JP2022080616A - Livestock desire discrimination program - Google Patents

Abstract

To automatically and highly accurately determine a livestock desire even without special skill and experience.SOLUTION: A livestock desire discrimination program for discriminating desires of livestock causes a computer to perform an information acquisition step and a discrimination step. The information acquisition step acquires acceleration information according to movements of livestock detected by acceleration sensors attached to bodies of the livestock to be discriminated and image information obtained by capturing the movements of the livestock. The discrimination step references a relation degree of three stages or more such as a combination of acceleration information for reference according to movements of livestock detected by acceleration sensors attached to bodies of livestock acquired in advance and image information for reference obtained by capturing the movements of the livestock and the desires of the livestock, and discriminates the desires of the livestock to be discriminated on the basis of the acceleration information and image information acquired by the information acquisition step.SELECTED DRAWING: Figure 3

Description

The present invention relates to a livestock desire discriminating program for discriminating the needs of mammalian livestock such as cattle, horses and pigs.

Mammalian livestock such as cows, horses, and pigs estrus or want food at a certain time, and even when they are sick, they want to do so. In particular, estrus is the period during which males are accepted, and mating takes place during this period, and estrus stops when conceived. Especially in livestock farmers, when artificial insemination is performed, it is necessary for the manager to accurately discover this estrus and seed it.
However, it is often difficult for even a skilled person to determine the desire of livestock. If the desire is missed, seeding will not be possible and it will be a great loss for livestock farmers. In addition, if the livestock are hungry and want food, but miss the feeding opportunity, the growth of the livestock will be stopped and the meat production will decrease. For this reason, it has been conventionally desired to provide a tool that assists in determining the desire of livestock.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object thereof is to automatically and highly accurately determine the desire in a livestock desire determination program for determining the desire of livestock. Is to provide a livestock desire discrimination program capable of.

In order to solve the above-mentioned problems, the livestock desire discrimination program according to the present invention is a livestock desire discrimination program for discriminating the needs of livestock, and the livestock is detected by an acceleration sensor attached to the body of the livestock to be discriminated. For reference according to the information acquisition step to acquire the acceleration information according to the movement and the image information obtained by capturing the movement of the livestock, and the movement of the livestock detected by the acceleration sensor attached to the body of the livestock acquired in advance. The combination of the acceleration information and the reference image information obtained by capturing the movement of the livestock, and the degree of association with the desire of the livestock in three or more stages are referred to, and the acceleration information and the image information acquired in the above information acquisition step are used. Based on the above, the computer is made to execute the discrimination step of discriminating the desire of the livestock to be discriminated.

Even if you do not have any special skills or experience, it is possible to determine the desires of livestock automatically and with high accuracy.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the livestock desire discrimination program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a livestock desire discrimination system 1 to which a livestock desire discrimination program to which the present invention is applied is implemented. The livestock desire determination system 1 includes an information acquisition unit 9, a search device 2 connected to the information acquisition unit 9, and a database 3 connected to the search device 2.

The information acquisition unit 9 is a device for a person using this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the search device 2 described later. The information acquisition unit 9 outputs the detected information to the search device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties. The information acquisition unit 9 may be configured by means for automatically fetching character strings and data posted on a site on the Internet. The information acquisition unit also includes an acceleration sensor attached to livestock.

The database 3 stores various information necessary for discriminating the desire for livestock. Information necessary for determining livestock needs includes reference acceleration information according to the movement of the livestock detected by the acceleration sensor attached to the body of the livestock acquired in advance, and reference image information that captures the movement of the livestock. , Reference audio information recorded livestock audio, reference food consumption information regarding livestock feed consumption acquired in advance, reference genital information obtained in advance for detecting the state of the livestock genital area, acquired in advance. Reference raw milk amount information that detects the amount of raw milk that can be collected from livestock, reference desire history information regarding the history of livestock needs acquired in advance, reference biological information that is detected in advance from each livestock, and livestock that have been acquired in advance. Reference attribute information regarding attributes, reference time zone information regarding the time zone in which the reference image information was captured, reference position information indicating the position of the livestock at the time when the reference image information was captured, and livestock to be discriminated. A dataset with the desires of is stored.

That is, in the database 3, such reference acceleration information, reference image information, reference audio information, reference food consumption information, reference genital area information, reference raw milk amount information, reference desire history information, Any one or more of the reference biological information, the reference attribute information, the reference time zone information, and the reference position information and the desire of the livestock to be discriminated are stored in association with each other.

