Method and system for analyzing incidents in herds

Abstract

Provided, according to one aspect of the present invention, is a method for analyzing incidents in a herd including a plurality of individual animals, the method comprising the steps of: grouping a plurality of individual animals on the basis of at least one criterion and extracting a reference behavior pattern for each group; generating information related to whether a current behavior is abnormal for each group with reference to the reference behavior pattern; and generating information related to an incident on the basis of the information related to whether the current behavior of each group is abnormal.

Description

Method and system for analyzing accidents in a group

[0001] The present invention relates to a method and system for analyzing accidents in a group.

[0002] If an accident occurs within a livestock barn, the user (manager) can detect it with the naked eye or through CCTV, but these surveillance methods are highly inefficient. To increase surveillance efficiency, existing technologies have been able to flag incidents or abnormal changes detected within livestock barns. However, these technologies utilize motion sensors to detect abnormal changes in livestock, so analysis is limited to individuals wearing sensors that are functioning normally.

[0003] However, there is a problem that in the case of individual monitoring technology using sensors mounted on each individual, there are many cases where the monitoring is not performed properly, as not all of the multiple individuals in the barn may be wearing sensors, and even if all of them are wearing sensors, the batteries may run out or malfunction and not function properly, or the sensors may be removed due to the individual's movement or treatment.

[0004] Accordingly, the inventor(s) of the present invention propose a technology capable of determining whether an accident occurred within a herd and information related to the accident by grouping multiple individuals using the above-described habits and analyzing whether there is any abnormality in the behavior of each group, targeting animals such as cows that have a consistent behavioral routine and show common behavioral characteristics within each herd.

[0005] The purpose of the present invention is to solve all of the problems of the above-mentioned prior art.

[0006] In addition, the present invention has another purpose of grouping a plurality of objects based on at least one criterion, extracting a criterion behavior pattern for each group, generating information related to whether the current behavior is abnormal for each group by referring to the criterion behavior pattern, and generating information related to an accident based on the information related to whether the current behavior is abnormal for each group.

[0007] In addition, another purpose of the present invention is to detect abnormal behavior of the entire flock that occurs due to a change in behavior of a portion of the flock, in a flock consisting of multiple individuals, to quickly and accurately determine whether an accident has occurred and where the accident occurred.

[0008] A representative configuration of the present invention to achieve the above purpose is as follows.

[0009] According to one aspect of the present invention, a method is provided, comprising the steps of grouping a plurality of objects based on at least one criterion and extracting a reference behavior pattern for each group, generating information related to whether current behavior is abnormal for each group by referring to the reference behavior pattern, and generating information related to an accident based on the information related to whether current behavior is abnormal for each group.

[0010] According to another aspect of the present invention, a system is provided, including a reference behavior pattern extraction unit that groups a plurality of objects based on at least one criterion and extracts a reference behavior pattern for each group, an abnormal behavior information generation unit that generates information related to whether current behavior is abnormal for each group by referring to the reference behavior pattern, and an accident information generation unit that generates information related to an accident based on the information related to whether current behavior is abnormal for each group.

[0011] In addition, a non-transitory computer-readable recording medium recording another method for implementing the present invention, another system, and a computer program for executing the method are further provided.

[0012] According to the present invention, a plurality of objects are grouped based on at least one criterion, a criterion behavior pattern is extracted for each group, information related to whether the current behavior is abnormal is generated for each group with reference to the criterion behavior pattern, and information related to an accident is generated based on the information related to whether the current behavior is abnormal for each group.

[0013] In addition, according to the present invention, for a flock consisting of multiple individuals, it is possible to quickly and accurately determine whether an accident has occurred and where the accident occurred by detecting abnormal behavior of the entire flock that occurs due to a change in behavior of a part of the flock.

[0014] FIG. 1 is a schematic diagram showing the overall configuration of a system for analyzing an accident in a group of multiple objects according to one embodiment of the present invention.

[0015] FIG. 2 is a drawing showing in detail the internal configuration of an accident analysis system (200) according to one embodiment of the present invention.

[0016] FIG. 3 is a drawing exemplarily showing a process of inferring an accident location using an accident analysis system (200) according to one embodiment of the present invention.

