Method and system for operating meat data management platform

Abstract

One embodiment of the present invention provides a method for operating a meat data management platform. The method comprises the steps of: receiving user information and registered meat information, including an image of meat to be registered, from a user terminal communicatively connected to the server; measuring at least one of the taste, tenderness, or grade of the meat included in the image of the meat to be registered; and matching and registering the measured meat measurement information, and the meat information to be registered and the user information. The present invention provides consistent and highly accurate measurements and evaluations of the taste, tenderness, and grade of meat through artificial intelligence, trained on meat images classified by the taste, tenderness, and grade of meat, for registering and managing meat data such as the grade, taste, and tenderness of meat, and can manage the data. In addition, a function that makes is simple and easy to register meat and peruse data can be provided to a user or a manager through a preset interface.

Description

Operation method and system for the meat data management platform

[0001] The present invention relates to a method and system for operating a meat data management platform using an artificial intelligence model, and more specifically, to an operating technology for a platform for registering and managing meat data such as meat grade, taste, and tenderness.

[0002] Meat consumption continues to rise globally. As of 2021, chicken ranked first in global meat consumption, followed by pork and beef.

[0003] In 2021, pork consumption reached 32.3 kg, while domestic beef consumption reached 12.4 kg per capita. Today's consumers have access to information about meat through various media outlets, and they use this information to purchase higher-quality meat. This is because there are significant differences in taste and price depending on the grade of meat.

[0004] When consumers purchase meat, the grade and taste of the meat are important factors, along with price and country of origin. Factors determining the grade and taste of meat include marbling, color, juiciness, and texture.

[0005] However, meat grades and flavor are difficult to judge or measure unless they are experts, and opinions on these factors can vary widely among experts. This is because key criteria for evaluating meat grades and flavor—such as marbling, color, texture, and palatability—vary among experts and across countries.

[0006] The lack of objective data to support the evaluation results is the biggest reason for the decline in reliability of evaluations of meat taste, tenderness, and grade (tenderness).

[0007] Accordingly, there is an increasing need for a method and system for operating a meat data management platform that can quickly and consistently measure and manage taste, age, and grade by analyzing a single meat photo based on data held by artificial intelligence that has learned a large number of meat photos by taste, age, and grade.

[0008] The present invention is intended to solve the problems of the prior art described above, and relates to a method and system for operating a meat data management platform using an artificial intelligence model that determines the grade of meat using an artificial intelligence model, performs a sensory evaluation by measuring the taste and tenderness (tenderness) of meat, and manages data on the taste, tenderness, and grade of meat.

[0009] However, the technical task that this embodiment seeks to achieve is not limited to the technical task described above, and other technical tasks may exist.

[0010] As a technical means for achieving the above-described technical task, an embodiment according to the first aspect of the present disclosure provides a method for operating a meat data management platform. The method comprises the steps of receiving registered meat information including user information and an image of a meat to be registered from a user terminal communicating with the server, measuring at least one of taste, year, and grade of the meat included in the image of the meat to be registered, and matching the measured meat measurement information with the meat information to be registered and user information to register the same.

[0011] In addition, an embodiment according to a second aspect of the present disclosure provides a meat data management platform operating system. The system receives registered meat information including user information and an image of the meat to be registered from a user terminal in communication with the server, measures at least one of the taste, age, and grade of the meat included in the image of the meat to be registered, and stores code that causes the measured meat measurement information to be matched with the registered meat information and user information to be registered.

[0012] The present invention provides consistent and highly accurate measurement and judgment of meat taste, tenderness, and grade by artificial intelligence that learns meat images classified by taste, tenderness, and grade, and registers and manages meat data such as grade, taste, and tenderness of meat, and can manage the data.

[0013] Additionally, it can provide users or administrators with a simple and easy way to register meat and view data through a preset interface.

[0014] FIG. 1 is a diagram illustrating an operating system of a meat data management platform according to one embodiment of the present invention.

[0015] Figure 2 is a drawing showing the detailed configuration of the server illustrated in Figure 1.

[0016] FIG. 3 is a drawing showing the main screen of an application according to one embodiment of the present invention.

[0017] Figures 4 to 8 are drawings showing a meat registration process according to one embodiment of the present invention.

[0018] FIG. 9 is a diagram illustrating meat data viewing according to one embodiment of the present invention.

[0019] Figure 10 is a drawing showing the main screen of an application according to the second embodiment of the present invention.

[0020] Figures 11 to 15 are drawings showing a meat registration process according to a second embodiment of the present invention.

[0021] FIGS. 16a to 23 are diagrams showing an additional data input process for meat according to a second embodiment of the present invention.

