KR20230025553A - Livestock disease control system and method - Google Patents

Abstract

According to the present invention, a livestock disease control system comprises: a monitoring device that monitors a livestock farm; a terminal device that performs at least one of information input and query input/output; an artificial intelligence learning model that determines a state by using external information received from at least one of the monitoring device and the terminal device; and a database in which the external information and a determination result of the artificial intelligence learning model are stored.

Description

Livestock disease control system and method for controlling livestock disease {Livestock disease control system and method}

The present invention relates to a livestock disease control system and method capable of controlling or preventing the spread of livestock diseases.

Many prior arts have been introduced for the prevention and prediction of outbreaks of livestock diseases.

Related patent documents include Korean Registered Patent No. 10-2108829 (hereinafter referred to as Prior Patent 1), No. 10-1995755 (hereinafter referred to as Prior Patent 2), No. 10-1546156 (hereinafter referred to as Prior Patent 3), and No. 10-1995755 (hereinafter referred to as Prior Patent 3). 1941527 (hereinafter referred to as Prior Patent 4), and No. 10-2197494 (hereinafter referred to as Prior Patent 5).

Prior Patent 1 is a livestock disease prediction and remote medical treatment system, which monitors livestock whose abnormality is predicted based on a critical value using biometric information of livestock and barn environment information through sensors before the onset of disease, and detects abnormalities through ontology and remote medical treatment. Disclosed is a system that provides accurate disease diagnosis and treatment information of predicted livestock.

The prior patent 2 attaches a radio tag with an identification number recorded to the livestock entity, analyzes the feed and water intake information pattern of the livestock, detects health abnormalities through this, and provides the detected abnormal symptom information in real time A technique for predicting livestock disease is disclosed.

Prior Patent 3 discloses a system for notifying the farmer of the risk of foot-and-mouth disease in advance.

The prior patent 4 discloses a technology of performing quarantine for visiting vehicles of livestock farms and recording and storing vehicle numbers and driver information.

Prior Patent 5 manages whether or not vehicles entering and exiting bases where vehicle sterilizers are installed are sterilized, and based on this data, livestock-related vehicles are regularly sterilized, thereby preventing infectious diseases from being transmitted through vehicles in advance. Introduce prevention techniques.

Prior Patent 1, Prior Patent 2, and Prior Patent 3 can monitor the presence or absence of diseases in livestock or internal problems, but cannot monitor external factors. Prior patent 4 and prior patent 5 cannot be internally monitored because disinfection, recording, and management are limited to visits.

Due to the above problem, the conventional system does not integrate each data management, so it is difficult to organically manage data and collect and analyze information. Because of this, the utilization of information is low, so there are limitations in preemptively responding to the spread of infectious diseases by detecting in real time and calculating the possibility of spreading infectious diseases. Unfortunately, after the spread of livestock diseases due to this problem, the government's countermeasures are lagging behind. In particular, since African swine fever (ASF) spreads rapidly, rapid detection of the outbreak and strong initial response are essential.

Registered Patent No. 10-2108829 Registered Patent No. 10-1995755 Registered Patent No. 10-1546156 Registered Patent No. 10-1941527 Registered Patent No. 10-2197494

The present invention is proposed in the background described above, and aims to establish a robust database so that the livestock disease control center can accurately grasp the initial occurrence of livestock disease.

In the present invention, the purpose of the database is to provide accurate livestock disease-related information based on object information that is difficult to malicious arbitrary manipulation.

An object of the present invention is to provide a system and method capable of preventing the outbreak environment of African swine fever and blocking the spread in case of outbreak.

The livestock disease control system of the present invention includes a monitoring device for monitoring a livestock farm; a terminal device that performs at least one of information input and query input/output; an artificial intelligence learning model that determines a state using external information input from at least one of the monitoring device and the terminal device; A database in which the external information and the judgment result of the artificial intelligence learning model are stored is included.

