WO2021251714A1 - Disease management method, disease management server, and disease management system for livestock - Google Patents

Abstract

Disclosed are a disease management method and apparatuses for performing same. The disease management method for livestock, comprising: a step of receiving biometric information of livestock from a biosensor capsule seated in the body of the livestock; a singularity detection step of detecting a singularity that deviates from a reference value, on the basis of the biometric information; a health condition monitoring step of increasing the disease probability of the livestock when the singularity is detected; and a disease detection step of determining a health condition abnormality of the livestock when the disease probability of the livestock is greater than or equal to a first threshold value, can increase the productivity of a farm by rapidly detecting individuals suspected of having a reproductive disorder (ovarian cyst) and enabling intensive care (veterinary examination and hormone prescription) on the farm.

Description

Livestock disease management method, disease management server and disease management system

The following embodiments relate to a disease management method for livestock, a disease management server, and a disease management system.

The livestock industry has grown significantly in terms of quantity due to the increase in breeding scale and population due to changes in diet. In particular, as the livestock industry changes from a small-scale livestock industry to a large-scale livestock industry, there are farms raising thousands or tens of thousands of livestock.

As the breeding scale increases, if livestock infectious diseases such as foot-and-mouth disease, mad cow disease, swine fever, and avian influenza (AI) occur, there is a problem that transmission occurs easily between livestock raised in a limited space. In severe cases, a large-scale death is required, which inflicts a major blow to the livestock industry.

In general, the barn refers to a building for raising livestock such as chickens, ducks, pigs or cattle, and such a barn is to facilitate the management while benefiting the growth of livestock.

However, a large number of livestock live in groups in the barn as described above, and when a contagious disease is induced according to the group life, diseases occur to all livestock in the barn, resulting in very serious damage.

In particular, a large number of livestock dies every year due to diseases such as mad cow disease, causing enormous damage to livestock farmers.

The examples below may provide technology for managing diseases in livestock.

In addition, the following embodiments can provide a technology in which a biocapsule sensor that transmits biometric information to determine the health status of livestock is orally administered directly to livestock without the need for an expensive gathering box, and is easily and conveniently installed.

According to an embodiment, receiving the biometric information of the livestock from a biosensor capsule arranged in the body of the livestock; a singularity detection step of detecting a singularity that deviates from a reference value based on the biometric information; a health status monitoring step of increasing the disease probability of the livestock when the singularity is detected; And when the disease probability of the livestock is greater than or equal to a first threshold value, it provides a disease detection step of determining an abnormality in the health state of the livestock.

The biometric information may include temperature information and activity information of the livestock.

The livestock activity information may include at least one of angular velocity information and acceleration information detected by the biosensor capsule.

The singularity detection step includes an estrus detection step of determining estrus when at least one of a first singularity in which the temperature information exceeds a temperature threshold and a second singularity in which the activity information exceeds an activity threshold is expressed. , when the estrus is detected, the increase in the disease probability of the livestock may increase.

In the step of detecting estrus, when the duration of the first singularity or the duration of the second singularity lasts longer than the first duration, it may be determined as estrus.

In the step of detecting estrus, when both the first singularity and the second singularity are expressed, it may be determined as estrus.

In the step of detecting estrus, when only one of the first singularity and the second singularity is expressed, when the duration of the expressed singularity continues for a second duration or more, it may be determined as estrus.

The health status monitoring step may include: the frequency of occurrence of estrus, irregularity of estrus onset, duration of estrus onset, and intensity of estrus onset so that the range of change in the disease probability of the livestock varies according to at least one selected from the group consisting of: can be adjusted

In the health status monitoring step, the higher the frequency of occurrence of estrus, the more irregular the expression of estrus, or the longer the duration of estrus, the greater the increase in the disease probability of the livestock.

When the estrus detection step is expressed three or more times within the second estrus cycle, the disease probability of the livestock may be adjusted to be equal to or greater than the first threshold value.

In the monitoring step, the disease probability of the livestock may be reduced after the singular point detection step and before the next singular point detection step.

When the disease probability is greater than or equal to a set value, the method may further include an alarm step of notifying the user of the disease of the livestock.

Abnormalities in the health of the livestock may be ovarian cysts.

The biosensor capsule may further include the step of inserting it into the stomach or vagina of the livestock.

According to another embodiment, a communication module for receiving the biometric information of the livestock from the biosensor capsule arranged in the body of the livestock; and a controller that analyzes the biometric information, determines the health status of the livestock based on the analysis result, and transmits information on the health status of the livestock to a user who manages the livestock, wherein the controller comprises: Detects a singularity deviating from a reference value based on biometric information, increases the disease probability of the livestock when the singularity is detected, and transmits an abnormal health status of the livestock to the user when the disease probability of the livestock is greater than or equal to a first threshold It provides a disease management server for livestock.

The biometric information includes temperature information and activity information of the livestock, and the controller is configured to include at least one of a first singularity point in which the temperature information exceeds a temperature threshold value and a second singularity point in which the activity information exceeds an activity threshold value When is expressed, it is determined as estrus, and it is possible to increase the disease probability of the livestock. The controller may control the change in the disease probability of the livestock to be different according to at least one selected from the group consisting of frequency of estrus expression, irregularity of estrus expression, duration of estrus expression, and intensity of estrus expression. .

The controller may adjust the disease probability of the livestock to be greater than or equal to the first threshold when the estrus detection step is expressed three or more times within the second estrous cycle.

Abnormalities in the health of the livestock may include ovarian cysts.

According to another embodiment, a biosensor capsule arranged in the body of the livestock to detect the biometric information of the livestock; a disease management server that receives the biometric information of livestock from the biosensor capsule through a communication network, analyzes the biometric information to generate health state information of the livestock, and transmits the health state information of the livestock to a user who manages the livestock; and a user device for receiving the health status information of livestock from the disease management server through a communication network, wherein the disease management server detects a singularity that deviates from a reference value based on the biometric information, and when the singularity is detected, the Increases the disease probability of livestock, wherein the user device, when the disease probability of the livestock is equal to or greater than a first threshold, receives a signal of abnormal health status of the livestock from the disease management server, provides a disease management system for livestock .