[0022] Figures 24 and 25 are drawings showing meat data verification according to the second embodiment of the present invention.

[0023] Figures 26 to 29 are diagrams showing meat data viewing according to the second embodiment of the present invention.

[0024] Figure 30 is a drawing showing a process of photographing meat according to one embodiment of the present invention.

[0025] Figure 31 is a flowchart illustrating the sequence of an operation method of a meat data management platform according to another embodiment of the present invention.

[0026] Hereinafter, the present disclosure will be described in detail with reference to the attached drawings. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein. In addition, the attached drawings are only intended to facilitate understanding of the embodiments disclosed in the present specification, and the technical concepts disclosed in the present specification are not limited by the attached drawings. All terms, including technical and scientific terms, used herein should be interpreted as having meanings generally understood by a person of ordinary skill in the technical field to which the present disclosure pertains. Terms defined in the dictionary should be interpreted as having additional meanings consistent with the relevant technical literature and the contents of the present disclosure, and shall not be interpreted in an extremely ideal or restrictive sense unless otherwise defined.

[0027] In order to clearly explain the present invention in the drawings, parts irrelevant to the description have been omitted, and the size, shape, and appearance of each component shown in the drawings may be modified in various ways. Identical / similar parts throughout the specification are given identical / similar drawing reference numerals.

[0028] Throughout the specification, when a part is said to be "connected (connected, in contact with, or coupled)" to another part, this includes not only cases where it is "directly connected (connected, in contact with, or coupled)" but also cases where it is "indirectly connected (connected, in contact with, or coupled)" with another member in between. Furthermore, when a part is said to "include (have or provide)" a certain component, this does not mean that it excludes other components, but rather that it may "include (have or provide)" other components, unless otherwise specifically stated.

[0029] In this specification, the term 'unit' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. In addition, one unit may be realized by using two or more pieces of hardware, and two or more units may be realized by one piece of hardware. Meanwhile, the '~ unit' is not limited to software or hardware, and the '~ unit' may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors. Therefore, as an example, the '~ unit' includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and '~sub-units' may be combined into a smaller number of components and '~sub-units' or further separated into additional components and '~sub-units'. Furthermore, the components and '~sub-units' may be implemented to activate one or more CPUs within the device or secure multimedia card.

[0030] The suffixes "module" and "part" used in the following description for components are assigned or used interchangeably solely for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. Furthermore, in describing the embodiments disclosed herein, detailed descriptions of related known technologies have been omitted if they are deemed to obscure the gist of the embodiments disclosed herein.

[0031] As used herein, ordinal terms such as "first," "second," etc., are used solely to distinguish one component from another and do not limit the order or relationship of the components. For example, the first component of the present disclosure may be referred to as the "second component," and similarly, the second component may also be referred to as the "first component." As used herein, singular forms should be construed to include plural forms, unless explicitly stated otherwise.

[0032] The "user terminal" mentioned below may be implemented as a computer or portable terminal that can access a server or other terminal via a network. Here, the computer may include, for example, a notebook, desktop, laptop, VR HMD (e.g., HTC VIVE, Oculus Rift, GearVR, DayDream, PSVR, etc.) equipped with a web browser. Here, the VR HMD includes all of the stand-alone models implemented independently for PC (e.g., HTC VIVE, Oculus Rift, FOVE, Deepon, etc.), mobile (e.g., GearVR, DayDream, Storm Magic, Google Cardboard, etc.), and console (PSVR) (e.g., Deepon, PICO, etc.). A portable terminal is, for example, a wireless communication device that ensures portability and mobility, and may include not only a smart phone, a tablet PC, and a wearable device, but also various devices equipped with communication modules such as Bluetooth (BLE, Bluetooth Low Energy), NFC, RFID, ultrasonic, infrared, WiFi, and LiFi. In addition, a "network" refers to a connection structure that enables information exchange between each node, such as terminals and servers, and includes a local area network (LAN), a wide area network (WAN), the Internet (WWW: World Wide Web), wired and wireless data communication networks, telephone networks, and wired and wireless television communication networks.Examples of wireless data communication networks include, but are not limited to, 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, Bluetooth, infrared, ultrasonic, visible light communication (VLC), and LiFi.

[0033] FIG. 1 is a diagram illustrating an operating system of a meat data management platform according to one embodiment of the present invention.

[0034] Referring to FIG. 1, the operating system of the meat data management platform includes a server (100) and a user terminal (200), and the server (100) and the user terminal (200) can be connected to each other through a communication network.

[0035] The server (100) may be implemented as a cloud computing server, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). Furthermore, the server may be implemented as a private cloud, public cloud, or hybrid cloud system, but the scope of the present invention is not limited thereto. Furthermore, the meat taste and tenderness measuring device (100) may be operated using an artificial intelligence model.