A control unit that performs control activities according to the judgment results of the artificial intelligence learning model, and includes a function of notifying an external institution associated with at least the occurrence of diseases in livestock may be further included in the control activities.

Each of the above elements may be connected through a network and/or a bus.

The monitoring device may include an environment sensor for detecting an environment; cameras monitoring the area; feed monitoring device for monitoring the feed condition; Vehicle recognition device for recognizing the vehicle; And at least one of a person recognition device for recognizing a person may be included.

The environmental sensor may include at least one of an air quality sensor, a temperature sensor, and a humidity sensor.

The camera may detect at least one of vehicles, livestock, foreign animals, and people.

The feed monitoring device may measure the height of the feed bin.

The vehicle recognition device may detect a license plate of the vehicle.

The person recognition device may detect a face of a person.

The database includes a plurality of tables; and a plurality of columns included in each of the plurality of tables.

Information recorded in at least one column of one of the plurality of tables is reference information, and may be registered in at least one column of another table among the plurality of tables.

Information that is not recorded in at least one column of another table among the plurality of tables may not be registered in at least one column of another table among the plurality of tables.

As a method of controlling livestock diseases, a vehicle information processing process for processing information related to incoming and outgoing vehicles, a disinfection information processing process for processing disinfection information of the farm environment, and a process for confirming the appearance of outside animals and processing the information External animal information processing process, livestock activity information processing process that processes information related to livestock movement activity, feed amount information processing process that processes information related to the amount of feed eaten by livestock, and processing and At least one of the disease information processing processes controlling the disease may be performed.

The foreign animal may be a wild boar, the livestock may be a pig, the disease may be African swine fever, and the vehicle information processing process may include driver information.

The vehicle information processing process may include recognizing and collecting vehicle license plates; Recognizing and collecting driver's fingerprint and facial information; The process of confirming that it has been disinfected; And it may include a process of issuing a disinfection certificate.

In order to perform the vehicle information processing process, an access list table, a vehicle access detail table, a vehicle table, a person access detail table, a farm table, and a user table may be linked to each other.

The disinfection information processing process includes a regular disinfection process for regularly performing disinfection; information collection process of collecting external information; Disinfection status information collection process to collect disinfection status and disinfection effect; And information transmission and recording process may be included.

The livestock activity information processing process includes a monitoring process of photographing livestock; Information collection process of collecting information; Livestock activity analysis process to analyze and measure the activity of livestock; And information transmission and recording process may be included.

The feed feeding amount information processing process may include a process of detecting a change amount of feed stored in a feed bin; Information collection process of collecting information; a change amount detection process for detecting a feed change amount in the feed bin; And information transmission and recording process may be included.

According to the present invention, it is possible to prevent the occurrence of African swine fever caused by the movement of wild boars. In addition, it is possible to strengthen prevention activities by finding out the risk of African swine fever.

According to the present invention, it is possible to maintain a database safely and robustly against the external environment, accurately manage livestock-related information, and effectively prevent the occurrence and spread of livestock diseases.

According to the present invention, by sharing all information with a wide-area management organization rather than a local area, it is possible to strictly manage and block the spread of livestock diseases.

1 is a block diagram of a livestock disease control system according to an embodiment.
2 is a view explaining a livestock disease control method according to an embodiment.
3 is a flowchart illustrating a vehicle information processing method in more detail;
4 is a flowchart of a database used in a vehicle information processing method;
5 is a flowchart illustrating a disinfection information processing method in more detail;
6 is a flowchart illustrating a method for processing external animal information in more detail;
7 is a flowchart showing a livestock activity information processing method in more detail;
8 is a flowchart of a database used in a disinfection information processing method, an external animal information processing method, and a livestock activity information processing method.
9 is a flow chart showing a feed feeding amount information processing method in more detail;
10 is a flow chart of a database used in a feed feeding amount information processing method;
11 is a flowchart of a database used in a disease information processing method;
Figure 12 is a view for explaining a database for analyzing the current state of the farm.
13 is a diagram for explaining a database for analyzing livestock activity.
14 is a diagram for explaining a database for analyzing the appearance of foreign animals.
15 is a diagram for explaining a database for analyzing vehicle/person access;
16 is a diagram illustrating a disease management database for managing diseases.

