CN109409334B - Inner container oil dirt cleaning platform - Google Patents
Inner container oil dirt cleaning platform Download PDFInfo
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- CN109409334B CN109409334B CN201811445840.3A CN201811445840A CN109409334B CN 109409334 B CN109409334 B CN 109409334B CN 201811445840 A CN201811445840 A CN 201811445840A CN 109409334 B CN109409334 B CN 109409334B
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Abstract
The invention relates to a liner oil dirt cleaning platform, which comprises: the light wave heating structure comprises a plastic shell, an inner container, a furnace chamber, a barbecue tube, a transformer, a magnetron, a capacitor, a microcrystal panel and a stirring sheet; the furnace chamber is used for packaging the inner container, the plastic shell is used for packaging the furnace chamber, and the stirring sheet is arranged in the furnace chamber; the transformer is arranged above the magnetron, the capacitor is arranged below the magnetron, and the microcrystalline panel is arranged on the rear wall of the liner; and the automatic cleaning equipment is arranged in the liner, is connected with the oil stain extraction equipment and is used for starting the automatic cleaning operation on the liner when the percentage of the number of the oil stain pixel points in the customized balanced image to the total number of the pixel points of the customized balanced image exceeds the limit. The invention can improve the automation degree of the light wave heating equipment.
Description
Technical Field
The invention relates to the field of light wave heating, in particular to a liner oil dirt cleaning platform.
Background
The light wave oven is an upgraded version of a household cooking oven called a microwave oven, the principle of the light wave oven is different from that of the microwave oven, but some compatible products exist in the market, and microwave heating and light wave heating can be compatible in the same equipment. The output power of the light wave oven is seven-eight-hundred watts mostly, but the light wave oven has a special energy-saving means. The light wave oven adopts light wave and microwave double high-efficiency heating, and can instantly generate huge heat.
Disclosure of Invention
In order to solve the technical problem that the oil stain of the current light wave heating equipment is difficult to detect and remove, the invention provides an inner container oil stain cleaning platform, based on the result of redundancy analysis of each target subimage in an image, a preset number of target subimages with the minimum redundancy are used as a plurality of output subimages, and the key is that the signal-to-noise ratio of the image is inversely proportional to the preset number; under the image processing mechanism selected by customization, the oil stain in the inner container is respectively identified, and whether the automatic oil stain cleaning processing is executed or not is determined based on the identification result.
According to an aspect of the present invention, there is provided a liner grease cleaning platform, the platform comprising:
the light wave heating structure comprises a plastic shell, an inner container, a furnace chamber, a barbecue tube, a transformer, a magnetron, a capacitor, a microcrystal panel and a stirring sheet; the furnace chamber is used for packaging the inner container, the plastic shell is used for packaging the furnace chamber, and the stirring sheet is arranged in the furnace chamber; the transformer is arranged above the magnetron, the capacitor is arranged below the magnetron, and the microcrystalline panel is arranged on the rear wall of the liner; the composite capture device comprises a first long cylinder framework, a second long cylinder framework, a third long cylinder framework and a main connecting piece, wherein the first long cylinder framework, the second long cylinder framework and the third long cylinder framework are buckled on the main connecting piece, the first long cylinder framework, the second long cylinder framework and the third long cylinder framework are perpendicular to each other, and the first long cylinder framework is used for obtaining a first high-definition image; the second long cylinder structure is used for obtaining a second high-definition image; the third long cylinder structure is used for obtaining a third high-definition image; the first parameter comparison device is respectively connected with the first long cylinder framework, the second long cylinder framework and the third long cylinder framework and is used for respectively receiving the first high-definition image, the second high-definition image and the third high-definition image, determining the image with the highest complexity in the first high-definition image, the second high-definition image and the third high-definition image as a filtering reference image, and outputting the type corresponding to the noise with the largest amplitude in the filtering reference image as a reference noise type; the data filtering device is connected with the first parameter comparison device and is used for respectively executing corresponding processing of a filtering algorithm corresponding to the reference noise type on the first high-definition image, the second high-definition image and the third high-definition image so as to respectively