By using the data collected from the biosensor capsule of the present invention, the disease management server quickly detects an object suspected of a reproductive disorder (ovarian cyst), and enables intensive care (veterinary examination and hormone prescription) of the farm, resulting in farm productivity can increase

1 is a schematic diagram of a disease management system according to an embodiment.

FIG. 2 is a schematic diagram of the biosensor capsule shown in FIG. 1 .

FIG. 3 is a schematic block diagram of the disease management server shown in FIG. 1 .

4A to 4D show examples of information on the health status of cattle provided from a disease management server.

FIG. 5 is a flowchart for explaining an operation method of the disease management server shown in FIG. 1 .

6 is a view showing the follicle shape and estrus according to hormonal changes in livestock.

7 is a table showing summary information about the experimental subject.

8 is a conceptual diagram of a Long Short-Term Memory (LSTM) - Fully Convolutional Networks (FCN) model.

9 is a graph showing bioinformation information obtained from a biosensor capsule of a normal individual.

10 is a graph showing biometric information obtained from a biosensor capsule of an experimental subject with ovarian cyst.

11 and 12 are graphs numerically showing the probability of estrus and ovarian cyst calculated based on temperature information and activity information of livestock.

13 is a table showing the experimental results of the test subject.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed in this specification are only exemplified for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiment according to the concept of the present invention These may be embodied in various forms and are not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention may have various changes and may have various forms, the embodiments will be illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes changes, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from other components, for example, without departing from the scope of rights according to the concept of the present invention, a first component may be named a second component, Similarly, the second component may also be referred to as the first component.

When an element is referred to as being “connected” or “connected” to another element, it is understood that it may be directly connected or connected to the other element, but other elements may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle. Expressions describing the relationship between elements, for example, “between” and “between” or “neighboring to” and “directly adjacent to”, etc. should be interpreted similarly.

The terminology used herein is used only to describe specific embodiments, and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprises" or "have" are intended to designate the presence of the specified features, numbers, steps, operations, components, parts, or combinations thereof, and include one or more other features or numbers, It should be understood that the possibility of the presence or addition of steps, operations, components, parts or combinations thereof is not precluded in advance.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present specification. does not

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference numerals in each figure indicate like elements.

1 is a schematic diagram of a disease management system according to an embodiment, and FIGS. 4A to 4D show examples of information on the health status of cattle provided from the disease management server 300 .

Referring to FIG. 1 , the disease management system 10 includes a plurality of cows (100-1 to 100-n, where n is a natural number greater than 1), a disease management server 300 , and a user device 400 .

The plurality of cows 100 - 1 to 100 - n may be equipped with a biosensor capsule 200 . The plurality of cattle 100 - 1 to 100 - n may be cattle managed by the user of the user device 400 . Also, some of the plurality of cattle 100 - 1 to 100 - n may be cattle managed by a user other than the user of the user device 400 .

The plurality of cows 100 - 1 to 100 - n may be assigned IDs to be managed by the disease management server 300 . For example, the plurality of cows 100 - 1 to 100 - n may be classified by an ID assigned to the biosensor capsule 200 and managed by the disease management server 300 .

The plurality of cows 100 - 1 to 100 - n may transmit biometric information to the disease management server 300 through the biosensor capsule 200 . In this case, the biosensor capsule 200 may be seated in a predetermined place in the living body of the plurality of cows 100-1 to 100-n. Accordingly, the biosensor capsule 200 may obtain biometric information from the plurality of cattle 100-1 to 100-n and transmit the biometric information to the disease management server 300 .

The disease management server 300 manages the plurality of cattle 100-1 to 100-n and provides a service for remotely managing the plurality of cattle 100-1 to 100-n to the user device 400 . can do. For example, the disease management server 300 may control the plurality of cows 100-1-100-n based on the biometric information obtained from the biosensor capsule 200 of the plurality of cows 100-1-100-n. We can provide services to manage

The disease management server 300 may analyze the biometric information, determine the health status of each of the plurality of cows 100-1 to 100-n based on the analysis result, and transmit the health status to the user device 400 . . The health condition may include at least one of a breeding-related condition for estrus and calving of the cow, a disease condition, a methane gas generation condition, and an activity condition of the rumen.

In addition, the disease management server 300 may provide an alarm classified according to a health state to the user device 400 .

The disease management service application capable of executing the service provided by the disease management server 300 may be installed and executed in the user device 400 and may perform a function related to the disease management service. For example, the disease management service application may be downloaded and installed from the disease management server 300 . As another example, the disease management service application may be downloaded and installed from an app store or an Android market.

The user device 400 may receive the health status of the plurality of cattle 100-1 to 100-n from the disease management server 300 and/or an alarm classified according to the health status. For example, the information on the health state may include information on estrus, fertilization, pregnancy, and delivery of cattle. In addition, the breeding-related state may include information on the latest estrous date, expected fertilization date, last fertilization date, estimated date of analysis, expected delivery date, dry milk date, latest delivery date, and expected estrus date of the cow.

For example, the health status information provided by the user device 400 from the disease management server 300 may be as shown in FIGS. 4A to 4D .

In this case, the user device 400 may be implemented as a personal computer (PC), a data server, or a portable electronic device.

Portable electronic devices include laptop computers, mobile phones, smart phones, tablet PCs, mobile internet devices (MIDs), personal digital assistants (PDAs), and enterprise digital assistants (EDAs). ), digital still camera, digital video camera, PMP (portable multimedia player), PND (personal navigation device or portable navigation device), handheld game console, e-book (e-book), or may be implemented as a smart device (smart device). The smart device may be implemented as a smart watch, a smart band, or a smart ring.

As described above, the user collectively remotely manages multiple objects, for example, a plurality of cows 100-1 to 100-n through the user device 400, continuously manages histories, reports and receives real-time status, You can respond quickly by receiving an alarm that is classified according to your health status.

FIG. 2 is a schematic diagram of the biosensor capsule shown in FIG. 1 .

Referring to FIG. 2 , the biosensor capsule 200 includes a plurality of sensors 210 to 250 , a battery 260 , a weight 270 , a communication module 280 , and an antenna 290 .