[0017] <Explanation of symbols>

[0018] 100: Communications network

[0019] 200: Accident Analysis System

[0020] 210: Reference behavior pattern extraction unit

[0021] 220: Abnormal Behavior Information Generation Unit

[0022] 230: Accident Information Generation Unit

[0023] 240: User Notifications

[0024] 250: Communications Department

[0025] 260: Control unit

[0026] 300: Device

[0027] The following detailed description of the present invention refers to the accompanying drawings, which illustrate specific embodiments in which the present invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention, while different from each other, are not necessarily mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be modified and implemented from one embodiment to another without departing from the spirit and scope of the present invention. Furthermore, it should be understood that the positions or arrangements of individual components within each embodiment may also be modified without departing from the spirit and scope of the present invention. Accordingly, the following detailed description is not to be taken in a limiting sense, and the scope of the present invention is to be construed to encompass the scope of the claims and all equivalents thereof. Like reference numerals in the drawings represent the same or similar elements throughout the several aspects.

[0028] Hereinafter, various preferred embodiments of the present invention will be described in detail with reference to the attached drawings so that a person having ordinary skill in the art to which the present invention pertains can easily practice the present invention.

[0029] Composition of the entire system

[0030] FIG. 1 is a schematic diagram showing the overall configuration of a system for analyzing an accident in a group of multiple objects according to one embodiment of the present invention.

[0031] As illustrated in FIG. 1, the entire system according to one embodiment of the present invention may include a communication network (100), an accident analysis system (200), and a device (300).

[0032] First, the communication network (100) according to one embodiment of the present invention can be configured regardless of the communication mode such as wired communication or wireless communication, and can be configured with various communication networks such as a local area network (LAN), a metropolitan area network (MAN), and a wide area network (WAN). Preferably, the communication network (100) referred to herein may be the well-known Internet or the World Wide Web (WWW). However, the communication network (100) is not necessarily limited thereto, and may include at least a portion of a well-known wired or wireless data communication network, a well-known telephone network, or a well-known wired or wireless television communication network.

[0033] For example, the communication network (100) may be a wireless data communication network that implements conventional communication methods such as WiFi communication, WiFi-Direct communication, Long Term Evolution (LTE) communication, 5G communication, Bluetooth communication (including Bluetooth Low Energy (BLE) communication), infrared communication, ultrasonic communication, etc., at least in part. As another example, the communication network (100) may be an optical communication network that implements conventional communication methods such as LiFi (Light Fidelity), etc., at least in part.

[0034] Next, the accident analysis system (200) according to one embodiment of the present invention can perform a function of grouping a plurality of objects based on at least one criterion, extracting a criterion behavior pattern for each group, generating information related to whether the current behavior is abnormal for each group by referring to the criterion behavior pattern, and generating information related to an accident based on the information related to whether the current behavior is abnormal for each group.

[0035] The configuration and function of the accident analysis system (200) according to the present invention will be described in detail below.

[0036] Next, a device (300) according to one embodiment of the present invention is a digital device that includes a function for communicating after connecting to an accident analysis system (200), and any digital device that has a memory means, a microprocessor, and a computing capability, such as a smart phone, a tablet, a smart watch, a smart band, smart glasses, a desktop computer, a notebook computer, a workstation, a PDA, a web pad, a mobile phone, etc., can be adopted as the device (300) according to the present invention.

[0037] In particular, the device (300) may include an application (not shown) that supports the user to receive services such as information related to an accident from the accident analysis system (200). Such an application may be downloaded from the accident analysis system (200) or an external application distribution server (not shown). Meanwhile, the characteristics of such an application may be generally similar to the reference behavior pattern extraction unit (210), abnormal behavior information generation unit (220), accident information generation unit (230), user notification unit (240), communication unit (250), and control unit (260) of the accident analysis system (200), which will be described later. Here, at least a part of the application may be replaced with a hardware device or firmware device that can perform functions substantially identical to or equivalent thereto, as necessary.

[0038] Configuration of the accident analysis system

[0039] Below, the internal configuration and functions of each component of the accident analysis system (200) that performs important functions for implementing the present invention will be examined.

[0040] FIG. 2 is a drawing showing in detail the internal configuration of an accident analysis system (200) according to one embodiment of the present invention.