The search device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be the one to be converted. The user can obtain a search solution by the search device 2.

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via an operation button, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 according to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is configured by a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program will be read and executed by the control unit 24.

The operation in the livestock desire discrimination system 1 having the above-described configuration will be described.

In the livestock desire determination system 1, it is premised that a combination of the reference acceleration information and the reference image information is formed, for example, as shown in FIG. The reference acceleration information is acceleration data detected by an acceleration sensor attached to the neck or body of a mammalian domestic animal such as a cow, a horse, or a pig, or a mammalian pet such as a dog or a cat. This reference acceleration information clearly reflects the movement of livestock. Cows in estrus are said to be restless and their walking speed and frequency may change, but such behavior will be reflected as reference acceleration information detected by the accelerometer. Cows in the estrous cycle also show behaviors that ride on other cows, and such behaviors are reflected as reference acceleration information detected by the acceleration sensor. In addition, hungry horses and cows may take actions to indicate their hunger, or they may lose energy and slow down, and such actions are reflected as reference acceleration information detected by the accelerometer. Will be.

The reference image information is composed of image data obtained by capturing such movements and behaviors of livestock with a camera. Acquire image data of behavior such as restlessness of cows in estrus, frequency and speed of walking, and riding on other cows, and acquire this by image analysis of the image data as needed. do. In addition, the image data of the operation when hungry or poor physical condition is acquired, and this is acquired by performing image analysis of the image data as necessary. If necessary, a deep learning technique may be used to automatically discriminate based on the feature amount of the analyzed image and convert it into data. Further, such behaviors and movements of livestock may be categorized based on the feature amount of the analysis image and automatically arranged.

As the input data, such reference acceleration information and reference image information are arranged side by side. The intermediate node 61 shown in FIG. 3 is a combination of reference image information and reference acceleration information as such input data. Each intermediate node 61 is further connected to an output. In this output, the desires A to E of the livestock are displayed. The desire of this livestock may be indicated as the time when the actual livestock estrus is estrus after days, hours, estrus after days, hours, etc., and the desire ends after days, hours, etc., or the starting point of the desire. It may indicate the date and time as well as the date and time of the end point of the desire. Or even if livestock are currently hungry, want to clean their bodies, want to drink water, want to excrete, and even if they are sick, they want to improve, and they want to improve their environment. good. According to the example of FIG. 3, Desire A has an estrus period from 10:00 on March 13 to 13:00 on March 14, and Desire B has an estrus period from 15:00 on March 15 to 6:00 on March 16. It may be composed of up to.

The determination of what the desire is may be determined by experts, experts, livestock farmers, etc., or it may be determined from the results of actual seeding, feeding, excretion, and washing. It may be something that is done. In such a case, if the seeding is actually performed and it succeeds, it is judged that the period is a desire, and if the seeding is performed and it fails, or if the seeding itself is not accepted, it is judged that it is not a desire. You may try to do it. Further, as a result of feeding, it may be determined from the goodness of biting, the amount of food consumed, and the like.

Through reference acceleration information and reference image information, various signs of livestock needs can be extracted, and a data set can be created from the extracted desire data. By creating and learning such a data set, it is possible to output what kind of desire the livestock has from the reference acceleration information and the reference image information.

Each combination (intermediate node) of the reference acceleration information and the reference image information is associated with each other through three or more levels of association with the desire as this output solution. The reference acceleration information and the reference image information are arranged on the left side through the degree of association, and the desires A, B, C, D, E, ... Are arranged on the right side through the degree of association. The degree of association indicates the degree of which desire is highly related to the reference acceleration information arranged on the left side and the reference image information. In other words, this degree of association is an index indicating what kind of desire each reference acceleration information and reference image information is likely to be associated with, and each reference acceleration information and reference image information. Therefore, it shows the accuracy in selecting the most suitable desire. In the example of FIG. 3, w13 to w22 are shown as the degree of association. These w13 to w22 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the desire as an output. On the contrary, the closer to one point, the lower the degree of relation between each combination as an intermediate node and the desire as an output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference acceleration information, the reference image information, and the desire in that case is suitable for determining the actual search solution, and analyzes these. , The degree of association shown in FIG. 3 is created by analysis.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference acceleration information P01 and the reference image information P16, the desire is analyzed from the past data. If there are many cases of A, set the degree of association that leads to A higher, and if there are many cases of "B", if there are few cases of A, increase the degree of association that leads to B and connect to A. Set the degree low. For example, in the example of the intermediate node 61a, the output of desires A and B is linked, but from the previous case, the degree of association of w13 connected to desire A is set to 7 points, and the degree of association of w14 connected to desire B is set to 2 points. It is set.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 3, the node 61b is a node in which the reference image information P14 is combined with the reference acceleration information P01, the degree of association of desire C is w15, and the degree of association of desire E is w16. It has become. The node 61c is a node in which the reference image information P15 and P17 are combined with respect to the reference acceleration information P02, and the degree of association of desire B is w17 and the degree of association of desire D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used when actually performing a search for determining a desire from now on. In such a case, image information is acquired in addition to acceleration information from the livestock to be discriminated to newly discriminate the desire. In addition to the acceleration information, the method of acquiring the image information is the same as the above-mentioned reference acceleration information and reference image information.

