CN110517217B - Computer integral data recognition device - Google Patents
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Abstract
The invention relates to an integral data identification device, comprising: the nearest neighbor interpolation device is used for receiving a storeroom captured image from a computer storeroom site, averagely dividing the storeroom captured image into blocks with the sizes of the corresponding blocks on the basis of the distance between the resolution ratio of the storeroom captured image and a preset resolution threshold value, selecting corresponding nearest neighbor interpolation processing with different strength on the basis of the pixel value variance of each block so as to obtain correction blocks for each block, and splicing the obtained correction blocks so as to obtain a nearest neighbor interpolation image; and the brightness analysis device is used for analyzing each object in the nearest neighbor interpolation image. The whole data identification device has wide application range and is convenient and practical. On the basis of targeted image processing, the method effectively judges the integral withering degree of plants in the computer storehouse, and can provide valuable reference data for watering time, so that the balance between watering times and growth effect is achieved.
Description
Technical Field
The invention relates to the field of computer maintenance, in particular to a computer overall data identification device.
Background
The computer microprocessor uses transistors as basic components, and as the processor is continuously improved and the updating speed is increased, the computer structure and components are also changed greatly. With the development of the photoelectric technology, the quantum technology and the biological technology, the development of a novel computer is greatly promoted.
The ALU and the control unit (both combined central processing unit, i.e., CPU) were gradually integrated onto one integrated circuit, called a microprocessor, since the 80's of the 20 th century. The working mode of the computer is very intuitive: in one clock cycle, the computer first fetches instructions and data from memory, then executes the instructions, stores the data, and then fetches the next instruction. This process is repeated until a termination instruction is obtained. The instruction set executed by the operator, interpreted by the controller, is a well-defined very limited set of simple instructions.
Disclosure of Invention
The invention aims to provide an overall data identification device, which comprises: the nearest neighbor interpolation device is used for receiving the storehouse capture image from the computer storehouse site, equally dividing the storehouse capture image into blocks with the corresponding block sizes based on the distance between the resolution of the storehouse capture image and a preset resolution threshold, selecting corresponding nearest neighbor interpolation processing with different forces based on the pixel value variance of each block to obtain correction blocks for each block, and splicing the obtained correction blocks to obtain a nearest neighbor interpolation image.
More specifically, in the whole data identification apparatus, the apparatus further includes: and the brightness analysis device is connected with the nearest neighbor interpolation device and used for receiving the nearest neighbor interpolation image, analyzing each object in the nearest neighbor interpolation image to obtain an object area corresponding to each object, confirming pixel points with brightness values larger than a first preset brightness threshold value and not on the boundary line of any object area as interference points, and confirming pixel points with brightness values smaller than a second preset brightness threshold value and not on the boundary line of any object area as interference points.
More specifically, in the whole data identification apparatus, the apparatus further includes: the pixel point identification device is connected with the brightness analysis device and used for receiving each interference point in the nearest neighbor interpolation image and executing the following operation on each interference point: and taking each interference point as a target point, and identifying the target point as a processing point when no pixel point with the brightness value larger than a first preset brightness threshold value or the brightness value smaller than a second preset brightness threshold value exists around the target point.
More specifically, in the whole data identification apparatus, the apparatus further includes: the pixel point processing device is respectively connected with the brightness analysis device and the pixel point identification device and is used for executing the following operations on each processing point in the nearest neighbor interpolation image: determining whether each surrounding pixel point of the processing point is a processing point, and performing weighted median filtering processing on each brightness value of each surrounding pixel point to obtain a processed brightness value of the processing point; the data merging equipment is connected with the pixel point processing equipment and is used for receiving each processed brightness value of each processing point and each brightness value of each non-processing point, and acquiring a data merging image corresponding to the nearest neighbor interpolation image based on each processed brightness value of each processing point and each brightness value of each non-processing point; the geometric mean de-noising device is connected with the data merging device and is used for receiving the data merging image and executing geometric mean de-noising processing on the data merging image to obtain a corresponding geometric mean de-noised image; the first segmentation device is used for identifying each object in the geometric mean de-noised image, comparing the size of each object to determine the largest-sized object in the geometric mean de-noised image, and performing image segmentation on the geometric mean de-noised image based on the size of the largest-sized object to obtain image blocks with the same size, wherein the larger the size of the largest-sized object is, the larger the obtained image blocks are; and the second segmentation equipment is respectively connected with the first segmentation equipment and the geometric mean de-noising equipment, and performs image block processing on the merged data image, wherein the image block processing has the same size as that of the first segmentation equipment, so as to obtain image blocks with the same size.
