KR102590406B1 - Biometric authentication apparatus based on livestock growth - Google Patents

Abstract

The livestock growth-based bioauthentication device includes a livestock health data receiver that receives livestock health data through a sensor coupled to the livestock while the livestock is alive, and a livestock disease model that is built by learning livestock health data on livestock with livestock diseases. A livestock disease model learning unit, a meat product safety evaluation unit that provides the livestock health data to the livestock disease model to collect livestock health status and evaluate meat product stability, and receives a livestock health grade request code linked to the meat product from the user. It includes a meat request code receiving unit, and a meat stability information processing unit providing meat stability information regarding the meat product stability evaluated while the livestock was alive based on the livestock health grade request code.

Description

Biometric authentication device based on livestock growth {BIOMETRIC AUTHENTICATION APPARATUS BASED ON LIVESTOCK GROWTH}

The present invention relates to livestock growth-based bioauthentication technology, and more specifically, to livestock growth-based bioauthentication technology that collects the health status of livestock while they are alive, evaluates meat product stability, and provides meat safety information to users. It concerns biometric authentication devices.

The livestock industry is changing from a labor-intensive industry to a technology- and knowledge-intensive industry using big data and ICT due to the aging of workers and a decrease in personnel.

A smart farm is a system that remotely and automatically manages the livestock breeding environment by incorporating ICT. Smart farms use ICT to monitor environmental information inside and outside the livestock shed and remotely control it with a smartphone. Recently, the biometric information of livestock generated by ICT equipment such as biometric information detectors (respiration, pulse, body temperature, etc.) and CCTV video analysis has been expanded. We are improving productivity by managing livestock breeding based on data.

Meanwhile, traceablity, which allows the distribution path of a product to be traced from the production stage to the final consumption or disposal stage, is being implemented as a legal regulation. For example, traceability refers to recording and managing information at each stage from the birth and raising of cattle to the sale of beef, thereby estimating the movement path of cattle and beef when necessary. However, the current history management only provides information such as the origin, date of birth, type, grade, and breeder of the meat product the user wishes to purchase, and the information was limited to determine the safety of the meat product.

Accordingly, there is a need for health information history management that can provide users with health history during the growing period of livestock before slaughter of the meat product.

Korean Patent No. 10-2014353 (2019.08.20) relates to a machine learning-based smart farm livestock management system, which includes a plurality of imaging units that generate image information of livestock raised in livestock barns, the shape of livestock according to breed, A database that stores comparative information for analyzing image information, such as behavioral characteristics, weight of livestock according to breed and size, and a plurality of image information collected in real time based on the comparative information are comprehensively analyzed to provide information on the population of livestock in the barn and An image analysis unit that generates weight information for each individual, a classification unit that classifies livestock by grade based on population information and weight information, and calculates the number of livestock that can be shipped, and a classification unit that calculates the number of livestock that can be shipped, population information, weight information, and livestock classified by grade. Discloses technology used in the management and shipping of livestock, including one or more terminal devices capable of checking the population and number of livestock available for shipment.

Korean Patent No. 10-2100737 (2020.04.08) relates to a system for providing information on the life cycle of livestock products by group. By grouping livestock products and providing life cycle information for each group, the life cycle of livestock products, which is different from the history system information that is information after slaughter, is provided. A variety of related information can be efficiently provided to consumers, and by grouping multiple plants produced from one body on the same day or multiple plants with similar birth dates produced from multiple mothers managed as a group, farms can efficiently provide each group. We disclose a technology that can provide consumers with highly accurate information on the life cycle of livestock products while managing livestock products.

Korean Patent No. 10-2014353 (2019.08.20) Korean Patent No. 10-2100737 (2020.04.08)

One embodiment of the present invention seeks to provide a bioauthentication device based on livestock growth that can collect the health status of livestock while they are alive, evaluate meat product safety, and provide meat safety information to users.

One embodiment of the present invention is to construct a livestock disease model and a livestock health model, evaluate livestock health grades according to the livestock disease model, and verify the safety of meat products as food according to the livestock health model for those below a certain standard. We aim to provide a growth-based bioauthentication device.