[0041] As illustrated in FIG. 2, an accident analysis system (200) according to one embodiment of the present invention may be configured to include a reference behavior pattern extraction unit (210), an abnormal behavior information generation unit (220), an accident information generation unit (230), a user notification unit (240), a communication unit (250), and a control unit (260). According to one embodiment of the present invention, at least some of the reference behavior pattern extraction unit (210), the abnormal behavior information generation unit (220), the accident information generation unit (230), the user notification unit (240), the communication unit (250), and the control unit (260) may be program modules that communicate with an external system (not shown). These program modules may be included in the accident analysis system (200) in the form of an operating system, an application program module, or other program modules, and may be physically stored in various known memory devices. In addition, these program modules may also be stored in a remote memory device capable of communicating with the accident analysis system (200). Meanwhile, these program modules include, but are not limited to, routines, subroutines, programs, objects, components, data structures, etc. that perform specific tasks or execute specific abstract data types, as described later in accordance with the present invention.

[0042] Meanwhile, although the accident analysis system (200) has been described as above, this description is exemplary, and it is obvious to those skilled in the art that at least some of the components or functions of the accident analysis system (200) may be realized within a device (300) or a server (not shown) or included within an external system (not shown) as needed.

[0043] First, the reference behavior pattern extraction unit (210) according to one embodiment of the present invention can perform a function of grouping a plurality of objects by at least one criterion and extracting a reference behavior pattern for each group.

[0044] The object of analysis of an accident within a group through the accident analysis system (200) according to one embodiment of the present invention may be an object having a characteristic of a clear routine of repeating the same action every unit of time (i.e., a habit of following a certain behavioral routine) and a characteristic of a common behavioral pattern among multiple objects included in a group (i.e., a simultaneous behavioral habit). Specifically, such an object may be a cow, sheep, goat, horse, or deer, and most specifically, a cow (mainly, a cow as livestock).

[0045] Grouping according to one embodiment of the present invention may mean grouping multiple entities according to a predetermined criterion. There may be at least one such criterion, and characteristics or attributes of the entities may be used as criteria. However, the type and content of these criteria may be varied as long as they are consistent with the purposes of the present invention.

[0046] According to one embodiment of the present invention, a reference behavior pattern is a behavior pattern that serves as a standard for determining abnormal behavior, and may refer to a normal behavior pattern. The reference behavior pattern described above may refer to a pattern analyzed for a unit period (e.g., 1 day) based on an individual performing the same behavior at the same time on each day. For example, cows repeat behaviors such as feeding, rumination, resting, moving, and social behavior (contact with other cows) at specific times during the day, and these repeated behaviors can be analyzed to determine the behavior at specific times during the day as a reference behavior pattern. For example, if a cow (or a herd of cows) usually engages in morning feeding and moving behavior between 5:00 AM and 7:00 AM, and rumination and resting behavior from 7:00 AM to 10:00 AM, the reference behavior pattern between 5:00 AM and 7:00 AM may be morning feeding and moving behavior, and the reference behavior pattern from 7:00 AM to 10:00 AM may be rumination and resting behavior.

[0047] Continuing, the reference behavior pattern extraction unit (210) according to one embodiment of the present invention can group a plurality of objects based on the cowshed or barn where the objects are located.

[0048] According to one embodiment of the present invention, grouping a plurality of objects based on the cowshed or barn in which the objects are located may mean dividing all objects into a plurality of groups by grouping objects in the same cowshed or barn into one group.

[0049] A barn according to one embodiment of the present invention may refer to a building or facility designed for raising animals, and multiple animals may be located within the barn. That is, a barn according to one embodiment of the present invention may be divided into one or more barn areas.

[0050] According to one embodiment of the present invention, a barn is a structure that is smaller in spatial size than a barn and is included in a barn, and may mean a minimum unit of space for raising individuals, and a plurality of individuals may be located in the barn.

[0051] Continuing, the reference behavior pattern according to one embodiment of the present invention may be a behavior that is repeated every hour.

[0052] As described above, a standard behavior pattern is a behavior that is repeated every unit of time. For example, if the unit of time is 1 day, the standard behavior pattern can be an analysis of behaviors that are repeated at a specific time period every day over the course of a day. In other words, by utilizing the characteristic that individuals belonging to each group all exhibit the same behavioral pattern at the same time (e.g., 4 PM), a behavior pattern that appears consistently every unit of time can be used as the standard behavior pattern.

[0053] Continuing, a group-specific baseline behavior pattern according to one embodiment of the present invention can be extracted based on the behavior of at least one object included in the group.