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. However, it is not limited to the following embodiments of the spirit of the present invention, and those skilled in the art who understand the spirit of the present invention can easily find other examples included within the scope of the same spirit by adding, changing, deleting, and adding elements. However, this may also be included within the scope of the present invention.

In the description of the drawings, the same or similar elements are assigned the same reference numerals regardless of reference numerals, and overlapping descriptions thereof may be omitted.

The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves.

In describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions thereof will be omitted.

The accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes and equivalents included in the spirit and technical scope of the present invention are included. It should be understood to include water or substitutes.

Terms including ordinal numbers, such as first and second, may be used to describe various components, but the components are not limited by the terms. These terms are only used for the purpose of distinguishing one component from another.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Singular expressions may include plural expressions unless the context clearly dictates otherwise.

In this application, terms such as "comprise" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

In the present invention, the livestock disease is most preferably applicable to African swine fever. However, it is not limited to African swine fever.

1 is a block diagram of a livestock disease control system according to an embodiment.

Referring to FIG. 1, the livestock disease control system according to an embodiment may be provided in a form in which each device is connected to a network/bus, which is exemplified as a communication system in which information moves. In the livestock disease control system, a monitoring device (3) provided to the farm and monitoring the farm, a terminal device (5) and an input/output (I/O) device for registering information on the livestock disease control system and/or performing query input/output (4), using the control unit 1 that controls various activities performed in the system, the database 100 in which various information is stored, external information input from the monitoring device and/or external information input from the terminal device An artificial intelligence learning model (2) that judges the situation, and/or an external agency (6) associated with disease occurrence may be included. The external institution may refer to a communication means of an external institution.

At least one of the farms may be included. The monitoring device 3 may be provided for each farm. The farm may be divided into one or more zones. A separate monitoring device may be provided for the zone.

The sensing device 3 includes an environment sensor 31 for detecting the environment for the area, a camera 32 for monitoring the area, a feed monitoring device 33 for monitoring the feed condition, and a vehicle recognition device for recognizing the vehicle ( 34), and/or a person recognition device 35 for recognizing a person. The environment sensor may include, for example, at least one of an air quality sensor, a temperature sensor, and a humidity sensor.

The monitoring device 3 may include a device for monitoring a regional range that may affect the farm. For example, a vehicle entering a farm may include a vehicle recognizing device 33 that recognizes a passing vehicle.

The terminal device 5 and the input/output (I/O) device 4 are devices for internal/external users to input various information that is not automatically input, and devices for inputting and outputting queries when necessary information is available. can include

The database 100 may store various types of information, such as monitoring device information, disease occurrence status information, livestock status, and/or information of an external agency notified upon disease outbreak. The stored information of the database 100 will be described in more detail later. The external institution 6 may be a public institution that manages the corresponding farm.

Various learning models can be accommodated in the artificial intelligence learning model 2, and when current information is input, judgment can be performed using the learning model. The artificial intelligence learning model 2 can determine the current situation using given information.

The control unit 1 can perform various actions necessary for controlling livestock diseases according to pre-recorded sequences. The controller 1 may perform an external action corresponding to the determined current state. For example, if the artificial intelligence learning model (2) determines that African swine fever is suspected using current information, it can immediately notify an external institution of the suspected fact.

The livestock disease control system and each component included in the system can be understood in more detail and accurately by the following livestock disease control method.

2 is a diagram illustrating a livestock disease control method according to an embodiment.

