CN114943737B - Flaky pastry quality evaluation method and device and readable storage medium - Google Patents

Flaky pastry quality evaluation method and device and readable storage medium Download PDF

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CN114943737B
CN114943737B CN202210874109.2A CN202210874109A CN114943737B CN 114943737 B CN114943737 B CN 114943737B CN 202210874109 A CN202210874109 A CN 202210874109A CN 114943737 B CN114943737 B CN 114943737B
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flaky pastry
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CN114943737A (en
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黄育东
王永来
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Shenzhen Zhongshi Chuangxin Food Co ltd
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Shenzhen Zhongshi Chuangxin Food Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G06T7/001—Industrial image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30128—Food products
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses a flaky pastry quality evaluation method, a flaky pastry quality evaluation device and a readable storage medium, which belong to the technical field of food quality evaluation, and comprise the steps of analyzing a real-time flaky pastry image by using a learning model through an acquired real-time flaky pastry image, comparing the real-time flaky pastry image with a standard flaky pastry image in the learning model, and substituting the standard flaky pastry image into a comparison evaluation model to obtain an expansion characteristic value of the real-time flaky pastry; extracting the chromatic value of the real-time flaky pastry, and obtaining the expansion characteristic value of the real-time flaky pastry by using a first uniform method according to the obtained baking degree of each region module by comparing with a standard color card; crushing the real-time flaky pastry, obtaining the crispness degree of each region module of the real-time flaky pastry through a crushing pressure value, and obtaining the characteristic value of the crispness degree of the real-time flaky pastry by utilizing a second uniform evaluation method; acquiring extreme degree factors of the roasting degree and the crispness degree; and obtaining the quality evaluation value of the real-time flaky pastry according to the normalization model.

Description

Flaky pastry quality evaluation method and device and readable storage medium
Technical Field
The invention relates to the technical field of food quality evaluation, in particular to a flaky pastry quality evaluation method and device and a readable storage medium.
Background
The crisp cake is named after a special fat baked crisp, and is characterized by golden yellow, clear layer, crisp but not crumbled, oily but not greasy, crisp and palatable. The existing cake products have various types, different components and different tastes, and bring delicious enjoyment to people. When manufacturers produce flaky pastries, the quality of the flaky pastries needs to be kept. The shortcakes are usually produced in batches, and the quality of a batch of shortcakes is grasped by examining the quality of individual shortcakes. The optimal quality range of the flaky pastry is predicted by inspecting the quality control production flow of the flaky pastry, so that a basis can be provided for enterprises to adopt raw materials, appropriate raw materials are adopted, qualified target products are produced under given production process conditions, the qualification rate is improved, waste is reduced, loss is reduced for the enterprises, and the enterprise profits are improved. At present, no method for inspecting the quality of the flaky pastry adopts manual quality inspection, has strong subjectivity and non-uniform standard, and is easy to cause inspection error.
Therefore, a technical problem to be solved urgently by those skilled in the art is how to provide a novel method for inspecting the quality of flaky pastry, so that the inspection standards are unified and the success rate of quality inspection is improved.
Disclosure of Invention
Therefore, the invention provides a flaky pastry quality evaluation method, a flaky pastry quality evaluation device and a readable storage medium, which are used for solving the problem of poor investigation effect in the prior art.
In order to achieve the above purpose, the invention provides the following technical scheme:
according to a first aspect of the invention, a flaky pastry quality evaluation method is provided, and comprises the following steps:
s1: a camera is called to collect a plurality of groups of standard flaky pastry images before and after baking and real-time flaky pastry images;
s2: analyzing the standard flaky pastry image by using a learning model to obtain an expansion coefficient of the standard flaky pastry; analyzing the real-time flaky pastry image by using a learning model to obtain an expansion coefficient of the real-time flaky pastry; the learning model equally divides the standard flaky pastry image and the real-time flaky pastry image into a plurality of region modules; the expansion coefficient of the real-time flaky pastry is compared with the expansion coefficient of the standard flaky pastry, and the expansion coefficient of the real-time flaky pastry is substituted into a comparison and evaluation model to obtain the expansion characteristic value of the real-time flaky pastry;
s3: extracting the colorimetric values of all region modules of the baked real-time flaky pastry, obtaining the baking degrees of all region modules of the real-time flaky pastry according to the pre-stored standard color card of the baking degree, and obtaining the colorimetric characteristic values of the real-time flaky pastry by utilizing a first uniform evaluation method;
s4: respectively carrying out crushing operation on each region module of the real-time flaky pastry to obtain the crushing pressure value of each region module of the real-time flaky pastry, obtaining the crispness degree of each region module of the real-time flaky pastry according to the crushing pressure value, and obtaining the characteristic value of the crispness degree of the real-time flaky pastry by utilizing a second uniform evaluation method;
s5: acquiring extreme degree factors of the baking degree and the crispness degree based on the baking degree and the crispness degree of each region module of the real-time flaky pastry;
s6: and substituting the expansion characteristic value, the chromaticity characteristic value, the crispness characteristic value and the extreme degree factor of the real-time flaky pastry into a normalization model to obtain a quality evaluation value of the real-time flaky pastry.