Based on the newly acquired acceleration information and image information in this way, the optimum desire is searched for. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired acceleration information is the same as or similar to P02 and the image information is the same as or similar to P17, the node 61d is associated via the degree of association. In this node 61d, the desire C is associated with w19 and the desire D is associated with the degree of association w20. In such a case, the desire C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the desire D, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

Further, an example of the degree of association w1 to w12 extending from the input is shown in Table 2 below.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

It should be noted that the livestock for which the reference acceleration information and the reference image information constituting the above-mentioned association degree are acquired and the livestock for which the discrimination target for newly acquiring the acceleration information and the image information are newly acquired do not have to be the same.

FIG. 4 shows an example in which a combination of the above-mentioned reference acceleration information, reference voice information, and a desire for the combination are set to three or more levels of association.

Reference audio information can be obtained by recording livestock audio. Livestock often raise or cry when they have some desire. Therefore, the desire of livestock can be determined by analyzing the voice of livestock. The voice of livestock can be detected, for example, through a microphone installed in a stable or the like.

As the reference voice information, the recorded voice may be frequency-analyzed, or the tone and strength thereof may be included in the analysis. This reference voice information may be obtained by acquiring voice in a certain period in time series and analyzing it.

In the example of FIG. 4, it is assumed that the input data is, for example, reference acceleration information P01 to P03 and reference voice information P18 to 21. The intermediate node shown in FIG. 4 is a combination of reference acceleration information and reference voice information as such input data. Each intermediate node is further linked to the output. In this output, the desire of livestock as an output solution is displayed.

Each combination (intermediate node) of the reference acceleration information and the reference voice information is associated with each other through three or more levels of association with the desire as this output solution. The reference acceleration information and the reference voice information are arranged on the left side through this degree of association, and the desires are arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the desire for the reference acceleration information and the reference voice information arranged on the left side. In other words, this degree of association is an index showing what kind of desire each reference acceleration information and reference voice information is likely to be associated with, and is most likely from the reference acceleration information and the reference voice information. It shows the accuracy in selecting each desire. In addition to the acceleration information, the desire changes depending on what the actual livestock voice is. Therefore, the optimum desire is searched for by combining the reference acceleration information and the reference voice information.

In the example of FIG. 4, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference acceleration information, the reference voice information, and the desire in that case is suitable for discriminating the actual search solution, and stores these. By analyzing and analyzing, the degree of association shown in FIG. 4 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference acceleration information P01 and the reference voice information P20, the desire of the livestock is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of desires A and B is linked, but from the previous case, the degree of association of w13 connected to desire A is set to 7 points, and the degree of association of w14 connected to desire B is set to 2 points. It is set.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference voice information P18 is combined with the reference acceleration information P01, the degree of association of desire C is w15, and the degree of association of desire E is w16. It has become. The node 61c is a node in which the reference voice information P19 and P21 are combined with respect to the reference acceleration information P02, and the degree of association of desire B is w17 and the degree of association of desire D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the acceleration information described above, the voice information of the livestock to be discriminated to newly determine the desire is acquired. The method of acquiring the voice information is the same as that of the reference voice information.

Such reference audio information and the method of capturing the audio information are based on the feature amount of the analyzed image by image analysis of the description of the document in which the above-mentioned audio information is described and, if necessary, deep learning technology. It may be automatically determined and converted into data.

