CN111815086A - Automatic big data sorting platform - Google Patents
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- CN111815086A CN111815086A CN201910289732.XA CN201910289732A CN111815086A CN 111815086 A CN111815086 A CN 111815086A CN 201910289732 A CN201910289732 A CN 201910289732A CN 111815086 A CN111815086 A CN 111815086A
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- 235000013311 vegetables Nutrition 0.000 claims abstract description 25
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 238000003384 imaging method Methods 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims description 32
- 238000012937 correction Methods 0.000 claims description 26
- 238000010606 normalization Methods 0.000 claims description 22
- 238000012163 sequencing technique Methods 0.000 claims description 15
- 238000000034 method Methods 0.000 claims description 6
- 235000020061 kirsch Nutrition 0.000 claims description 3
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G06T7/11—Region-based segmentation
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Abstract
The invention relates to an automatic big data sorting platform, which comprises: a feature identification device for performing dish target recognition on the curve-processed image based on the imaging feature of each dish to obtain the number of each dish target in the curve-processed image; the vegetable system analysis device is used for counting the number of the vegetable targets owned by each vegetable system in the curve processing image based on the type of the vegetable system to which each vegetable target belongs; and a ranking control device for ranking the various cuisine based on the number of cuisine targets possessed by each cuisine in the curve processing image. The automatic big data sorting platform simplifies operation and is reliable in data. The number of the dish targets owned by each kind of dish is counted based on the type of the dish to which each kind of dish target belongs, and various kinds of dishes are sequenced based on the number of the dish targets owned by each kind of dish, so that the layout of subsequent operation activities is facilitated.
Description
Technical Field
The invention relates to the field of automatic control, in particular to an automatic big data sorting platform.
Background
Automatic control means that a certain operating state or parameter of a machine, equipment or production process is automatically operated according to a predetermined rule by an external device or apparatus without direct human involvement. Automatic control is in contrast to manual control concepts.
The research of the automatic control technology is beneficial to freeing human from complex, dangerous and fussy working environment and greatly improving the control efficiency. Automatic control is a branch of engineering science. He involves automatic influence on the dynamic system using feedback principles to bring the output value close to what we want. From a process point of view, it is based on mathematical system theory. What we call automatic control today is a branch of the theory of control that arose in the middle of the twentieth century. The underlying conclusions are presented by norbert wiener, ludaff kalman.
Disclosure of Invention
The invention needs to have the following two important points:
(1) the method comprises the steps of performing targeted detection on the number of various dish objects which are completely placed in a dining hall at present, counting the number of the dish objects owned by each kind of dish system based on the type of the dish system to which each kind of dish object belongs, and sequencing the various dish systems based on the number of the dish objects owned by each kind of dish system so as to wirelessly send sequencing results for sequencing the various dish systems to a mobile phone of a restaurant manager, so that the restaurant manager can refer to the sequencing results to conveniently perform layout of subsequent operation activities;
(2) and identifying each target occupying the pixel points in the image, wherein the number of the targets exceeds the number of the pixel points, and taking the number of the targets as the number of the field targets to determine whether to execute vertical sharpening processing with larger operation amount.
According to an aspect of the invention, an automated big data sorting platform is provided, the platform comprising:
the characteristic identification device is connected with the curve processing device and used for carrying out dish target identification on the curve processing image based on the imaging characteristic of each dish so as to obtain the number of each dish target in the curve processing image;
the vegetable system analysis device is connected with the characteristic identification device and used for receiving the number of various vegetable targets in the curve processing image and counting the number of the vegetable targets owned by each vegetable system in the curve processing image based on the type of the vegetable system to which each vegetable target belongs;
the sorting control device is connected with the dish series analysis device and is used for sorting various dish series based on the number of the dish targets owned by each dish series in the curve processing image;
the wireless sending equipment is connected with the sequencing control equipment and is used for wirelessly sending sequencing results for sequencing various cuisines to a mobile phone of a restaurant manager;
the fisheye capturing device is arranged on the roof of the dining hall and used for performing image capturing action on the lower dining hall to obtain a corresponding hall captured image;
the homomorphic filtering device is connected with the fisheye capturing device and used for receiving the hall captured image and executing homomorphic filtering processing on the hall captured image to obtain and output a filtered image;
the foreground stripping equipment is connected with the homomorphic filtering equipment and used for receiving the filtered image, taking pixel points of gray values in the filtered image between a foreground upper limit gray value and a foreground lower limit gray value as foreground pixel points, and forming a field foreground image corresponding to the filtered image based on each foreground pixel point in the filtered image;
the target identification device is connected with the foreground stripping device and used for receiving the field foreground image, identifying each target occupying the pixels with the number exceeding the number limit in the field foreground image and outputting the number of each target as the number of the field targets;
the vertical direction sharpening device is respectively connected with the homomorphic filtering device and the target identification device and is used for receiving the filtered image and executing vertical direction sharpening processing on the filtered image when the number of the received field targets is less than or equal to a preset number threshold value so as to obtain and output a corresponding vertical direction sharpened image;
and the operator sharpening device is connected with the vertical direction sharpening device and is used for carrying out sharpening operation based on a Kirsch operator on the received vertical direction sharpened image so as to obtain a corresponding operator sharpened image.