The invention has at least the following key invention points:
(1) based on green component values of pixel points of each plant region in the image subjected to targeted processing in the computer warehouse, the withering degree of the corresponding plant region is judged, so that the green plant withering degree of the whole computer warehouse is judged, and integral reference data is provided for the subsequent determination of whether plant watering is carried out or not;
(2) after the geometric mean denoising processing is carried out on the image, carrying out random noise amplitude contrast analysis of a selected region on the image before and after the geometric mean denoising processing;
(3) based on the random noise amplitude comparison analysis result of the selected region of the image before and after the geometric mean denoising, determining whether the subsequent wiener denoising processing needs to be executed on the image after the geometric mean denoising processing;
(4) and confirming the pixel points with the brightness values larger than the first preset brightness threshold value and not on the boundary line of any object region as interference points, and also confirming the pixel points with the brightness values smaller than the second preset brightness threshold value and not on the boundary line of any object region as interference points, and further identifying each processing point needing filtering processing from each interference point, thereby improving the image signal processing effect.
The whole data identification device has wide application range and is convenient and practical. On the basis of targeted image processing, the method effectively judges the integral withering degree of plants in the computer storehouse, and can provide valuable reference data for watering time, so that the balance between watering times and growth effect is achieved.
Detailed Description
Some green plants can effectively absorb toxic chemical substances generated by house decoration, such as chlorophytum comosum, sansevieria trifasciata, cymbidium once and monstera deliciosa, and have particularly strong capability of absorbing formaldehyde; the goldfish grass, the morning glory and the pink can convert sulfur dioxide with strong toxicity into a sulfate compound with no toxicity or low toxicity through oxidation; the cycas, the chrysanthemum, the pomegranate, the camellia and the like can effectively clear harmful substances such as sulfur dioxide, chlorine, carbon monoxide, nitrogen peroxide and the like.
Generally, the relative humidity in the room should not be lower than 30%, and if the humidity is too low or too high, the adverse effect on human health can be caused. Planting some indoor requirements with high requirements on moisture, such as scindapsus aureus, ivy, rhododendron, fern and the like, can increase the indoor humidity in a natural way to form a natural humidifier.
Researches show that orchid, alocasia esculenta, red cinnamon and the like are natural dust collectors, and cilia on plants can intercept and adsorb particles and smoke floating in the air. If there are a sufficient number of such plants in the room, the content of floating microorganisms and floating dust in the room will be reduced.
At present, a plurality of plants are usually placed in a computer storehouse to reduce the environmental deterioration degree caused by too many computers in the storehouse, however, the plants are placed for a long time to cause withering to a certain degree, the withering degree of each plant and even different plants under the same plant are constantly changed and different, how to judge the whole withering degree of the plants in the storehouse to reduce the watering times and ensure the healthy growth of the plants is one of the problems to be solved at present.
In order to overcome the defects, the invention provides an overall data identification device which can effectively solve the corresponding technical problems.
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
An overall data recognition apparatus comprising:
the nearest neighbor interpolation device is used for receiving the storehouse capture image from the computer storehouse site, equally dividing the storehouse capture image into blocks with the corresponding block sizes based on the distance between the resolution of the storehouse capture image and a preset resolution threshold, selecting corresponding nearest neighbor interpolation processing with different forces based on the pixel value variance of each block to obtain correction blocks for each block, and splicing the obtained correction blocks to obtain a nearest neighbor interpolation image.
Next, a specific configuration of the entire data recognition apparatus according to the present invention will be further described.