Based on the newly acquired acceleration information and voice information in this way, the desire of livestock is searched for. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired acceleration information is the same as or similar to P02 and the voice information is P21, the node 61d is associated via the degree of association, and this node. In 61d, desire C is associated with w19 and desire D is associated with a degree of association w20. In such a case, the desire C having the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the desire D, which has the lowest degree of association but the association itself is recognized, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

FIG. 5 shows an example in which a combination of the above-mentioned reference image information, reference audio information, and a desire for the combination are set to three or more levels of association. In this way, the degree of association between the combination of the reference image information, the reference audio information, and the desire for the combination is acquired in advance. At the time of actual determination, the image information and audio information of the livestock to be discriminated are acquired. Then, after referring to the degree of association, the desire of the livestock is determined in the same manner as described above based on the newly acquired image information and audio information.

In FIG. 6, in addition to the above-mentioned reference acceleration information and reference image information, a combination of reference food consumption information and the desire of livestock for the combination are set to three or more levels of association. Here is an example.

Reference food consumption is information that indicates the actual food consumption of livestock. This reference food consumption information may be obtained by the breeder measuring the food consumption actually ingested by the livestock each time. As the measuring method, the weight of the bait box containing the bait may be measured before and after the bait, and the consumption of the bait may be calculated from the difference value. In addition, the inside of the container containing food (hay, etc.) may be imaged from an external camera, and the obtained image information may be analyzed before and after feeding to calculate the consumption of food. ..

In such a case, as shown in FIG. 6, the degree of association is such that the set of the combination of the reference acceleration information, the reference image information, and the reference food consumption information is the node 61a to 61e of the intermediate node as described above. Will be expressed as.

For example, in FIG. 6, in the node 61c, the reference acceleration information P02 is associated with the association degree w3, the reference image information P15 is associated with the association degree w7, and the reference food consumption information W is associated with the association degree w11. Similarly, in the node 61e, the reference acceleration information P03 is associated with the association degree w5, the reference image information P15 is associated with the association degree w8, and the reference food consumption information V is associated with the association degree w10.
The reference food consumption information may be obtained as one data of changes in food consumption over time. This makes it possible to grasp the tendency of whether the consumption of the latest food has decreased or increased as compared with the consumption of the normal food.

Similarly, when such a degree of association is set, the desire of the livestock is discriminated based on the acceleration information newly acquired from the livestock to be discriminated, the image information, and the food consumption information.

In determining this desire, the degree of association shown in FIG. 6 acquired in advance is referred to. For example, it can be specified that the acceleration information is the same as or similar to the reference acceleration information P02, the acquired image information is the same as or similar to the reference image information P15, and the acquired food consumption information is the reference food consumption. When it can be identified that the quantity information W is, the combination is associated with the node 61c, in which the desire B is associated with the degree of association w17 and the desire D is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the desire of the livestock to be discriminated is obtained by actually acquiring the acceleration information, the image information, and the food consumption information.

It is said that the desire of livestock is reflected in the amount of food consumed, and it is possible to discriminate the desire of livestock with high accuracy by referring to such food consumption information.

In FIG. 7, in addition to the above-mentioned reference acceleration information and reference image information, a combination of reference vulva information and the desire of livestock for the combination are set to three or more levels of association. An example is shown.

The reference vulva information is information indicating the vulva of livestock. This reference vulva information may be composed of image information obtained by capturing the vulva of livestock with a camera, or is input as data after actually visually observing and observing the vulva and grasping the situation. It may be a thing. Further, the reference vulva information may be composed of data as a result of detecting and confirming the presence or absence of estrus mucus coming out of the vulva by an image, a sensor, or visual recognition by a stable worker.

In such a case, as shown in FIG. 7, the degree of association is such that the set of the combination of the reference acceleration information, the reference image information, and the reference vulva information is the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 7, the reference acceleration information P02 is associated with the reference degree w3, the reference image information P15 is associated with the association degree w7, and the reference vulva information W is associated with the association degree w11. Similarly, in the node 61e, the reference acceleration information P03 is associated with the association degree w5, the reference image information P15 is associated with the association degree w8, and the reference vulva information V is associated with the association degree w10.

Similarly, when such a degree of association is set, the desire of the livestock is discriminated based on the acceleration information newly acquired from the livestock to be discriminated, the image information, and the vulva information.