Further, the learning model is obtained by using multiple groups of data through machine learning training, the multiple groups of data comprise first data and second data, the first data are images before baking the standard flaky pastry, and the second data are images after baking the standard flaky pastry.
Further, the learning model establishes a plane coordinate system by taking the central points of the standard flaky pastry image and the real-time flaky pastry image as original points, and equally divides the standard flaky pastry image and the real-time flaky pastry image under the plane coordinate system to obtain the coordinates of the region module.
Further, the first uniformity evaluation method includes:
establishing a real-time flaky pastry chroma degree factor set M = { M = { (M) } 1 ，m 2 ，m 3 ，m 4 ，m 5 ，m 6 ，m 7 }; wherein m is 1 Representing burnt, m 2 Represents relatively paste, m 3 Represents partial pasting, m 4 Standing for good roasting, m 5 Is representative of partial deficit, m 6 Represents less, m 7 Representing the roast lack;
establishing fuzzy evaluation preset weight B = [ B ] corresponding to the real-time flaky pastry chroma degree factor set 1 ，b 2 ，b 3 ，b 4 ，b 5 ，b 6 ，b 7 ](ii) a Wherein, b 1 A predetermined weight representing a burnt batter, b 2 A predetermined weight representing a more fuzzy b 3 A predetermined weight representing partial blurring, b 4 Representing a predetermined weight of roasting, b 5 A predetermined weight representing a bias, b 6 A default weight representing a lower weight, b 7 A preset weight representing a roast shortage;
establishing a fuzzy evaluation preset matrix for the real-time flaky pastry chroma degree uniformity evaluation factors
Figure 825360DEST_PATH_IMAGE001
(ii) a Wherein r is 11 ，r 17 ，r 71 ，r 77 Presetting an empirical value for the chroma degree of the real-time flaky pastry;
selecting a weight value U 1 Carrying out weighted evaluation on the baking chromaticity degree of the real-time flaky pastry to obtain a chromaticity characteristic value theta of the real-time flaky pastry 2 ；
The formula of the chromaticity characteristic value of the real-time flaky pastry is as follows:
Figure 815313DEST_PATH_IMAGE002
（1）。
further, the second uniformity evaluation method includes:
establishing a real-time crisp degree factor set N = { N = N of the flaky pastry 1 ，n 2 ，n 3 ，n 4 ，n 5 ，n 6 ，n 7 }; wherein n is 1 Standing for bake hardness, n 2 Is meant to be harder, n 3 Representing a hardness of n 4 Representing crunchy texture, n 5 Representing a partial softness, n 6 Is representative of softer, n 7 Standing for baking to be soft;
establishing fuzzy evaluation preset weight D = [ D ] corresponding to real-time crisp cake degree factor set 1 ，d 2 ，d 3 ，d 4 ，d 5 ，d 6 ，d 7 ](ii) a Wherein d is 1 A predetermined weight representing the degree of hardness of baking, d 2 Representing a harder predetermined weight, d 3 Representing a hard preset weight, d 4 A predetermined weight representing crispness, d 5 A predetermined weight representing a softness, d 6 Representing a softer preset weight, d 7 Representing the preset weight of the roasting softness;
establishing a fuzzy evaluation preset matrix for real-time crisp degree uniformity evaluation factors of the flaky pastry
Figure 596187DEST_PATH_IMAGE003
(ii) a Wherein w 11 ，w 17 ，w 71 ，w 77 Presetting an empirical value for the degree of crispness of the real-time flaky pastry;
selecting a weight value U 2 And baking the real-time flaky pastryCarrying out weighted evaluation on the crispness degree to obtain the characteristic value theta of the crispness degree of the real-time flaky pastry 3 ；
The formula of the feature value of the crispness of the real-time flaky pastry is as follows:
Figure 987985DEST_PATH_IMAGE004
（2）。
further, the comparative evaluation model is:
Figure 828902DEST_PATH_IMAGE005
（3）
wherein, theta 1 Is the expansion characteristic value, P, of the real-time flaky pastry 2 Is the expansion coefficient, P, of the real-time flaky pastry 1 The expansion coefficient of the standard flaky pastry is shown.