[0036] The server receives registered meat information including user information and an image of the meat to be registered from a user terminal connected to the communication.

[0037] The server measures at least one of the following: taste, age, and grade for the meat included in the image of the meat to be registered.

[0038] The server matches the measured meat measurement information with the registered meat information and user information and registers them.

[0039] The user terminal can transmit registered meat information, including images of the meat to be registered and user information, to the server. Furthermore, the user terminal can receive and view visualized data on the meat's taste, age, and grade from the server.

[0040] Figure 2 is a drawing showing the detailed configuration of the server illustrated in Figure 1.

[0041] Referring to FIG. 2, the server (100) may include a communication module (110), a processor (120), and a memory (130).

[0042] The communication module (110) may include a device including hardware and software necessary to transmit and receive signals such as control signals or data signals through wired or wireless connections with other network devices.

[0043] The communication module (110) can receive registered meat images and user information from the user terminal (200). Furthermore, the communication module (110) can provide the user terminal (200) with data visualized with regard to taste, age, and grade of the meat. However, this is not limited thereto, and, if necessary, the communication module (110) can receive images or videos from external devices and databases other than the user terminal (200).

[0044] The processor (120) may include various types of devices that control and process data. The processor (120) may refer to a data processing device built into hardware that has a physically structured circuit to perform functions expressed in code or instructions included in a program.

[0045] In one example, the processor (120) may be implemented in the form of a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., but the scope of the present invention is not limited thereto.

[0046] The processor (120) performs operations according to the code stored in the memory (130).

[0047] The memory (130) can store at least one of information and data input to the communication module (110), information and data required for functions performed by the processor (120), and data generated according to the execution of the processor (120).

[0048] Memory (130) should be interpreted as a general term for a non-volatile storage device that maintains stored information even when no power is supplied, and a volatile storage device that requires power to maintain stored information. Memory (130) may include magnetic storage media or flash storage media in addition to a volatile storage device that requires power to maintain stored information, but the scope of the present invention is not limited thereto.

[0049] The memory (130) is electrically connected to the processor (120) and stores at least one code to be executed by the processor (120). The memory (130) stores code that, when executed by the processor (120), causes the processor (120) to perform the following functions and procedures.

[0050] The memory stores code that causes the server to receive registered meat information, including user information and an image of the meat to be registered, from a user terminal connected to the server. For example, the user information may include the user terminal's owner's name, company name, address, email address, and ID, and the user information may be stored in a user information table.

[0051] And the registered meat information can receive a meat registration request by receiving the breed, part, and cross-sectional image of the meat from the user terminal. In addition, the registered meat information further includes a meat basic table, a fresh meat and processed meat sensory evaluation table, a heated meat sensory evaluation table, an electronic tongue experiment table, and a deep aging history table. The meat basic table includes a meat management number, a meat production user, a sex, a grade, a year of birth, a farm, a farm manager, and an individual identification number for the meat. The fresh meat and processed meat sensory evaluation table includes marbling, meat color, texture, and meat juiciness. The heated meat sensory evaluation table includes flavor, moisture, tenderness, umami, and preference. The electronic tongue experiment table includes collagen, sourness, bitterness, umami, and richness. The deep aging history table can include a date and time.

[0052] The memory stores code that causes the meat to be measured for at least one of the following characteristics: taste, age, duration, and grade. For example, the taste, age, duration, and grade of the meat may be measured using an artificial intelligence model trained to measure at least one of the following characteristics: grade, taste, duration, and year of the meat in an input meat image. In this case, the measurement may be provided based on analysis of the meat using any one of an RGB camera, a spectral camera, and a hyperspectral camera.

[0053] The memory stores code that causes the meat to measure at least one of the following characteristics: taste, age, duration, and grade, based on registered meat information. For example, the duration may include the consumption period, processing period, and distribution period of the meat, and the consumption period, processing period, and distribution period may be measured based on a second time point, from the first time point when the meat is first registered, to measure the duration of the meat.

[0054] The memory stores a code that causes the measured meat measurement information to match the registered meat information and user information.

[0055] The memory stores a code that causes a unique management number to be assigned to each piece of meat, and a QR code containing the completed information to be generated and provided when the registration of the meat is complete.

[0056] The memory stores code that receives a data viewing request for a specific type of meat from a user terminal and provides corresponding data. For example, the data may be supporting data including color distribution, and the supporting data may be provided in a visualized form, such as grade, taste, and year. Furthermore, the data may further include QR codes, deep aging data, cross-sectional photographic data, sensory evaluation data, taste data, and experimental data.