In determining this desire, the degree of association shown in FIG. 7 acquired in advance is referred to. For example, it can be specified that the acceleration information is the same as or similar to the reference acceleration information P02, the acquired image information is the same as or similar to the reference image information P15, and the acquired vulva information is the reference vulva information. When it can be identified as W, the combination is associated with the node 61c, in which the desire B is associated with the degree of association w17 and the desire D is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the desire of the livestock to be discriminated from which the acceleration information, the image information, and the vulva information are actually newly acquired is obtained.

Especially in estrus female livestock, it is said that the condition of the vulva changes and mucus is released from the vulva, and by referring to such vulva information, the desire of the livestock can be discriminated with high accuracy. Is possible.

In FIG. 8, in addition to the above-mentioned reference acceleration information and reference image information, a combination of reference raw milk amount information and the desire of livestock for the combination are set to three or more levels of association. An example is shown.

The reference raw milk amount information is the amount of raw milk that can be collected from female livestock, and can be detected particularly from livestock that can collect raw milk such as dairy cows and goats. This reference raw milk amount information indicates the amount of raw milk collected per day, once, or per unit time.

In such a case, as shown in FIG. 8, the degree of association is such that the set of the combination of the reference acceleration information, the reference image information, and the reference raw milk amount information is the node 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 8, in the node 61c, the reference acceleration information P02 is associated with the association degree w3, the reference image information P15 is associated with the association degree w7, and the reference raw milk amount information W is associated with the association degree w11. Similarly, in the node 61e, the reference acceleration information P03 is associated with the association degree w5, the reference image information P15 is associated with the association degree w8, and the reference raw milk amount information V is associated with the association degree w10.

Similarly, when such a degree of association is set, the desire of the livestock is discriminated based on the acceleration information newly acquired from the livestock to be discriminated, the image information, and the raw milk amount information.

In determining this desire, the degree of association shown in FIG. 8 acquired in advance is referred to. For example, it can be specified that the acceleration information is the same as or similar to the reference acceleration information P02, the acquired image information is the same as or similar to the reference image information P15, and the acquired raw milk amount information is the reference raw milk amount information. When it can be identified as W, the combination is associated with the node 61c, in which the desire B is associated with the degree of association w17 and the desire D is associated with the degree of association w18. As a result of such a degree of association, based on w17 and w18, the desire of the livestock to be discriminated from which the acceleration information, the image information, and the raw milk amount information are newly acquired is actually obtained.

Since the desire of livestock may be reflected in the amount of raw milk that can be collected, it is possible to determine the desire of livestock with high accuracy by referring to such raw milk amount information.

Further, according to the present invention, the desire history information regarding the past desire history of each livestock to be discriminated may be further acquired. This desire history information includes the type and timing of the desire determined each year, the conception history if the livestock has been conceived and delivered many times, the process from conception to delivery, the postpartum situation, and delivery. Includes the situation of the child. Further, in the case of appetite, the desire history information may be composed of history information such as the type and amount of food given daily and the amount of food consumed. In addition to this, for the livestock, when the acquired (acceleration information, image information, ... raw milk amount information) was seeded at what timing, what kind of result was obtained, and at what timing the feed was given. It may indicate what kind of result the consumption amount and the bite were.

When discriminating a desire, the discriminating accuracy may be improved by referring to such desire history information. The desires determined through the above-mentioned degree of association may be further modified based on the desire history information, or the weighting may be changed. For example, in the desire history information, for livestock whose desires come relatively early every year, the desires determined through the degree of association may be set earlier, or the weighting of the degree of association leading to the earlier desires may be increased. You may do it.

The reference to such desire history information may be performed in the same manner at all the above-mentioned degree of association.

Further, in the present invention, the reference food consumption information may be applied as an alternative to the reference voice information in the degree of association shown in FIG. In such a case, there are three or more stages of a combination having a reference image information that captures the movement of the livestock acquired in advance, a reference feed consumption information regarding the consumption amount of the livestock food that has been acquired in advance, and a desire of the livestock. The degree of association is formed.

Next, the image information obtained by capturing the motion of the livestock to be discriminated and the food consumption information regarding the food consumption of the livestock to be discriminated are acquired in the same manner as described above. Then, with reference to the formed degree of association, the desire of the livestock is determined based on the reference image information according to the acquired image information and the reference food consumption information according to the acquired food consumption information. The details of the method for discriminating the desire are the same as described above.