The biosensor capsule 200 may be seated in a certain place in the body of the cow, for example, into the stomach and seated in the rumen or in the vagina. In this case, the biosensor capsule 200 may be protected by a case (not shown) for protecting the inside from moisture or gastric acid in the living body.

The acceleration sensor 210 may obtain acceleration information of the cow. For example, the acceleration sensor 210 may detect an acceleration value of a cow in real time and generate acceleration information.

The biosensor capsule 200 may change direction while in the animal's body (eg, in the rumen and/or in the honeycomb of a cow). Therefore, detecting animal movement and/or gastrointestinal activity in only one or both axes may yield inaccurate results.

Accordingly, the acceleration sensor 210 is a 3-axis or 6-axis capable of detecting the movement of livestock and/or gastrointestinal activity in each of the Cartesian coordinate “x”, “y” and “z” axes. It may be an axial accelerometer. As the number of sensing axes increases, more accurate motion can be detected. For example, using a 6-axis accelerometer may increase precision compared to measuring activity with a 3-axis accelerometer.

The acceleration vector magnitude (v) can be calculated from values measured with a 3-axis accelerometer by calculating the square root of the sum of the squares of each of the "x", "y" and "z" coordinate axes.

In order to remove the error rate generated by the movement caused by the shaking of the biosensor capsule 200 itself in the body of the livestock or other acceleration forces (eg, gravity) acting on the livestock, the vector size is differentiated to give more information about the actual movement of the livestock. You can get specific information.

In addition to detecting movement of the livestock and/or gastrointestinal activity, accelerometer 221 may be configured to detect gastrointestinal contractions. Since the biosensor capsule 200 may be disposed in the stomach (eg, in the rumen or honeycomb of a ruminant livestock), the biosensor capsule 200 may be affected, such as by movement, by the gastrointestinal contraction of the livestock. In this case, the biosensor capsule 200 may be configured to have sufficient weight and/or density to be retained in the lower part of the animal's stomach and/or to be moved and positioned on the wall of the livestock's stomach.

The biosensor capsule 200 coupled to move like the stomach of a livestock is a biosensor capsule corresponding to the movement of the stomach so that the accelerometer 221 of the biosensor capsule 200 detects the movement of the stomach such as contraction 200) by matching and correcting the movements, the values can be referred to for extracting the movement values of the stomach.

For example, a typical ruminant contraction can, under normal health conditions, perform three gastrointestinal contractions every minute during rumen activity, i.e., every 20 seconds.

Non-rumination time may indicate a livestock health condition in which the livestock no longer feeds and/or intermittently.

Thus, managers can detect when a livestock becomes “non-ruminant” by observing changes and/or a decrease in the frequency of gastrointestinal contractions of the livestock.

Prolonged periods of time during which livestock are “non-ruminant” can indicate serious health conditions.

In addition, livestock may exhibit "non-rumination" before the onset of unusual health-state symptoms (eg increased/decreased movement or temperature).

The gyro sensor 220 may obtain information on the angular velocity of the cow. For example, the gyro sensor 220 may detect an angular velocity value of a cow in real time and generate angular velocity information.

Based on the principle of the gyroscope, MEMS technology can be applied to implement a chip-type gyro sensor, and the small size can be mounted in the biosensor capsule 200 .

The movement of the biosensor capsule 200 may be sensed using the acceleration sensor 210 and the gyro sensor 220 , and as a result, the activity of the cow may be detected. The roll, pitch, and yaw of the biosensor capsule can be detected by combining the acceleration detected by the acceleration sensor and the angular velocity of the gyro sensor.

The temperature sensor 230 may acquire temperature information in the stomach or vagina of the cow. For example, the temperature sensor 230 may sense the temperature value of the cow's stomach in real time and generate temperature information. The temperature sensor 230 may include a thermistor, a thermocouple, or a platinum resistance thermometer. The temperature in the stomach or vagina of the cow and the temperature of the cow may have a constant difference in the value.

The methane sensor 240 may acquire methane gas generation information in the stomach of cattle. For example, the methane sensor 240 may detect the amount of methane gas generated in the stomach of cattle in real time, and generate methane gas generation information.

The pH sensor 250 may acquire information about the pH of the cow's stomach or vagina. For example, the pH sensor 250 may sense the pH concentration in the stomach of cattle in real time and generate pH information.

The electrical conductivity sensor is a sensor that measures the electrical conductivity of substances present in the stomach or vagina. Electrical conductivity is an index indicating the degree to which an electric current flows well between a material or an object positioned between a pair of sensor electrodes. Conductors such as metals have high electrical conductivity and insulators such as glass have low electrical conductivity.

While it is also possible to measure the electrical conductivity between two points on a solid such as a metal, it is also possible to measure the electrical conductivity in a liquid containing electrolyte ions. The electrical conductivity of a liquid increases as the concentration of electrolyte ions contained in the liquid increases. The unit is dS/m or mS/cm, and the electrical conductivity may vary depending on the pH concentration and temperature of the liquid. Electrical conductivity varies somewhat depending on the type of ions contained in the liquid, but may vary in proportion to the total salt concentration.

Acceleration information, angular velocity information, temperature information, methane gas generation information, electrical conductivity information, and pH information may be transmitted to the disease management server 300 through the communication module 280 and the antenna 290 .

The battery 260 may supply power to each of the components 210 , 220 , 230 , 240 , 250 , 280 , and 290 of the biosensor capsule 200 . For example, the battery 260 may be implemented as various types of batteries.

Battery 260 may include a battery energy storage device such as a lithium ion battery, a lead battery, a nickel cadmium battery, and the like.

In other embodiments, battery 260 may include a generator. The generator may include a piezoelectric generator or a mass/AC power source generator that generates electric power from movement and/or kinetic activity or vibration of the biosensing capsule 200 within the host livestock.

In another embodiment, the generator may be a heat-activated generator for generating electrical energy from the body heat of the host livestock. The generator may include a microbial fuel cell (MFC) configured to generate electrical energy derived from bacteria that is supplied to organic material in the stomach (ie, the rumen or honeycomb) of the livestock.

The generator may be disposed outside the health sensor device housing. Battery 260 may include both a battery power storage device and a generator; In this embodiment, the power generated by the generator may be stored in a battery power storage device.