The automatic big data sorting platform simplifies operation and is reliable in data. The number of the dish targets owned by each kind of dish is counted based on the type of the dish to which each kind of dish target belongs, and various kinds of dishes are sequenced based on the number of the dish targets owned by each kind of dish, so that the layout of subsequent operation activities is facilitated.
Detailed Description
The following will describe in detail an embodiment of the automated big data sorting platform of the present invention.
Image recognition is an important area of artificial intelligence. In order to create a computer program that simulates human image recognition activities, different image recognition models have been proposed. Such as a template matching model. This model considers that a certain image is recognized and that it is necessary to have a memory pattern, also called template, of this image in past experience. If the current stimulus matches the template in the brain, the image is identified. For example, if there is an a-template in the brain, the letter a is recognized if its size, orientation, shape are identical to the a-template. The model is simple and clear and can be easily applied to practical use. However, this model emphasizes that the image must be completely matched with the template in the brain for recognition, and in fact, a person can recognize not only an image that is completely matched with the template in the brain, but also an image that is not completely matched with the template. For example, one can recognize not only a specific letter a, but also various letters a of a print, a script, an incorrect direction, and different sizes. At the same time, the number of images that a person can recognize is large, and it is not possible if each image recognized has a corresponding template in the brain.
In the prior art, in order to meet the eating habits and requirements of customers, a restaurant needs to continuously perform manual form filling investigation on the customers, however, the investigation mode is too original, on one hand, the time of the customers needs to be consumed, and the impatience of the customers is caused, and on the other hand, the data of the form filling of the customers is not real enough, and the misleading is easily caused to the follow-up operation activities of restaurant operators.
In order to overcome the defects, the invention builds an automatic big data sorting platform and can effectively solve the corresponding technical problem.
The automatic big data sorting platform shown according to the embodiment of the invention comprises:
the characteristic identification device is connected with the curve processing device and used for carrying out dish target identification on the curve processing image based on the imaging characteristic of each dish so as to obtain the number of each dish target in the curve processing image;
the vegetable system analysis device is connected with the characteristic identification device and used for receiving the number of various vegetable targets in the curve processing image and counting the number of the vegetable targets owned by each vegetable system in the curve processing image based on the type of the vegetable system to which each vegetable target belongs;
the sorting control device is connected with the dish series analysis device and is used for sorting various dish series based on the number of the dish targets owned by each dish series in the curve processing image;
the wireless sending equipment is connected with the sequencing control equipment and is used for wirelessly sending sequencing results for sequencing various cuisines to a mobile phone of a restaurant manager;
the fisheye capturing device is arranged on the roof of the dining hall and used for performing image capturing action on the lower dining hall to obtain a corresponding hall captured image;
the homomorphic filtering device is connected with the fisheye capturing device and used for receiving the hall captured image and executing homomorphic filtering processing on the hall captured image to obtain and output a filtered image;
the foreground stripping equipment is connected with the homomorphic filtering equipment and used for receiving the filtered image, taking pixel points of gray values in the filtered image between a foreground upper limit gray value and a foreground lower limit gray value as foreground pixel points, and forming a field foreground image corresponding to the filtered image based on each foreground pixel point in the filtered image;
the target identification device is connected with the foreground stripping device and used for receiving the field foreground image, identifying each target occupying the pixels with the number exceeding the number limit in the field foreground image and outputting the number of each target as the number of the field targets;
the vertical direction sharpening device is respectively connected with the homomorphic filtering device and the target identification device and is used for receiving the filtered image and executing vertical direction sharpening processing on the filtered image when the number of the received field targets is less than or equal to a preset number threshold value so as to obtain and output a corresponding vertical direction sharpened image;
the operator sharpening device is connected with the vertical direction sharpening device and is used for carrying out sharpening operation based on a Kirsch operator on the received vertical direction sharpened image so as to obtain a corresponding operator sharpened image;
the minimum value filtering device is connected with the operator sharpening device and is used for carrying out minimum value filtering processing on the received operator sharpened image so as to obtain and output a corresponding minimum value filtering image;
the curve processing device is connected with the minimum value filtering device and used for receiving the minimum value filtering image and adjusting the radian of each curve in the minimum value filtering image to be below a preset curve maximum radian threshold value so as to obtain and output a corresponding curve processing image;
and the vertical direction sharpening device is further used for stopping performing vertical direction sharpening processing on the filtered image when the number of the received field targets is larger than a preset number threshold, and outputting the filtered image as a vertical direction sharpened image.
