JP7037924B2 - Specific equipment, specific method, and program - Google Patents

Description

The present invention relates to a specific device, a specific method, and a program.

It is known that fattening cattle are prone to abnormalities such as difficulty in standing up in the late fattening stage. If the difficulty of standing up is left unattended, the cow may die due to suffocation, etc. ing.

In addition, it is known that breeding cattle are also prone to abnormalities such as difficulty in standing up before and after childbirth. I'm checking if there isn't.

Furthermore, the cow may have difficulty standing due to abnormalities such as flatulence. Flatulence is a disease that can occur in livestock of ruminants such as cattle, and may develop when, for example, overeating a fermentable feed such as green grass. When bloating develops, the cow's abdomen swells, making it difficult for the cow to stand up.

Abe Ryo, "Agricultural Science Seminar Basics of Livestock Breeding", New Edition, Agricultural Fisheries Village Cultural Association, April 2008, p.109, p.122-124.

However, for example, in a large-scale dairy farm with hundreds to thousands of cows, whether there are any abnormalities (difficulty standing including flatulence etc.) in the cows by patrol the barn. Is difficult to confirm.

The embodiment of the present invention has been made in view of the above points, and an object thereof is to identify an abnormality in livestock.

In order to solve the above problems, an embodiment of the present invention is a specific device for identifying an abnormality in a livestock, which is a storage means for storing acceleration data measured by an acceleration sensor mounted on the livestock, and a predetermined time. The first acquisition means for acquiring one or more acceleration data between the storage means and the first acquisition means for calculating the standard deviation based on the one or more acceleration data acquired by the first acquisition means. The calculation means, the second calculation means for calculating a predetermined index value from the standard deviation calculated by the first calculation means, and the index value calculated by the second calculation means are preset. If it exceeds a predetermined threshold value, it is characterized by having a first specific means for identifying that an abnormality has occurred in the livestock.

It is possible to identify abnormalities in livestock.

It is a figure which shows an example of the whole structure of the specific system which concerns on 1st Embodiment. It is a figure which shows an example of the measurement data stored in the measurement data storage part which concerns on 1st Embodiment. It is a figure which shows an example of the functional structure of the specific processing part which concerns on 1st Embodiment. It is a flowchart which shows an example of the abnormality identification processing which concerns on 1st Embodiment. It is a figure explaining an example of calculation of an index value. It is a figure which shows the comparative example at the time of foraging behavior and the time of abnormal behavior. It is a figure which shows an example of the whole structure of the specific system which concerns on the 2nd Embodiment. It is a figure which shows an example of the measurement data stored in the measurement data storage part which concerns on the 2nd Embodiment. It is a figure which shows an example of the functional structure of the specific processing part which concerns on the 2nd Embodiment. It is a flowchart which shows an example of the abnormality identification processing which concerns on the 2nd Embodiment. It is a figure explaining an example of the posture identification of a cow.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. Hereinafter, as an example of livestock, a case of identifying an abnormality in cattle will be described. However, livestock are not limited to cattle.

[First Embodiment]
<Overall configuration>
First, the overall configuration of the specific system 1 for identifying an abnormality such as difficulty in standing up of a cow will be described with reference to FIG. FIG. 1 is a diagram showing an example of the overall configuration of the specific system according to the first embodiment.

As shown in FIG. 1, the specific system 1 according to the present embodiment includes a specific device 10 for identifying an abnormality in a cow and one or more tags 20 attached to the cow. It is preferable that the tag 20 is fixedly attached to the neck portion of the cow.

The tag 20 is a device attached to a cow. One tag 20 is attached to one cow. The tag 20 includes an acceleration sensor that measures the acceleration of a cow wearing the tag 20 (acceleration of three axes of X-axis, Y-axis, and Z-axis).

The tag 20 transmits measurement data including an acceleration sensor value measured by an acceleration sensor to the specific device 10 at predetermined time intervals (for example, every 2 seconds). The measurement data transmitted to the specific device 10 is stored (stored) in the measurement data storage unit 200 described later.