Battery 260 may include a power monitor that may be used to monitor power and/or voltage levels of battery power storage and/or generators. A plurality of power-saving operations may be performed using the power state information provided by the power monitor.

The weight 270 may serve to add weight so that the biosensor capsule 200 is injected into the cow's living body and is not discharged to the outside, and enters and settles in a certain place in the cow's body, for example, the stomach. That is, the weight 270 may have sufficient weight to be seated on the cow's stomach without the biosensor capsule 200 being discharged outside in the process of chewing the cud of the cow.

The fact that the biosensor capsule 200 is not discharged to the outside by the weight 270 will be described as follows.

Cows, among herbivores, are ruminants, which, because they do not know when they will be attacked by carnivores, quickly graze grass without chewing, put it in their stomach, and then ruminate (chew the cud) food again in a safe place.

The stomach of a ruminant has four parts. Similarly, a cow has four latitudes. The first stomach (eg, lump) is a space for storing food eaten quickly by the cow, and occupies 80% of the total volume of the cow's stomach. The second stomach (eg, the honeycomb stomach) is responsible for mixing food and exhaling it back into the mouth. Food that is chewed back in the mouth passes through the 3rd stomach (on the folds) and the 4th (on the folds) and moves to the intestine.

For example, food that is swallowed without chewing becomes lumps and enters the first, largest stomach (the lump), where it becomes moist and soft and enters the second stomach (the honeycomb). The food is divided into reasonably good sizes here, but at leisure, the food is brought back to the mouth from the second stomach (the honeycomb) and begins to be chewed again. Therefore, the cow is chewing something all day long. When chewed and swallowed again, food moves to the 3rd stomach (on the folds), the 4th (on the folds), and finally moves to the intestine. Food can be digested through this process.

This complex process takes more than three days, and the cow can get all the nutrients in the food through this digestive process. In general, a healthy cow starts rumination from 30 to 40 minutes after feeding, and the rumination is 6-8 times for 40-50 minutes and 50-60 kg. That is, cows eat 8 hours a day, meditate 8 hours, and rest 8 hours.

When the biosensor capsule 200 is injected into a living body of a cow, the biosensor capsule 200 may be mostly seated in a certain place in the body of the cow, for example, the first stomach (humus). This is because the 1st stomach (hump) occupies 80% of the total volume of the cow's stomach, and it is composed of several spaces and has a suitable place for seating.

As described above, the cow is a ruminant that chews the cud, and every moment, it swallows the food in the first stomach (hummus) and the second stomach (beehive stomach) and recalls a considerable amount of weight. In this process, the biosensor capsule 200 may be mixed with food and come out again through the mouth.

In addition, the first stomach (hump) consists of several spaces, and the food in the cow's stomach is often mixed with water. At this time, if the biosensor capsule 200 does not have sufficient weight, it floats on the food due to moisture in the process of mixing it with the food, and is discharged into the mouth of the cow together with the food during the chewing process of the cow.

Accordingly, the biosensor capsule 200 including the weight 270 moves by mixing with food in various spaces in the cow's stomach and sinks to the bottom of the moving space by gravity, and the biosensor capsule 200 naturally moves. The plurality of sensors 210 to 250 included in the biosensor capsule 200 can accurately measure the internal information of a cow by being seated at an appropriate position and not being discharged out of the living body.

The communication module 280 and the antenna 290 are for performing communication between the biosensor capsule 200 and the disease management server 300 . That is, the biosensor capsule 200 and the disease management server 300 may exchange signals (or data) with each other through the communication module 280 and the antenna 290 .

When the amount of data to be transmitted is small, the communication distance may be much more important for data transmission communication over long distances than for high throughput data transmission. In order to satisfy both the amount of data to be transmitted and the transmission distance, the battery consumption must be large and the performance of the antenna must be good.

If the data rate is reduced by 1/4, the transmission distance can be doubled. In the case of the biosensor capsule 200 inserted into the body of livestock, it is difficult to use a high-power short-wave signal capable of transmitting a signal over a long distance. Data measured by the biosensor capsule 200 may be transmitted using a sub-1 GHz band, which is a relatively low frequency.

In order for a radio signal to penetrate the body of a livestock, it may be preferable to use a long wave efficiently or for medical reasons. For example, for long-distance communication, there is a problem in the amount of power consumption is large, and if the power consumption is large, the life of the battery is shortened. There may be problems with repeating.

The communication module 280 of the present invention may use a communication method such as Bluetooth and Wi-Fi, and in particular, BLE, WiFi HaLow, etc. designed with low power consumption may be used. The communication module 280 of the present invention may transmit a signal using a frequency band of Sub-1 GHz.

Even if such a low-frequency signal of 1 GHz or less is used, the radio wave is attenuated while passing through the body of the livestock, so that the signal capable of long-distance transmission of 6.4 km can be reduced to about 100 m to several hundred m. A signal may be transmitted to the disease management server 300 through a repeater or a separate device within a transmission distance.

When the wireless repeater is installed far outside the communication distance from the biosensor capsule 200 , it may be difficult to transmit data from the biosensor capsule 200 to the disease management server 300 directly through the wireless repeater. In such a case, a second device may be installed in another part of the animal's body (not shown).

The second device may be configured to receive data from the biosensor capsule 200 installed in the body of the livestock and transmit it to a wireless repeater (or a drone (not shown)) or a base station. Examples of the second device may include a necklace, an ear-tag (not shown), and the like.

The communication module 280 may be implemented as a LORA communication module. Accordingly, without the need for a gathering device between the biosensor capsule 200 and the disease management server 300 , the biosensor capsule 200 connects to a communication network within 20 km through the communication module 280 and transmits biometric information to the disease management server 300 . ) can be sent to

The plurality of sensors 210 to 250 may be integrated on a printed circuit board (PCB). Further, the communication module 280 and/or the antenna 290 may also be integrated on the silver PCB.

As described above, the biosensor capsule 200 can be directly orally administered to cattle without the need for an expensive gathering box, and thus can be easily and conveniently installed.

FIG. 3 is a schematic block diagram of the disease management server shown in FIG. 1 .

Referring to FIG. 3 , the disease management server 300 may include a communication module 310 and a controller 330 .