[0057] The memory stores code that receives a data correction request from a user terminal when there is an error in the data and causes the data to be updated to reflect the corrections entered by the user terminal.

[0058] FIG. 3 is a drawing showing the main screen of an application according to one embodiment of the present invention.

[0059] Referring to Figure 3, you can log in by entering your ID (301) and password (302) on the application's main screen. Once logged in, you can select and perform meat registration (303) or data management (304). If you are using the application for the first time, you must register before logging in. Registration can be done by entering user information including your name, company name, address, email address, and ID.

[0060] Figures 4 to 8 are drawings showing a meat registration process according to one embodiment of the present invention.

[0061] Referring to Fig. 4, for meat registration, basic meat information input (401), cross-sectional meat photography (402), and fresh meat sensory evaluation (403) can be performed. For meat registration, a history number can be input (404), and the next button (406) can be pressed on the search result provision screen (405) to input basic meat information.

[0062] Referring to Fig. 5, the type and part (501) of meat can be selected for inputting basic meat information (401). For the type of meat (502), beef can be selected, and for the part (503), the meat can be divided into large and small parts to select detailed parts. Once all selections are completed, the save button (504) is pressed to complete inputting basic meat information (505), and a cross-section photograph of the meat can be input (402).

[0063] Referring to Fig. 6, for meat cross-section photography input (402), a shooting date (601) can be selected and a photographer and a photo can be input (602). When a photo is taken (603) using the camera of the user terminal, the entered shooting date, photographer and photo can be provided as a result (604). If there is no problem with the result (604), the meat cross-section photography can be completed (701) by inputting a save button (605).

[0064] Referring to Fig. 7, when the meat cross-section photograph is completed (701), the fresh meat sensory evaluation input (403) can be performed. The fresh meat sensory evaluation can be completed by selecting marbling, meat color, texture, surface texture, and overall preference (702), and when the fresh meat sensory evaluation is completed (703), the meat registration can be completed by entering the save button (704).

[0065] Referring to Figure 8, one can see a screen where meat registration is complete and a management number is being generated (801) and a screen where the management number has been generated (802). After completing all processes, one can move to the main screen by pressing the Go to Home button (803).

[0066] FIG. 9 is a diagram illustrating meat data viewing according to one embodiment of the present invention.

[0067] Referring to Figure 9, a data viewing screen can be provided (901) including management numbers, registrants, and management items. Furthermore, the data can be sorted (902) by filtering by search period or by most recent or past date. Data corresponding to the selected filter can be provided (903).

[0068] Figure 10 is a drawing showing the main screen of an application according to the second embodiment of the present invention.

[0069] Referring to Figure 10, you can log in by entering your ID (1001) and password (1002) on the application's main screen. After logging in, you can select and perform meat registration (1003) or data management (1004).

[0070] Figures 11 to 15 are drawings showing a meat registration process according to a second embodiment of the present invention.

[0071] Referring to Figure 11, for meat registration, basic meat information input (1101), cross-sectional meat photography (1102), and fresh meat sensory evaluation (1103) can be performed. For meat registration, a history number can be input (1104), and then the next button (1106) can be pressed on the search results screen (1105) to input basic meat information.

[0072] Referring to Fig. 12, the type and part (1201) of meat can be selected for inputting basic meat information (1101). For the type of meat (1202), beef can be selected, and for the part (1203), the meat can be divided into large and small parts to select detailed parts. Once all selections are completed, the save button (1204) is pressed to complete inputting basic meat information (1205), and a cross-sectional photograph of the meat (1206) can be taken.

[0073] Referring to Fig. 13, for meat cross-section photography input (1102), a shooting date (1301) can be selected, and a photographer and a photo can be input (1302). When a photo is taken (1303) using the camera of the user terminal, the entered shooting date, photographer and photo can be provided as a result (1304). If there is no problem with the result (1304), the save button (1305) can be input to complete the meat cross-section photography (1401).

[0074] Referring to Fig. 14, when the meat cross-section photograph is completed (1401), the fresh meat sensory evaluation input (1103) can be performed. The fresh meat sensory evaluation can be completed by selecting marbling, meat color, texture, surface texture, and overall preference (1402), and when the fresh meat sensory evaluation is completed (1403), the meat registration can be completed by entering the save button (1404).

[0075] Referring to Figure 15, one can see a screen where meat registration is complete and a management number is being generated (1501) and a screen where the management number has been generated (1502). After completing all processes, the desired task can be performed by pressing the Go to Home button (1503) or the Enter Additional Information button (1504).

[0076] FIGS. 16a to 23 are diagrams showing an additional data input process for meat according to a second embodiment of the present invention.

