JP7386667B2 - pet health management system - Google Patents

Description

The present invention relates to a pet health management system that detects the condition of a pet.

Against the backdrop of a stressed and aging society in recent years, there is a shift toward a pet-friendly society in which people seek comfort from pets and accept pets as members of their families. Furthermore, the relationship between people and pets is changing from a relationship of ``keeping and being kept'' as pets to one of ``family members and partners.'' Because of this relationship, the term "companion animal" for pets is also becoming widespread.

On the other hand, pets are aging faster than before, and people are becoming more aware of their health. Recently, it is believed that early detection of pre-symptomatic disease, which is a state that is on the verge of becoming a disease, and appropriate treatment can extend the healthy lifespan of a pet and lead to a longer lifespan for the pet. To this end, various methods have been proposed for understanding and managing the health status of pets.

For example, Patent Documents 1 and 2 describe a pet monitoring device that includes various sensors on a collar or the like worn on a pet and collects information regarding the pet such as the pet's movement, body temperature, and body position. Further, Patent Document 1 also describes that a plurality of sensors are used to detect movement and awakening of a pet.

Special table 2018-517400 publication Japanese Patent Application Publication No. 2008-131862

By the way, as pets, especially cats, are increasingly being kept indoors, the proportion of pets that do not wear collars is increasing, and there are even more cats that do not wear collars. Therefore, if a collar equipped with a sensor is attached to a cat that does not wear a collar, there is a problem in that the stress of the pet is likely to increase.

In addition, the method described in Patent Document 1 requires multiple sensors to detect movement and wakefulness of pets, and in the case of multiple pets, it is necessary to judge the health status of each individual. Therefore, it is necessary to attach multiple sensors to each pet. Therefore, there is a problem that the cost increases.

The present invention has been made in view of the above-mentioned problems, and an object of the present invention is to provide a pet health management system that can detect whether a pet is sick while suppressing stress in the pet while suppressing cost increases. With the goal.

The pet health management system of the present invention includes a state detection sensor that periodically detects the state of a pet and outputs a physical quantity indicating the state of the pet as state data, and a state detection sensor that detects the health state of the pet based on the state data. and a state management device that extracts first time data indicating the sleeping time of the pet from time series data of the state data periodically detected and output by the state detection sensor. a first time data extraction section; a number data extraction section that extracts number data indicating the number of times the pet's position changes during sleep from the first time data; , a determining unit that determines whether or not the pet is pre-symptomatic; the determining unit compares the first time data and the number of times data with a preset normal range; When the first time data or the number of times data is outside the normal range, and the number of times it is outside the normal range is a set number or more consecutively, it is determined that the pet is not sick. It is something.

As described above, according to the present invention, based on the first time data indicating the sleeping time extracted from the status data detected by the status detection sensor and the frequency data indicating the number of times the pet's position has changed, it is possible to determine whether the pet is not sick. It is determined whether or not there is. Thereby, it is possible to detect whether or not a pet is sick while suppressing stress on the pet and suppressing an increase in costs.

1 is a block diagram showing an example of the configuration of a pet health management system according to Embodiment 1. FIG. It is a schematic diagram showing the 1st example of the installation place of a state detection sensor. It is a schematic diagram showing the 2nd example of the installation place of a state detection sensor. 2 is a functional block diagram showing an example of the configuration of the state management device in FIG. 1. FIG. 5 is a hardware configuration diagram showing an example of the configuration of the state management device in FIG. 4. FIG. 5 is a hardware configuration diagram showing another example of the configuration of the state management device in FIG. 4. FIG. It is a graph for explaining the condition of a pet. It is a graph which shows an example of time series data of state data. It is a graph for explaining detection of a pet's pre-illness using sleep time. It is a graph for explaining detection of a pet's pre-illness using sleep time. 7 is a flowchart illustrating an example of the flow of state detection processing by the state management device according to the first embodiment. FIG. 2 is a block diagram showing an example of the configuration of a pet health management system according to a second embodiment. FIG. 3 is a block diagram showing an example of the configuration of a pet health management system according to Embodiment 3. FIG. FIG. 12 is a block diagram showing an example of the configuration of a pet health management system according to Embodiment 4. FIG. FIG. 12 is a block diagram showing an example of the configuration of a pet health management system according to Embodiment 5. FIG. 16 is a functional block diagram showing an example of the configuration of the state management device shown in FIG. 15. FIG. FIG. 7 is a block diagram showing an example of the configuration of a pet health management system according to a sixth embodiment. 18 is a functional block diagram showing an example of the configuration of the state management device in FIG. 17. FIG. FIG. 7 is a block diagram showing an example of the configuration of a pet health management system according to Embodiment 7. FIG. FIG. 12 is a block diagram showing an example of the configuration of a pet health management system according to Embodiment 8. FIG. FIG. 9 is a block diagram showing an example of the configuration of a pet health management system according to a ninth embodiment.