The communication module 310 may perform communication between the disease management server 300 and the biosensor capsule 200 . Also, the communication module 310 may communicate between the disease management server 300 and the user device 400 . That is, the disease management server 300 and the biosensor capsule 200 may exchange signals (or data) with each other through the communication module 310 , and the disease management server 300 and the user device 400 may communicate with each other through the communication module. Signals (or data) may be exchanged with each other through 310 .

The controller 330 may control the overall operation of the disease management server 300 . The controller 330 may receive biometric information from the biosensor capsule 200 through the communication module 310 .

The controller 330 may analyze the biometric information and determine the health status of the cow based on the analysis result. The health condition may include at least one of a breeding-related condition for estrus and calving of the cow, a disease condition, a methane gas generation condition, and an activity condition of the rumen.

For example, the controller 330 may determine the estrus state and/or the calving state of the cow based on angular velocity information, acceleration information, and temperature information included in the biometric information.

As another example, the controller 330 may determine the gastric activity state of the cow based on angular velocity information, acceleration information, and PH concentration information included in the biometric information.

As another example, the controller 330 may determine the amount of methane gas generated in the stomach of the cow based on the methane gas generation information included in the biometric information.

As another example, the controller 330 may determine the disease state of the cow based on angular velocity information, acceleration information, temperature information, pH concentration, and methane gas generation information included in the biometric information.

The controller 330 may transmit information on the health state to the user device 400 through the communication module 310 . In this case, the information on the breeding-related state may include information on estrus, fertilization, pregnancy, and delivery of cattle. In addition, the information on the breeding-related state may include information on the latest estrous date, the expected fertilization date, the last fertilization date, the estimated date of analysis, the expected delivery date, the expected dry milk date, the latest delivery date, and the expected estrous date of the cow.

In addition, the disease management server 300 provides an alarm (eg, a temperature rise warning alarm, a temperature drop warning alarm, an expected delivery date alarm, an appraisal date alarm, a dry pregnancy date alarm, or an estrus due date alarm, etc.) classified according to the health status. may be provided to the device 400 .

A more detailed operation of the controller 330 will be described later.

A module in the present specification may mean hardware capable of performing functions and operations according to each name described in this specification, or may mean computer program code capable of performing specific functions and operations, , or an electronic recording medium on which a computer program code capable of performing specific functions and operations is loaded, for example, may refer to a processor or a microprocessor.

In other words, a module may mean a functional and/or structural combination of hardware for carrying out the technical idea of the present invention and/or software for driving the hardware.

FIG. 5 is a flowchart for explaining an operation method of the disease management server shown in FIG. 1 .

Referring to FIG. 5 , the controller 330 may receive bovine biometric information from the biosensor capsule 200 seated on the cow through the communication module 310 ( S510 ).

The controller 330 may analyze the biometric information and determine the health status of the cow based on the analysis result ( S530 ).

The controller 330 may transmit information on the health state of the cattle to the user who manages the cattle through the communication module 310 ( S550 ). In this case, the health state may include at least one of a breeding-related state for estrus and calving of the cow, a disease state, a methane gas generation state, and an activity state of the rumen.

Although it has been described that the biosensor capsule 200 is seated on the stomach of a cow in the flowchart of FIG. 5 , the biosensor capsule 200 may be applied to any type of biosensor capsule 200 that can be inserted into the vagina or inserted into the body of a livestock in a separate manner.

Among the health conditions of livestock that can be detected using the biosensor capsule 200 , a reproduction disorder of livestock can be identified as a representative example. A representative example of the cause of reproductive disorders is an ovarian cyst, and if the reproductive disorder is detected at an early stage, the damage of the reproductive disorder can be minimized, which can contribute to the productivity of cattle and the economic feasibility of the farm.

6 is a view showing the shape of the follicle (follicle) and the time of estrus according to hormonal changes in livestock.

Livestock such as pigs and cattle repeat estrus at regular intervals unless the female is pregnant, and the estrus cycle is about 21 days (around 3 weeks), including 4-5 days in the follicular phase and 16 days in the luteal phase. The follicle 31 in the female ovary is influenced by follicle stimulating hormone (FSH, 11) secreted from the pituitary gland to promote growth and development, and estrogen 12 is secreted from the fully developed follicle 31 .

Estrogen (12) affects the hypothalamus or pituitary of the brain, and as the follicle grows, the amount of estrogen (12) secreted increases. When the estrogen (12) exceeds a certain concentration, the livestock is introduced into the estrous period (20), and the estrogen (12) affects the hypothalamus and pituitary gland of the brain to induce the pituitary gland to secrete luteinizing hormone (LH, 13). . When the luteinizing hormone 13 is secreted, the mature follicle 31 ovulates (the egg is discharged into the uterus) and the follicle 31 is converted into the corpus luteum 32 . Immediately after ovulation, the concentration of estrogen (12) is lowered, and the concentration of progesterone (14) secreted from the corpus luteum (32) increases rapidly.

The corpus luteum 32 disappears after about fortnight, and the concentration of progesterone 14 also changes according to the development cycle of the corpus luteum. Progesterone 14 serves to maintain pregnancy by suppressing uterine contractions and suppressing ovulation, and if pregnancy does not occur, the corpus luteum 32 degenerates and the concentration of progesterone 14 decreases.

Ovarian cyst (OC: ovarian cyst) is a functional disease caused by abnormal secretion of these hormones, and occurs due to decreased luteinizing hormone (LH) secretion or excessive follicle stimulating hormone (FSH) secretion.

It refers to the presence of one or more structures in the ovary with a diameter of 2.5 cm or more that are larger than that of a mature follicle, and are present in the ovary for more than 10 days. According to the Korean cattle disease management, about 70% of reproductive disorders in Korean cattle are caused by ovarian cysts, which mainly occur 1 to 4 months after delivery.

There are three types of ovarian cysts: follicular cyst (FC), luteal cyst (LC), and cystic corpora lutea (CL). Among them, follicular cyst is the most common disease causing failure of ovulation and corpus luteum formation.

This is an abnormality in hormone secretion due to pregnancy and childbirth, and typical symptoms include nymphomania, which is frequent estrus, and estrus at a ratio of about 80:20. If the estrus period is different from the normal cycle due to ovarian ovarian cyst, it is easy to miss the breeding time, and the increase in breeding interval and fertilization frequency is a cause of lowering the productivity of cattle.