Referring to FIG. 2, the livestock disease control method according to the embodiment includes a vehicle information processing method (S1) for processing information related to a vehicle being pulled in and out, and a disinfection information processing method (S2) for processing disinfection information of a farm environment. ), an external animal information processing method (S3) for confirming the appearance of external animals such as wild boars and processing related information, a livestock activity information processing method (S4) for processing information related to livestock movement activity, and/ Alternatively, a feed amount information processing method (S5) for processing information related to the amount of feed eaten by livestock may be included. In addition to this, a disease information processing method for processing and controlling disease outbreaks may be further included. When the livestock is a pig and the disease is African swine fever, the external animal information processing method (S3) may be a wild boar information processing method.

The execution of the livestock disease control method is information collection using each device of the monitoring device 3 of the livestock disease control system, judgment using the artificial intelligence learning model 2, control of the control unit 1, and D/B It can be performed using the stored information of (100). Of course, the input/output of information using the terminal device 5 and information linkage of the external organization 6 can also be performed in the same way.

3 is a flowchart illustrating the vehicle information processing method in more detail.

Referring to FIG. 3, the vehicle information processing method (S1) includes a process of recognizing and collecting vehicle license plates (S11), a process of recognizing and collecting driver's fingerprints and facial information (S12), measuring whether the vehicle is overloaded A collecting process (S14) may be performed.

Images of the vehicle information and driver information may be recognized as object information by an object detection model (S13). The object detection model may be performed by a process of determining using the artificial intelligence learning model (2). Vehicle, driver, and overload information may be input and stored into the database 100 (S17). The vehicle and driver information may be notified to an external agency (6). It can be confirmed that the vehicle and driver information has been disinfected (S15). In the case of disinfection, it may be issued as a digital disinfection certificate (S16).

According to this method, the issuance of a disinfection certificate, which was managed manually, can be automatically recognized and computerized for vehicle and driver information. Therefore, user convenience can be enhanced by issuing and confirming a digital disinfection certificate. Vehicle license plate recognition is convenient because the license plate is photographed through a camera and the license plate is recognized using a deep learning-based image object detection model. The driver's fingerprint and facial information are collected, matched with vehicle information, and automatically computerized to maintain the information stored in the database more robustly. A vehicle weight detection sensor may be installed to measure whether the vehicle is overloaded. It is easy to possess and check by issuing and managing digital disinfection certificates based on computerized information.

4 shows the flow of the database used in the vehicle information processing method.

Referring to FIG. 4 , each table may include a plurality of columns. For example, the 'farm' table may include columns such as farm number, farm name, farm address, region, management institution, and/or farm owner as columns. FK (Foreign Key) is an external key, meaning that the FK-checked column uses external reference information. Each head of the arrow is a location where reference information is stored, and the tail of the arrow is where the reference information is used. For example, the 'Region' column of the 'Farms' table is linked to the 'Region' column of the 'Related Institutions/Facilities and Relevant Offices' table. In this case, the 'region' column of the 'farm' table may be registered by referring to the information of the 'region' column of the 'relevant institution/facility and related government office' table corresponding to the head of the arrow. Therefore, if there is no information on the farm registered in the 'Region' column of the 'Related Institution/Facilities and Relevant Offices' table corresponding to the head of the arrow, regional information cannot be registered in the 'Region' column of the 'Farms' table. . Therefore, farms must have their own farm information registered in advance with related institutions/facility and regional authorities. In addition, in order for the farm to be registered in the 'Region' column of the 'Related Institution/Facilities and Relevant Offices' table, refer to the regional information in the 'Region Rating Information' table. Therefore, a certain farm can be registered together with the 'region' column of the 'region level information' table.

As mentioned above, all information is used in time series for information reference using the foreign key. Therefore, all necessary information can be provided in the preceding process. It is a structure in which any arbitrary user cannot change only his/her own information. Therefore, it is possible to more safely maintain and manage information related to the disease. The information processing method described above can be equally applied to all information processing methods.

Referring to FIG. 4 , 'entry list' 121, 'vehicle access details' 122, 'vehicle' 123, 'person access details' 124, 'farm' 111, and 'user' There is a table of (112).