Embodiments of the present invention will be described below. The present invention is not limited to the following embodiments, and can be variously modified without departing from the spirit of the present invention. Further, the present invention includes all combinations of configurations that can be combined among the configurations shown in the following embodiments. Further, in each figure, the same reference numerals are the same or equivalent, and this is common throughout the entire specification.

Embodiment 1.
A pet health management system according to the first embodiment will be described. The pet health management system according to the first embodiment acquires information indicating the condition of a pet such as a cat, and detects whether the pet is sick or not based on the acquired information.

[Configuration of pet health management system 100]
FIG. 1 is a block diagram showing an example of the configuration of a pet health management system according to the first embodiment. As shown in FIG. 1, the pet health management system 100 includes a state management device 1, a state detection sensor 2, and an output device 3. The state management device 1 and the state detection sensor 2 are connected wirelessly or by wire. Further, the state management device 1 and the output device 3 are connected wirelessly or by wire.

The state detection sensor 2 detects a physical quantity indicating the state of the pet that comes into contact with the state detection sensor 2. For example, the condition detection sensor 2 detects one or more physical quantities such as the temperature, weight, or pressure of the pet it comes into contact with. The state detection sensor 2 outputs the detected physical quantity as state data. Note that in the first embodiment, the state detection sensor 2 is not attached directly to the pet using a collar or the like, but is installed at a specific location such as the pet's favorite location, for example.

FIG. 2 is a schematic diagram showing a first example of the installation location of the state detection sensor. As shown in FIG. 2, the state detection sensor 2 is provided, for example, on a pet sheet 50 used as a bed for a pet. Note that the condition detection sensor 2 is preferably provided under the pet sheet 50 so as not to come into direct contact with the pet. Furthermore, in order to ensure that the pet comes into contact with the state detection sensor 2 via the pet sheet 50, the pet sheet 50 may be provided with a stepped portion 50A so that the pet can lean against the stepped portion 50A. .

FIG. 3 is a schematic diagram showing a second example of the installation location of the state detection sensor. As shown in FIG. 3, the state detection sensor 2 is provided in a box 60 with an open top that is used as a bed for a pet, for example. In addition, also in this case, the state detection sensor 2 is preferably provided inside the bottom surface or on the back side of the bottom surface of the box body 60 so as not to come into direct contact with the pet.

The output device 3 in FIG. 1 notifies the user of the detection result of the pet's condition output from the condition management device 1 using video, audio, or the like. The output device 3 is provided with, for example, an LED (Light Emitting Diode), and visually informs the user of the pet's status by turning on, turning off, or blinking the LED. Further, the output device 3 may be provided with an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) display, and may notify the user of the pet's condition by video, image, text, or the like. Furthermore, the output device 3 may be provided with a speaker, for example, and may aurally notify the user of the pet's condition using audio.

The condition management device 1 periodically acquires condition data detected and output by the condition detection sensor 2, and detects whether the pet is sick or not based on the acquired condition data.

FIG. 4 is a functional block diagram showing an example of the configuration of the state management device shown in FIG. 1. As shown in FIG. As shown in FIG. 4, the state management device 1 includes a data acquisition section 11, a first time data extraction section 12, a frequency data extraction section 13, a comparison judgment section 14, and a storage section 15. The state management device 1 realizes various functions by executing software on an arithmetic device such as a microcomputer, or is configured with hardware such as a circuit device that realizes various functions.

The data acquisition unit 11 acquires state data that is a physical quantity detected by the state detection sensor 2 . The data acquisition unit 11 periodically acquires status data from the status detection sensor 2.