[0054] In one embodiment of the present invention, the object to be analyzed for an accident within a group through another accident analysis system (200) may be an object having a behavioral synchronization habit as described above. Specifically, behavioral synchronization is a phenomenon in which multiple objects within a group (or multiple objects grouped according to one embodiment of the present invention) imitate each other's actions, thereby making the behavior of the entire group identical or similar. By using this, a standard behavioral pattern for each group according to one embodiment of the present invention can be extracted based on the behavior of at least one object included in the group.

[0055] Next, the abnormal behavior information generation unit (220) according to one embodiment of the present invention can perform a function of generating information related to whether the current behavior is abnormal for each group by referring to a standard behavior pattern.

[0056] The meaning of the abnormal behavior information generation unit (220) according to one embodiment of the present invention generating information related to whether the current behavior is abnormal for each group by referring to the reference behavior pattern may mean comparing the reference behavior pattern extracted for each group with the current behavior pattern, determining the current behavior pattern as an abnormal behavior in response to a difference derived from the comparison exceeding a threshold value, and generating related information. Specifically, since the reference behavior pattern is a behavior that is repeated every unit of time, comparing the reference behavior pattern with the current behavior pattern may mean determining whether the current behavior pattern is normal based on the reference behavior pattern of the time zone corresponding to the current time zone.

[0057] Continuing, information related to whether the current behavior is abnormal according to one embodiment of the present invention may include at least one of the type of abnormal behavior, the degree of difference from the reference behavior pattern, the location of the group in which the abnormal behavior appears, and the time at which the abnormal behavior occurred.

[0058] The type of abnormal behavior according to one embodiment of the present invention may mean a type of behavior (transfer behavior or social transfer) that can affect the behavior pattern of the group to which the individual belongs or the behavior pattern of another adjacent group as an abnormal behavior exhibited by the individual.

[0059] Specifically, (1) accidents in which an individual is startled by a sudden external stimulus (such as a loud noise or a specific movement of another individual) and suddenly makes a large movement, (2) accidents in which a body part is injured (such as a broken horn) by colliding with another individual or structure, (3) accidents in which an individual is injured (such as a fracture) by slipping while running, (4) accidents in which an individual is unable to move because part of its body is caught in a structure in the barn and makes a commotion, and (5) accidents in which an individual escapes the barn and wanders around outside the barn. These are all behaviors that agitate the entire group and cause a change in behavioral patterns as a result of an individual's sudden behavior. These can all be considered a type of abnormal behavior.

[0060] For example, if an individual gets their mouth caught in a cage's bars, they may exhibit unusual behavior, such as struggling to escape. This behavior can cause the entire group to become agitated and anxious, potentially leading to behavior patterns that deviate from the norm.

[0061] Meanwhile, the types of abnormal behavior described above may also include the severity (risk) of the abnormal behavior. Specifically, the severity of abnormal behavior can refer to a scale indicating that even identical abnormal behaviors differ in degree. For example, even if the cause of two accidents is the same, such as an individual getting their mouth caught in a metal bar, the severity may differ depending on the time of occurrence, duration, and degree of the individual's struggle. Similarly, even identical abnormal behaviors can be classified into different types of abnormal behavior depending on their different severity levels.

[0062] The degree of difference from the reference behavior pattern according to one embodiment of the present invention may mean the probability of corresponding to an outlier calculated through a method of determining an outlier.

[0063] The method for determining the above-described outliers can be a method using an anomaly score threshold or a support vector machine (SVM) method.

[0064] Specifically, the method of using an anomaly score threshold may mean a method of calculating an anomaly score based on the difference between a standard behavior pattern and a current behavior pattern, and then determining the current behavior pattern as an anomaly when the calculated anomaly score exceeds a threshold (which may be changed as needed).

[0065] Meanwhile, the method of using a support vector machine is a method that can be applied to two-dimensional data generated by corresponding two criteria for judging abnormal behavior to each axis. Specifically, it can mean a method of finding a hyperplane (i.e., a one-dimensional line) in two-dimensional planar data where one axis (e.g., the x-axis) is the sitting behavior frequency of an object (sitting values) and the other axis (e.g., the y-axis) is the activity values ​​of an object, and then distinguishing normal behavior (normal data) from abnormal behavior (abnormal data) based on the hyperplane. Here, the hyperplane is generated as a result of the learning of the support vector machine and can form a boundary that includes most of the normal data (i.e., it learns to set a hyperplane that includes normal data points as much as possible during the learning process), and data outside the boundary can be classified as outliers.