The specific device 10 is one or more computers that identify abnormalities in cattle (for example, difficulty in standing up in the late fattening stage, difficulty in standing up before and after childbirth, difficulty in standing up due to flatulence, etc.). The specific device 10 has a specific processing unit 100 and a measurement data storage unit 200.

The specific processing unit 100 identifies an abnormality in cattle based on the measurement data stored in the measurement data storage unit 200. The specific processing unit 100 is realized by a process of causing a CPU (Central Processing Unit) or the like to execute one or more programs installed in the specific device 10.

The measurement data storage unit 200 stores the measurement data received from the tag 20. The measurement data storage unit 200 can be realized by using, for example, an auxiliary storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The measurement data storage unit 200 stores a plurality of measurement data at predetermined time intervals (for example, every 2 seconds).

The configuration of the specific system 1 shown in FIG. 1 is an example, and may be another configuration. For example, the specific device 10 may be composed of a plurality of computers. Further, for example, a device (cloud server or the like) connected to the specific device 10 via a network may have a part of the functions of the specific processing unit 100.

<Measurement data stored in the measurement data storage unit 200>
Here, the measurement data stored in the measurement data storage unit 200 according to the present embodiment will be described with reference to FIG. 2. FIG. 2 is a diagram showing an example of measurement data stored in the measurement data storage unit 200 according to the first embodiment. When the specific device 10 receives the measurement data from the tag 20, the specific processing unit 100 may store (store) the received measurement data in the measurement data storage unit 200.

As shown in FIG. 2, the measurement data storage unit 200 stores one or more measurement data for each tag ID that identifies the tag. Since one tag 20 is attached to one cow, the tag ID may be information for identifying the cow (individual identification information for the cow).

Each measurement data includes a date and time and an accelerometer value. The date and time is, for example, the date and time when the tag 20 transmits the measurement data. The date and time may be the date and time when the specific device 10 receives the measurement data.

The acceleration sensor value is a value of acceleration measured by the acceleration sensor included in the tag 20. The acceleration sensor value includes an X component indicating an acceleration component in the X-axis direction, a Y component indicating an acceleration component in the Y-axis direction, and a Z component indicating an acceleration component in the Z-axis direction. For example, the measurement data of the date and time "t ₁ " includes an X component "x ₁ ", a Y component "y ₁ ", and a Z component "z ₁ " of the acceleration sensor value.

As described above, in the measurement data stored in the measurement data storage unit 200 according to the present embodiment, the measurement data including the date and time and the acceleration sensor value is accumulated (stored) for each tag ID.

<Functional configuration of the specific processing unit 100>
Next, the functional configuration of the specific processing unit 100 according to the present embodiment will be described with reference to FIG. FIG. 3 is a diagram showing an example of the functional configuration of the specific processing unit 100 according to the present embodiment.

As shown in FIG. 3, the specific processing unit 100 includes an acquisition unit 101, a preprocessing unit 102, a norm calculation unit 103, a standard deviation calculation unit 104, an index value calculation unit 105, and an abnormality identification unit 106. Have.

The acquisition unit 101 acquires measurement data from the measurement data storage unit 200. At this time, the acquisition unit 101 acquires measurement data for a predetermined time (for example, 10 minutes) from the measurement data storage unit 200 for each tag ID, for example.

The pre-processing unit 102 performs pre-processing on the measurement data acquired by the acquisition unit 101. The preprocessing is, for example, a defect complement (resampling) process of measurement data, a noise reduction process, and the like.

The norm calculation unit 103 calculates the L2 norm of the acceleration sensor value included in the measurement data after the preprocessing.

The standard deviation calculation unit 104 calculates the standard deviation of the L2 norm calculated by the norm calculation unit 103 in predetermined time (for example, 1 minute) units.