The whole data identification device may further include:
and the brightness analysis device is connected with the nearest neighbor interpolation device and used for receiving the nearest neighbor interpolation image, analyzing each object in the nearest neighbor interpolation image to obtain an object area corresponding to each object, confirming pixel points with brightness values larger than a first preset brightness threshold value and not on the boundary line of any object area as interference points, and confirming pixel points with brightness values smaller than a second preset brightness threshold value and not on the boundary line of any object area as interference points.
The whole data identification device may further include:
the pixel point identification device is connected with the brightness analysis device and used for receiving each interference point in the nearest neighbor interpolation image and executing the following operation on each interference point: and taking each interference point as a target point, and identifying the target point as a processing point when no pixel point with the brightness value larger than a first preset brightness threshold value or the brightness value smaller than a second preset brightness threshold value exists around the target point.
The whole data identification device may further include:
the pixel point processing device is respectively connected with the brightness analysis device and the pixel point identification device and is used for executing the following operations on each processing point in the nearest neighbor interpolation image: determining whether each surrounding pixel point of the processing point is a processing point, and performing weighted median filtering processing on each brightness value of each surrounding pixel point to obtain a processed brightness value of the processing point;
the data merging equipment is connected with the pixel point processing equipment and is used for receiving each processed brightness value of each processing point and each brightness value of each non-processing point, and acquiring a data merging image corresponding to the nearest neighbor interpolation image based on each processed brightness value of each processing point and each brightness value of each non-processing point;
the geometric mean de-noising device is connected with the data merging device and is used for receiving the data merging image and executing geometric mean de-noising processing on the data merging image to obtain a corresponding geometric mean de-noised image;
the first segmentation device is used for identifying each object in the geometric mean de-noised image, comparing the size of each object to determine the largest-sized object in the geometric mean de-noised image, and performing image segmentation on the geometric mean de-noised image based on the size of the largest-sized object to obtain image blocks with the same size, wherein the larger the size of the largest-sized object is, the larger the obtained image blocks are;
the second segmentation equipment is respectively connected with the first segmentation equipment and the geometric mean de-noising equipment, and is used for performing image block processing on the merged data image, wherein the image block processing is the same as the image block processing of the first segmentation equipment in size, so as to obtain image blocks with the same size;
a sharpening identification device, connected to the first segmentation device and the second segmentation device respectively, for taking a mean value of a plurality of random noise amplitudes of a plurality of image blocks, which are in an L shape within the geometric mean denoised image, in each image block output by the first segmentation device as a first sharpening mean value, and taking a mean value of a plurality of random noise amplitudes of a plurality of image blocks, which are in an L shape within the data convergence image, in each image block output by the second segmentation device as a second sharpening mean value;
the wiener processing device is respectively connected with the sharpening identification device and the geometric mean value denoising device and is used for executing wiener denoising processing on the geometric mean value denoised image when the first sharpening mean value is less than 1.2 times of the second sharpening mean value so as to obtain a wiener denoised image;
the wilting degree identification equipment is connected with the wiener processing equipment and used for identifying each plant area from the received wiener de-noising image based on plant imaging characteristics, determining the wilting degree of the fingerprint area based on the mean value of each green component value of each pixel point in each plant area, and carrying out mean value calculation on each wilting degree of each plant area to obtain the corresponding green wilting degree of the storehouse;
the voice playing equipment is connected with the wilting degree recognition equipment and is used for sending a storehouse watering signal and playing a voice notification file corresponding to the storehouse watering signal when the wilting degree of the green plants in the storehouse is lower than a preset wilting degree threshold value;
the wiener processing device is further configured to stop performing wiener denoising processing on the geometric mean denoising image when the first sharpened mean is more than 1.2 times of the second sharpened mean, and output the geometric mean denoising image as a wiener denoising image;
wherein, in the wilting degree recognition device, determining the wilting degree of the fingerprint area based on the mean value of the green component values of the pixel points in each plant area includes: the larger the mean value of the green component values of the pixel points in each plant area is, the lower the wilting degree of the fingerprint area is determined.
The whole data identification device may further include:
the fiber receiving and sending equipment is connected with the wiener processing equipment and used for receiving the wiener de-noising image and sending the wiener de-noising image through a fiber communication link;
the whole data recognition device is characterized in that:
in the pixel point identification device, when a pixel point with a brightness value larger than a first preset brightness threshold value or a pixel point with a brightness value smaller than a second preset brightness threshold value exists around the target point, the target point is identified as a non-processing point.