One embodiment of the present invention secures the reliability of the safety evaluation by periodically collecting the health status of the livestock through a sensor coupled to the livestock and performing meat product safety evaluation by setting the weight of the livestock health status of the most recent section before slaughter to a high level. We would like to provide a bioauthentication device based on livestock growth that can

Among the embodiments, the livestock growth-based bio-authentication device includes a livestock health data receiver that receives livestock health data through a sensor coupled to the livestock while the livestock is alive, and a livestock health data receiver that learns livestock health data about livestock suffering from livestock diseases. A livestock disease model learning unit that builds a disease model, a meat product safety evaluation unit that collects livestock health status by providing the livestock health data to the livestock disease model and evaluates meat product stability, and livestock health linked to meat products from users. It includes a meat request code receiving unit that receives a grade request code, and a meat stability information processing unit that provides meat stability information regarding the meat product stability evaluated while the livestock was alive based on the livestock health grade request code.

The livestock disease model learning unit can learn body temperature, pH, movement, intake activity, rumination activity, and rest activity collected through a sensor inserted into the living body of livestock suffering from the livestock disease as the livestock health data.

The livestock disease model learning unit may construct a livestock health model by learning livestock health data about livestock that are not affected by the livestock disease.

The meat product stability evaluation unit may verify the meat product stability by providing the livestock health data to the livestock health model when the meat product stability is below a certain standard.

The meat product safety evaluation unit may determine the urgency of the livestock health data and collect the livestock health status immediately or periodically.

The meat product stability evaluation unit may evaluate the meat product stability of the meat product by modeling the livestock health status collected while the livestock is alive as a finite time series meat stability model.

The meat product stability evaluation unit may generate the time series meat stability model by performing a weighted average of the collected livestock health status.

The meat product safety evaluation unit may perform the weighted average by highly evaluating the weight of the livestock health status of the most recent section before slaughter of the livestock.

The meat product safety evaluation unit determines the disease validity period of the livestock health status according to the livestock disease, and when the disease validity period has elapsed, the livestock health status can be increased in advance.

If the meat product stability is below a certain standard, the meat safety information processing unit may provide the user with information on health problems that may occur due to consumption of the livestock.

The disclosed technology can have the following effects. However, since it does not mean that a specific embodiment must include all of the following effects or only the following effects, the scope of rights of the disclosed technology should not be understood as being limited thereby.

The livestock growth-based bioauthentication device according to an embodiment of the present invention can collect the health status of livestock while they are alive, evaluate meat product safety, and provide meat safety information to users.

The livestock growth-based bioauthentication device according to an embodiment of the present invention constructs a livestock disease model and a livestock health model, evaluates the livestock health grade according to the livestock disease model, and uses the livestock health model as food for those below a certain standard. Meat product safety can be verified.

The livestock growth-based bioauthentication device according to an embodiment of the present invention periodically collects the health status of livestock through a sensor coupled to the livestock and evaluates meat product safety by setting the weight of the livestock health status of the most recent section before slaughter to a high level. The reliability of the stability evaluation can be secured by performing .

1 is a diagram illustrating a bio-authentication system based on livestock growth according to an embodiment of the present invention.
FIG. 2 is a diagram explaining the system configuration of the biometric authentication device in FIG. 1.
FIG. 3 is a diagram explaining the functional configuration of the biometric authentication device in FIG. 1.
FIG. 4 is a flowchart explaining the livestock growth-based bioauthentication process performed in the bioauthentication device in FIG. 1.

Since the description of the present invention is only an example for structural and functional explanation, the scope of the present invention should not be construed as limited by the examples described in the text. In other words, since the embodiments can be modified in various ways and can take various forms, the scope of rights of the present invention should be understood to include equivalents that can realize the technical idea. In addition, the purpose or effect presented in the present invention does not mean that a specific embodiment must include all or only such effects, so the scope of the present invention should not be understood as limited thereby.

Meanwhile, the meaning of the terms described in the present invention should be understood as follows.

Terms such as “first” and “second” are used to distinguish one component from another component, and the scope of rights should not be limited by these terms. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected to the other component, but that other components may exist in between. On the other hand, when a component is referred to as being “directly connected” to another component, it should be understood that there are no other components in between. Meanwhile, other expressions that describe the relationship between components, such as "between" and "immediately between" or "neighboring" and "directly neighboring" should be interpreted similarly.