The index value calculation unit 105 calculates a predetermined index value from the standard deviation during a predetermined time (for example, 1 hour). The index value calculation unit 105 uses, for example, a preset upper limit value and a lower limit value, and the standard deviation exceeds the upper limit value during a predetermined time (for example, 5 minutes, 10 minutes, 1 hour, etc.). After that, the number of times the standard deviation falls below the lower limit value and the number of times the standard deviation falls below the lower limit value and then exceeds the upper limit value may be counted, and this count value may be used as an index value. At this time, the index value calculation unit 105 does not count if the standard deviation exceeds the upper limit value and then exceeds the upper limit value again (without falling below the lower limit value). Similarly, the index value calculation unit 105 does not count if the standard deviation falls below the lower limit and then falls below the lower limit again (without exceeding the upper limit).

The index value calculation unit 105 may calculate an index value other than the above. For example, when the number of times the standard deviation exceeds the upper limit value is K _s and the number of times the standard deviation falls below the lower limit value is _Ki , the index value calculation unit 105 may use min (K _s , K _i ), K _s + K _i , or the like. May be used as an index value.

The abnormality specifying unit 106 determines whether or not the index value calculated by the index value calculation unit 105 exceeds a preset threshold value. Then, when it is determined that the index value exceeds the threshold value, the abnormality specifying unit 106 identifies that an abnormality has occurred in the cow. On the other hand, when it is determined that the index value does not exceed the threshold value, the abnormality specifying unit 106 identifies that no abnormality has occurred in the cow.

When the abnormality specifying unit 106 identifies that an abnormality has occurred in the cow, for example, the abnormality specifying unit 106 notifies an alert or the like indicating that the abnormality has occurred in the cow. As the notification destination of the alert, for example, it may be displayed on the display of the specific device 10, or may be transmitted to another device (for example, a PC, a smartphone, a tablet terminal, etc.) connected to the specific device 10. ..

<Process to identify abnormalities in cattle>
Hereinafter, a process for identifying an abnormality that has occurred in cattle will be described with reference to FIG. FIG. 4 is a flowchart showing an example of the abnormality specifying process according to the first embodiment. The process shown in FIG. 4 is repeatedly executed every 10 minutes, for example. However, 10 minutes is an example and may be repeatedly executed at arbitrary time intervals.

First, the acquisition unit 101 acquires measurement data for a predetermined time (for example, 10 minutes) from the measurement data storage unit 200 for each tag ID (step S11). In this way, the acquisition unit 101 acquires measurement data in a predetermined time unit (for example, in units of 10 minutes) from the measurement data storage unit 200 for each cow (tag ID). It should be noted that such a predetermined time is not limited to 10 minutes, and for example, the user of the specific device 10 can set it to an arbitrary time.

Hereinafter, the case where the measurement data 1, the measurement data 2, ..., And the measurement data N1 of the tag ID “Tag1” for ₁₀ minutes is acquired by the acquisition unit 101 will be described.

Next, the preprocessing unit 102 performs preprocessing on the measurement data acquired by the acquisition unit 101 (step S12). That is, the preprocessing unit 102 performs defect complementation (resampling) processing, noise reduction processing, and the like for measurement data. The defect completion process is a process for complementing a defect when the data cannot be acquired in order to improve the accuracy of the collected data. Further, the noise reduction processing is a processing for removing data indicating a momentary movement of a cow (for example, a movement of momentarily shaking the body, a movement of momentarily causing the body to be greatly shaken, etc.). As a result, the measurement data 1, the measurement data 2, ..., And the measurement data _N2 after the pretreatment can be obtained.

Next, the norm calculation unit 103 calculates the L2 norm of the acceleration sensor value included in the measurement data after the preprocessing (step S13). That is, the norm calculation unit 103 includes the L2 norm 1 of the acceleration sensor value 1 included in the measurement data 1, the L2 norm 2, ... Of the acceleration sensor value 2 included in the measurement data 2, and the acceleration included in the measurement data N ₂ . The L2 norm N ₂ of the sensor value N ₂ is calculated.

Next, the standard deviation calculation unit 104 calculates the standard deviation of the L2 norm calculated by the norm calculation unit 103 in predetermined time (for example, 1 minute) units (step S14). That is, the standard deviation calculation unit 104 calculates the standard deviation for the L2 norm 1, L2 norm 2, ..., L2 norm N ₂ in 1-minute units. The standard deviation calculated by the standard deviation calculation unit 104 is stored in, for example, an auxiliary storage device such as an HDD or SSD.