[0077] Referring to Fig. 16a, when the additional information input button (1504) is input in Fig. 15, the screen provided for adding data provides a load button (1601) for adding data, and additional information can be input to the searched data (1603) through uploading an Excel file, scanning a QR code, or entering a management number (1602) in the management number import pop-up. For scanning a QR code, as shown in Fig. 16b, when the QR scan button (1604) is input, a preset screen (1605) is provided, and when the QR code is displayed on the camera, the corresponding code can be automatically input (1606). Alternatively, the Excel file upload button (1607) can be input and a file can be registered.

[0078] Referring to Fig. 17, deep aging data input (1701), heated meat sensory evaluation data input (1702), electronic tongue data input (1703), and experimental data input (1704) can be performed to input additional information. Deep aging data input (1701) displays the order, time, and date (1705), and when adding deep aging data, the deep aging date in month, day, and year and the ultrasonic treatment time can be input (1706).

[0079] Referring to Fig. 18, if the deep aging date is entered as July 1, 2022 (1801) and the ultrasonic treatment time is entered as 6 hours and 31 minutes (1802) and saved, the total number of times (3 times) and the time taken (19 hours and 33 minutes) of the first, second, and third deep aging data can be confirmed (1803).

[0080] Referring to FIG. 19, a screen asking whether to perform a fresh meat sensory evaluation is provided as a pop-up (1901), and fresh meat sensory evaluation items (1902) including marbling, meat color, texture, surface juice, and overall preference may be provided.

[0081] Referring to Figure 20, deep aging data and fresh meat sensory evaluation are completed (2001), and heated meat sensory evaluation data can be input (1702). The heated meat sensory evaluation data can include flavor, juiciness, tenderness, umami, and preference (2002).

[0082] Referring to Figure 21, deep aging data, fresh meat sensory evaluation, and heated meat sensory evaluation are completed (2101), and electronic tongue data input (1703) can be performed. The electronic tongue data can include sourness, richness, savoryness, and aftertaste (2102).

[0083] Referring to Fig. 22, deep aging data, fresh meat sensory evaluation, cooked meat sensory evaluation, and electronic tongue data are completed (2201), and experimental data input (1704) can be performed. The experimental data can include DL meat juice loss, CL cooking loss, PH, WBSF shear rate, cathepsin activity, and MFI microfabrication index (2202). When the input of experimental data is completed, a screen for completing deep aging data, fresh meat sensory evaluation, cooked meat sensory evaluation, electronic tongue data, and experimental data (2204) can be provided.

[0084] Referring to FIG. 23, a screen may be displayed showing that additional data entry is complete and data is being registered (2301), and that additional data registration is complete (2302) and a QR code print button (2303) is displayed. When the QR code print button (2303) is pressed, a screen showing that a QR code is being printed (2304) may be provided.

[0085] Figures 24 and 25 are drawings showing meat data verification according to the second embodiment of the present invention.

[0086] Referring to Fig. 24, a meat data confirmation page (2401) including a management number, registrant, and management items is provided, and the meat data can be confirmed through filtering (2402) of the inquiry period, registrant, and breeding type.

[0087] Referring to Figure 25, a history (2501) including the history number, farm, slaughterhouse, slaughter date, breed or livestock type, sex, grade, and date of birth can be checked by entering the history number. In addition, meat cross-section data (2502) including the shooting date, photographer, and meat image can be checked, and fresh meat sensory evaluation data (2503) of the meat can be checked.

[0088] Figures 26 to 29 are diagrams showing meat data viewing according to the second embodiment of the present invention.

[0089] Referring to Figure 26, a meat data viewing page (2601) including a management number, registrant, and management items is provided, and meat data can be checked through filtering (2602) by inquiry period, registrant, and breed. Furthermore, by selecting any meat data, basic information and additional information including basic meat information, meat cross-section, and fresh meat sensory evaluation can be checked (2603).

[0090] Referring to FIGS. 27 to 29, deep aging data (2701), fresh meat sensory evaluation (2702), heated meat sensory evaluation data (2703), electronic tongue data (2801), experimental data (2802), meat history (2803), meat information (2901), meat cross-section image (2902), and fresh meat sensory evaluation (2903) can be viewed.

[0091] As a third embodiment of the present invention, the server may be referred to as a meat grading device or a meat taste and tenderness measuring device.

[0092] At this time, the meat grading device or meat flavor and tenderness measuring device can receive a meat image from a user terminal, segment the meat to be evaluated, crop it, and then resize the image. This can minimize information loss. Transfer learning can then be performed on the resized image using a pre-trained AI model. The AI ​​model can then be used to grade the meat. Alternatively, the resized meat image can be latently vectorized using the AI ​​model, and flavor and tenderness can be measured based on previously learned grade-specific marbling, meat color, texture, juiciness, and preference evaluation values, and a sensory evaluation table can be generated.