In order to accurately detect an ovarian cyst, it is necessary to check whether the size of the ovary is 2.5 cm or more through rectal ultrasonography.

By using the biosensor capsule 200, which is a bio-insertable sensor of the present invention, biomarkers can be collected and an ovarian cyst can be identified through artificial intelligence (AI) analysis.

Artificial intelligence refers to a field that studies artificial intelligence or methodologies that can make it, and machine learning refers to a field that defines various problems dealt with in the field of artificial intelligence and studies methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through constant experience.

7 is related to the information of the test subject, the test subject was selected from five Korean cattle breeding cattle farms in which data was accumulated for a predetermined period or more after the administration of the biocapsule. Among the individuals with sufficient information to analyze the characteristics of each individual by accumulating data before the experiment, 50 animals that completed delivery in December 2018 were selected as the experimental subjects.

The estrus cycle after recurrent estrus for each individual in the experimental period (2019/01/01 ~ 2019/04/30) was compared/observed. Recurrent estrus refers to the first estrus that occurs after the lactation phase is completed after delivery.

Experimental subjects were selected at a rate of 10-20% of the total individuals for each farm, and as of the start date of the experiment (2019/01/01), all subjects were 24 months of age or older and had completed childbirth. In particular, since ovarian cysts appear in livestock from Gyeongsan (who have had a baby), individuals from Gyeongsan were selected and tested.

Biomarkers of cattle were measured using the biosensor capsule 200 . The temperature sensor and the 3-axis acceleration sensor mounted on the biosensor capsule measured the temperature change and activity of the cattle at 10-minute intervals, respectively. When the temperature of cattle is measured with a temperature sensor included in the biosensor capsule 200, bioinformation without external influence can be collected. According to embodiments, the activity may be measured not only with the 3-axis accelerometer but also with the 6-axis accelerometer or the like.

The measurement period may be set longer than this. The shorter the period, the easier it is to detect estrus, but the measurement period may be adjusted in consideration of the battery consumption of the biosensor capsule 200 .

The core temperature of cattle can be precisely measured in units of 0.1°C. Changes in temperature information can be monitored by using the measured value as it is, but the average and standard deviation can be used to represent the average value and the difference as a normalized graph.

Behavioral characteristics such as mounting, increased gait, or lethargy of cattle can be analyzed based on a vector value synthesizing three-axis acceleration values. For example, the activity can be measured by Equation 1 below.

[Equation 1]

Behavioral aspects of livestock, such as excessive gait or lame cows, often result in large measure of an individual's activity. Acceleration data of multiple axes may be used to distinguish the activity information due to the movement of the livestock and the activity information due to the riding behavior of the livestock. The activity information can detect the presence or absence of estrus by patterning the movement direction according to a specific action (eg, Sangga) that appears during estrus.

Temperature information and activity information acquired from the biosensor capsule 200 may be analyzed using an AI algorithm. An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model having problem-solving ability, which is composed of artificial neurons (nodes) that form a network by combining synapses. An artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates an output value.

The artificial neural network may include an input layer, an output layer, and optionally a plurality of hidden layers. Each layer may include a plurality of neurons, and the artificial neural network may include neurons and synapses connecting the neurons. In the artificial neural network, each neuron may output a function value of an activation function with respect to input signals input through a synapse, a weight, and a bias.

Model parameters refer to parameters determined through learning, and include the weight of synaptic connections and the bias of neurons. In addition, the hyperparameter refers to a parameter to be set before learning in a machine learning algorithm, and includes a learning rate, the number of iterations, a mini-batch size, an initialization function, and the like.

The purpose of learning the artificial neural network can be seen as determining the model parameters that minimize the loss function. The loss function may be used as an index for determining optimal model parameters in the learning process of the artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

Supervised learning refers to a method of training an artificial neural network in a state where a label for the training data is given, and the label is the correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state where no labels are given for training data. Reinforcement learning can refer to a learning method in which an agent defined in an environment learns to select an action or sequence of actions that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is also called deep learning (deep learning), and deep learning is a part of machine learning. Hereinafter, machine learning is used in a sense including deep learning.

Among AI application techniques, using a machine learning system that combines FCN (Fully Convolutional Networks), a Semantic Segmentation Algorithm Model, and LSTM (Long Short-Term Memory), which is used for time-series data linkage analysis, multi-class classification can be performed.

The LSTM technique is a learning method for processing time series data like the RNN (Recurrent Neural Network) technique. It can record input information longer than the RNN technique, so it is possible to learn long data.

In the RNN technique, the prediction data is calculated by using input data from the previous information to the next information. However, if the distance between the previous information and the current information increases because the data becomes too long, the backpropagation gradient gradually decreases, which significantly reduces the learning ability. LSTM is a technique devised to solve the above problem. The LSTM is a structure in which the cell-state is added to the hidden state of the RNN, and may consist of information to be discarded from the input cell state, information to be input to the long term layer, and a process of updating and outputting existing information.

The FCN technique is a method to obtain improved data by synthetically multiplying raw data with a filter. The FCN technique can reduce the noise of raw data, so that more reliable predictions can be obtained.

8 is a schematic diagram of an algorithm for predicting ovarian cyst by putting temperature information and activity information into the LSTM-FCN model.

The LSTM-FCN model classifies an individual's estrus and ovarian cyst signs by normalizing the types of biomarker changes in a certain range from 0 to 1. The probability of an ovarian cyst can be determined by performing machine learning training and testing of the Microsoft Azure Machine Learning Tool based on 500 million cattle data to determine whether an actual ovarian cyst has occurred.

In particular, it is possible to compensate for the noise appearing in the collected data due to the difference of each individual by reflecting the difference by identifying the cycle of each estrous individual through the temperature information and activity information before pregnancy and childbirth of the experimental subject.

More specifically, as shown in FIG. 8 , the input time series information of the system consists of temperature information and activity information. Temperature information can be normalized by using the mean and standard deviation to express the difference from the mean.