obtain a first filtering image, a second filtering image and a third filtering image; the first identification device is used for receiving the first filtering image, the second filtering image and the third filtering image, executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, and identifying whether a target pixel point is carried out on each pixel point in the filtering image based on a preset target gray threshold range; the SGRAM chip is connected with the first identification equipment, and is used for pre-storing a preset target gray threshold range and a mapping relation between an image signal-to-noise ratio and a preset number; the first processing device is connected with the first identification device and used for executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, receiving each target pixel point, fitting each target pixel point and removing an isolated target pixel point to obtain each target sub-image in the filtering image; the second identification device is respectively connected with the SGRAM chip and the first processing device and is used for executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, analyzing the redundancy of each target sub-image and sequencing the redundancy of each target sub-image so as to take the preset number of target sub-images with the minimum redundancy as a plurality of output sub-images; the object analysis device is connected with the second recognition device and used for receiving a plurality of output sub-images of the first filtering image, the second filtering image and the third filtering image, outputting the output sub-image with the most objects in the first filtering image as a first reference sub-image, outputting the output sub-image with the most objects in the second filtering image as a second reference sub-image and outputting the output sub-image with the most objects in the third filtering image as a third reference sub-image; the data comparison equipment is connected with the object analysis equipment and used for comparing the signal-to-noise ratio of the first reference sub-image, the signal-to-noise ratio of the second reference sub-image and the signal-to-noise ratio of the third reference sub-image and outputting the filtering image corresponding to the reference sub-image with the maximum signal-to-noise ratio as a customized selection image; the equalization processing equipment is connected with the data comparison equipment and is used for receiving the customized selection image, executing self-adaptive equalization processing on the customized selection image to obtain a corresponding self-adaptive equalization image and outputting the customized equalization image; the oil stain extraction equipment is connected with the equalization processing equipment and is used for executing the following processing on each pixel point in the customized equalization image: determining whether the gray value of the pixel point falls between an upper gray threshold and a lower gray threshold of the oil stain, and if the gray value of the pixel point falls between the upper gray threshold and the lower gray threshold, determining the pixel point as the oil stain pixel point; the automatic cleaning equipment is arranged in the inner container, is connected with the oil stain extraction equipment and is used for starting automatic cleaning operation on the inner container when the percentage of the number of the oil stain pixel points in the customized equilibrium image to the total number of the pixel points of the customized equilibrium image exceeds the limit; wherein, in the SGRAM chip, the image signal-to-noise ratio is inversely proportional to the preset number.
More specifically, in the liner grease cleaning platform: the second identification device further comprises a signal-to-noise ratio measurement unit for measuring a real-time signal-to-noise ratio of the filtered image.
More specifically, in the liner grease cleaning platform: the second identification device further determines a preset number required by the second identification device based on the real-time signal-to-noise ratio and the mapping relationship.
More specifically, in the liner grease cleaning platform: the object analysis device and the data comparison device are integrated on a printed circuit board and share the same quartz oscillator.
More specifically, in the liner grease cleaning platform: identifying whether each pixel point in the filtered image is a target pixel point based on a preset target gray threshold range comprises the following steps: and when the gray value of the pixel point is within the preset target gray threshold range, determining the pixel point as a target pixel point.
More specifically, in the liner grease cleaning platform: identifying whether each pixel point in the filtered image is a target pixel point based on a preset target gray threshold range comprises the following steps: and when the gray value of the pixel point is out of the preset target gray threshold range, determining the pixel point as a non-target pixel point.
More specifically, in the clean platform of inner bag grease dirt still includes:
and the second parameter comparison equipment is connected with the data filtering equipment and is used for acquiring the dynamic ranges corresponding to the first filtering image, the second filtering image and the third filtering image respectively.