The whole data recognition device is characterized in that:
and in the pixel point identification equipment, determining each pixel point except each interference point in the nearest neighbor interpolation image as a non-interference point.
The whole data recognition device is characterized in that:
in the pixel processing device, the farther the surrounding pixels are from the processing point, the smaller the weighted value used by the surrounding pixels participating in the weighted median filtering processing is, and when the surrounding pixels are interference points, the smaller the weighted value used by the surrounding pixels participating in the weighted median filtering processing is than the weighted value used by the surrounding pixels which are non-interference points participating in the weighted median filtering processing.
The whole data recognition device is characterized in that:
in the nearest neighbor interpolation device, the closer the resolution of the storehouse captured image is to the preset resolution threshold, the larger corresponding blocks into which the storehouse captured image is equally divided are;
in the pixel point identification device, the second preset brightness threshold is smaller than the first preset brightness threshold.
The whole data recognition device is characterized in that:
in the nearest neighbor interpolation device, for each block, the greater the variance of the pixel values of the block, the smaller the intensity of the selected nearest neighbor interpolation processing;
the pixel point processing equipment consists of signal receiving sub-equipment, signal processing sub-equipment and signal sending sub-equipment, and the signal processing sub-equipment is respectively connected with the signal receiving sub-equipment and the signal sending sub-equipment.
In addition, the optical fiber is a short term for optical fiber, and is a fiber made of glass or plastic, which can be used as a light transmission means. The principle of transmission is total reflection of light. The fine optical fiber is enclosed in a plastic sheath so that it can be bent without breaking. Generally, a Light Emitting Diode (LED) or a laser beam is used as a transmitter at one end of the optical fiber to transmit an optical pulse to the optical fiber, and a photosensor is used as a receiver at the other end of the optical fiber to detect the pulse.
In the multimode optical fiber, the core diameter is 50 μm and 62.5 μm, which are approximately equivalent to the thickness of human hair. The diameter of the single-mode optical fiber core is 8-10 μm, and 9/125 μm is commonly used. The core is surrounded by a glass envelope, commonly referred to as a cladding, of lower refractive index than the core, which keeps the light rays within the core. Further on the outside is a thin plastic outer jacket, i.e. a coating, for protecting the cladding. The optical fibers are typically bundled and protected by an outer jacket. The core is usually a double-walled concentric cylinder of silica glass with a small cross-sectional area, which is brittle and easily broken, and therefore requires the addition of a protective layer.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. An overall data recognition apparatus, comprising:
the nearest neighbor interpolation device is used for receiving a storeroom captured image from a computer storeroom site, averagely dividing the storeroom captured image into blocks with the sizes of the corresponding blocks on the basis of the distance between the resolution ratio of the storeroom captured image and a preset resolution threshold value, selecting corresponding nearest neighbor interpolation processing with different strength on the basis of the pixel value variance of each block so as to obtain correction blocks for each block, and splicing the obtained correction blocks so as to obtain a nearest neighbor interpolation image;
the brightness analysis device is connected with the nearest neighbor interpolation device and used for receiving the nearest neighbor interpolation image, analyzing each object in the nearest neighbor interpolation image to obtain an object area corresponding to each object, confirming pixel points with brightness values larger than a first preset brightness threshold value and not on the boundary line of any object area as interference points, and confirming pixel points with brightness values smaller than a second preset brightness threshold value and not on the boundary line of any object area as interference points;
the pixel point identification device is connected with the brightness analysis device and used for receiving each interference point in the nearest neighbor interpolation image and executing the following operation on each interference point: taking each interference point as a target point, and identifying the target point as a processing point when no pixel point with the brightness value larger than a first preset brightness threshold value or the brightness value smaller than a second preset brightness threshold value exists around the target point;
the pixel point processing device is respectively connected with the brightness analysis device and the pixel point identification device and is used for executing the following operations on each processing point in the nearest neighbor interpolation image: determining whether each surrounding pixel point of the processing point is a processing point, and performing weighted median filtering processing on each brightness value of each surrounding pixel point to obtain a processed brightness value of the processing point;