Further, the normalization model is:
Figure 306151DEST_PATH_IMAGE006
（4）
wherein, lambda is the quality evaluation value of the real-time flaky pastry, theta 1 Is the expansion characteristic value of the real-time flaky pastry 2 Is a real-time color characteristic value of the flaky pastry 3 Is the characteristic value of the degree of crispness, k, of the flaky pastry in real time 1 For the preset weight, k, of the expansion characteristic of the real-time flaky pastry 2 For the preset weight, k, of the colorimetric characteristic value of the flaky pastry in real time 3 Is the preset weight of the feature value of the degree of crispness of the real-time flaky pastry,
Figure 890716DEST_PATH_IMAGE007
for the first extreme of the preset function,
Figure 933759DEST_PATH_IMAGE008
a function is preset for the second terminal.
According to a second aspect of the present invention, there is provided a flaky pastry quality evaluating apparatus comprising:
the high-definition camera is used for collecting the original image of the real-time flaky pastry;
the pressure sensor is used for acquiring the crushing pressure value of each region module of the real-time flaky pastry;
a memory for storing computer execution instructions;
a processor for executing the computer-executable instructions stored in the memory to cause the apparatus to perform any of the above methods of assessing the quality of a crisp.
Further, the high-definition camera preprocesses the acquired original image: and selecting an effective area where the real-time flaky pastry is located from the original image to obtain a first image, removing the background in the first image, and only reserving the image information of the real-time flaky pastry to obtain a second image.
According to a third aspect of the present invention, there is provided a readable storage medium comprising a program and instructions, wherein when the program or instructions are run on a computer, the method of assessing the quality of a flaky pastry according to any one of the above is implemented.
The invention has the following advantages:
according to the method, the real-time flaky pastry is compared with the standard flaky pastry through a machine learning training method, and the obtained expansion characteristic value is more objective and accurate; the colorimetric values of all the region modules obtained by segmenting the image of the real-time flaky pastry accord with the visual characteristics of human beings, and the obtained image is more visual and effective; the expansion characteristic value and the crispness characteristic value obtained by homogenizing the region modules are more objective and reasonable; and substituting the acquired extreme degree factors of the baking degree and the crispness degree into the normalization model, so that the influence of extreme conditions such as excessive pasting and excessive softening in the baking process is reduced, and the quality evaluation value of the real-time flaky pastry is more objective and accurate. The automatic learning evaluation is adopted, the quality evaluation standard of the flaky pastry is unified, and the success rate of quality inspection is improved.
Drawings
Fig. 1 is a flow chart of the flaky pastry quality evaluation method provided by the invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
According to a first aspect of the present invention, there is provided a flaky pastry quality evaluating method, comprising the steps of:
s1: and a camera is called to acquire a plurality of groups of standard flaky pastry images before and after baking and real-time flaky pastry images. Under the condition that the illumination and the relative position of the high-definition camera and the flaky pastry baking tray are kept consistent, the original image of the flaky pastry is collected.
S2: analyzing the standard flaky pastry image by using a learning model to obtain the expansion coefficient of the standard flaky pastry; and analyzing the real-time flaky pastry image by using a learning model to obtain the expansion coefficient of the real-time flaky pastry.