In this case, in order to be recorded in the vehicle and 'vehicle access details', it may be preceded that the borrower is registered as a user in the 'user name' column of the 'user' table, and the vehicle number is registered in the 'vehicle' table. . After that, when the vehicle is recognized by the monitoring device 3, vehicle access details may be stored with reference to the owner and the vehicle. In the case where there is no reference information, vehicle entry/exit details cannot be stored and entry/exit may not be permitted.

In this case, in order for a person to be recorded in the 'vehicle access history', the farm subject to access must be registered in the 'Farm Number' column of the 'Farm' table, and the driver must be registered in the 'User Name' column of the 'User' table. It may be preceded by being registered as . After that, when the driver of the vehicle is recognized by the monitoring device 3, the driver and the farm are called, and the driver corresponding to the farm is referred to, and the driver can be stored together in the vehicle access history. In the case where there is no reference information, vehicle entry/exit details cannot be stored and entry/exit may not be permitted.

Before being recorded in the 'vehicle access history' 132 table, the result of the processing of the artificial intelligence learning model 2 will be recorded.

The flow of data in the database and the confirmation process by reference information can be similarly performed in all information processing using the database below.

5 is a flowchart illustrating a disinfection information processing method in more detail.

Referring to FIG. 5, the disinfection information processing method (S2) includes a regular disinfection process (S21) for regularly performing disinfection, an information collection process (S22) for collecting external information, and a disinfection status and disinfection effect. It may include a disinfection status information collection process (S23), and information transmission and recording (S24).

In the information collection process, it may be confirmed by detecting an image such as spraying disinfectant, measuring air quality and disinfectant ingredients using a sensor, and/or inputting a disinfection record book.

According to this method, it is possible to accurately recognize whether or not to disinfect with a deep learning-based sensor object detection model through an AIoT environmental sensor. In addition, automatic disinfection detection and manual management details can be computerized to accurately and conveniently manage.

6 is a flowchart illustrating the external animal information processing method in more detail.

The outside animal information processing method (S3) includes a monitoring process of photographing the farm neighborhood with a camera (S31), an information collection process of collecting information (S32), and a process of determining whether an outside animal has appeared (S33). , and information transmission and recording (S34).

The information collection process may use images such as thermal images and general images. The ectoanimal may exemplify a wild boar. The external animal appearance discrimination process (S33) can be performed by the artificial intelligence learning model (2). In order to determine the appearance of foreign animals using images, a learning model learned in advance using a sufficient learning model may be stored in the artificial intelligence learning model 2. The same goes for other learning models without specific explanations.

According to the present method, it is possible to accurately detect a wild boar through a deep learning-based image and a thermal image object model using AIoT thermal imaging CCTV. Simple wild boar detection and reduced labor costs can be expected through unmanned wild boar detection.

7 is a flowchart showing the livestock activity information processing method in more detail.

The livestock activity information processing method (S4) includes a monitoring process of photographing livestock in breeding facilities (S41), an information collection process of collecting information (S42), and a livestock activity analysis process of analyzing and measuring livestock activity (S43). , and information transmission and recording (S44).

The information collection process may use images such as thermal images and general images. Livestock can exemplify pigs. The livestock activity analysis process (S43) can be divided into a process of identifying livestock individuals and a process of obtaining movement information of the identified livestock. The process of identifying the individual of the livestock may use an artificial intelligence learning device. The process of acquiring the movement line information may use SORT.

According to this method, the information of each object can be computerized through pig individual object recognition and tracking through a deep learning-based video object detection model for objects seen through the AIoT CCTV camera. Accordingly, it is possible to quickly identify an object exhibiting an abnormal behavior.

8 shows the flow of the database used in the disinfection information processing method, the external animal information processing method, and the livestock activity information processing method.

Referring to FIG. 8, 'vaccination history' (131), 'sensor component detection history' (132), 'disinfection history' (134), 'pig group activity history' (136), 'wild boar haunting history' (138) It can be exemplified by a table recording this information.