More specifically, in the liner grease cleaning platform: the second parameter comparison device is further configured to take the image with the largest dynamic range among the first filtered image, the second filtered image, and the third filtered image as a parameter comparison image.
More specifically, in the clean platform of inner bag grease dirt still includes:
and the data splicing equipment is connected with the data filtering equipment and is used for combining the first filtering image, the second filtering image and the third filtering image to obtain a combined filtering image.
More specifically, in the clean platform of inner bag grease dirt still includes:
and the instant display screen is respectively connected with the second parameter comparison equipment and the data splicing equipment and is used for respectively displaying the parameter comparison image and the combined filtering image.
Detailed Description
The following will describe in detail the embodiment of the inner container oil stain cleaning platform of the present invention.
The light wave oven is convenient, fast, healthy and environment-friendly, and can quickly enter common families, and the following problems need to be noticed during cooking:
1. various high temperature resistant containers can be used in the lightwave oven, but if microwave cooking power is selected, it is preferable not to use a metal or metal-containing container. Since metal has a reflecting effect on microwaves, not only is food difficult to cook, but the reflected microwaves can damage parts of the microwave oven, and the service life is affected.
2. If the frozen food is cooked, the food is thawed first. When thawing, attention should be paid to: firstly, using microwave low-power gear to make it uniformly unfreeze; secondly, when the food with different thickness is thawed to half, in order to prevent a certain part from being cooked, the food can be firstly suspended for a while and then is continuously thawed; thirdly, food thawed once is not too much nor too thick, the thickness of meat food is preferably not more than 3 cm, and the thickness of other food is not more than 5 to 7 cm.
3. Canned and bagged food sealed by heating with a microwave oven is avoided, which is easy to cause explosive rupture of the sealed food except for specially marked microwave food. If the preservative film is added on the container for holding food in order to prevent the water from evaporating, a plurality of small holes are punched.
4. Frying food with microwave oven is forbidden. Generally, the fried food needs to be heated slowly, but the heating speed of light waves and microwaves is high, so that danger is easy to occur.
In order to overcome the defects, the invention builds the inner container oil dirt cleaning platform and can effectively solve the corresponding technical problem.
The inner container oil stain cleaning platform shown according to the embodiment of the invention comprises:
the light wave heating structure comprises a plastic shell, an inner container, a furnace chamber, a barbecue tube, a transformer, a magnetron, a capacitor, a microcrystal panel and a stirring sheet;
the furnace chamber is used for packaging the inner container, the plastic shell is used for packaging the furnace chamber, and the stirring sheet is arranged in the furnace chamber;
the transformer is arranged above the magnetron, the capacitor is arranged below the magnetron, and the microcrystalline panel is arranged on the rear wall of the liner;
the composite capture device comprises a first long cylinder framework, a second long cylinder framework, a third long cylinder framework and a main connecting piece, wherein the first long cylinder framework, the second long cylinder framework and the third long cylinder framework are buckled on the main connecting piece, the first long cylinder framework, the second long cylinder framework and the third long cylinder framework are perpendicular to each other, and the first long cylinder framework is used for obtaining a first high-definition image; the second long cylinder structure is used for obtaining a second high-definition image; the third long cylinder structure is used for obtaining a third high-definition image;
the first parameter comparison device is respectively connected with the first long cylinder framework, the second long cylinder framework and the third long cylinder framework and is used for respectively receiving the first high-definition image, the second high-definition image and the third high-definition image, determining the image with the highest complexity in the first high-definition image, the second high-definition image and the third high-definition image as a filtering reference image, and outputting the type corresponding to the noise with the largest amplitude in the filtering reference image as a reference noise type;
the data filtering device is connected with the first parameter comparison device and is used for respectively executing corresponding processing of a filtering algorithm corresponding to the reference noise type on the first high-definition image, the second high-definition image and the third high-definition image so as to respectively obtain a first filtering image, a second filtering image and a third filtering image;
the first identification device is used for receiving the first filtering image, the second filtering image and the third filtering image, executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, and identifying whether a target pixel point is carried out on each pixel point in the filtering image based on a preset target gray threshold range;