The learning model is obtained by using a plurality of groups of data through machine learning training, the plurality of groups of data comprise first data and second data, the first data are images before baking the standard flaky pastry, and the second data are images after baking the standard flaky pastry. The learning model establishes a plane coordinate system by taking the central points of the standard flaky pastry image and the real-time flaky pastry image as original points, equally divides the standard flaky pastry image and the real-time flaky pastry image under the plane coordinate system, and divides the standard flaky pastry image and the real-time flaky pastry image into a plurality of region modules to obtain the coordinates of the region modules. And solving to obtain the area of each region module according to the coordinates of each vertex of the region module. The flaky pastry can gradually expand and become larger in the baking process, and the area of the flaky pastry also becomes larger. Because the flaky pastry is divided equally, the area in the area module of the flaky pastry becomes larger along with the expansion of the flaky pastry.
And inputting the first data and the second data of the standard flaky pastry into the learning model, and obtaining the change of the standard flaky pastry before and after the area expansion in each region module according to the machine learning of the learning model so as to obtain the expansion coefficient of the standard flaky pastry. And inputting the data of the real-time flaky pastry image into the learning model, and calculating and analyzing to obtain the expansion coefficient of the real-time flaky pastry. And comparing the expansion coefficients of the real-time flaky pastries with the expansion coefficients of the standard flaky pastries, and substituting the comparison and evaluation model to obtain the expansion characteristic value of the real-time flaky pastries.
The comparative evaluation model is:
Figure 820943DEST_PATH_IMAGE005
（3）
wherein, theta 1 Is the expansion characteristic value, P, of the flaky pastry in real time 2 Is the expansion coefficient, P, of the real-time flaky pastry 1 The expansion coefficient of the standard flaky pastry is shown.
S3: and simultaneously taking the divided region modules of the real-time flaky pastry as pixel blocks, extracting the colorimetric values of the region modules of the baked real-time flaky pastry, and obtaining the baking degree of the region modules of the real-time flaky pastry according to the pre-stored standard color card of the baking degree. The color depth of the area module is darker than that of the standard color card to indicate that the roasting degree of the area module is close to the baking degree, and the color depth of the area module is lighter than that of the standard color card to indicate that the roasting degree of the area module is close to the baking shortage degree, so that the roasting degree factor of each area module is obtained.
And obtaining the chromaticity characteristic value of the real-time flaky pastry by utilizing a first uniform evaluation method.
A first uniformity evaluation method comprising:
establishing a real-time flaky pastry chroma degree factor set M = { M = { M = } 1 ，m 2 ，m 3 ，m 4 ，m 5 ，m 6 ，m 7 }; wherein m is 1 Representing burnt, m 2 Represents relatively paste, m 3 Represents partial pasting, m 4 M stands for good roast 5 Represents a bias, m 6 Represents less, m 7 Representing the roasting shortage;
establishing fuzzy evaluation preset weight B = [ B ] corresponding to real-time flaky pastry chroma degree factor set 1 ，b 2 ，b 3 ，b 4 ，b 5 ，b 6 ，b 7 ](ii) a Wherein, b 1 A predetermined weight representing a baking batter, b 2 A predetermined weight representing a blurriness, b 3 A predetermined weight representing partial blurring, b 4 Representing a predetermined weight of roasting, b 5 A predetermined weight representing a bias, b 6 A predetermined weight representing a lower amount, b 7 A preset weight representing a roast shortage;
establishing a fuzzy evaluation preset matrix for real-time flaky pastry chroma degree uniformity evaluation factors
Figure 644543DEST_PATH_IMAGE001
(ii) a Wherein r is 11 ，r 17 ，r 71 ，r 77 Presetting an empirical value for the chroma degree of the real-time flaky pastry;
selecting a weight value U 1 Carrying out weighted evaluation on the baking chromaticity degree of the real-time flaky pastry to obtain the chromaticity characteristic value theta of the real-time flaky pastry 2 ；
The formula of the chromaticity characteristic value of the real-time flaky pastry is as follows:
Figure 908165DEST_PATH_IMAGE002
（1）。
s4: and respectively carrying out crushing operation on each region module of the real-time flaky pastry, obtaining the crushing pressure value of each region module of the real-time flaky pastry, and obtaining the crispness degree of each region module of the real-time flaky pastry according to the crushing pressure value. The pressure value born by the real-time flaky pastry is smaller as the real-time flaky pastry is more crispy, so that the crispness degree of each region module of the real-time flaky pastry is judged according to the magnitude of the pressure value.
And obtaining the feature value of the crispness of the real-time flaky pastry by utilizing a second uniform evaluation method.