Next, the index value calculation unit 105 calculates a predetermined index value from the standard deviation during the predetermined time (for example, 1 hour) (step S15). That is, the index value calculation unit 105 acquires, for example, the standard deviation during the past hour from the auxiliary storage device and calculates a predetermined index value. The predetermined index value counts the number of times the standard deviation exceeds the upper limit and then falls below the lower limit and the number of times the standard deviation falls below the lower limit and then exceeds the upper limit during a predetermined time. It is calculated by.

Here, the calculation of the index value will be described with reference to FIG. FIG. 5 is a diagram illustrating an example of calculation of an index value.

As shown in FIG. 5, when the vertical axis is the standard deviation and the horizontal axis is the time, the number of times the standard deviation exceeds the upper limit and then falls below the lower limit in one hour, and the standard deviation is the lower limit. The index value is calculated by counting the number of times the upper limit is exceeded after the value falls below. For example, in the example shown in FIG. 5, it is counted as 3 times between 6:00 and 6:10.

The lower limit value and the upper limit value are determined in advance based on, for example, an empirical rule, but the lower limit value is, for example, larger than the standard deviation calculated from the acceleration sensor value while the cow is lying down. It is preferable to set a high value. Further, as the upper limit value, for example, it is preferable to set a value equal to or greater than the standard deviation calculated from the acceleration sensor value during the state in which the cow is eating (the state of eating food). .. The upper limit is learned by a machine learning method, for example, using the accelerometer value during the state in which the cow is eating (or the state of movement similar to that at the time of feeding) as the correct answer data. The standard deviation determined in may be set.

Further, the lower limit value and the upper limit value may be determined for each cow or may be determined in common for all cows. By determining for each cow, for example, a lower limit value and an upper limit value can be set in consideration of the behavioral characteristics of each cow. When determining for each cow, the upper limit value and the lower limit value may be further set based on the past operation data of the cow. For example, a higher upper limit may be set for cows that are statistically judged to have a lot of movement. In other words, the upper limit value and the lower limit value may be set according to the characteristics of each cow.

Further, the time width (time width between predetermined times) in which the index value is calculated by the index value calculation unit 105 may differ depending on the type of abnormality to be determined. For example, in the case of a highly urgent illness such as flatulence, the width is set to 1 hour, while in the case of delivery, the width is set to 6 hours.

Next, the abnormality specifying unit 106 determines whether or not the index value calculated by the index value calculation unit 105 exceeds a preset threshold value. Then, when it is determined that the index value exceeds the threshold value, the abnormality specifying unit 106 identifies that an abnormality has occurred in the cow. On the other hand, when the abnormality specifying unit 106 determines that the index value does not exceed the threshold value, it identifies that no abnormality has occurred in the cow (step S15). As the threshold value, for example, about 15 to 20 can be considered. However, the threshold value is not limited to this.

For example, different threshold values may be used depending on the type of abnormality to be determined. More specifically, the threshold is set higher when cattle suffering from a certain disease continuously experience movements that exceed or fall below the upper limit. On the other hand, when a large movement suddenly occurs in a cow suffering from another certain disease, the threshold value is set lower.

Based on the above, the specific device 10 according to the present embodiment can identify abnormalities occurring in cattle (for example, difficulty in standing up in the late fattening stage, difficulty in standing up before and after childbirth, difficulty in standing up due to flatulence, etc.). ..

Here, as an example, FIG. 6 shows a comparative example of the L2 norm and the standard deviation during feeding behavior and abnormal behavior (difficulty standing). FIG. 6 is a diagram showing a comparative example between the feeding behavior and the abnormal behavior.

FIG. 6A is an L2 norm at the time of feeding behavior. Further, FIG. 6 (b) shows the L2 norm at the time of abnormal behavior. It can be seen that the L2 norm is often a prominent value during abnormal behavior as compared with that during feeding behavior.