The first time data extraction unit 12 extracts first time data from the state data acquired by the data acquisition unit 11. The first time data indicates the sleeping time that is the pet's cumulative sleeping time included within a set period A that will be described later.

The frequency data extraction unit 13 extracts frequency data from the first time data extracted by the first time data extraction unit 12. The number of times data indicates the number of times the pet's position has changed, which is the number of times the pet's position has changed during the cumulative sleeping time of the pet, which is the first time data.

The comparison/determination unit 14 performs various comparisons and determinations to determine the health condition of the pet. For example, the comparison judgment unit 14 compares the first time data extracted by the first time data extraction unit 12 with a preset normal range, and determines whether the pet's sleeping time within the set period A is within the normal range. Determine whether it exists or not. Further, the comparison/determination unit 14 compares the frequency data extracted by the frequency data extraction unit 13 with the normal range, and determines whether the number of times the pet's position changes within the set period A is within the normal range. The normal range is a set value provided for the first time data and the number of position changes, respectively, in order to determine that the pet's health condition is good.

The comparison/judgment unit 14 compares the number of times the first time data is continuously outside the normal range with a preset number of times. Further, the comparison/judgment unit 14 compares the number of times that the number of times data is continuously outside the normal range with a preset number of times. The set number of times is a value set to prevent the pet's health condition from being erroneously determined to be disease-free. Then, the comparison/judgment unit 14 determines whether the pet is not sick or not based on the number of consecutive times outside the normal range of the first time data and the number of consecutive times outside the normal range of the frequency data.

The storage unit 15 stores various values used in each part of the state management device 1. For example, the storage unit 15 stores state data acquired by the data acquisition unit 11. Furthermore, the storage unit 15 stores the normal range, set number of times, etc. used by the comparison/judgment unit 14. Furthermore, the storage unit 15 stores setting amounts used when the first time data extraction unit 12 and the number of times data extraction unit 13 each extract the first time data and the number of times data.

The storage unit 15 is provided with a time data database (appropriately referred to as "time data DB") 151 and a number data database (appropriately referred to as "number of times data DB") 152. The time data DB 151 stores time data extracted by the first time data extraction unit 12. The time data DB 151 is referred to when the comparison/determination unit 14 determines whether or not the pet is pre-symptomatic based on the first time data. The number data DB 152 stores the number data extracted by the number data extraction unit 13. The frequency data DB 152 is referred to when the comparison/judgment unit 14 determines whether or not the pet is pre-symptomatic based on the frequency data.

FIG. 5 is a hardware configuration diagram showing an example of the configuration of the state management device shown in FIG. 4. As shown in FIG. When the various functions of the state management device 1 are executed by hardware, the state management device 1 in FIG. 4 is configured with a processing circuit 31, as shown in FIG. 5. The functions of the data acquisition section 11, first time data extraction section 12, frequency data extraction section 13, comparison/determination section 14, and storage section 15 in FIG. 4 are realized by the processing circuit 31.

When each function is executed by hardware, the processing circuit 31 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate). Array), or a combination of these. The functions of the data acquisition section 11, first time data extraction section 12, frequency data extraction section 13, comparison/judgment section 14, and storage section 15 may be realized by the processing circuit 31, or the functions of each section may be implemented in one It may be realized by the processing circuit 31.

FIG. 6 is a hardware configuration diagram showing another example of the configuration of the control device in FIG. 4. When the various functions of the state management device 1 are executed by software, the state management device 1 in FIG. 4 is configured with a processor 41 and a memory 42, as shown in FIG. The functions of the data acquisition section 11, first time data extraction section 12, frequency data extraction section 13, comparison and determination section 14, and storage section 15 are realized by the processor 41 and the memory 42.

When each function is executed by software, the functions of the data acquisition section 11, first time data extraction section 12, frequency data extraction section 13, comparison judgment section 14, and storage section 15 can be performed by software, firmware, or software and firmware. This is realized by a combination of Software and firmware are written as programs and stored in memory 42. The processor 41 reads and executes programs stored in the memory 42 to realize the functions of each section.

Examples of the memory 42 include nonvolatile or volatile semiconductor memories such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable and Programmable ROM), and EEPROM (Electrically Erasable and Programmable ROM). is used. Further, as the memory 42, a removable recording medium such as a magnetic disk, a flexible disk, an optical disk, a CD (Compact Disc), an MD (Mini Disc), and a DVD (Digital Versatile Disc) may be used.