A second uniformity evaluation method comprising:
establishing a real-time crisp degree factor set N = { N = 1 ，n 2 ，n 3 ，n 4 ，n 5 ，n 6 ，n 7 }; wherein n is 1 Standing for bake hardness, n 2 Is meant to be harder, n 3 Representing a hardness of n 4 Representing a crisp character, n 5 Representing a partial softness, n 6 RepresentsSofter, n 7 The representative is baking softness;
establishing fuzzy evaluation preset weight D = [ D ] corresponding to real-time crisp cake degree factor set 1 ，d 2 ，d 3 ，d 4 ，d 5 ，d 6 ，d 7 ](ii) a Wherein d is 1 A predetermined weight representing the degree of hardness of baking, d 2 Representing a harder predetermined weight, d 3 Representing a hard preset weight, d 4 A predetermined weight representing crispness, d 5 A predetermined weight representing a softness bias, d 6 Representing a softer preset weight, d 7 Representing the preset weight of the roasting softness;
establishing a fuzzy evaluation preset matrix for real-time crisp degree uniformity evaluation factors of the flaky pastry
Figure 133610DEST_PATH_IMAGE003
(ii) a Wherein w 11 ，w 17 ，w 71 ，w 77 Presetting an empirical value for the degree of crispness of the real-time flaky pastry;
selecting a weight value U 2 Carrying out weighted evaluation on the baking crispness degree of the real-time flaky pastry to obtain the characteristic value theta of the crispness degree of the real-time flaky pastry 3 ；
The formula of the feature value of the crispness degree of the real-time flaky pastry is as follows:
Figure 926117DEST_PATH_IMAGE004
（2）。
s5: and acquiring extreme degree factors of the baking degree and the crispness degree based on the baking degree and the crispness degree of each region module of the real-time flaky pastry.
The extreme factor of the roasting degree is baking paste m 1 And am of browing 7 . The extreme factor of crispness is hard-baking n 1 And baking soft n 7 . If the module in a certain area of the flaky pastry is baked too extremely, the quality of the whole real-time flaky pastry is poor. The extreme factor is put into the normalization model, so that the extreme factor of a module in a certain region of the flaky pastry is prevented from being homogenized, and the accuracy of the real-time flaky pastry quality evaluation value is improved.
S6: and substituting the expansion characteristic value, the chromaticity characteristic value, the crispness characteristic value and the extreme factor of the real-time flaky pastry into the normalization model to obtain a quality evaluation value of the real-time flaky pastry.
The normalized model is:
Figure 502591DEST_PATH_IMAGE006
（4）
wherein, lambda is the quality evaluation value of the real-time flaky pastry, theta 1 Is the expansion characteristic value of the real-time flaky pastry 2 Is a real-time color characteristic value of the flaky pastry, theta 3 Is the characteristic value of the degree of crispness, k, of the flaky pastry in real time 1 For the preset weight, k, of the expansion characteristic of the real-time flaky pastry 2 For the preset weight, k, of the colorimetric characteristic value of the real-time flaky pastry 3 Is the preset weight of the feature value of the degree of crispness of the real-time flaky pastry,
Figure 304325DEST_PATH_IMAGE007
for the first extreme of the preset function,
Figure 384277DEST_PATH_IMAGE008
a function is preset for the second terminal.
Wherein the first extreme refers to the most mushy and hard area module of the real-time flaky pastry baking, and the second extreme refers to the least soft area module of the real-time flaky pastry baking.
The expansion characteristic value and the crispness characteristic value obtained by homogenizing the region modules are objective and reasonable. And substituting the acquired extreme degree factors of the baking degree and the crispness degree into the normalization model, so that the influence of extreme conditions such as excessive pasting and excessive softening in the baking process is reduced, and the quality evaluation value of the real-time flaky pastry is more objective, accurate and comprehensive.
And a quality evaluation grade table is arranged outside the crisp cake, and the quality evaluation grade table divides the quality of the crisp cake into excellent, good and poor. And comparing the obtained quality evaluation value with a quality evaluation grade table to obtain the quality of the tested real-time flaky pastry. And judging the quality of the flaky pastries on the same production line according to the tested real-time flaky pastries.