The 'inoculation history' 131 can record the vaccination date and the like using reference information of the 'farm' table using an external key.

The 'sensor component detection history' 132 may record detected components and the like using reference information of the 'environmental sensor' 133 table. The 'environmental sensor' table may use reference information of the 'farm number' column of the 'farm' table and the information of the 'region' column of the 'zone' 135 table. The 'region' table may be set as a single zone by grouping each zone divided into several zones of the farm or by grouping several farms that can be monitored with a single camera if the farm is small.

The 'disinfection history' 134 may use reference information of information in the 'farm number' column of the 'farm' table and the 'region' column of the 'region' 135 table. In other words, disinfection history can be recorded only for pre-registered areas and farms. This allows data structures to operate more robustly.

The 'pig group activity details' (136) is the 'farm number' column of the 'farm' table, the 'region' column of the 'region' (135) table, and the 'cctv' column of the 'cctv' (137) table. information can be used as reference information. Accordingly, the data structure can operate more robustly. Since the 'cctv' table 137 refers to both the 'farm' table and the 'region' table, it is possible to accurately identify which farm and which zone it corresponds to.

The 'wild boar haunting history' 138 may use information in the 'farm number' column of the 'farm' table and the 'cctv' column of the 'cctv' 137 table as reference information.

In the 'disinfection history' (134), 'pig group activity details' (136), and 'wild boar appearance details' (138) tables, before the detailed information is recorded, the result of processing of the artificial intelligence learning model (2) may be included. Through this, more accurate information can be stored according to the accurate judgment result of artificial intelligence.

9 is a flowchart showing a feed feeding amount information processing method in more detail.

Referring to FIG. 9, the feed feeding amount information processing method (S5) includes a process of detecting a change amount of feed stored in a feed bin (S51), an information collection process of collecting information (S52), and a feed change amount in the bin. It may include a change amount detection process (S53) for detecting, and information transmission and recording (S54).

According to the present method, it is possible to computerize the information by detecting the change in feed amount in the bin using a depth information sensor. Accordingly, illegal feed feeding can be confirmed. Accordingly, it is possible to check the appropriate feed provision/purchase time and feed feeding details.

10 shows the flow of the database used in the feed feeding amount information processing method.

Referring to FIG. 10 , with reference to the 'farm number' column of the 'farm' table, details of the 'farm' column of the 'feed purchase details' 141 table may be recorded. With reference to the 'farm number' column of the 'farm' table, details of the 'farm' column of the 'feed area' 142 table may be recorded. The feed area may refer to each area in which feed is stored. The feed area may mean each bin. The feed area can detect the amount of feed currently stored in the bin.

The 'feed area' column of the 'feed area' 142 table may be referred to as the 'feed area' column of the 'feed inventory entry and exit history' 143 table. The feeding amount of feed can be determined using the 'feed inventory entry and exit history' (143). That is, information related to a more accurate feed feeding amount can be obtained using the input/output amount of feed and the current feed amount. In addition, illegal feed feeding can be confirmed based on the correlation between the 'feed purchase details' (141) and the 'feed inventory entry and exit details' (143).

11 shows the flow of the database used in the disease information processing method.

Referring to FIG. 11, the information of the 'Regional Rating Information' (153) table, the 'Related Institutions/Facilities and Regional Authorities' (154) table, and the 'Related Institution Facilities' (155) table are recorded in the 'Farm' table. do.

When a livestock disease, for example, African swine fever occurs in a certain farm, information from the 'farm' table and the 'user' table may be recorded in the 'asf report receipt details' table (152).

Information on farms whose entry and exit is restricted according to the influence of a certain farm may be stored in the 'restricted period' table (151), using the information in the 'farm' table as reference information. The start date and time of the restriction and the end date and time of the restriction may be recorded in the 'limited period' table.