Further, in the present invention, the reference desire history information may be applied as an alternative to the reference voice information in the degree of association shown in FIG. In such a case, a combination having a reference image information that captures the motion of the livestock acquired in advance, a reference desire history information regarding the history of the desire of the livestock acquired in advance, and a relationship of three or more stages of the desire of the livestock. Form the degree.

Next, the image information obtained by capturing the motion of the livestock to be discriminated and the desire history information regarding the past desire history of each livestock to be discriminated are acquired in the same manner as described above. Then, with reference to the formed degree of association, the desire of the livestock is determined based on the reference image information according to the acquired image information and the reference desire history information according to the acquired desire history information. The details of the method for discriminating the desire are the same as described above.

Further, in the present invention, the reference biometric information may be applied as a substitute for the reference voice information in the degree of association shown in FIG. In such a case, a combination having a reference image information that captures the movement of the livestock acquired in advance and a reference biological information previously detected from each livestock forms a degree of association of three or more levels with the desire of the livestock. Keep it.

The reference biological information and biological information include all biological information detected from domestic animals, and are composed of, for example, body temperature, heart rate, pulse, blood oxygen concentration, blood carbon dioxide amount, and the like. The body temperature may be measured through, for example, a thermo sensor, and the heart rate and blood oxygen concentration may be measured by using, for example, a chip sensor, an IC tag having a sensing function, or the like. Since such biometric information may also express a desire, this is used as an explanatory variable.

Next, the image information obtained by capturing the motion of the livestock to be discriminated and the biological information regarding the history of the past desires of each livestock to be discriminated are acquired in the same manner as described above. Then, the desire of the livestock is discriminated based on the reference image information according to the acquired image information and the reference biological information according to the acquired biological information by referring to the formed degree of association. The details of the method for discriminating the desire are the same as described above.

Further, in the present invention, the reference attribute information may be applied as a substitute for the reference voice information in the degree of association shown in FIG. In such a case, a combination having a reference image information that captures the movement of the livestock acquired in advance and a reference attribute information regarding the attributes of the livestock acquired in advance forms a degree of association of three or more levels with the desire of the livestock. I will do it.

The reference attribute information and attribute information are composed of data such as age, sex, type, pedigree, past medical history, past birth history, size and weight of livestock. These data may be manually input by a livestock farmer or the like. Since such attribute information may also express a desire, this is used as an explanatory variable.

Next, the image information obtained by capturing the motion of the livestock to be discriminated and the attribute information regarding the attributes of the livestock to be discriminated are acquired in the same manner as described above. Then, the desire of the livestock is determined based on the reference image information according to the acquired image information and the reference attribute information according to the acquired attribute information by referring to the formed degree of association. The details of the method for discriminating the desire are the same as described above.

Further, in the present invention, the reference time zone information may be applied as a substitute for the reference voice information in the degree of association shown in FIG. In such a case, there are three or more stages of a combination having reference image information that captures the movement of the livestock acquired in advance, reference time zone information regarding the time zone in which the reference image information is captured, and the desire of the livestock. Form the degree of association.

The reference time zone information and the time zone information may be, for example, grouped in the morning or afternoon, or may be indicated by an hourly or minute pitch as the time zone in which the reference image information is captured. .. Since the behavior of livestock desires may differ depending on the time of day, this is also used as an explanatory variable.

Next, the image information obtained by capturing the motion of the livestock to be discriminated and the time zone information relating to the time zone in which the image information is captured are acquired in the same manner as described above. Then, the desire of the livestock is determined based on the reference image information according to the acquired image information and the reference time zone information according to the acquired time zone information by referring to the formed degree of association. The details of the method for discriminating the desire are the same as described above.

Further, in the present invention, the reference position information may be applied as a substitute for the reference voice information in the degree of association shown in FIG. In such a case, a combination having reference image information that captures the movement of the livestock acquired in advance, reference position information that indicates the position of the livestock at the time when the reference image information is captured, and the desire of the livestock. Form three or more levels of association.

The reference position information and the position information may be composed of the position coordinates detected by, for example, GPS or the like as the position where the reference image information is captured. Since the behavior of the livestock's desire may differ depending on the position of the livestock, this is also used as an explanatory variable.

Next, the image information obtained by capturing the motion of the livestock to be discriminated and the position information indicating the position of the livestock at the time when the image information is captured are acquired in the same manner as described above. Then, the desire of the livestock is determined based on the reference image information according to the acquired image information and the reference position information according to the acquired position information by referring to the formed degree of association. The details of the method for discriminating the desire are the same as described above.