FIG. 6 (c) shows the standard deviation during feeding behavior. Further, FIG. 6 (d) shows the standard deviation at the time of abnormal behavior. It can be seen that the vertical swing is larger during abnormal behavior than during feeding behavior.

Thereby, by appropriately setting the upper limit value and the lower limit value, it is possible to identify the abnormality of the cow with high accuracy. By increasing the difference between the upper limit value and the lower limit value, false detection can be reduced, but detection omission becomes large. On the contrary, by reducing the difference between the upper limit value and the lower limit value, false detection becomes large, but detection omission can be reduced. Therefore, for example, when the difference between the upper limit value and the lower limit value is large, it is preferable to set the threshold value relatively small. On the other hand, for example, when the difference between the upper limit value and the lower limit value is made small, it is preferable to set the threshold value relatively large.

Further, the upper limit value may be set stepwise. For example, a first upper limit value and a second upper limit value having a value larger than the first upper limit value may be set. At this time, for example, a first threshold value corresponding to the first upper limit value and a second threshold value corresponding to the second upper limit value may be set as the threshold value. The first threshold counts, for example, the number of times the standard deviation exceeds the first upper limit and then falls below the lower limit, and the number of times the standard deviation falls below the first lower limit and then exceeds the upper limit. It is the counted value. Similarly, the second threshold is, for example, the number of times the standard deviation exceeds the second upper limit and then falls below the lower limit, and the number of times the standard deviation falls below the second lower limit and then exceeds the upper limit. It is a count value that counts and. Here, the lower limit is set to a constant value. The lower limit value may be set stepwise in the same manner as the upper limit value.

[Second embodiment]
Next, the second embodiment will be described. In the second embodiment, a case where an abnormality in cattle is identified with higher accuracy by using barometric pressure data will be described. In the second embodiment, the differences from the first embodiment will be mainly described, and the description of the same components as those in the first embodiment will be omitted.

<Overall configuration>
First, the specific system 1 according to the present embodiment will be described with reference to FIG. 7. FIG. 7 is a diagram showing an example of the overall configuration of the specific system 1 according to the second embodiment.

As shown in FIG. 7, the specific system 1 according to the present embodiment further includes a reference atmospheric pressure sensor 30 for measuring a reference atmospheric pressure. Further, the tag 20 according to the present embodiment further includes a barometric pressure sensor for measuring barometric pressure.

The tag 20 according to the present embodiment transmits measurement data including the barometric pressure sensor value measured by the barometric pressure sensor to the specific device 10 at predetermined time intervals (for example, every 2 seconds).

Further, the reference atmospheric pressure sensor 30 is installed at a predetermined position in the barn (for example, on the ground in the barn) and measures the reference atmospheric pressure. The reference atmospheric pressure sensor 30 transmits reference atmospheric pressure data including a reference atmospheric pressure sensor value indicating the measured atmospheric pressure to the specific device 10 at predetermined time intervals (for example, every 2 seconds). The reference atmospheric pressure data transmitted to the specific device 10 is stored (stored) in the reference atmospheric pressure data storage unit 300, which will be described later.

Here, the specific device 10 according to the present embodiment further has a reference atmospheric pressure data storage unit 300.

The reference atmospheric pressure data storage unit 300 stores the reference atmospheric pressure data received from the reference atmospheric pressure sensor 30. The reference atmospheric pressure data storage unit 300 can be realized by using, for example, an auxiliary storage device such as an HDD or SSD. The reference atmospheric pressure data storage unit 300 stores a plurality of reference atmospheric pressure data at predetermined time intervals (for example, every 2 seconds).

For example, the cow may be equipped with an acceleration sensor and a barometric pressure sensor separately instead of the tag 20.

<Measurement data stored in the measurement data storage unit 200>
Here, the measurement data stored in the measurement data storage unit 200 according to the present embodiment will be described with reference to FIG. FIG. 8 is a diagram showing an example of measurement data stored in the measurement data storage unit 200 according to the second embodiment.