On the other hand, although shown in FIGS. 4, 8, 10, and 11, it can be understood that the content of a column that is not described is stored information as the name of the column itself. Therefore, the contents shall be included as the description contents of the present invention.

The flow of the database described above explains how each data necessary for the operation of the livestock disease control method is connected and operated with each other. By ensuring that information in a certain table uses information in another table as reference information, it is difficult to change data and authenticity of the information can be secured.

The database 100 of the present invention may store a plurality of information. Stored information can be classified according to the role of the information described in FIGS. 4, 8, 10, and 11. For example, the information includes a database for analyzing farm status, a database for analyzing livestock activity, a database for analyzing the appearance of outside animals, a database for analyzing vehicle/person access, and a database for managing diseases. A database may be included.

12 is a diagram illustrating a database for analyzing the current state of a farm.

Referring to FIG. 12 , farm analysis data may include basic farm information, farm disinfection history, farm feed information, and the like. The farm information may include information such as contact information, location, and jurisdiction. The disinfection history of the farm may include information related to farm disinfection, such as sensing time through a barn environmental information sensor, air components, and handwritten input. The feed information of the farm may include automatic usage information through feed-related purchase/stock/feed bin sensing and automatic and manual information of manual usage details.

The analysis data of the farm is 1) information such as the 'farm' table, 'region' table, 'affiliation' table, 'environmental sensor' table, and 'feed purchase history' table, and 2) the table of 1) It may include information of a table in which reference information referred to is stored.

13 is a diagram for explaining a database for analyzing livestock activity.

Referring to FIG. 13 , livestock activity analysis data may include information of all related equipment such as cameras, sensors, and computers that detect pig herd activity of livestock, for example, pigs. It may include activity data and abnormal behavior data of all pigs collected through the equipment and additional manual management.

The livestock activity analysis data may include 1) information such as the 'pig herd activity history' table and the 'cctc' table, and 2) information of a table in which reference information referred to by the table 1) is stored.

14 is a diagram for explaining a database for analyzing the appearance of foreign animals.

Referring to FIG. 14 , external animal appearance analysis data may include information of all related equipment such as cameras, sensors, and computers that detect external animals, for example, wild boars. This may include any wild boar detection data collected through the above equipment and additional manual management.

The external animal application analysis data may include 1) the 'wild boar haunting details' table, 2) information of a table in which reference information referred to by the table 1) is stored.

15 is a diagram illustrating a database for analyzing vehicle/person access.

Referring to FIG. 15 , vehicle/person access analysis data is all vehicle/person data related to the farm and may include information on vehicles and people entering and exiting the farm. Vehicle information may include information such as vehicle number, owner, and vehicle type. Personal information may include information such as name, contact information, and address. The access and disinfection list may include information on the reason for access, access time, access farm, disinfection status, and the above access vehicle and access person.

For the vehicle/person access analysis data, 1) the 'User' table, the 'Access List' table, the 'Vehicle Access Details' table, the 'Vehicle' table, and the 'People Access Details' table, 2) The tables in 1) are referenced. It may include information of a table in which reference information to be stored.

16 is a diagram illustrating a disease management database for managing diseases.

Referring to FIG. 16 , disease, eg African swine fever (ASF) management data, can manage overall information related to ASF.

Report status management can include data such as report reception and management, and current status. It can include information such as the reported farm, farm location, date and time of outbreak detection, date and time of report, reporter, status of reception, and whether or not reception has been processed. Control status management can include control information when farm access/disinfection control is required by reporting and external/internal information. Control status management may further include information such as control farms and control period. Competent farm management is a management bankbook registered in the ASF surveillance system, and can include farm information in the jurisdiction so that farm information can be checked and contacted when reporting and controlling.

The disease management database may further include information that provides organic information and enables management by integrating collected data such as farm, pig herd activity, wild boar detection, and vehicle/human information.

The disease management database may include: 1) a 'limited period' table, an 'asf report submission details' table, and 2) information on a table in which reference information referred to by the table 1) is stored.