[0066] The degree of deviation from the above-described standard behavioral pattern or the probability of it being an outlier may be proportional to the severity of the abnormal behavior.

[0067] Meanwhile, the method for calculating the degree of difference from the above-described standard behavior pattern is exemplary, and the method is not limited to the above example, and any type of method or mechanism suitable for the purpose of the present invention may be used.

[0068] The time at which abnormal behavior occurred according to one embodiment of the present invention may mean the time at which abnormal behavior occurred by a specific individual within a group or the time at which abnormal behavior occurred by group.

[0069] Specifically, if the time at which abnormal behavior occurred by a specific individual within a group is referred to as the time at which the specific individual first exhibited abnormal behavior, it can mean the time at which the specific individual first exhibited abnormal behavior, when the entire group to which the specific individual belongs was affected by the specific individual's abnormal behavior and showed a current behavior pattern that was different from the standard behavior pattern.

[0070] Meanwhile, the time at which abnormal behavior occurs in each group may be different from the standard behavior pattern as the influence of abnormal behavior may spread between multiple groups. In this case, the time at which the current behavior pattern in each group differs from the standard behavior pattern can be expressed as the 'time at which abnormal behavior occurred in each group.'

[0071] Meanwhile, the time at which the abnormal behavior occurred can be acquired simultaneously with the detection of the abnormal behavior through a specific device, or the time at which the abnormal behavior occurred for each individual or each group can be inferred using information related to whether the current behavior is abnormal (type of abnormal behavior, degree of difference from the standard behavior pattern, etc.).

[0072] For example, among the types of abnormal behavior described above, the degree to which an individual or group is affected by the occurrence of an abnormal situation in the surroundings and the speed at which the influence of the abnormal situation spreads may vary depending on the severity of the abnormal behavior, so the time of occurrence of the abnormal behavior can be inferred by considering the severity.

[0073] Next, the accident information generation unit (230) according to one embodiment of the present invention can perform a function of generating information related to an accident based on information related to whether the current behavior of each group is abnormal.

[0074] According to one embodiment of the present invention, generating information related to an accident based on information related to whether the current behavior of each group is abnormal may mean inferring various information related to an accident based on information related to whether the various current behaviors are abnormal as described above.

[0075] Meanwhile, the above-described inference can be performed by an artificial intelligence model trained to determine the cause of the accident, the time of accident occurrence by group, the time of first accident occurrence, the accident propagation pattern between groups, and the location of first accident occurrence, based on information related to whether the current behavior is abnormal.

[0076] Continuing, information related to an accident according to one embodiment of the present invention may include at least one of the type of accident, the location where the accident occurred, and the time when the accident occurred.

[0077] According to one embodiment of the present invention, the type of accident may be information about the abnormal behavior of a specific individual (in this case, the type of accident can be immediately identified through detection devices such as CCTV installed in the barn or barn), or it may refer to the type of accident cause analyzed based on the abnormal behavior of a specific individual. For example, if a specific individual exhibits abnormal behavior such as running around, the cause of the accident may be due to a dangerous structure inside the barn or barn, but it may also be due to feed not being supplied at the normal time (for example, an automatic feeding system malfunctioning). In conclusion, the type of accident may refer to the cause of the accident, and the cause of the accident may be related to the solution provided to the user.

[0078] According to one embodiment of the present invention, the point of occurrence of an accident may refer to the point of the initial accident. The influence of abnormal behavior within an individual may spread, causing abnormal behavior to occur in multiple individuals or groups. This overall abnormal behavior is often caused by the initial abnormal behavior (accident) occurring in a specific individual. In this case, knowing the point of occurrence of the initial accident allows the user to respond quickly.

[0079] Meanwhile, an embodiment of the process of inferring the point of accident occurrence will be described in more detail below.

[0080] According to one embodiment of the present invention, the time of accident occurrence may refer to the time of accident occurrence for each group or the time of the initial accident occurrence. Specifically, if the user can determine the time of the initial accident occurrence, they can determine the elapsed time since the accident occurred, thereby determining how long the accident lasted and how serious the current situation is.