Further, as shown in FIG. 9, as the reference information U, reference acceleration information, reference image information, reference audio information, reference food consumption information, reference genital area information, reference raw milk amount information, etc. As the reference information V, the reference acceleration information, the reference image information, the reference audio information, the reference food consumption information, the reference genital information, and the reference, which are combined with the reference information U to form the degree of association. The raw milk amount information may be used for discrimination based on the degree of association between these combinations. The output solution is the desire described above.

At this time, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the desire of livestock without any special skill or experience. Further, according to the present invention, it is possible to make a judgment of this search solution with higher accuracy than that made by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the above-mentioned input data and output data are not completely the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A dataset may be created between the data and the output data and trained.

Further, according to the present invention, there is a feature that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

By discriminating the desires of livestock with higher credibility and less misunderstanding based on the degree of association expressed by such numerical values of three or more stages, multiple candidates for possible search solutions can be considered. Under the circumstances, it is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided, for example, via a public communication network such as the Internet. Further, when knowledge, information, and data regarding acceleration information, image information, audio information, food consumption information, genital area information, and raw milk amount information are acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is done by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts, except when it is based on information that can be obtained from the public communication network. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating a trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and training the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

In the above-described embodiment, the reference image information is used as the basic reference information, and the reference acceleration information, the reference voice information, the reference food consumption information, and the reference information are used as other reference information. An example of a case where the degree of association is configured by a combination of any of genital area information, raw milk volume information for reference, desire history information for reference, biological information for reference, attribute information for reference, time zone information for reference, and position information for reference. I explained it to you, but it is not limited to this. As the basic reference information, reference acceleration information other than reference image information, reference audio information, reference food consumption information, reference genital area information, reference raw milk amount information, reference desire history information, reference Biological information, reference attribute information, reference time zone information, and reference position information are used, and as other reference information, any reference information other than the reference information that is the basis thereof is combined to form a degree of association. You may do so.

Further, according to the present invention, as shown in FIG. 10, it is possible to use three or more levels of association between the reference information as the keynote and the reference adoption eligibility. In such a case, the solution search will be performed based on the degree of association between the reference information according to the newly acquired information and the recruitment eligibility level of three or more levels.

Similarly, in these cases, when the information corresponding to the reference information used as the learning data is input, the solution search is performed based on the above-mentioned method.

The search solution obtained through the degree of association may be further modified based on other reference information, or the weighting may be changed.

When any of the above-mentioned reference information is used as the keynote reference information, the other reference information here corresponds to any reference information other than the keynote reference information.

For example, as one of the other reference information, when the body temperature in the reference biological information is very high, it is assumed that it has been previously determined that the physical condition is poor. At this time, as the desire of livestock, a process of increasing the weighting of the search solution "I want to improve my physical condition" is performed, in other words, a process of increasing the probability that "I want to improve my physical condition" leads to the search solution. Set in advance to do so.

For example, another reference information G is an analysis result that more suggests the livestock's desire "want to feel better", and the reference information F is an analysis that more suggests the livestock's desire "fullness". It shall be the result. In this way, after setting with the reference information, if the actually acquired information is the same as or similar to the reference information G, the process of increasing the weighting of the livestock desire "I want to improve my physical condition" is performed. .. On the other hand, when the actually acquired information is the same as or similar to the reference information F, a process of increasing the weighting of the livestock desire "fullness" is performed. That is, the degree of association itself that leads to the desire of livestock may be controlled based on the reference information F to H. Alternatively, after the desire of livestock is determined only by the above-mentioned degree of association, the search solution obtained may be modified based on the reference information F to H. In the latter case, how to modify the desire of livestock as a search solution based on the reference information F to H will reflect what was designed on the system side each time.

Further, the reference information is not limited to the case where it is composed of any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such a case, the higher the desire of the livestock suggested by the reference information, the higher the correction of the desire of the livestock as the search solution obtained through the degree of association, and the suggestion of the reference information. In cases where the desire of livestock is lower, the desire of livestock as a search solution obtained through the degree of association may be modified to be lower.