As shown in FIG. 8, the measurement data storage unit 200 stores one or more measurement data for each tag ID that identifies the tag. In addition, each measurement data further includes a barometric pressure sensor value. The barometric pressure sensor value is a barometric pressure value measured by the barometric pressure sensor included in the tag 20.

As described above, in the measurement data stored in the measurement data storage unit 200 according to the present embodiment, measurement data including the date and time, the acceleration sensor value, and the pressure sensor value is accumulated (stored) for each tag ID. ) Has been.

<Functional configuration of the specific processing unit 100>
Next, the functional configuration of the specific processing unit 100 according to the present embodiment will be described with reference to FIG. FIG. 9 is a diagram showing an example of the functional configuration of the specific processing unit 100 according to the second embodiment.

As shown in FIG. 9, the specific processing unit 100 further has a posture specifying unit 107. Further, the acquisition unit 101 according to the present embodiment acquires the reference atmospheric pressure data from the reference atmospheric pressure data storage unit 300. At this time, the acquisition unit 101 acquires the reference atmospheric pressure data for the same time as the measurement data acquired from the measurement data storage unit 200.

The posture specifying unit 107 is based on the barometric pressure sensor value included in the measurement data acquired by the acquisition unit 101 and the reference barometric pressure sensor value included in the reference barometric pressure data acquired by the acquisition unit 101, and the posture of the cow (that is, that is). , "Standing" or "lying"). Here, "standing" is a state in which a cow is standing. In addition, "lying" is a state in which a cow is lying down.

When the cow is difficult to stand, the cow's posture is "lying". Therefore, by specifying the posture of the cow by the posture specifying unit 107, the abnormality specifying unit 106 according to the present embodiment "stands up" even when it is determined that the index value exceeds the threshold value. If it is, it can be identified that no abnormality has occurred in the cow. This makes it possible to prevent erroneous detection (that is, a situation in which the abnormality specifying unit 106 identifies that an abnormality has occurred in the cow while the cow is standing).

<Process to identify abnormalities in cattle>
Hereinafter, the process of identifying the abnormality that has occurred in the cow will be described with reference to FIG. 10 in consideration of the posture of the cow. FIG. 10 is a flowchart showing an example of the abnormality specifying process according to the second embodiment. The process shown in FIG. 10 is repeatedly executed every 10 minutes, for example. However, 10 minutes is an example and may be repeatedly executed at arbitrary time intervals.

Since the processes of steps S11 to S15 of FIG. 10 are the same as those of FIG. 4, the description thereof will be omitted.

Following step S15, the acquisition unit 101 acquires reference atmospheric pressure data for the same time as in step S11 from the reference atmospheric pressure data storage unit 300 (step S21). That is, for example, when the measurement data between the date and time t ₁ to t _n (n is an integer of 1 or more) is acquired in step S11, the acquisition unit 101 similarly obtains the reference between the date and time t ₁ to t _n . Atmospheric pressure data is acquired from the reference atmospheric pressure data storage unit 300.

Next, the posture specifying unit 107 calculates the atmospheric pressure difference sensor value during a predetermined time (for example, 10 minutes) from the measurement data acquired by the acquisition unit 101 and the reference atmospheric pressure data (step S22). The pressure difference sensor value is the difference between the pressure sensor value included in the measurement data and the reference pressure sensor value included in the reference pressure data at the same date and time as the measurement data or the date and time corresponding to the measurement data.

Next, the posture specifying unit 107 identifies the posture of the cow from the differential barometric pressure sensor value calculated in step S22 above (step S23).

Here, an example of specifying the posture of a cow will be described with reference to FIG. 11. FIG. 11 is a diagram illustrating an example of specifying the posture of a cow.

Since the tag 20 is attached to the position of the cow's neck, when the differential pressure sensor value is relatively high, it indicates that the cow's neck is in the low position. On the other hand, when the differential barometric pressure sensor value is relatively low, it indicates that the position of the cow's neck is high. Therefore, as shown in FIG. 11, when the differential barometric pressure sensor value is relatively higher than other times, it can be identified that the cow is lying down.