Singular expressions should be understood to include plural expressions, unless the context clearly indicates otherwise, and terms such as “comprise” or “have” refer to the specified features, numbers, steps, operations, components, parts, or them. It is intended to specify the existence of a combination, and should be understood as not excluding in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

Identification codes (e.g., a, b, c, etc.) for each step are used for convenience of explanation, and each step may occur in a different order from the specified order unless a specific order is clearly stated in the context. . That is, each step may occur in the same order as specified, may be performed substantially simultaneously, or may be performed in the opposite order.

The present invention can be implemented as computer-readable code on a computer-readable recording medium, and the computer-readable recording medium includes all types of recording devices that store data that can be read by a computer system. . Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices, and are also implemented in the form of a carrier wave (e.g., transmission via the Internet). It also includes Additionally, the computer-readable recording medium can be distributed across computer systems connected to a network, so that computer-readable code can be stored and executed in a distributed manner.

All terms used herein, unless otherwise defined, have the same meaning as commonly understood by a person of ordinary skill in the field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as consistent with the meaning they have in the context of the related technology, and cannot be interpreted as having an ideal or excessively formal meaning unless clearly defined in the present invention.

Figure 1 is a diagram showing a bio-authentication system based on livestock growth according to an embodiment of the present invention.

Referring to FIG. 1, the livestock breeding-based bioauthentication system 100 includes a user terminal 110, a sensor 130, a bioauthentication device 150, and a database 170.

The user terminal 110 may correspond to a computing device that can receive information about the safety of meat products. The user terminal 110 may be implemented as a smartphone, a laptop, or a computer, but is not necessarily limited thereto, and may also be implemented as a variety of devices such as a tablet PC. The user terminal 110 may be connected to the biometric authentication device 150 through a network, and a plurality of user terminals 110 may be simultaneously connected to the biometric authentication device 150.

Additionally, the user terminal 110 may install and run a dedicated program or application for querying meat stability information regarding meat product stability. The user terminal 110 may be implemented including a camera module capable of reading a livestock health grade request code coupled to a meat product.

The sensor 130 can be coupled to the livestock 200 to measure livestock health data. The sensor 130 can be inserted into the living body of the livestock 200 to measure biometric and activity information of the livestock 200. The sensor 130 may be administered into the oral cavity of the livestock 200 and placed on the body of the livestock 200. For example, if the livestock 200 is a cow, the sensor 130 may be seated in the rumen of the cow and measure the internal state of the rumen. The sensor 130 may be connected to the biometric authentication device 150 through a network. The sensor 130 can measure the body temperature, pH, movement, etc. of the livestock 200 and transmit the measurement data to the biometric authentication device 150 at set intervals.

The bioauthentication device 150 collects the health status of the livestock 200 through the sensor 130 coupled to the livestock 200 while the livestock 200 is alive, evaluates meat product stability, and provides meat stability information to the user terminal 110. It can be implemented as a server corresponding to a computer or program. The biometric authentication device 150 may be connected to the user terminal 110 through a wired network or a wireless network such as Bluetooth or WiFi, and may communicate with the user terminal 110 through the network. The biometric authentication device 150 can be connected to the sensor 130 inserted into the living body of the livestock 200 through a wireless network such as Bluetooth or WiFi, and can communicate with the sensor 130 through the network.

The database 170 may correspond to a storage device that stores various information required during the operation of the biometric authentication device 150. The database 170 can build and store a livestock disease model based on livestock health data on livestock with livestock diseases and a livestock health model based on livestock health data on livestock without livestock diseases, and can maintain livestock health while the livestock are alive. The state can be collected and stored, but is not necessarily limited to this, and the bioauthentication device 150 can store information collected or processed in various forms during the bioauthentication process based on livestock growth.

FIG. 2 is a diagram explaining the system configuration of the biometric authentication device in FIG. 1.

Referring to FIG. 2, the biometric authentication device 150 may be implemented including a processor 210, a memory 230, a user input/output unit 250, and a network input/output unit 270.

The processor 210 can execute a procedure that processes each step in the process of operating the biometric authentication device 150, and can manage the memory 230 that is read or written throughout the process. ) can schedule the synchronization time between volatile and non-volatile memory. The processor 210 can control the overall operation of the biometric authentication device 150 and is electrically connected to the memory 230, the user input/output unit 250, and the network input/output unit 270 to control the data flow between them. You can. The processor 210 may be implemented as a central processing unit (CPU) of the biometric authentication device 130.