We further propose other embodiments included in the spirit of the present invention.

The database 100 has been indicated and described as being densely provided in any node of the system. However, it is not limited thereto, and the 'ASF report receipt detail' table, which is preferably controlled by the external institution 6, may be stored in the database of the external institution 6. In addition, vaccination history may be stored in an external institution 6 that performs health management.

According to the present invention, it is possible to strictly control livestock diseases, especially African swine fever, so that harm caused by livestock diseases can be reduced.

1: control unit
2: AI learning model
3: Monitoring device
5: terminal device
6: external agency
100: database

Claims (12)

monitoring devices that monitor livestock farms;
a terminal device that performs at least one of information input and query input/output;
an artificial intelligence learning model that determines a state using external information input from at least one of the monitoring device and the terminal device;
a database in which the external information and the decision result of the artificial intelligence learning model are stored;
A controller that performs control activities according to the judgment results of the artificial intelligence learning model, and includes a function of notifying an external institution related to at least the occurrence of livestock diseases in the control activities; and
A livestock disease control system in which each of the above elements is connected by a network and/or a bus. According to claim 1,
In the monitoring device,
An environmental sensor that senses the environment;
cameras monitoring the area;
feed monitoring device for monitoring the feed condition;
Vehicle recognition device for recognizing the vehicle; and
A livestock disease control system that includes at least one of the human recognition devices for recognizing people. According to claim 2,
The environmental sensor may include at least one of an air quality sensor, a temperature sensor, and a humidity sensor,
The camera can detect at least one of people, vehicles, livestock, and foreign animals,
The feed monitoring device can measure the height of the feed bin,
The vehicle recognition device can detect the license plate of the vehicle,
The human recognition device is a livestock disease control system capable of detecting a human face. According to claim 1,
In the database,
multiple tables; and
A plurality of columns included in each of the plurality of tables are included,
Information recorded in at least one column of any one of the plurality of tables is reference information, and can be registered in at least one column of another table among the plurality of tables. According to claim 4,
Information not recorded in at least one column of another table among the plurality of tables cannot be registered in at least one column of another table among the plurality of tables. As a method of controlling livestock diseases,
a vehicle information processing process for processing information related to a vehicle being pulled in and out;
A disinfection information processing process that processes information to disinfect the farm environment;
An external animal information processing process of confirming the appearance of an external animal and processing the information;
Livestock activity information processing process for processing information related to the movement of livestock;
Feed amount information processing process for processing information related to the amount of feed eaten by livestock; and
A livestock disease control method in which processing at the time of disease outbreak and disease information processing process for controlling the disease are performed. According to claim 6,
The livestock disease control method of claim 1 , wherein the foreign animal is a wild boar, the livestock is a pig, the disease is African swine fever, and the vehicle information processing process includes driver information. According to claim 6,
The vehicle information processing process,
The process of recognizing and collecting license plates;
Recognizing and collecting driver's fingerprint and facial information;
The process of confirming that it has been disinfected; and
A livestock disease control method that includes the process of issuing a disinfection certificate. According to claim 8,
In order to perform the vehicle information processing process,
A livestock disease control method in which an access list table, a vehicle access details table, a vehicle table, a person access details table, a farm table, and a user table are linked to each other. According to claim 6,
The disinfection information processing process,
regular disinfection process with regular disinfection;
information collection process of collecting external information;
Disinfection status information collection process to collect disinfection status and disinfection effect; and
Livestock disease control method including information transmission and recording process. According to claim 6,
The livestock activity information processing process,
Livestock surveillance process;
Information collection process of collecting information;
Livestock activity analysis process to analyze and measure the activity of livestock; and
Livestock disease control method including information transmission and recording process. According to claim 6,
The feed feeding amount information processing process,
A process of detecting a change amount of feed stored in the feed bin;
information collection process of collecting information;
a change amount detection process for detecting a feed change amount in the feed bin; and
Livestock disease control method including information transmission and recording process.

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