the SGRAM chip is connected with the first identification equipment, and is used for pre-storing a preset target gray threshold range and a mapping relation between an image signal-to-noise ratio and a preset number;
the first processing device is connected with the first identification device and used for executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, receiving each target pixel point, fitting each target pixel point and removing an isolated target pixel point to obtain each target sub-image in the filtering image;
the second identification device is respectively connected with the SGRAM chip and the first processing device and is used for executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, analyzing the redundancy of each target sub-image and sequencing the redundancy of each target sub-image so as to take the preset number of target sub-images with the minimum redundancy as a plurality of output sub-images;
the object analysis device is connected with the second recognition device and used for receiving a plurality of output sub-images of the first filtering image, the second filtering image and the third filtering image, outputting the output sub-image with the most objects in the first filtering image as a first reference sub-image, outputting the output sub-image with the most objects in the second filtering image as a second reference sub-image and outputting the output sub-image with the most objects in the third filtering image as a third reference sub-image;
the data comparison equipment is connected with the object analysis equipment and used for comparing the signal-to-noise ratio of the first reference sub-image, the signal-to-noise ratio of the second reference sub-image and the signal-to-noise ratio of the third reference sub-image and outputting the filtering image corresponding to the reference sub-image with the maximum signal-to-noise ratio as a customized selection image;
the equalization processing equipment is connected with the data comparison equipment and is used for receiving the customized selection image, executing self-adaptive equalization processing on the customized selection image to obtain a corresponding self-adaptive equalization image and outputting the customized equalization image;
the oil stain extraction equipment is connected with the equalization processing equipment and is used for executing the following processing on each pixel point in the customized equalization image: determining whether the gray value of the pixel point falls between an upper gray threshold and a lower gray threshold of the oil stain, and if the gray value of the pixel point falls between the upper gray threshold and the lower gray threshold, determining the pixel point as the oil stain pixel point;
the automatic cleaning equipment is arranged in the inner container, is connected with the oil stain extraction equipment and is used for starting automatic cleaning operation on the inner container when the percentage of the number of the oil stain pixel points in the customized equilibrium image to the total number of the pixel points of the customized equilibrium image exceeds the limit;
wherein, in the SGRAM chip, the image signal-to-noise ratio is inversely proportional to the preset number.
Next, the specific structure of the liner grease cleaning platform of the present invention will be further described.
In the inner container oil stain cleaning platform: the second identification device further comprises a signal-to-noise ratio measurement unit for measuring a real-time signal-to-noise ratio of the filtered image.
In the inner container oil stain cleaning platform: the second identification device further determines a preset number required by the second identification device based on the real-time signal-to-noise ratio and the mapping relationship.
In the inner container oil stain cleaning platform: the object analysis device and the data comparison device are integrated on a printed circuit board and share the same quartz oscillator.
In the inner container oil stain cleaning platform: identifying whether each pixel point in the filtered image is a target pixel point based on a preset target gray threshold range comprises the following steps: and when the gray value of the pixel point is within the preset target gray threshold range, determining the pixel point as a target pixel point.
In the inner container oil stain cleaning platform: identifying whether each pixel point in the filtered image is a target pixel point based on a preset target gray threshold range comprises the following steps: and when the gray value of the pixel point is out of the preset target gray threshold range, determining the pixel point as a non-target pixel point.
In the clean platform of inner bag grease dirt still includes:
and the second parameter comparison equipment is connected with the data filtering equipment and is used for acquiring the dynamic ranges corresponding to the first filtering image, the second filtering image and the third filtering image respectively.
In the inner container oil stain cleaning platform: the second parameter comparison device is further configured to take the image with the largest dynamic range among the first filtered image, the second filtered image, and the third filtered image as a parameter comparison image.