The memory 230 may be implemented as a non-volatile memory such as a solid state drive (SSD) or a hard disk drive (HDD) and may include an auxiliary memory used to store all data required for the biometric authentication device 150. It may include a main memory implemented as volatile memory such as RAM (Random Access Memory).

The user input/output unit 250 may include an environment for receiving user input and an environment for outputting specific information to the user. For example, the user input/output unit 250 may include an input device including an adapter such as a touch pad, touch screen, on-screen keyboard, or pointing device, and an output device including an adapter such as a monitor or touch screen. In one embodiment, the user input/output unit 250 may correspond to a computing device connected through a remote connection, and in such case, the biometric authentication device 130 may be performed as an independent server.

The network input/output unit 270 includes an environment for connecting with external devices or systems through a network, for example, Local Area Network (LAN), Metropolitan Area Network (MAN), Wide Area Network (WAN), and VAN ( It may include an adapter for communication such as a Value Added Network).

FIG. 3 is a diagram functionally explaining the biometric authentication device in FIG. 1.

Referring to FIG. 3, the bio-authentication device 150 includes a livestock health data receiving unit 310, a livestock disease model learning unit 320, a meat product stability evaluation unit 330, a meat request code receiving unit 340, and meat stability information. It includes a processing unit 350 and a control unit 360.

The livestock health data receiver 310 may receive livestock health data through the sensor 130 coupled to the livestock 200 while the livestock 200 is alive. In one embodiment, the livestock health data receiver 310 is connected to the sensor 130 through a wireless network and may periodically collect health data of the livestock 200 from the sensor 130. Here, the sensor 130 can be inserted into the living body of the livestock 200 to measure biometric information of the livestock 200. The livestock health data receiver 310 may periodically collect livestock health data such as body temperature, pH, movement, drinking water or feed intake activity, rumination activity, and resting activity of the livestock 200 through the sensor 130.

The livestock disease model learning unit 320 can build a livestock disease model by learning livestock health data about livestock suffering from livestock diseases. In one embodiment, the livestock disease model learning unit 320 learns body temperature, pH, movement, intake activity, rumination activity, and resting activity collected through a sensor inserted into the living body of livestock suffering from livestock disease as livestock health data to determine livestock disease. You can build a model. Livestock diseases vary depending on the livestock species, including bacterial and parasitic diseases, viral diseases, avian diseases, and malignant livestock infectious diseases. The livestock disease model learning unit 320 can construct a livestock disease model for predicting the possibility of livestock disease by deriving the correlation between livestock health data of livestock suffering from livestock disease and the livestock disease through machine learning.

The livestock disease model learning unit 320 can build a livestock health model by learning livestock health data about livestock that are not affected by livestock diseases. The livestock disease model learning unit 320 learns body temperature, pH, movement, intake activity, rumination activity, and rest activity collected through biometric sensors of livestock that are not affected by livestock disease as livestock health data to build a livestock health model. You can. The livestock disease model learning unit 320 can build a livestock health model by analyzing livestock health data of livestock without livestock diseases compared to livestock health data of livestock with livestock diseases through machine learning. Here, the livestock health model can be used as a standard to verify the safety of meat products after slaughter of livestock 200.

The meat product safety evaluation unit 330 can provide livestock health data to a livestock disease model to determine livestock health status and collect livestock health status to evaluate meat product stability. In one embodiment, the meat product safety evaluation unit 330 predicts and predicts the possibility of livestock disease by using livestock health data received through the sensor 130 coupled to the livestock 200 as input to the constructed livestock disease model. The livestock health status of livestock 200 can be determined depending on the degree of livestock disease possibility. If the meat product stability is below a certain standard, the meat product stability evaluation unit 330 can provide livestock health data to the livestock health model to verify meat product stability. Here, the meat product safety evaluation unit 330 can pre-evaluate the safety of the meat product after slaughter through the health status of the livestock 200 while the livestock 200 is alive. The meat product safety evaluation unit 330 performs a meat product safety evaluation based on the collected livestock health status, and if the result is below a certain standard, the livestock health data is used as input to the livestock health model, and the livestock health data of livestock that is not affected by livestock disease is used as input to the livestock health model. Meat product safety can be verified through comparison. In one embodiment, the meat product safety evaluation unit 330 may change the meat product safety evaluation when the livestock health data of livestock whose meat product stability is evaluated below a certain standard is similar to the livestock health data of livestock not suffering from livestock disease. there is.