[Detection of pre-symptomatic disease]
Next, a pre-symptomatic detection method using the pet health management system 100 according to the first embodiment will be described. In general, it is known that the sleeping state of pets, especially cats, such as sleeping time and number of changes in position during sleep, changes depending on their physical condition. In this way, a pet's health condition is manifested in sleep characteristics. Therefore, if the characteristics of a pet's sleep can be detected, it is possible to determine the pet's health condition. In addition, pets often sleep in a specific place, such as a favorite place, although this varies depending on environmental conditions such as temperature.

Therefore, the pet health management system 100 according to the first embodiment detects the characteristics of a pet's sleep at a specific location, and detects whether the pet is sick or not based on the detection results. Specifically, the pet health management system 100 installs a state detection sensor 2 at a specific location, and uses the state detection sensor 2 to detect a physical quantity indicating the state of the pet. Then, the pet health management system 100 extracts the sleeping time at the location and the number of changes in position during sleep from the state data, which is a physical quantity detected by the state detection sensor 2, and based on the extraction results, the pet health management system 100 Detect.

A method for extracting the pet's sleeping time and the number of changes in position will be described below. FIG. 7 is a graph for explaining the state of the pet. In FIG. 7, the horizontal axis indicates time, and the vertical axis indicates state data, which is a physical quantity detected by the state detection sensor 2. In FIG. That is, FIG. 7 shows time-series data of state data. Among the time-series data of the state data, a period in which the state data shows a high value indicates that the pet is staying at a specific place where the state detection sensor 2 is installed. Further, a period in which the status data shows a low value indicates that the pet is away from a specific location.

In general, pets have a set time for sleeping. Therefore, in the first embodiment, among the time-series data of the state data shown in FIG. is extracted.

(Extraction of sleep time)
As shown in FIG. 7, the stay time of the pet includes a state in which the pet is sleeping and a state in which the pet stays for temporary rest. Among these, during a stay for temporary rest, the amount of time that the pet continuously contacts the state detection sensor 2 is short, so the amount of increase and decrease in the value of the state data per unit time is smaller than during sleep. Conceivable.

Therefore, during sleeping time, the amount of change in state data per unit time is larger than during a stay due to temporary rest. In other words, the sleeping time is the time from when the state data greatly increases to when the state data greatly decreases per unit time. Specifically, the sleeping time is the time from the time when the state data per unit time increases by a preset first setting amount or more to the time when the state data decreases by the first setting amount or more.

In the example shown in FIG. 7, the ranges indicated by times t1 and t2 within the set period A are determined to be sleeping hours, and the cumulative sleeping time of the pet during the set period A is "t1+t2".

(Extraction of number of position changes)
The number of times the body position has changed can be determined from the number of times that a change in the state data, which is determined to be a change in the body position, is detected within the time that is determined to be the sleeping time. For example, a change in position such as turning over in bed is considered to be a case where the pet temporarily leaves the state detection sensor 2 during sleeping time and then immediately comes into contact with the state detection sensor 2. Therefore, the number of position changes is detected when the state data per unit time changes by a second set amount or more and a third set amount or less during a time that is determined to be sleep time.

In the example shown in FIG. 7, the state that satisfies the condition that the amount of change in the state data per unit time is equal to or greater than the second set amount and equal to or less than the third set amount occurs three times during the sleep time t1. The number of posture changes during t1 is three times. Similarly, during sleep time t2, two changes in position occur. Therefore, in the example shown in FIG. 7, the number of times the position changes within the set period A is five.

(Setting of setting period A)
Here, the setting of the setting period A will be explained. When looking at time-series data of status data by date, common patterns may exist. For example, if your pet is a cat, the cat may sleep in a somewhat fixed pattern. Therefore, when a common pattern exists in the time-series data of status data on a plurality of dates, the area including the common pattern is set as the setting period A.

FIG. 8 is a graph showing an example of time-series data of state data. In FIG. 8, the horizontal axis indicates time, and the vertical axis indicates state data, which is a physical quantity detected by the state detection sensor 2. In FIG. Moreover, in FIG. 8, time-series data of the state data on the first day, the second day, and the Xth day after acquiring the state data is shown.