1 Livestock desire discrimination system 2 Search device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimating unit 28 Storage unit 61 node

Claims (11)

In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring acceleration information according to the movement of the livestock detected by an acceleration sensor attached to the body of the livestock to be discriminated and image information obtained by capturing the movement of the livestock.
The combination of the reference acceleration information according to the movement of the livestock detected by the acceleration sensor attached to the body of the livestock acquired in advance and the reference image information obtained by imaging the movement of the livestock, and the desire of the livestock. It is characterized in that a computer is made to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the acceleration information acquired in the information acquisition step and the image information with reference to the degree of association of three or more stages. Livestock desire discrimination program. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring acceleration information according to the movement of the livestock detected by an acceleration sensor attached to the body of the livestock to be discriminated and audio information recorded from the livestock's voice.
The combination of the reference acceleration information according to the movement of the livestock detected by the acceleration sensor attached to the body of the livestock acquired in advance and the reference voice information obtained by recording the voice of the livestock, and the desire of the livestock. Livestock characterized by having a computer execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the acceleration information acquired in the information acquisition step and the voice information based on the degree of association of three or more stages. Desire discrimination program. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information obtained by capturing the movement of the livestock to be discriminated and audio information recorded with the voice of the livestock.
The above information acquisition step is based on the combination of the reference image information obtained by capturing the motion of the livestock acquired in advance, the reference audio information obtained by recording the voice of the livestock, and the degree of association with the desire of the livestock in three or more stages. A livestock desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the image information acquired in the above and the audio information. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information that captures the movement of the livestock to be discriminated and food consumption information regarding the food consumption of the livestock to be discriminated.
Based on the combination of the reference image information that captures the movement of the livestock acquired in advance and the reference food consumption information regarding the consumption amount of the livestock that has been acquired in advance, and the degree of association with the desire of the livestock in three or more stages. , A livestock desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the image information acquired in the information acquisition step and the food consumption information. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information that captures the movement of the livestock to be discriminated and desire history information regarding the history of past needs for each livestock to be discriminated.
Based on the combination of the reference image information that captures the movement of the livestock acquired in advance, the reference desire history information regarding the history of the desire of the livestock acquired in advance, and the degree of association with the desire of the livestock in three or more stages, the above. A livestock desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the image information acquired in the information acquisition step and the desire history information. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information obtained by capturing the movement of the livestock to be discriminated and biological information detected from the livestock to be discriminated.
In the above information acquisition step, based on the combination of the reference image information obtained by capturing the motion of the livestock acquired in advance, the reference biological information detected from each livestock in advance, and the degree of association with the desire of the livestock in three or more stages. A livestock desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the acquired image information and biological information. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information obtained by capturing the movement of the livestock to be discriminated and attribute information related to the attributes of the livestock to be discriminated.
The above information acquisition step is based on the combination of the reference image information obtained by capturing the motion of the livestock acquired in advance, the reference attribute information regarding the attributes of the livestock acquired in advance, and the degree of association with the desire of the livestock in three or more stages. A livestock desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the image information acquired in the above and the attribute information. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information obtained by capturing the movement of the livestock to be discriminated and time zone information relating to the time zone in which the above image information was captured, and
Based on the combination of the reference image information that captures the movement of the livestock acquired in advance and the reference time zone information regarding the time zone in which the reference image information was captured, and the degree of association of three or more stages with the desire of the livestock. , A livestock desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the image information acquired in the information acquisition step and the time zone information. In the livestock desire discrimination program that discriminates the desire of livestock,
An information acquisition step for acquiring image information that captures the movement of the livestock to be discriminated and position information indicating the position of the livestock at the time when the above image information is captured.
There are three or more stages of a combination of reference image information that captures the movement of the livestock acquired in advance, reference position information that indicates the position of the livestock at the time when the reference image information is captured, and the desire of the livestock. A livestock desire discrimination program characterized by having a computer execute a discrimination step for discriminating the desire of the livestock to be discriminated based on the image information acquired in the information acquisition step based on the degree of association and the position information. The livestock desire discrimination program according to any one of claims 1 to 9, wherein in the discrimination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. In the pet desire discrimination program that discriminates the needs of pets
An information acquisition step for acquiring image information obtained by capturing the motion of a pet to be discriminated and voice information recorded with the voice of the pet.
The above information acquisition step is based on the combination of the reference image information obtained by capturing the motion of the pet acquired in advance, the reference audio information recorded with the voice of the pet, and the degree of association with the pet's desire in three or more stages. A pet desire discrimination program characterized by causing a computer to execute a discrimination step for discriminating the desire of the pet to be discriminated based on the image information acquired in the above and the voice information.

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