Next, the detailed structure of the automated big data sorting platform of the present invention will be further described.
The automatic big data sorting platform can further comprise:
and the dynamic random access storage equipment is respectively connected with the curve processing equipment and the minimum value filtering equipment and is used for receiving and temporarily storing the curve processing image.
In the automatic big data sorting platform:
the vegetable system analysis device, the sequencing control device, the minimum value filtering device, the operator sharpening device, the curve processing device and the vertical sharpening device are respectively realized by adopting CPLD chips with different models.
The automatic big data sorting platform can further comprise:
and the normalization correction device is respectively connected with the fisheye capturing device and the homomorphic filtering image and is used for receiving the hall captured image and equally dividing the hall captured image into blocks with corresponding block sizes based on the distance between the definition grade of the hall captured image and the preset lower limit definition grade.
In the automatic big data sorting platform:
the normalization correction device selects corresponding normalization correction processing with different forces based on the pixel value variance of each block to obtain adjustment blocks for each block, and combines the obtained adjustment blocks to obtain a normalization correction image.
In the automatic big data sorting platform:
in the normalization correction device, the larger the resolution level of the hall captured image is from a preset lower limit resolution level, the larger the corresponding block into which the hall captured image is equally divided, and in the normalization correction device, the larger the variance of the pixel value of each block is, the smaller the degree of the selected normalization correction process is.
In the automatic big data sorting platform:
the normalized correction device is further configured to send the normalized correction image to the homomorphic filtering device in place of the hall captured image.
The automatic big data sorting platform can further comprise:
and the definition improving device is connected with the normalization correction device and used for performing definition improving operation on the hall captured image when the definition grade of the hall captured image is smaller than the preset lower limit definition grade before the normalization correction device performs normalization correction on the hall captured image, replacing the hall captured image with the hall captured image after the definition improving operation is performed and inputting the replaced hall captured image into the normalization correction device, and not performing the definition improving operation on the hall captured image when the definition grade of the hall captured image is larger than or equal to the preset lower limit definition grade.
In addition, the CPLD has the characteristics of flexible programming, high integration level, short design and development period, wide application range, advanced development tool, low design and manufacturing cost, low requirement on hardware experience of designers, no need of testing for standard products, strong confidentiality, popular price and the like, and can realize large-scale circuit design, so that the CPLD is widely applied to prototype design and product production (generally less than 10,000) of products. CPLD devices are used in almost all applications where small-scale, general-purpose digital integrated circuits are used. The CPLD device has become an indispensable component of electronic products, and its design and application become a necessary skill for electronic engineers. The CPLD is a digital integrated circuit in which a user constructs logic functions according to his or her own needs. The basic design method is to generate corresponding target files by means of an integrated development software platform and methods such as schematic diagrams, hardware description languages and the like, and to transmit codes to a target chip through a download cable (programming in the system) so as to realize the designed digital system.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
Although the present invention has been described with reference to the above embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be subject to the scope defined by the claims of the present application.