According to a second aspect of the present invention, there is provided a flaky pastry quality evaluating apparatus comprising:
and the high-definition camera is used for acquiring the original image of the real-time flaky pastry. Preprocessing the acquired original image by the high-definition camera: selecting an effective area where the real-time flaky pastry is located from the original image to obtain a first image, removing the background in the first image, and only keeping the image information of the real-time flaky pastry to obtain a second image.
The pressure sensor is used for acquiring the crushing pressure values of the region modules of the real-time flaky pastry and judging the crispness degree of the real-time flaky pastry according to the crushing pressure values;
a memory for storing computer execution instructions;
a processor for executing computer-executable instructions stored in the memory to cause the apparatus to perform any of the methods of crisp quality assessment described above.
According to a third aspect of the present invention there is provided a readable storage medium comprising a program and instructions, the method of assessing the quality of a biscuit described in any of the above when the program or instructions are run on a computer being implemented.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (8)

1. The flaky pastry quality evaluation method is characterized by comprising the following steps of:
s1: a camera is called to collect a plurality of groups of standard flaky pastry images before and after baking and real-time flaky pastry images;
s2: analyzing the standard flaky pastry image by using a learning model to obtain an expansion coefficient of the standard flaky pastry; analyzing the real-time flaky pastry image by using a learning model to obtain an expansion coefficient of the real-time flaky pastry; the learning model equally divides the standard flaky pastry image and the real-time flaky pastry image into a plurality of region modules; the expansion coefficient of the real-time flaky pastry is compared with the expansion coefficient of the standard flaky pastry, and the expansion coefficient of the real-time flaky pastry is substituted into a comparison and evaluation model to obtain the expansion characteristic value of the real-time flaky pastry;
s3: extracting the colorimetric values of all region modules of the baked real-time flaky pastry, obtaining the baking degrees of all region modules of the real-time flaky pastry according to the pre-stored standard color card of the baking degree, and obtaining the colorimetric characteristic values of the real-time flaky pastry by utilizing a first uniform evaluation method;
s4: respectively carrying out crushing operation on each region module of the real-time flaky pastry to obtain the crushing pressure value of each region module of the real-time flaky pastry, obtaining the crispness degree of each region module of the real-time flaky pastry according to the crushing pressure value, and obtaining the characteristic value of the crispness degree of the real-time flaky pastry by utilizing a second uniform evaluation method;
s5: acquiring extreme degree factors of the baking degree and the crispness degree based on the baking degree and the crispness degree of each region module of the real-time flaky pastry;
s6: substituting the expansion characteristic value, the chromaticity characteristic value, the crispness characteristic value and the extreme degree factor of the real-time flaky pastry into a normalization model to obtain a quality evaluation value of the real-time flaky pastry;
the second uniformity evaluation method includes:
establishing a real-time crisp degree factor set N = { N = N of the flaky pastry 1 ，n 2 ，n 3 ，n 4 ，n 5 ，n 6 ，n 7 }; wherein n is 1 Standing for bake hardness, n 2 Is relatively hard, n 3 Representing a hardness of n 4 Representing a crisp character, n 5 Representing a partial softness, n 6 Is representative of softer, n 7 Standing for baking to be soft;
establishing fuzzy evaluation preset weight D = [ D ] corresponding to real-time crisp cake degree factor set 1 ，d 2 ，d 3 ，d 4 ，d 5 ，d 6 ，d 7 ](ii) a Wherein d is 1 A predetermined weight representing the degree of hardness of baking, d 2 Representing a harder predetermined weight, d 3 Representing a hard presetWeight, d 4 A predetermined weight representing crispness, d 5 A predetermined weight representing a softness bias, d 6 Representing a softer preset weight, d 7 Representing the preset weight of the roasting softness;
establishing fuzzy evaluation preset matrix for real-time crisp cake degree uniformity evaluation factors
Figure DEST_PATH_IMAGE002
(ii) a Wherein, w 11 ，w 17 ，w 71 ，w 77 Presetting an empirical value for the crisp degree of the real-time flaky pastry;
selecting a weight value U 2 Carrying out weighted evaluation on the baking crispness degree of the real-time flaky pastry to obtain the characteristic value theta of the crispness degree of the real-time flaky pastry 3 ；
The formula of the feature value of the crispness of the real-time flaky pastry is as follows:
Figure DEST_PATH_IMAGE004
；
the normalization model is as follows:
Figure DEST_PATH_IMAGE006
wherein lambda is the quality evaluation value of the real-time flaky pastry and theta 1 Is the expansion characteristic value of the real-time flaky pastry 2 Is a real-time color characteristic value of the flaky pastry, theta 3 Is the characteristic value of the degree of crispness, k, of the flaky pastry in real time 1 For the preset weight, k, of the expansion characteristic of the real-time flaky pastry 2 For the preset weight, k, of the colorimetric characteristic value of the real-time flaky pastry 3 Is the preset weight of the feature value of the degree of crispness of the real-time flaky pastry,
Figure DEST_PATH_IMAGE008
for the first extreme of the preset function to be,
Figure DEST_PATH_IMAGE010
for second terminal presettingA function.