[0081] Continuing, the type of accident and the point of occurrence of the accident according to one embodiment of the present invention can be inferred by synthesizing the degree of difference from the reference behavior pattern, the location of the group in which the abnormal behavior appears, and the time at which the abnormal behavior occurred.

[0082] Because the barn or shelter according to one embodiment of the present invention is very large and may contain a large number of objects, it may be difficult to pinpoint the location of an accident simply by identifying the location of the barn or shelter. Therefore, accurately providing the location of the accident can be crucial to enable users to respond to the accident as quickly as possible.

[0083] The accident information generation unit (230) according to one embodiment of the present invention can infer the type of accident and the point of occurrence of the accident by synthesizing at least one of (1) factors affecting the speed at which the influence of abnormal behavior is spread, such as the degree of difference or severity from a reference behavior pattern, (2) geographical information, such as the relative or absolute location of each group (which may be a friendly or right-wing location) or the location of the group in which the abnormal behavior appears, and (3) temporal information, such as the time at which the abnormal behavior of each group first appeared.

[0084] For example, the more the deviation from the standard behavior pattern, the more severe the abnormal behavior, and the more severe the abnormal behavior, the faster the abnormal behavior spreads to surrounding groups, so this can be taken into account when determining the location of the initial accident.

[0085] That is, when abnormal behavior spreads throughout the entire barn, it is important for the user to find out where the initial abnormal behavior occurred in order to identify and resolve the cause of the problem. Therefore, the accident information generation unit (230) can infer or reverse-calculate the initial accident location by synthesizing the speed at which the abnormal behavior spreads, the location of each group, and the time of accident occurrence for each group, and provide the location to the user.

[0086] The above-described inference process will be described with reference to FIG. 3 as follows. In a situation where multiple objects are divided and located in cowsheds A (410), B cowshed (420), and C cowshed (430), the accident analysis system (200) according to one embodiment of the present invention is used to find the point of initial accident occurrence (440). In this case, grouping by cowshed can be performed by the reference behavior pattern extraction unit (210), and a reference behavior pattern for each group can be extracted from at least one object included in each cowshed. Then, the abnormal behavior information generation unit (220) can detect the occurrence of abnormal behavior by comparing the reference behavior pattern extracted above with the current behavior pattern. In order to accurately determine where the accident first occurred within a large cowshed, the accident information generation unit (230) can infer the exact point of initial accident occurrence by synthesizing the time when the abnormal behavior appeared for each group and the type (severity) of the accident. For example, considering that the group corresponding to C barn (430) has the highest severity (i.e., the difference between the current behavior pattern and the reference behavior pattern is the greatest), it can be inferred that the initial occurrence point is located inside C barn (430), and further considering that the group corresponding to B barn (420) has the next highest severity and the group corresponding to A barn (410) has the lowest severity, it can be inferred that the point (440) within C barn (430) that is somewhat distant from A barn (410) and close to B barn (420) is the initial accident occurrence point.

[0087] Meanwhile, the above-described inference process is described in a very simplified manner to aid understanding, and the actual inference process is performed through more factors and more sophisticated algorithms. Therefore, the method by which the accident analysis system (200) according to one embodiment of the present invention generates (infers) information related to an accident, including the point of initial accident occurrence, is not limited by the above-described example.

[0088] Meanwhile, the accident analysis system (200) according to one embodiment of the present invention can infer the cause of simultaneous abnormal behaviors instead of inferring the type and location of the accident in response to the analysis that abnormal behaviors occurred simultaneously in multiple objects or the entire livestock shed (cow shed and barn).

[0089] Specifically, if an accident occurs at a specific location in a cowshed or allied group and the abnormal behavior spreads throughout the group, the initial point of occurrence can be inferred based on the group-specific abnormal behavior occurrence pattern as described above. However, if abnormal behavior occurs simultaneously across all individuals, it is difficult to conclude that the accident occurred at a specific location and the abnormal behavior spread throughout the group, making inferring the initial point of occurrence inappropriate. In such cases, instead of identifying the initial point of occurrence, the cause of the simultaneous and multiple abnormal behaviors exhibited by all individuals can be inferred.