The meat product safety evaluation unit 330 can determine the urgency of livestock health data and collect livestock health status immediately or periodically. In one embodiment, the meat product safety evaluation unit 330 may evaluate the meat product stability of the meat product by modeling the livestock health status collected while the livestock is alive as a finite time series meat stability model. Here, the meat product stability evaluation unit 330 can generate a time series meat stability model by performing a weighted average of the collected livestock health status. The meat product safety evaluation unit 330 may perform a weighted average by highly evaluating the weight of the livestock health status of the most recent section before slaughtering the livestock. The time-series meat stability (stationary) model can be defined by the following equation.

[Equation]

Here, Model_tsms represents the time series meat stability model, x represents the livestock health status collected periodically while the livestock is alive, and W represents the weight of the livestock health status.

The meat product safety evaluation unit 330 determines the disease validity period of the livestock health status according to the livestock disease, and when the disease validity period has elapsed, the livestock health status can be increased in advance.

The meat request code receiver 340 may receive a livestock health grade request code associated with a meat product from the user. The user can read the livestock health grade request code bound to the meat product through the user terminal 110 and transmit the read livestock health grade request code through the network to request provision of information on the safety of the meat product. In one embodiment, the livestock health grade request code may be attached to the packaging of the meat product in the form of a serial number or a barcode or QR code that can be used to check the safety evaluation of the meat product. The user can input the serial number directly through the user terminal 110 or use the camera module of the user terminal 110 to capture an image of the livestock health grade request code in the form of a barcode or QR code attached to the meat product.

The meat stability information processing unit 350 may provide the user terminal 110 with meat stability information regarding meat product stability that was evaluated while the livestock 200 was alive based on the livestock health grade request code. In one embodiment, the meat safety information processing unit 350 may provide the user with information on health problems that may occur due to consumption of the livestock if the meat product safety is below a certain standard.

The control unit 260 can control the overall operation of the biometric authentication device 150, and includes a livestock health data receiving unit 310, a livestock disease model learning unit 320, a meat product stability evaluation unit 330, and a meat request code receiving unit. Processing and data flow between the 340 and the meat stability information processing unit 350 can be controlled.

FIG. 4 is a flowchart explaining the livestock growth-based bioauthentication process performed in the bioauthentication device in FIG. 1.

In Figure 4, the biometric authentication device 150 receives livestock health data through the sensor 130 coupled to the livestock 200 while the livestock 200 is alive (step S410). In one embodiment, the biometric authentication device 150 receives biometric information and movement information of the livestock 200 from the sensor 130 through the livestock health data receiver 310 to determine the temperature, pH, movement, and body temperature of the livestock 200. Livestock health data including eating activity, rumination activity, and resting activity can be collected.

The biometric authentication device 150 builds a livestock disease model by learning livestock health data about livestock suffering from livestock disease (step S420). In one embodiment, the bio-authentication device 150 can machine learn livestock health data as input through the livestock disease model learning unit 320 to build a livestock disease model that can predict the possibility of livestock disease from livestock health data. there is.

The biometric authentication device 150 builds a livestock health model by learning livestock health data about livestock that are not affected by livestock diseases (step S430). In one embodiment, the bio-authentication device 150 performs machine learning on livestock health data as input through the livestock disease model learning unit 320 to predict the likelihood of the animal being healthy without contracting a livestock disease from the livestock health data. You can build a model.

The bioauthentication device 150 provides livestock health data to the livestock disease model to determine livestock health status and collects livestock health status to evaluate meat product stability (step S440). In one embodiment, the bio-authentication device 150 creates a livestock disease model from the livestock health data of the livestock 200 collected through the sensor 130 while the livestock 200 is alive through the meat product stability evaluation unit 330. Based on this, the livestock health status of livestock 200 can be determined. Here, livestock health status can be determined through prediction of the possibility of livestock disease. The meat product safety evaluation unit 330 can evaluate the safety of meat products of the livestock 200 based on the livestock health status while the livestock 200 is alive. For example, the higher the possibility of livestock disease, the lower the safety of meat products, and conversely, the lower the possibility of livestock disease, the higher the safety of meat products.