[0093] At this time, the artificial intelligence models used for transfer learning and latent vectorization can utilize CNN (Convolutional Neural Network) and ViT (Vision Transformer).

[0094] CNN is a deep learning model that detects image features through convolution operations and uses these to classify input data or extract meaningful information and learn.

[0095] ViT is a deep learning model that uses an attention mechanism for image processing, and is effective in extracting and classifying global information by converting images into tokens.

[0096] More specifically, ViT implements image processing within a Transformer-structured model, leveraging the Transformer's multi-head attention structure rather than using a CNN for vision tasks. This allows ViT to achieve superior performance compared to other CNN-based models and consumes less computational resources during the training process.

[0097] At this time, large values ​​are emphasized based on the Q (Query), K (Key), and V (Value) values, which are the key element representations for learning.

[0098] Learning management for AI models like the above can be accomplished using pre-configured libraries. These libraries support tracking machine learning model experiments and model sharing.

[0099] A meat grading device or a meat flavor and tenderness measuring device can receive a meat image from a user terminal and display it by combining a color distribution graph of the corresponding grade. At this time, the meat image and the color distribution graph can be arranged to overlap or side by side. Furthermore, an artificial intelligence model can be used to obtain a sensory evaluation table. The sensory evaluation table can include marbling, meat color, texture, surface juiciness, and preference.

[0100] And the meat grading device or the meat taste and tenderness measuring device can provide a visualization of the contribution of each grade of meat using a color distribution graph for each grade. At this time, the meat image can be combined with the color distribution graph to display it. In addition, the meat can be classified into meat grades including 1++, 1+, 1, 2, and 3, or the taste and tenderness of the meat can be classified, and the results can be visualized by applying Grad-CAM technology for each grade or taste and tenderness of the corresponding meat to show which part has made the greatest contribution. The closer to red in the meat image, the greater the contribution to the model's prediction.

[0101] Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for visualizing the prediction results of deep learning models. It can highlight the contribution of specific parts of an image to the model's decisions. This allows for a visual understanding of which parts the model primarily utilizes and facilitates interpretation of the model's predictions.

[0102] And this is a technique to visualize the meat image received from the user terminal by the meat grading device or the meat taste and tenderness measuring device by resizing it to the same size as the original image after making the latent vector into a square using the ViT (Vision Transformer) based model, which is an artificial intelligence model.

[0103] Techniques used to visualize images include Grad-CAM (Gradient-weighted Class Activation Mapping) and Attention Map.

[0104] The results of the taste and tenderness evaluation for raw meat (fresh meat) can provide marbling, meat color, texture, juiciness, and palatability based on the original image, and can provide marbling, meat color, texture, juiciness, palatability, and an AI grade number based on the image visualized by the AI ​​model. In addition, the QR code, management number, registrant email, and storage time can be provided together.

[0105] Additionally, the taste and age evaluation results for processed meat can provide marbling, meat color, texture, juiciness, palatability, sequence number, and time based on the original image, and marbling, meat color, texture, juiciness, palatability, sequence number, time, and AI grade number based on the image visualized by the AI ​​model. Additionally, a QR code, management number, registrant email, and storage time can be provided.

[0106] Figure 30 is a drawing showing a process of photographing meat according to one embodiment of the present invention.

[0107] Referring to Figure 30, meat and measuring equipment are prepared (3001), and a cross-section of the meat is photographed (3002) using the measuring equipment, thereby obtaining an image (3003) of the cross-section of the meat. The measuring equipment may be a general RGB camera, a spectral camera, or a hyperspectral camera. Furthermore, one or more of the following characteristics can be measured from the photographed image of the meat: taste, age, duration, and grade.

[0108] In this case, the period may be any of the following: consumption period, processing period, or distribution period. These periods may be directly related to the freshness of the meat based on the number of days elapsed. Accordingly, the camera may include a sensor capable of detecting the degree of meat spoilage.

[0109] Figure 31 is a flowchart illustrating the sequence of an operation method of a meat data management platform according to another embodiment of the present invention.

[0110] The elevator safety door control method described below can be performed by the operating system of the meat data management platform described above with reference to FIGS. 1 to 30 . Therefore, the content of the embodiments of the present disclosure described above with reference to FIGS. 1 to 30 can be equally applied to the embodiments described below, and any overlapping content with the above description will be omitted. The steps described below do not necessarily have to be performed in order, and the order of the steps can be set in various ways, and the steps can be performed almost simultaneously.