[0090] The above-mentioned cause of simultaneous abnormal behavior in all individuals may not be due to behavioral transfer between individuals, but rather may refer to factors that can simultaneously affect the entire population. For example, factors that can simultaneously affect the entire population may include (1) infectious diseases (e.g., foot-and-mouth disease, bovine spongiform encephalitis, and bovine diarrhea virus), (2) environmental stress (e.g., heat stress), (3) toxin poisoning (e.g., when specific feed or water is contaminated and contains toxins), and (4) gas exposure (e.g., accumulation of toxic gases such as ammonia and hydrogen sulfide in a barn).

[0091] Continuing, the accident analysis system (200) according to one embodiment of the present invention, in response to the simultaneous occurrence of abnormal behavior in all individuals, may infer factors that may simultaneously affect the entire group described above (instead of inferring the location of the initial accident occurrence), or may infer factors based on information collected using other devices installed in the barn or alley. The other devices described above may be, for example, (1) a thermometer, a wearable activity sensor, or an image analysis system in the case of an infectious disease, (2) a temperature and humidity sensor, a thermal imaging camera, or a ventilation or cooling control system in the case of environmental stress, (3) a feed analysis kit, a water quality monitoring sensor, a smart feeder, or a smart waterer (to detect decreased intake) in the case of toxin poisoning, and (4) a gas detection sensor or a ventilation system sensor in the case of gas exposure.

[0092] Next, the accident analysis system (200) according to one embodiment of the present invention may further include a user notification unit (240).

[0093] The user notification unit (240) according to one embodiment of the present invention can perform a function of notifying the user of information related to an accident.

[0094] In one embodiment of the present invention, a user notification may include, in addition to incident-related information, information related to the abnormality of the current behavior described above and an urgency level calculated based on at least one of the incident-related information. For example, the urgency level may be set higher in proportion to the degree of difference between the current behavior pattern and the baseline behavior pattern.

[0095] Meanwhile, the type of information or notification method included in the user notification regarding the incident may vary depending on the urgency level described above. For example, if the urgency level is low, relatively less information regarding the incident may be included, or the user notification may be delivered via a method that is as intrusive as possible to the user (e.g., app notifications or text messages). If the urgency level is high, relatively more information regarding the incident may be included (i.e., more detailed information may be delivered), or the user notification may be delivered via a method that is more easily noticeable to the user (e.g., including a specific warning sound or using a phone call).

[0096] Continuing, the user notification unit (240) according to one embodiment of the present invention can inform the user of a response plan for the accident along with information related to the accident.

[0097] The user notification unit (240) according to one embodiment of the present invention can select the most appropriate response measure based on the severity, urgency, or type of the accident, and notify the user of this along with information related to the accident.

[0098] For example, if a cow gets stuck in a specific structure (such as a cage, fence, narrow passage, or feed trough) at a specific location within a barn, the system can suggest appropriate rescue tools such as a lifter or sling along with the initial accident location, and further, considering the possibility of the cow being injured, it can suggest a veterinarian's phone number or automatically call a veterinarian to provide a response plan for the accident.

[0099] Meanwhile, in response to the accident analysis system (200) according to one embodiment of the present invention as described above inferring factors that can simultaneously affect the entire group, the user notification unit (240) can notify the user that an accident that can affect the entire livestock farm has occurred, and can also notify the user of a response plan that can resolve the analyzed factors.

[0100] For example, if an infectious disease is inferred to have occurred in a livestock farm, the user notification unit (240) may provide information about animals suspected of being infected, suggest a method for isolating infected animals, suggest a method for contacting quarantine authorities or veterinarians, or automatically call quarantine authorities or veterinarians without user intervention.

[0101] In conclusion, utilizing the accident analysis system (200) of the present invention not only enables monitoring of accidents that pose a threat to livestock management, but also provides users with various information and solutions necessary to resolve accidents when they occur, enabling rapid response to accidents and preventing injury and death of livestock. This ultimately enhances animal welfare while simultaneously increasing productivity and minimizing property damage to the user (farmer).

[0102] Next, the communication unit (250) according to one embodiment of the present invention can perform a function that enables data transmission and reception from / to the reference behavior pattern extraction unit (210), the abnormal behavior information generation unit (220), the accident information generation unit (230), and the user notification unit (240).