the data merging equipment is connected with the pixel point processing equipment and is used for receiving each processed brightness value of each processing point and each brightness value of each non-processing point, and acquiring a data merging image corresponding to the nearest neighbor interpolation image based on each processed brightness value of each processing point and each brightness value of each non-processing point;
the geometric mean de-noising device is connected with the data merging device and is used for receiving the data merging image and executing geometric mean de-noising processing on the data merging image to obtain a corresponding geometric mean de-noised image;
the first segmentation device is used for identifying each object in the geometric mean de-noised image, comparing the size of each object to determine the largest-sized object in the geometric mean de-noised image, and performing image segmentation on the geometric mean de-noised image based on the size of the largest-sized object to obtain image blocks with the same size, wherein the larger the size of the largest-sized object is, the larger the obtained image blocks are;
the second segmentation equipment is respectively connected with the first segmentation equipment and the geometric mean de-noising equipment, and is used for performing image block processing on the merged data image, wherein the image block processing is the same as the image block processing of the first segmentation equipment in size, so as to obtain image blocks with the same size;
a sharpening identification device, connected to the first segmentation device and the second segmentation device respectively, for taking a mean value of a plurality of random noise amplitudes of a plurality of image blocks, which are in an L shape within the geometric mean denoised image, in each image block output by the first segmentation device as a first sharpening mean value, and taking a mean value of a plurality of random noise amplitudes of a plurality of image blocks, which are in an L shape within the data convergence image, in each image block output by the second segmentation device as a second sharpening mean value;
the wiener processing device is respectively connected with the sharpening identification device and the geometric mean value denoising device and is used for executing wiener denoising processing on the geometric mean value denoised image when the first sharpening mean value is less than 1.2 times of the second sharpening mean value so as to obtain a wiener denoised image;
the wilting degree identification equipment is connected with the wiener processing equipment and used for identifying each plant area from the received wiener de-noising image based on plant imaging characteristics, determining the wilting degree of the fingerprint area based on the mean value of each green component value of each pixel point in each plant area, and carrying out mean value calculation on each wilting degree of each plant area to obtain the corresponding green wilting degree of the storehouse;
the voice playing equipment is connected with the wilting degree recognition equipment and is used for sending a storehouse watering signal and playing a voice notification file corresponding to the storehouse watering signal when the wilting degree of the green plants in the storehouse is lower than a preset wilting degree threshold value;
the wiener processing device is further configured to stop performing wiener denoising processing on the geometric mean denoising image when the first sharpened mean is more than 1.2 times of the second sharpened mean, and output the geometric mean denoising image as a wiener denoising image;
wherein, in the wilting degree recognition device, determining the wilting degree of the fingerprint area based on the mean value of the green component values of the pixel points in each plant area includes: the larger the mean value of the green component values of the pixel points in each plant area is, the lower the wilting degree of the fingerprint area is determined.
2. The holistic data recognition device of claim 1, wherein the device further comprises:
and the optical fiber transceiving equipment is connected with the wiener processing equipment and used for receiving the wiener de-noising image and sending the wiener de-noising image through an optical fiber communication link.
3. The integral data recognition device as recited in claim 2, wherein:
in the pixel point identification device, when a pixel point with a brightness value larger than a first preset brightness threshold value or a pixel point with a brightness value smaller than a second preset brightness threshold value exists around the target point, the target point is identified as a non-processing point.
4. The integral data recognition device as recited in claim 3, wherein:
and in the pixel point identification equipment, determining each pixel point except each interference point in the nearest neighbor interpolation image as a non-interference point.
5. The integral data recognition device as recited in claim 4, wherein:
in the pixel processing device, the farther the surrounding pixels are from the processing point, the smaller the weighted value used by the surrounding pixels participating in the weighted median filtering processing is, and when the surrounding pixels are interference points, the smaller the weighted value used by the surrounding pixels participating in the weighted median filtering processing is than the weighted value used by the surrounding pixels which are non-interference points participating in the weighted median filtering processing.
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