For example, if a common pattern appears in the time-series data of status data on multiple dates, the setting period A for extracting the sleeping time and the number of changes in position during sleep is set to the period that includes the common pattern. Ru. In the example shown in FIG. 8, two stay times appear between 0:00 a.m. and 9:00 a.m. in the state data from day 1 to day X. Therefore, here, the period from 0:00 a.m. to 9:00 a.m. is set as the setting period A.

Note that the pet's condition, such as sleeping hours, may change as the day passes even if the pet's health condition is good. Then it is preferable. Further, the setting period A may be arbitrarily determined by the user, for example.

(detection of pre-symptomatic disease)
Next, a method for detecting whether a pet is ill using the sleeping time and the number of changes in position detected as described above will be described. Here, an example will be described in which a pet's pre-illness is detected using sleep time.

When detecting whether a pet is sick using sleep time, a normal range for determining that the pet is in good health is set in advance for the sleep time. If the sleeping time is within the normal range, it is determined that the pet is in good health.

Next, when the sleep time falls outside the normal range, the number of consecutive times the sleep time falls outside the normal range from that point on is counted. Then, when the number of times the sleeping time falls outside the normal range exceeds a preset number of times, it is determined that the pet is not sick. This is to prevent the pet's health condition from being erroneously determined to be disease-free if the sleeping time falls outside the normal range only once due to erroneous sensor detection or the like.

FIGS. 9 and 10 are graphs for explaining detection of pre-symptomatic conditions in pets using sleeping time. In FIGS. 9 and 10, the horizontal axis indicates the date, and the vertical axis indicates the sleep time t_sl, which is the first time data. That is, FIGS. 9 and 10 show changes in sleep time t_sl by date. Note that the sleeping time t_sl is the cumulative sleeping time within the set period A described above.

FIG. 9 shows an example in which the sleep time t_sl is shorter than normal. As shown in FIG. 9, when the sleep time t_sl falls outside the normal range on date X1, from this point on, the number of times the sleep time t_sl consecutively falls outside the normal range is counted. In this example, since the number of consecutive times the sleeping time t_sl is outside the normal range is equal to or greater than the set number of times, it is determined that the pet is not sick.

FIG. 10 shows an example in which the sleeping time t_sl is longer than normal. In the example shown in FIG. 10, the sleep time t_sl falls outside the normal range on date X2, and thereafter, the number of consecutive times the sleep time t_sl falls outside the normal range is equal to or greater than the set number of times. Therefore, in this case, similarly to the example of FIG. 9, it is determined that the pet is not sick.

In addition, in this example, a case has been described in which a pet's pre-sickness is detected using sleep time, but it is also possible to similarly detect a pet's pre-symptomatic status using the number of changes in position.

(Status detection processing)
FIG. 11 is a flowchart showing an example of the flow of state detection processing by the state management device according to the first embodiment. In step S1, the data acquisition unit 11 of the state management device 1 acquires state data based on the physical quantity detected by the state detection sensor 2. The data acquisition unit 11 stores the acquired state data in the storage unit 15.

In step S2, the comparison/determination unit 14 determines whether state data has been acquired for the set period A. If the state data has been acquired for the set period A (step S2: YES), the process moves to step S3. On the other hand, if the status data has not been acquired for the set period A (step S2: NO), the process returns to step S1, and the process of step S1 is repeated until the status data has been acquired for the set period A.

In step S3, the first time data extraction unit 12 reads out time series data of the acquired state data from the storage unit 15, and extracts first time data indicating the sleeping time of the pet from the time series data of the read state data. . Then, the first time data extraction unit 12 stores the extracted first time data in the time data DB 151 of the storage unit 15.

Further, in step S4, the frequency data extraction unit 13 extracts frequency data indicating the number of times of position changes in the first time data extracted in step S3. Then, the number data extraction unit 13 stores the extracted number data in the number data DB 152 of the storage unit 15.

In step S5, the comparison/determination unit 14 compares the extracted first time data with the normal range stored in the storage unit 15, and determines whether the first time data is within the normal range. As a result of the comparison, if the first time data is within the normal range (step S5: YES), the process moves to step S7.