In the clean platform of inner bag grease dirt still includes:
and the data splicing equipment is connected with the data filtering equipment and is used for combining the first filtering image, the second filtering image and the third filtering image to obtain a combined filtering image.
In the clean platform of inner bag grease dirt still includes:
and the instant display screen is respectively connected with the second parameter comparison equipment and the data splicing equipment and is used for respectively displaying the parameter comparison image and the combined filtering image.
In addition, the SGRAM is an abbreviation of Synchronous Graphics DRAM, which means that the Synchronous Graphics RAM is a video memory specially designed for a video card, is a video memory with stronger Graphics read-write capability, and is formed by improving SDRAM. The method improves the defect of low video memory transmission rate of low performance in the past, and creates conditions for improving the performance of the display card. The SGRAM reads and writes data not one by one, but uses a Block as a unit, thereby reducing the times of reading and writing the whole memory and improving the efficiency of a graphic controller.
By adopting the inner container oil stain cleaning platform, aiming at the technical problem that the oil stain of the light wave heating equipment is difficult to detect and remove in the prior art, based on the result of the redundancy analysis of each target subimage in the image, the preset number of target subimages with the minimum redundancy is taken as a plurality of output subimages, and the key is that the signal-to-noise ratio of the image is inversely proportional to the preset number; under the image processing mechanism selected by customization, identifying the oil stain conditions in the inner container respectively, and determining whether to execute automatic oil stain cleaning processing based on the identification result; thereby solving the technical problem.
It is to be understood that while the present invention has been described in conjunction with the preferred embodiments thereof, it is not intended to limit the invention to those embodiments. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (8)
1. The utility model provides a clean platform of inner bag grease dirt which characterized in that includes:
the light wave heating structure comprises a plastic shell, an inner container, a furnace chamber, a barbecue tube, a transformer, a magnetron, a capacitor, a microcrystal panel and a stirring sheet;
the furnace chamber is used for packaging the inner container, the plastic shell is used for packaging the furnace chamber, and the stirring sheet is arranged in the furnace chamber;
the transformer is arranged above the magnetron, the capacitor is arranged below the magnetron, and the microcrystalline panel is arranged on the rear wall of the liner;
the composite capture device comprises a first long cylinder framework, a second long cylinder framework, a third long cylinder framework and a main connecting piece, wherein the first long cylinder framework, the second long cylinder framework and the third long cylinder framework are buckled on the main connecting piece, the first long cylinder framework, the second long cylinder framework and the third long cylinder framework are perpendicular to each other, and the first long cylinder framework is used for obtaining a first high-definition image; the second long cylinder structure is used for obtaining a second high-definition image; the third long cylinder structure is used for obtaining a third high-definition image;
the first parameter comparison device is respectively connected with the first long cylinder framework, the second long cylinder framework and the third long cylinder framework and is used for respectively receiving the first high-definition image, the second high-definition image and the third high-definition image, determining the image with the highest complexity in the first high-definition image, the second high-definition image and the third high-definition image as a filtering reference image, and outputting the type corresponding to the noise with the largest amplitude in the filtering reference image as a reference noise type;
the data filtering device is connected with the first parameter comparison device and is used for respectively executing corresponding processing of a filtering algorithm corresponding to the reference noise type on the first high-definition image, the second high-definition image and the third high-definition image so as to respectively obtain a first filtering image, a second filtering image and a third filtering image;
the first identification device is used for receiving the first filtering image, the second filtering image and the third filtering image, executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, and identifying whether a target pixel point is carried out on each pixel point in the filtering image based on a preset target gray threshold range;
the SGRAM chip is connected with the first identification equipment, and is used for pre-storing a preset target gray threshold range and a mapping relation between an image signal-to-noise ratio and a preset number;
the first processing device is connected with the first identification device and used for executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, receiving each target pixel point, fitting each target pixel point and removing isolated target pixel points to obtain each target sub-image in the filtering image;