Then, the normalized temperature information and activity information are input into long-term short-term memory (LSTM) and one-dimensional convolution layers according to each time frame. The probability value [probability of ovarian cyst state, probability of normal state] is connected and processed through the softmax function to produce an output value. If the cow is definitely determined to be in the ovarian cyst state, as in [Equation 2] below, the output value becomes [100, 0] as a percentage, and in the normal state, the output value becomes [0, 100].

[Equation 2] Probability of ovarian ovarian tumor state = 100 - Probability of normal state.

9 is temperature information and activity information of an individual in a normal state, and FIG. 10 is temperature information and activity information of an individual in a state of ovarian cyst.

It is data for about 2 months from the date of recurrent estrus, and the shaded part of the activity information is the average value of the activity information for 1 hour, and when there is a sudden movement, a vertical line is drawn and the activity rapidly increases.

In the normal-state individual of FIG. 9 , including recurrent estrus, estrus was detected three times in two months, and in the ovarian cyst-state object of FIG. 10 , estrus was detected four times in two months including recurrent estrus. It can be determined that the behavior is caused by an ovarian cyst by detecting more frequent estrus compared to the normal pattern.

Referring to FIGS. 9 and 10 , when the temperature information and the activity information of the livestock exceed the respective thresholds, it can be determined that the singularity is expressed.

Although an embodiment using only temperature information and activity information is illustrated, when other indicators are used (eg, electrical conductivity), when each information exceeds a threshold, it can be determined that a singularity has been expressed.

When at least one of data collected from a plurality of sensors exceeds a threshold, it may be determined that a singularity has occurred.

Whether or not estrus may be determined based on the appearance time and duration of the first singular point in which the temperature information exceeds the temperature threshold and the second singular point in which the activity information exceeds the activity threshold.

When the first singularity and the second singularity appear at the same time and the first duration (for example, 1 to 2 hours) continues, it may be determined as estrus. In general, estrus lasts about 18 hours, so if the first singularity and the second singularity are expressed in too short a time (20-30 minutes), it may not be judged as estrus.

Alternatively, when only one of the first singularity and the second singularity appears and the expression of the singularity continues for a second duration or longer, this case may also be determined as estrus. The second duration may be different from the second duration for the first singularity and the second duration for the second singularity. The second duration may be set longer than the first duration, which is a duration when the first singularity and the second singularity are expressed simultaneously (eg, 6 hours or more).

A normal estrus cycle is derived based on the data of the previously analyzed population, and if estrus occurs in a period different from that, it can be judged as abnormal estrus. For example, since cows generally exhibit estrus for about 21 days or so, if they have an estrus cycle of less than 19 days or more than 23 days, it can be determined as abnormal estrus.

The controller 330 of the disease management server 300 may determine estrus by using temperature information and activity information, and if there is a large difference from previous information by using temperature information and activity information collected before childbirth for each individual, it is abnormal It can be judged as estrus (which does not fit into the normal estrus cycle).

11 and 12 show, for example, the controller 330 of the disease management server 300 of FIG. 3 based on the temperature graph and activity graph and the temperature information and activity information of the individual showing the ovarian cyst pattern AI algorithm It is a graph showing the probability of estrus and the probability of ovarian cyst.

It is a graph showing the temperature information and activity information measured from about 7 weeks (42 days) after childbirth, and the point determined by the time of estrus is indicated by arrows and original characters.

The circled parts of the temperature graph of (a) and the activity graph of (b) show the first and second singularities that exceed the temperature threshold and the activity threshold. (c) The graph is a graph showing the probability of estrus (dotted line) and the probability of ovarian cyst (solid line), and the arrow part indicates the part determined to be in estrus.

When the first singularity and the second singularity occur at the same time, it can be determined as estrus, and the point determined to be in estrus is indicated by an arrow in (c). When the first singularity and the second singularity appear at the same time, the probability of estrus increases.

When the temperature and activity are lowered, estrus is also terminated and the probability of estrus is also lowered. The presence of estrus may increase an individual's probability of an ovarian cyst. In particular, the shorter the frequency of estrus, the greater the probability of an ovarian cyst.

As shown in FIG. 11, when the first estrus (recurrent estrus) appears quickly after childbirth (①), the probability of an ovarian cyst increases, but when recurrent estrus is late as shown in FIG. 12 (①), the probability of an ovarian cyst during recurrent estrus have.

As time passes after estrus, the probability of an ovarian cyst decreases slowly, but if estrus is detected again, the probability of an ovarian cyst may increase again. When only one of the first singularity and the second singularity appears, it may not be judged as estrus, but the probability of an ovarian cyst as an abnormal symptom may increase at a small level compared to that judged by estrus.

As shown in FIG. 11, when 4 or more estrus including recurrent estrus is detected around 21 days, which is one cycle, the probability of an ovarian cyst becomes 100 in the 4th estrus, and an alarm suspected of being an ovarian cyst is provided to the user device 400. can

Alternatively, as shown in FIG. 12 , when three or more estrus are detected including recurrent estrus for a short period, it is determined as an ovarian cyst, and an alarm may be provided to the user device 400 .

The ovarian cyst probability is not determined simply by the number of occurrences of estrus, but the increase in the ovarian cyst probability may vary depending on the length of each estrus cycle, irregularity of estrus cycle, intensity of estrus, and duration of estrus.

13 is a table showing the results of determining whether or not ovarian cyst is present in each individual by changing the probability of ovarian cyst according to temperature information, activity information, and presence or absence of estrus by determining estrus based on temperature information and activity information in this way.

Seven out of 50 experimental animals were judged to be ovarian cysts, and no ovarian cysts were found in farm A, 2 mice, farm B 3, and farm C. One each was found on farm D and farm E. The ovarian cyst is judged to be an ovarian cyst from the 19th to 48th days from the date of recurrent estrus, with the exception of the smallest and largest values, usually between the date of recurrent estrus and the first (21 days) and second estrus cycles (42 days). appear.

Although there is a difference for each individual, estrus was expressed 4 times for an average of 30 days, and it can be seen that estrus entered about every 10 days, which is about a week and a half earlier than the normal cycle.

By predicting the presence or absence of an ovarian cyst through the biometric information detected through the biosensor capsule 200 and extracting an object to be precisely examined by the veterinarian, it is possible to reduce the wastage of manpower for unnecessary examination. In this experiment, only 7 out of 50 animals were tested.