Further, if the first time data is outside the normal range (step S5: NO), the process moves to step S6. In step S6, the comparison/judgment unit 14 refers to the time data DB 151 and compares the number of consecutive times the first time data is outside the normal range with the set number of times read out from the storage unit 15. As a result of the comparison, if the number of times the first time data is outside the normal range is less than the set number of times (step S6: NO), the process moves to step S7.

In step S7, the comparison/judgment unit 14 compares the extracted frequency data with the normal range stored in the storage unit 15, and determines whether the frequency data is within the normal range. As a result of the comparison, if the frequency data is within the normal range (step S7: YES), the comparison determination unit 14 determines that the pet's health condition is good in step S9.

If the number of times data is outside the normal range (step S7: NO), the process moves to step S8. In step S8, the comparison/judgment unit 14 refers to the number data DB 152 and compares the number of consecutive times the number data falls outside the normal range with the set number of times read out from the storage unit 15. As a result of the comparison, if the number of times the number data falls outside the normal range is less than the set number of times (step S8: NO), the comparison judgment unit 14 determines that the pet's health condition is good in step S9. do.

On the other hand, in step S6, if the number of times the first time data is outside the normal range is equal to or greater than the set number of times (step S6: YES), the comparison determination unit 14 determines that the pet is not sick in step S10. do. Furthermore, in step S8, if the number of times the frequency data falls outside the normal range is equal to or greater than the set number of times (step S8: YES), the comparison/judgment unit 14 also determines that the pet is not sick in step S10. do.

Then, in step S11, the state management device 1 outputs information indicating the determination result to the output device 3. As a result, the determination result regarding the state of the pet is output from the output device 3, and the user is notified of the state of the pet.

As described above, in the pet health management system 100 according to the first embodiment, the first time data indicating the sleeping time of the pet is extracted from the time series data of the state data, and the first time data indicating the sleeping time of the pet is extracted from the time series data of the state data. Number of times data indicating the number of times of position change is extracted. Then, based on the extracted first time data and frequency data, it is determined whether the pet is not sick. As a result, the pet's health condition can be detected with one condition detection sensor 2, so it is possible to suppress the pet's stress, suppress cost increases, and detect whether the pet is sick.

In the pet health management system 100, the time from the time when the state data increases by more than a first preset amount to the time when it decreases by more than the first preset amount is determined to be the pet's sleeping time, and this sleeping time is the first hour. Extracted as data. This allows the pet's sleeping time to be easily detected.

In the pet health management system 100, the number of times the state data per unit time in the first time data changes by more than a second set amount and less than a third set amount is extracted as frequency data. Thereby, the number of changes in position during sleep can be easily detected.

In the pet health management system 100, the first time data and the number of times data are compared with a preset normal range. Then, when the first time data or the number of times data falls outside the normal range, and the number of times that the first time data or the number of times falls outside the normal range continues to exceed a set number of times, it is determined that the pet is not sick. Thereby, for example, when the first time data or the number of times data falls outside the normal range only once, it is possible to prevent the pet's health condition from being erroneously determined to be disease-free.

Embodiment 2.
Next, Embodiment 2 will be described. The pet health management system according to the second embodiment differs from the first embodiment in that the pet's surface temperature is detected as a physical quantity. In the second embodiment, parts common to those in the first embodiment are denoted by the same reference numerals, and detailed description thereof will be omitted.

[Configuration of pet health management system 200]
FIG. 12 is a block diagram showing an example of the configuration of the pet health management system according to the second embodiment. As shown in FIG. 12, the pet health management system 200 includes a condition management device 1, a temperature sensor 202, and an output device 3. The state management device 1 and the temperature sensor 202 are connected wirelessly or by wire.

In the second embodiment, a temperature sensor 202 is used as the state detection sensor 2 described in the first embodiment. The temperature sensor 202 detects the surface temperature of the pet that comes into contact with the temperature sensor 202 as a physical quantity. The temperature sensor 202 outputs the detected surface temperature as state data (temperature data).

The condition management device 1 according to the second embodiment periodically acquires temperature data detected and output by the temperature sensor 202, and detects whether the pet is sick or not based on the acquired temperature data. Note that the configuration of the state management device 1 is the same as the configuration according to Embodiment 1 shown in FIG. 4, so a description thereof will be omitted.