Next, the abnormality specifying unit 106 identifies the abnormality of the cow in consideration of the posture of the cow identified in step S23 above (step S24). That is, for example, when the posture of the cow specified in step S23 is "standing", the abnormality specifying unit 106 identifies that no abnormality has occurred in the cow. On the other hand, for example, when the posture of the cow specified in step S23 above is "lying", the abnormality specifying unit 106 determines whether or not the index value exceeds the threshold value, as in step S15 of FIG. do. This makes it possible to identify abnormalities in cattle with high accuracy.

The posture of the cow may be specified, for example, after step S11 or step S12. At this time, for example, when the posture of the cow is specified as "standing", it is possible not to execute the subsequent processing. That is, when the posture of the cow is specified as "standing", it is possible not to calculate the L2 norm or the standard deviation.

Based on the above, in the specific device 10 according to the present embodiment, when identifying abnormalities occurring in cattle (for example, difficulty in standing up in the late fattening stage, difficulty in standing up before and after childbirth, difficulty in standing up due to flatulence, etc.), It is possible to identify the occurrence of anomalies with higher accuracy by considering the posture of the cow.

The present invention is not limited to the above-described embodiment disclosed specifically, and various modifications and modifications can be made without departing from the scope of claims.

1 Specified system 10 Specified device 20 Tags 30 Reference pressure sensor 100 Specified processing unit 101 Acquisition unit 102 Preprocessing unit 103 Norm calculation unit 104 Standard deviation calculation unit 105 Index value calculation unit 106 Abnormality identification unit 107 Attitude specification unit 200 Measurement data storage unit 300 Reference pressure data storage unit

Claims (5)

It is a specific device that identifies abnormalities in livestock.
A storage means for storing acceleration data measured by an acceleration sensor attached to the livestock, and a storage means.
A first acquisition means for acquiring one or more acceleration data during a predetermined time from the storage means,
A first calculation means for calculating the standard deviation based on the one or more acceleration data acquired by the first acquisition means, and
A second calculation means for calculating a predetermined index value from the standard deviation calculated by the first calculation means, and a second calculation means.
When the index value calculated by the second calculation means exceeds a predetermined threshold value set in advance, the first specific means for identifying the occurrence of an abnormality in the livestock and the first specific means.
Have,
The second calculation means is
A specific device, characterized in that the index value is calculated based on the magnitude relationship between the standard deviation and the preset upper limit value and lower limit value during a predetermined time . The specific device according to claim 1 , wherein at least one of the upper limit value and the lower limit value is set according to the characteristics of each livestock. The storage means stores the first barometric pressure data and the second barometric pressure data measured by the first barometric pressure sensor attached to the livestock and the second barometric pressure sensor installed at a predetermined position, respectively.
A second acquisition means for acquiring one or more first barometric pressure data and a second barometric pressure data from the storage means during a predetermined time,
It has one or more first atmospheric pressure data acquired by the second acquisition means and a second specific means for specifying the posture of the livestock from the second atmospheric pressure data.
The first specific means is
The invention according to claim 1 or 2 , wherein when the posture of the livestock is specified to be lying down by the second specific means, it is determined whether or not the index value exceeds the threshold value. Specific device. A specific device for identifying an abnormality in livestock, which has a storage means for storing acceleration data measured by an acceleration sensor attached to the livestock.
A first acquisition procedure for acquiring one or more acceleration data during a predetermined time from the storage means,
The first calculation procedure for calculating the standard deviation based on the acceleration data of one or more acquired by the first acquisition procedure, and the first calculation procedure.
A second calculation procedure for calculating a predetermined index value from the standard deviation calculated by the first calculation procedure, and a second calculation procedure.
When the index value calculated by the second calculation procedure exceeds a predetermined threshold value set in advance, the first specific procedure for identifying that an abnormality has occurred in the livestock and the first specific procedure.
And run
The second calculation procedure is
A specific method characterized in that the index value is calculated based on the magnitude relationship between the standard deviation and the preset upper limit value and lower limit value during a predetermined time . A program for making a computer function as each means of the specific device according to any one of claims 1 to 3 .

Images