Claims (8)
1. An automated big data sorting platform, the platform comprising:
the characteristic identification device is connected with the curve processing device and used for carrying out dish target identification on the curve processing image based on the imaging characteristic of each dish so as to obtain the number of each dish target in the curve processing image;
the vegetable system analysis device is connected with the characteristic identification device and used for receiving the number of various vegetable targets in the curve processing image and counting the number of the vegetable targets owned by each vegetable system in the curve processing image based on the type of the vegetable system to which each vegetable target belongs;
the sorting control device is connected with the dish series analysis device and is used for sorting various dish series based on the number of the dish targets owned by each dish series in the curve processing image;
the wireless sending equipment is connected with the sequencing control equipment and is used for wirelessly sending sequencing results for sequencing various cuisines to a mobile phone of a restaurant manager;
the fisheye capturing device is arranged on the roof of the dining hall and used for performing image capturing action on the lower dining hall to obtain a corresponding hall captured image;
the homomorphic filtering device is connected with the fisheye capturing device and used for receiving the hall captured image and executing homomorphic filtering processing on the hall captured image to obtain and output a filtered image;
the foreground stripping equipment is connected with the homomorphic filtering equipment and used for receiving the filtered image, taking pixel points of gray values in the filtered image between a foreground upper limit gray value and a foreground lower limit gray value as foreground pixel points, and forming a field foreground image corresponding to the filtered image based on each foreground pixel point in the filtered image;
the target identification device is connected with the foreground stripping device and used for receiving the field foreground image, identifying each target occupying the pixels with the number exceeding the number limit in the field foreground image and outputting the number of each target as the number of the field targets;
the vertical direction sharpening device is respectively connected with the homomorphic filtering device and the target identification device and is used for receiving the filtered image and executing vertical direction sharpening processing on the filtered image when the number of the received field targets is less than or equal to a preset number threshold value so as to obtain and output a corresponding vertical direction sharpened image;
the operator sharpening device is connected with the vertical direction sharpening device and is used for carrying out sharpening operation based on a Kirsch operator on the received vertical direction sharpened image so as to obtain a corresponding operator sharpened image;
the minimum value filtering device is connected with the operator sharpening device and is used for carrying out minimum value filtering processing on the received operator sharpened image so as to obtain and output a corresponding minimum value filtering image;
the curve processing device is connected with the minimum value filtering device and used for receiving the minimum value filtering image and adjusting the radian of each curve in the minimum value filtering image to be below a preset curve maximum radian threshold value so as to obtain and output a corresponding curve processing image;
and the vertical direction sharpening device is further used for stopping performing vertical direction sharpening processing on the filtered image when the number of the received field targets is larger than a preset number threshold, and outputting the filtered image as a vertical direction sharpened image.
2. The automated big data sorting platform of claim 1, wherein the platform further comprises:
and the dynamic random access storage equipment is respectively connected with the curve processing equipment and the minimum value filtering equipment and is used for receiving and temporarily storing the curve processing image.
3. The automated big data sorting platform of claim 2, wherein:
the vegetable system analysis device, the sequencing control device, the minimum value filtering device, the operator sharpening device, the curve processing device and the vertical sharpening device are respectively realized by adopting CPLD chips with different models.
4. The automated big data sorting platform of claim 3, wherein the platform further comprises:
and the normalization correction device is respectively connected with the fisheye capturing device and the homomorphic filtering image and is used for receiving the hall captured image and equally dividing the hall captured image into blocks with corresponding block sizes based on the distance between the definition grade of the hall captured image and the preset lower limit definition grade.
5. The automated big data sorting platform of claim 4, wherein:
the normalization correction device selects corresponding normalization correction processing with different forces based on the pixel value variance of each block to obtain adjustment blocks for each block, and combines the obtained adjustment blocks to obtain a normalization correction image.
6. The automated big data sorting platform of claim 5, wherein:
in the normalization correction device, the larger the resolution level of the hall captured image is from a preset lower limit resolution level, the larger the corresponding block into which the hall captured image is equally divided, and in the normalization correction device, the larger the variance of the pixel value of each block is, the smaller the degree of the selected normalization correction process is.
7. The automated big data sorting platform of claim 6, wherein:
the normalized correction device is further configured to send the normalized correction image to the homomorphic filtering device in place of the hall captured image.
8. The automated big data sorting platform of claim 7, wherein the platform further comprises:
and the definition improving device is connected with the normalization correction device and used for performing definition improving operation on the hall captured image when the definition grade of the hall captured image is smaller than the preset lower limit definition grade before the normalization correction device performs normalization correction on the hall captured image, replacing the hall captured image with the hall captured image after the definition improving operation is performed and inputting the replaced hall captured image into the normalization correction device, and not performing the definition improving operation on the hall captured image when the definition grade of the hall captured image is larger than or equal to the preset lower limit definition grade.
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| CN201910289732.XA CN111815086A (en) | 2019-04-11 | 2019-04-11 | Automatic big data sorting platform |
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| CN201910289732.XA CN111815086A (en) | 2019-04-11 | 2019-04-11 | Automatic big data sorting platform |
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