[0111] Referring to FIG. 31, the operating method of the meat data management platform includes a step of receiving registered meat information (S100), a step of measuring at least one of the year and grade (S200), a step of matching and registering meat information and user information (S300), a step of providing data corresponding to meat (S400), and a data update step (S500).

[0112] The registration meat information reception step (S100) is a step for receiving registration meat information, including user information and an image of the meat to be registered, from a user terminal connected to the server. The user information may include the user terminal's owner's name, company name, address, email address, and ID, and the user information may be stored in a user information table. Furthermore, the registration meat information may receive a meat registration request by receiving the meat species, cut, and cross-sectional image from the user terminal.

[0113] In addition, the registered meat information further includes a meat basic table, a fresh meat and processed meat sensory evaluation table, a heated meat sensory evaluation table, an electronic tongue experiment table, and a deep aging history table, wherein the meat basic table includes a meat management number, a meat production user, a sex, a grade, a year of birth, a farm, a farm manager, and an individual identification number for the meat, wherein the fresh meat and processed meat sensory evaluation table includes marbling, meat color, texture, and meat juiciness, wherein the heated meat sensory evaluation table includes flavor, moisture, tenderness, umami, and preference, wherein the electronic tongue experiment table includes collagen, sourness, richness, umami, and aftertaste, and wherein the deep aging history table may include a date and time.

[0114] Based on this registered meat information, at least one of the taste, year, period, and grade of the meat can be measured.

[0115] The step (S200) of measuring at least one of the year and grade is a step of measuring at least one of the taste, year, period, and grade of the meat included in the image of the meat to be registered. At this time, the taste, year, period, and grade of the meat can be measured using an artificial intelligence model trained to measure at least one of the grade, taste, period, and year of the meat included in the input meat image. And the period is

[0116] The consumption period, processing period, and distribution period of the meat are included, and the consumption period, processing period, and distribution period can be measured based on a second time point at which the period of the meat is measured from a first time point at which the meat is first registered. In addition, the measurement can be provided based on analysis of the meat using any one of an RGB camera, a spectral camera, and a hyperspectral camera.

[0117] The step (S300) of matching and registering meat information and user information is a step for matching and registering measured meat measurement information with the target meat information and user information. For example, a unique management number may be assigned to each meat, and upon completion of registration of the meat, a QR code containing the completed information may be generated and provided.

[0118] The meat-related data provision step (S400) is a step in which a request for viewing data on a particular type of meat is received from the user terminal and data corresponding to the meat is provided. The data may further include a QR code, deep aging data, cross-sectional photographic data, sensory evaluation data, taste data, and experimental data.

[0119] The data update step (S500) is a step for receiving a data correction request from the user terminal when there is an error in the data, and updating the data by reflecting the correction information entered by the user terminal.

[0120] An embodiment of the present invention may also be implemented in the form of a recording medium containing computer-executable instructions, such as program modules, executed by a computer. Computer-readable media may be any available media that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. Furthermore, computer-readable media may include all computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.

[0121] Although the methods and systems of the present invention have been described with respect to specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

[0122] Those skilled in the art will appreciate that the present disclosure can be easily modified into other specific forms based on the above description without changing the technical spirit or essential characteristics of the present disclosure. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The scope of the present disclosure is indicated by the following claims, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present disclosure. The scope of the present application is indicated by the following claims rather than the above detailed description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present application.

Claims

1. A method for operating a meat data management platform performed by a server, a) a step of receiving registered meat information including user information and an image of the meat to be registered from a user terminal connected to the server; and b) a step of measuring at least one of the taste, year, period, and grade of the meat included in the image of the meat to be registered; and c) A method for operating a meat data management platform, comprising a step of matching and registering the meat measurement information measured according to step b) with the meat information and user information to be registered according to step a).

2. In paragraph 1, The taste, year, period and grade of the meat in step b) above are as follows: A method for operating a meat data management platform, wherein the method is measured using an artificial intelligence model trained to measure at least one of the grade, taste, period, and year of meat included in an input meat image.

3. In paragraph 1, The above user information is, A method for operating a meat data management platform, wherein the user terminal's owner name, company name, address, email address, and ID are included, and the user information is stored in a user information table.

4. In paragraph 1, Registered meat information is, A method for operating a meat data management platform, wherein a meat registration request is received by receiving a meat type, part, and cross-sectional image from the user terminal.

5. In paragraph 4, Registered meat information is, It further includes a meat basic table, a fresh meat and processed meat sensory evaluation table, a heated meat sensory evaluation table, an electronic tongue experiment table, and a deep aging history table. The above meat basic table includes the meat management number, meat production user, sex, grade, year of birth, farm, farm manager and individual identification number for the meat. The above fresh meat and processed meat sensory evaluation table includes marbling, meat color, texture, and meat juiciness. The above heated meat sensory evaluation table includes flavor, moisture, tenderness, savory taste, and preference. The above electronic tongue experiment table includes collagen, sourness, richness, umami and aftertaste, The above deep aging history table includes date and time, Step b) above, A method for operating a meat data management platform, wherein at least one of taste, year, period, and grade of the meat is measured based on the registered meat information.