2. The method of claim 1, wherein the learning model is derived by machine learning training using a plurality of sets of data, the plurality of sets of data including a first data and a second data, the first data being an image of the standard shortcake before baking and the second data being an image of the standard shortcake after baking.
3. The flaky pastry quality evaluation method as claimed in claim 2, wherein the learning model establishes a plane coordinate system by taking the central point of the standard flaky pastry image and the real-time flaky pastry image as an origin, and equally divides the standard flaky pastry image and the real-time flaky pastry image under the plane coordinate system to obtain the coordinates of the region module.
4. The method of evaluating the quality of a flaky pastry as set forth in claim 1, wherein the first uniformity evaluating method comprises:
establishing a real-time flaky pastry chroma degree factor set M = { M = { M = } 1 ，m 2 ，m 3 ，m 4 ，m 5 ，m 6 ，m 7 }; wherein m is 1 Representing burnt, m 2 Represents relatively pasty, m 3 Represents partial pasting, m 4 M stands for good roast 5 Is representative of partial deficit, m 6 Represents less, m 7 Representing the roast lack;
establishing fuzzy evaluation preset weight B = [ B ] corresponding to the real-time flaky pastry chroma degree factor set 1 ，b 2 ，b 3 ，b 4 ，b 5 ，b 6 ，b 7 ](ii) a Wherein, b 1 A predetermined weight representing a baking batter, b 2 A predetermined weight representing a blurriness, b 3 A predetermined weight representing partial blurring, b 4 A preset weight representing roasting, b 5 A predetermined weight representing a bias, b 6 A predetermined weight representing a lower amount, b 7 A preset weight representing a roast shortage;
establishing a factor for evaluating the uniformity of the chroma degree of the real-time flaky pastryFuzzy evaluation preset matrix of
Figure DEST_PATH_IMAGE012
(ii) a Wherein r is 11 ，r 17 ，r 71 ，r 77 Presetting an empirical value for the chroma degree of the real-time flaky pastry;
selecting a weighted value U 1 Carrying out weighted evaluation on the baking chromaticity degree of the real-time flaky pastry to obtain a chromaticity characteristic value theta of the real-time flaky pastry 2 ；
The formula of the chromaticity characteristic value of the real-time flaky pastry is as follows:
Figure DEST_PATH_IMAGE014
。
5. the method of evaluating the quality of a flaky pastry according to claim 1, wherein the comparative evaluation model is:
Figure DEST_PATH_IMAGE016
wherein, theta 1 Is the expansion characteristic value, P, of the flaky pastry in real time 2 Is the expansion coefficient, P, of the real-time flaky pastry 1 The expansion coefficient of the standard flaky pastry is shown.
6. A flaky pastry quality evaluating device is characterized by comprising:
the high-definition camera is used for collecting the original image of the real-time flaky pastry;
the pressure sensor is used for acquiring the crushing pressure value of each region module of the real-time flaky pastry;
a memory for storing computer execution instructions;
a processor for executing the computer-executable instructions stored by the memory to cause the apparatus to perform the method of flaky pastry quality evaluation as claimed in any one of claims 1 to 5.
7. The flaky pastry quality evaluating device as claimed in claim 6, wherein the high-definition camera carries out pretreatment on the collected original image: and selecting an effective area where the real-time flaky pastry is located from the original image to obtain a first image, removing the background in the first image, and only reserving the image information of the real-time flaky pastry to obtain a second image.
8. A readable storage medium, characterized by comprising a program and instructions, which when run on a computer, the biscuit quality evaluation method according to any one of claims 1 to 5 is implemented.
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