[0103] Finally, the control unit (260) according to one embodiment of the present invention can perform a function of controlling the flow of data between the reference behavior pattern extraction unit (210), the abnormal behavior information generation unit (220), the accident information generation unit (230), the user notification unit (240), and the communication unit (250). That is, the control unit (260) according to one embodiment of the present invention can control the flow of data from / to the outside of the accident analysis system (200) or the flow of data between each component of the accident analysis system (200), thereby controlling the reference behavior pattern extraction unit (210), the abnormal behavior information generation unit (220), the accident information generation unit (230), the user notification unit (240), and the communication unit (250) to perform their own functions.

[0104] The embodiments of the present invention described above may be implemented in the form of program commands that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the computer-readable recording medium may be specially designed and configured for the present invention or may be known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program commands, such as ROMs, RAMs, and flash memories. Examples of program commands include not only machine language codes generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. Hardware devices may be changed into one or more software modules to perform processing according to the present invention, and vice versa.

[0105] Although the present invention has been described above with specific details such as specific components and limited examples and drawings, these are provided only to help a more general understanding of the present invention, and the present invention is not limited to the above examples, and those with ordinary knowledge in the technical field to which the present invention pertains can make various modifications and changes based on this description.

[0106] Therefore, the idea of ​​the present invention should not be limited to the embodiments described above, and not only the scope of the patent claims described below but also all scopes equivalent to or equivalently modified from the scope of the patent claims are considered to fall within the scope of the idea of ​​the present invention.

Claims

1. A method for analyzing accidents within a group consisting of multiple objects. A step of grouping multiple objects based on at least one criterion and extracting a criterion behavior pattern for each group; A step of generating information related to whether the current behavior is abnormal for each group by referring to the above standard behavior pattern, and A step of generating information related to an accident based on information related to whether the current behavior of each group is abnormal method.

2. In paragraph 1, In the above-mentioned standard behavior pattern extraction step, multiple objects are grouped based on the cowshed or barn where the objects are located. method.

3. In paragraph 1, The above standard behavior pattern is a behavior that is repeated every hour. method.

4. In paragraph 1, The above group-specific standard behavior pattern is extracted based on the behavior of at least one object included in the group. method.

5. In paragraph 1, Information related to whether the current behavior is abnormal includes at least one of the following: the type of abnormal behavior, the degree of difference from the standard behavior pattern, the location of the group in which the abnormal behavior appears, and the time when the abnormal behavior occurred. method.

6. In paragraph 1, Information related to the above accident includes at least one of the type of accident, the location of the accident, and the time of the accident. method.

7. In paragraph 6, The type of the above accident and the location of the accident are inferred by comprehensively considering the degree of difference from the standard behavior pattern, the location of the group where the abnormal behavior appeared, and the time when the abnormal behavior occurred. method.

8. In paragraph 1, Further comprising a step of informing the user of information related to the above-determined accident. method.

9. In paragraph 8, In the step of notifying the user, the user is also informed of the response plan for the accident. method.

10. A non-transitory computer-readable recording medium recording a computer program for executing the method according to paragraph 1.

11. A system for analyzing accidents within a group of multiple objects. A reference behavior pattern extraction unit that groups multiple objects by at least one criterion and extracts a reference behavior pattern for each group. An abnormal behavior information generation unit that generates information related to whether the current behavior is abnormal for each group by referring to the above standard behavior pattern, and An accident information generation unit that generates accident-related information based on information related to whether the current behavior of each group is abnormal. System.

12. In paragraph 11, The above-mentioned standard behavior pattern extraction unit groups multiple individuals based on the cowshed or barn where the individuals are located. System.

13. In paragraph 11, The above standard behavior pattern is a behavior that is repeated every hour. System.

14. In paragraph 11, The above group-specific standard behavior pattern is extracted based on the behavior of at least one object included in the group. System.

15. In paragraph 11, Information related to whether the current behavior is abnormal includes at least one of the following: the type of abnormal behavior, the degree of difference from the standard behavior pattern, the location of the group in which the abnormal behavior appears, and the time when the abnormal behavior occurred. System.

16. In paragraph 11, Information related to the above accident includes at least one of the type of accident, the location of the accident, and the time of the accident. System.

17. In paragraph 16, The type of the above accident and the location of the accident are inferred by comprehensively considering the degree of difference from the standard behavior pattern, the location of the group where the abnormal behavior appeared, and the time when the abnormal behavior occurred. System.

18. In paragraph 11, Further comprising a user notification section that informs the user of information related to the above-determined accident. System.

19. In paragraph 18, The above user notification section informs the user of the response plan for the accident. System.

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