6. In paragraph 1, Step c) above, Each of the above meats is assigned a unique management number, A method for operating a meat data management platform, further comprising a step of generating and providing a QR code containing the completed information when the registration of the above meat is completed.

7. In paragraph 1, d) further comprising a step of receiving a data viewing request for any one meat from the user terminal and providing data corresponding to the meat, A method for operating a meat data management platform, wherein the above data is base data including color distribution, and the base data provides visualizations of grade, taste, and year.

8. In paragraph 7, A method for operating a meat data management platform, wherein the above data further includes QR code, deep aging data, cross-sectional photograph data, sensory evaluation data, taste data, and experimental data.

9. In paragraph 1, e) A method for operating a meat data management platform, further comprising a step of receiving a data correction request from the user terminal and updating the data by reflecting the correction input by the user terminal if there is an error in the data in the step d).

10. In paragraph 2, The above period Including the consumption period, processing period and distribution period of the above meat, A method for operating a meat data management platform, wherein the above-mentioned consumption period, processing period, and distribution period are measured based on a second point in time at which the period of the meat is measured from the first point in time at which the meat is first registered.

11. In paragraph 1, A method for operating a meat data management platform, wherein the above measurement is provided based on analysis of the meat through any one of an RGB camera, a spectral camera, and a hyperspectral camera.

12. Communication module; at least one processor; and A memory electrically connected to the processor and storing at least one code to be executed by the processor, The above memory, when executed through the processor, causes the processor to: A meat data management platform operating system that receives registered meat information including user information and an image of the meat to be registered from a user terminal communicating with the server, measures at least one of the taste, year, period, and grade of the meat included in the image of the meat to be registered, and stores a code that causes the measured meat measurement information to be matched with the meat information to be registered and user information to be registered.

13. In paragraph 12, The taste, year, period and grade of the above meat are as follows: A meat data management platform operating system, which measures at least one of the grade, taste, period, and year of meat included in an input meat image using an artificial intelligence model trained to measure the meat.

14. In paragraph 12, The above user information is, A meat data management platform operating system, comprising the owner's name, company name, address, email, and ID of the user terminal, and wherein the user information is stored in a user information table.

15. In paragraph 12, Registered meat information is, A meat data management platform operating system that receives meat species, parts, and cross-sectional images from the user terminal and receives a meat registration request.

16. In paragraph 15, Registered meat information is, It further includes a meat basic table, a fresh meat and processed meat sensory evaluation table, a heated meat sensory evaluation table, an electronic tongue experiment table, and a deep aging history table. The above meat basic table includes the meat management number, meat production user, sex, grade, year of birth, farm, farm manager and individual identification number for the meat. The above fresh meat and processed meat sensory evaluation table includes marbling, meat color, texture, and meat juiciness. The above heated meat sensory evaluation table includes flavor, moisture, tenderness, savory taste, and preference. The above electronic tongue experiment table includes collagen, sourness, richness, umami and aftertaste, The above deep aging history table includes date and time, Step b) above, A meat data management platform operating system that measures at least one of taste, year, and grade of the meat based on the registered meat information.

17. In paragraph 12, The above memory, when executed through the processor, causes the processor to: Each of the above meats is assigned a unique management number, A meat data management platform operating system that stores a code that causes a QR code containing the completed information to be generated and provided when the registration of the above meat is completed.

18. In paragraph 12, The above memory, when executed through the processor, causes the processor to: A meat data management platform operating system that receives a request for viewing data on any one meat from the user terminal, provides data corresponding to the meat, receives a request for modifying data from the user terminal if there is an error in the data, and stores code that causes the data to be updated by reflecting modifications entered by the user terminal.

19. In paragraph 18, The above data further includes QR code, deep aging data, cross-sectional photograph data, sensory evaluation data, taste data, and experimental data, and is a meat data management platform operating system.

20. In paragraph 13, The above period Including the consumption period, processing period and distribution period of the above meat, A method for operating a meat data management platform, wherein the above-mentioned consumption period, processing period, and distribution period are measured based on a second point in time at which the period of the meat is measured from the first point in time at which the meat is first registered.

21. In paragraph 12, A meat data management platform operating system, wherein the above measurement is provided based on analysis of the meat through any one of an RGB camera, a spectral camera, and a hyperspectral camera.

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