven at the current moment and before the food materials are roasted to serve as a plurality of images to be processed;
the parameter extraction mechanism is respectively connected with the content conversion equipment and the model analysis equipment and is used for taking a plurality of images to be processed as input data of the deep neural network model after training to operate the deep neural network model after training, and obtaining three output data of the deep neural network model after training to be respectively used as set baking temperature, set rolling times and set baking duration;
wherein, a plurality of minutes visual angle conversion images that acquire when carrying out the baking operation each time are regarded as the input data of degree of depth neural network model, and the baking temperature, the number of times of rolling and the baking duration that will user set for when carrying out the baking operation each time are regarded as three output data of degree of depth neural network model, and training including the degree of depth neural network model: performing roasting operation once, training the deep neural network model once, and after the deep neural network model training corresponding to the historical roasting operation times is finished successively, taking the obtained deep neural network model as a deep neural network model after the training is finished;
wherein the performing of the parameter normalization process on the plurality of captured images at the divisional angles acquired each time the baking operation is performed to obtain the plurality of converted images at the divisional angles includes: and sequentially performing resolution normalization processing, contrast normalization processing and signal-to-noise ratio normalization processing on the multiple sub-view captured images acquired during each baking operation to obtain multiple sub-view converted images.
2. The neural network-based automated parameter setting system of claim 1, wherein:
sequentially performing resolution normalization, contrast normalization and signal-to-noise normalization on a plurality of sub-view captured images acquired each time a baking operation is performed to obtain a plurality of sub-view converted images, comprising: performing a size scaling operation on each of the sub-perspective captured images to complete a resolution normalization process on the plurality of sub-perspective captured images.
3. The neural network-based automated parameter setting system of claim 2, wherein:
performing a size scaling operation on each of the sub-perspective captured images to complete a resolution normalization process on the plurality of sub-perspective captured images comprises: the resolutions of the plurality of images after the resolution normalization processing are equal.
4. The neural network-based automated parameter setting system of claim 3, wherein:
sequentially performing resolution normalization, contrast normalization and signal-to-noise normalization on a plurality of sub-view captured images acquired each time a baking operation is performed to obtain a plurality of sub-view converted images, comprising: performing a contrast lifting operation on one or more of the plurality of sub-perspective captured images to complete contrast normalization processing of the plurality of sub-perspective captured images.
5. The neural network-based automated parameter setting system of claim 4, wherein:
performing a contrast lifting operation on one or more of the plurality of perspective captured images to complete contrast normalization processing of the plurality of perspective captured images comprises: the contrast of the plurality of images after the contrast normalization process is equal.
6. The neural network-based automated parameter setting system of claim 5, wherein:
sequentially performing resolution normalization, contrast normalization and signal-to-noise normalization on a plurality of sub-view captured images acquired each time a baking operation is performed to obtain a plurality of sub-view converted images, comprising: performing a filtering operation on one or more of the plurality of view angle captured images to complete signal to noise ratio normalization processing of the plurality of view angle captured images.
7. The neural network-based automated parameter setting system of claim 6, wherein:
performing a filtering operation on one or more of the plurality of view angle captured images to complete signal to noise ratio normalization processing of the plurality of view angle captured images comprises: and the signal-to-noise ratios of the plurality of images after the signal-to-noise ratio normalization processing are equal.
8. The neural network-based automated parameter setting system of claim 7, further comprising:
and the temperature control mechanism is arranged on the front panel of the oven, is connected with the parameter extraction mechanism and is used for setting the baking temperature of the baking to be the received set baking temperature.
9. The neural network-based automated parameter setting system of claim 8, further comprising:
and the frequency control mechanism is arranged on the front panel of the oven, connected with the parameter extraction mechanism and used for setting the rolling frequency of the baking to be the received set rolling frequency.
10. The neural network-based automated parameter setting system of claim 9, further comprising:
and the time length control mechanism is arranged on the front panel of the oven, connected with the parameter extraction mechanism and used for setting the baking time length of the baking to be the received set baking time length.
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| CN115086472A (en) * | 2022-06-13 | 2022-09-20 | 泰州亚东广告传媒有限公司 | Mobile phone APP management platform based on key frame information |
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| GB202111697D0 (en) | 2021-09-29 |
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