In order not to miss an ovarian cyst individual, even a small change can increase the rate of increase of the ovarian cyst probability, thereby preventing the omission of an ovarian cyst individual.

However, in this case, as the number of ovarian cyst tests increases, the data increases, so the probability of ovarian cyst can be adjusted according to temperature change, activity change, and estrus by using existing information according to AI (artificial intelligence) analysis. Using the information collected from the biosensor capsule 200 of the present invention, the disease management server 300 quickly detects an object suspected of a reproductive disorder (ovarian cyst), and intensive care of the farm (veterinary examination and hormone prescription) This can increase the productivity of the farm.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA). , a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible from the above description by those skilled in the art. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

The present invention can be applied to a disease control method for livestock, a disease management server, and a disease management system.

Claims (20)

1. receiving biometric information of the livestock from a biosensor capsule arranged in the body of the livestock;

   a singularity detection step of detecting a singularity that deviates from a reference value based on the biometric information;

   a health status monitoring step of increasing the disease probability of the livestock when the singularity is detected; and

   When the disease probability of the livestock is greater than or equal to a first threshold value, including a disease detection step of judging that the health condition of the livestock is abnormal;

   Methods of disease control in livestock.
2. According to claim 1,

   The biometric information is characterized in that it includes temperature information and activity information of the livestock,

   Methods of disease control in livestock.
3. 3. The method of claim 2,

   The livestock activity information comprises at least one of angular velocity information and acceleration information detected by the biosensor capsule,

   Methods of disease control in livestock.
4. 3. The method of claim 2,

   The singular point detection step is

   and an estrus detection step of determining estrus when at least one of a first singularity point in which the temperature information exceeds a temperature threshold value and a second singularity point in which the activity information exceeds an activity threshold value is expressed;

   The health status monitoring step is

   When the estrus is detected, characterized in that the increase in the disease probability of the livestock increases,

   Methods of disease control in livestock.
5. 5. The method of claim 4,

   The estrus detection step is

   When the duration of the first singularity or the duration of the second singularity lasts longer than the first duration, characterized in that it is determined as estrus,

   Methods of disease control in livestock.
6. 5. The method of claim 4,

   The step of detecting estrus,

   When both the first singularity and the second singularity are expressed, characterized in that it is determined as estrus,

   Methods of disease control in livestock.
7. 5. The method of claim 4,

   The step of detecting estrus,

   When either one of the first singularity and the second singularity is expressed, when the duration of the expressed singularity continues for a second duration or longer, it is determined as estrus,

   Methods of disease control in livestock.
8. 5. The method of claim 4,

   The health status monitoring step is

   The change in the disease probability of the livestock is different according to at least one selected from the group consisting of the frequency of the estrus expression, the irregularity of the estrus expression, the duration of the estrus expression, and the intensity of the estrus expression,

   Methods of disease control in livestock.
9. 9. The method of claim 8,

   The health status monitoring step is

   The higher the frequency of expression of estrus, the more irregular the expression of estrus, or the longer the duration of estrus, the greater the increase in the disease probability of the livestock,

   Methods of disease control in livestock.
10. 5. The method of claim 4,

    The health status monitoring step is

    When the estrus detection step is expressed three or more times within the second estrus cycle, the disease probability of the livestock is controlled so that the first threshold value or more,

    Methods of disease control in livestock.
11. According to claim 1,

    The health status monitoring step is

    After the singularity detection step, the disease probability of the livestock is reduced before the next singularity detection step,

    Methods of disease control in livestock.
12. According to claim 1,

    When the disease probability is greater than or equal to a set value, further comprising an alarm step of notifying the user of the disease of the livestock,

    Methods of disease control in livestock.
13. The method of claim 1,

    The health condition of the livestock is characterized in that the ovarian cyst is,

    Methods of disease control in livestock.
14. The method of claim 1,

    Further comprising the step of inserting the biosensor capsule into the stomach or vagina of livestock,

    Methods of disease control in livestock.
15. a communication module for receiving biometric information of the livestock from a biosensor capsule arranged in the body of the livestock; and

    and a controller that analyzes the biometric information, determines the health status of the livestock based on the analysis result, and transmits information on the health status of the livestock to a user who manages the livestock,

    The controller is

    Detecting a singularity that deviates from the reference value based on the biometric information,

    When the singularity is detected, the disease probability of the livestock is increased,

    When the disease probability of the livestock is greater than or equal to a first threshold, transmitting an abnormal health condition of the livestock to the user,

    Livestock disease management server.
16. 16. The method of claim 15,

    The biometric information includes temperature information and activity information of the livestock,

    The controller is

    When at least one of a first singularity in which the temperature information exceeds a temperature threshold value and a second singularity in which the activity information exceeds an activity threshold value are expressed, it is determined as estrus, and the disease probability of the livestock is increased doing,

    Livestock disease management server.
17. 17. The method of claim 16,

    The controller is

    The change in the disease probability of the livestock is different according to at least one selected from the group consisting of the frequency of the estrus expression, the irregularity of the estrus expression, the duration of the estrus expression, and the intensity of the estrus expression,

    Livestock disease management server.
18. 18. The method of claim 17,

    The controller is

    When the estrus detection step is expressed three or more times within the second estrus cycle, the disease probability of the livestock is controlled so that the first threshold value or more,

    Livestock disease management server.
19. 16. The method of claim 15,

    The health condition of the livestock is characterized in that the ovarian cyst is,

    Livestock disease management server.
20. a biosensor capsule arranged in the body of the livestock to detect the biometric information of the livestock;

    a disease management server that receives biometric information of livestock from the biosensor capsule through a communication network, analyzes biometric information to generate health state information of livestock, and transmits health state information of livestock to a user who manages livestock; and

    and a user device for receiving health status information of livestock from the disease management server through a communication network,

    The disease management server detects a singularity that deviates from a reference value based on the biometric information, and increases the disease probability of the livestock when the singularity is detected,

    The user device, when the disease probability of the livestock is greater than or equal to a first threshold, receiving a signal of abnormal health status of the livestock from the disease management server,

    Livestock disease management system.

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