the second identification device is respectively connected with the SGRAM chip and the first processing device and is used for executing the following actions aiming at one of the first filtering image, the second filtering image and the third filtering image, analyzing the redundancy of each target sub-image and sequencing the redundancy of each target sub-image so as to take the preset number of target sub-images with the minimum redundancy as a plurality of output sub-images;
the object analysis device is connected with the second recognition device and used for receiving a plurality of output sub-images of the first filtering image, the second filtering image and the third filtering image, outputting the output sub-image with the most objects in the first filtering image as a first reference sub-image, outputting the output sub-image with the most objects in the second filtering image as a second reference sub-image and outputting the output sub-image with the most objects in the third filtering image as a third reference sub-image;
the data comparison equipment is connected with the object analysis equipment and used for comparing the signal-to-noise ratio of the first reference sub-image, the signal-to-noise ratio of the second reference sub-image and the signal-to-noise ratio of the third reference sub-image and outputting the filtering image corresponding to the reference sub-image with the maximum signal-to-noise ratio as a customized selection image;
the equalization processing equipment is connected with the data comparison equipment and is used for receiving the customized selection image, executing self-adaptive equalization processing on the customized selection image to obtain a corresponding self-adaptive equalization image and outputting the customized equalization image;
the oil stain extraction equipment is connected with the equalization processing equipment and is used for executing the following processing on each pixel point in the customized equalization image: determining whether the gray value of the pixel point falls between an upper gray threshold and a lower gray threshold of the oil stain, and if the gray value of the pixel point falls between the upper gray threshold and the lower gray threshold, determining the pixel point as the oil stain pixel point;
the automatic cleaning equipment is arranged in the inner container, is connected with the oil stain extraction equipment and is used for starting automatic cleaning operation on the inner container when the percentage of the number of the oil stain pixel points in the customized equilibrium image to the total number of the pixel points of the customized equilibrium image exceeds the limit;
wherein, in the SGRAM chip, the image signal-to-noise ratio is inversely proportional to the preset number;
the second identification device further comprises a signal-to-noise ratio measurement unit for measuring the real-time signal-to-noise ratio of the filtered image;
the second identification device further determines a preset number required by the second identification device based on the real-time signal-to-noise ratio and the mapping relationship.
2. The inner container grease dirt cleaning platform of claim 1, characterized in that:
the object analysis device and the data comparison device are integrated on a printed circuit board and share the same quartz oscillator.
3. The inner container grease dirt cleaning platform of claim 2, characterized in that:
identifying whether each pixel point in the filtered image is a target pixel point based on a preset target gray threshold range comprises the following steps: and when the gray value of the pixel point is within the preset target gray threshold range, determining the pixel point as a target pixel point.
4. The inner container grease dirt cleaning platform of claim 3, characterized in that:
identifying whether each pixel point in the filtered image is a target pixel point based on a preset target gray threshold range comprises the following steps: and when the gray value of the pixel point is out of the preset target gray threshold range, determining the pixel point as a non-target pixel point.
5. The liner grease cleaning platform of claim 4, wherein the platform further comprises:
and the second parameter comparison equipment is connected with the data filtering equipment and is used for acquiring the dynamic ranges corresponding to the first filtering image, the second filtering image and the third filtering image respectively.
6. The inner container grease dirt cleaning platform of claim 5, characterized in that:
the second parameter comparison device is further configured to take the image with the largest dynamic range among the first filtered image, the second filtered image, and the third filtered image as a parameter comparison image.
7. The liner grease cleaning platform of claim 6, wherein the platform further comprises:
and the data splicing equipment is connected with the data filtering equipment and is used for combining the first filtering image, the second filtering image and the third filtering image to obtain a combined filtering image.
8. The liner grease cleaning platform of claim 7, wherein the platform further comprises:
and the instant display screen is respectively connected with the second parameter comparison equipment and the data splicing equipment and is used for respectively displaying the parameter comparison image and the combined filtering image.
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