If the meat product stability is below a certain standard, the bioauthentication device 150 verifies the meat product stability by providing livestock health data to the livestock health model (step S450). In one embodiment, the bio-authentication device 150 evaluates the meat product stability through the meat product stability evaluation unit 330, and when the meat product stability of the livestock is below a certain standard (healthy food standard), the livestock collected from the livestock is Based on the health data of the livestock health model, the livestock health status can be re-determined and the meat product stability can be verified with the re-determined livestock health status. The meat product stability evaluation unit 330 can change the verification results by reflecting them on the meat product stability.

The biometric authentication device 150 receives the livestock health grade request code associated with the meat product from the user (step S460). When a user wants to know the safety of a meat product, he or she can read the livestock health grade request code bound to the meat product through the user terminal 110 and transmit it to the bio-authentication device 150.

The bioauthentication device 150 provides meat stability information regarding meat product stability that was evaluated while the livestock was alive based on the received livestock health grade request code (step S470). In one embodiment, the bio-authentication device 150 identifies livestock from the livestock health grade request code through the meat stability information processing unit 350 and sends meat product stability information evaluated during growth of the identified livestock to the user terminal 110. It can inform users about the safety of meat products.

Although the present application has been described above with reference to preferred embodiments, those skilled in the art may make various modifications and changes to the present application without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can do it.

100: Bio-certification system based on livestock growth
110: user terminal 130: sensor
150: Biometric authentication device 170: Database
200: Livestock
310: Livestock health data receiving unit 320: Livestock disease model learning unit
330: Meat product stability evaluation unit 340: Meat request code reception unit
350: meat stability information processing unit 360: control unit

Claims (10)

A livestock health data receiver that receives livestock health data through a sensor coupled to the livestock while the livestock is alive;
A livestock disease model learning department that builds a livestock disease model by learning livestock health data about livestock affected by livestock diseases;
a meat product safety evaluation unit that provides the livestock health data to the livestock disease model to collect livestock health status and evaluate meat product safety;
A meat request code receiving unit that receives a livestock health grade request code associated with a meat product from the user; and
It includes a meat stability information processing unit that provides meat stability information regarding the meat product stability evaluated while the livestock was alive based on the livestock health grade request code,
The livestock disease model learning department
Build a livestock health model by learning livestock health data about livestock that are not affected by the livestock disease,
The meat product safety evaluation department
Predict the possibility of livestock disease by using the livestock health data as input to the livestock disease model, determine the livestock health status according to the predicted livestock disease possibility, and evaluate the meat product safety,
Even if the possibility of the livestock disease is predicted, if the meat product stability is below a certain standard, the livestock health data is provided to the livestock health model to verify the meat product safety by comparing it with livestock health data on livestock that are not affected by the livestock disease. do,
A bio-authentication device based on livestock growth, characterized in that the meat product safety evaluation is changed when the livestock health data of livestock whose meat product safety is evaluated below a certain standard is similar to the livestock health data of livestock not affected by the livestock disease. .
The method of claim 1, wherein the livestock disease model learning unit
A bio-authentication device based on livestock growth, characterized in that it learns body temperature, pH, movement, intake activity, rumination activity, and rest activity collected through a sensor inserted into the living body of livestock suffering from the livestock disease as the livestock health data.
delete delete The method of claim 1, wherein the meat product stability evaluation unit
A bio-authentication device based on livestock growth, characterized in that it determines the urgency of the livestock health data and collects the livestock health status immediately or periodically.
The method of claim 5, wherein the meat product stability evaluation unit
A bio-authentication device based on livestock growth, characterized in that it evaluates the meat product stability of the meat product by modeling the livestock health status collected while the livestock is alive as a finite time series meat stability model.
The method of claim 6, wherein the meat product stability evaluation unit
A bio-authentication device based on livestock growth, characterized in that the time series meat stability model is generated by weighting the collected livestock health status.
The method of claim 7, wherein the meat product stability evaluation unit
A bio-authentication device based on livestock growth, characterized in that the weighted average is performed by highly evaluating the weight of the most recent livestock health status before slaughtering the livestock.
The method of claim 8, wherein the meat product stability evaluation unit
A bio-authentication device based on livestock growth, characterized in that it determines the disease validity period of the livestock health condition according to the livestock disease, and increases the livestock health condition in advance when the disease validity period has elapsed.
The method of claim 1, wherein the meat stability information processing unit
A bio-authentication device based on livestock growth, characterized in that when the meat product stability is below a certain standard, the user is provided with information on health problems that may occur due to consumption of the livestock.