[Status detection processing]
The first time data extraction unit 12 of the state management device 1 extracts first time data, which is the cumulative sleeping time in the set period A, from the temperature data from the temperature sensor 202 acquired by the data acquisition unit 11. Further, the frequency data extraction unit 13 extracts frequency data indicating the number of times the body changes position during sleep from the temperature data.

The method of extracting the first time data and frequency data from the temperature data is the same as in the first embodiment. That is, the time from the time when the temperature data per unit time increases by the first set amount or more to the time when the temperature data decreases by the first set amount or more becomes the first time data indicating the sleeping time. In addition, the value counted when the temperature data per unit time changes by more than the second set amount and less than the third set amount in the first time data determined as the sleeping time becomes the number of posture changes. Then, as in the first embodiment, the condition management device 1 detects whether the pet is sick or not based on the extracted first time data and frequency data.

As described above, in the pet health management system 200 according to the second embodiment, the temperature sensor 202 that detects the surface temperature of the pet as a physical quantity and outputs the detected surface temperature as state data is used as the state detection sensor 2. used. Thereby, it is possible to detect whether a pet is sick from the pet's surface temperature.

Embodiment 3.
Next, Embodiment 3 will be described. The pet health management system according to the third embodiment differs from the first and second embodiments in that the pet's weight is detected as a physical quantity. In addition, in this Embodiment 3, the same code|symbol is attached|subjected to the part common with Embodiment 1 and 2, and detailed description is abbreviate|omitted.

[Configuration of pet health management system 300]
FIG. 13 is a block diagram showing an example of the configuration of a pet health management system according to the third embodiment. As shown in FIG. 13, the pet health management system 300 includes a condition management device 1, a weight sensor 302, and an output device 3. The state management device 1 and the weight sensor 302 are connected wirelessly or by wire.

In the third embodiment, a weight sensor 302 is used as the state detection sensor 2 described in the first embodiment. The weight sensor 302 detects the weight of the pet present on the weight sensor 302 as a physical quantity. The weight sensor 302 outputs the detected weight as state data (weight data).

The condition management device 1 according to the third embodiment periodically acquires the weight data detected and output by the weight sensor 302, and detects whether or not the pet is sick based on the acquired weight data. Note that the configuration of the state management device 1 is the same as the configuration according to Embodiment 1 shown in FIG. 4, so a description thereof will be omitted.

[Status detection processing]
The first time data extraction unit 12 of the state management device 1 extracts first time data, which is the cumulative sleeping time in the set period A, from the weight data from the weight sensor 302 acquired by the data acquisition unit 11. Further, the frequency data extraction unit 13 extracts frequency data indicating the number of times the body changes position during sleep from the weight data.

The method of extracting the first time data and frequency data from the weight data is the same as in the first embodiment. That is, the time from the time when the weight data per unit time increases by the first set amount or more to the time when the weight data decreases by the first set amount or more becomes the first time data indicating the sleeping time. Further, the value counted when the weight data per unit time changes by a second set amount or more and a third set amount or less in the first time data determined as the sleeping time becomes the number of posture changes. Then, as in the first embodiment, the condition management device 1 detects whether the pet is sick or not based on the extracted first time data and frequency data.

As described above, in the pet health management system 300 according to the third embodiment, the weight sensor 302 that detects the weight of the pet as a physical quantity and outputs the detected weight as status data is used as the status detection sensor 2. . Thereby, it is possible to detect whether a pet is sick from the weight, which is the pet's weight.

Embodiment 4.
Next, Embodiment 4 will be described. The pet health management system according to the fourth embodiment differs from the first to third embodiments in that the pressure applied by the pet is detected as a physical quantity. Note that in the fourth embodiment, parts common to those in the first to third embodiments are given the same reference numerals, and detailed description thereof will be omitted.

[Configuration of pet health management system 400]
FIG. 14 is a block diagram showing an example of the configuration of a pet health management system according to the fourth embodiment. As shown in FIG. 14, the pet health management system 400 includes a condition management device 1, a pressure sensor 402, and an output device 3. The state management device 1 and the pressure sensor 402 are connected wirelessly or by wire.

In the fourth embodiment, a pressure sensor 402 is used as the state detection sensor 2 described in the first embodiment. The pressure sensor 402 detects the pressure applied to the pressure sensor 402 by the pet as a physical quantity. The pressure sensor 402 outputs the detected pressure as state data (pressure data).

The condition management device 1 according to the fourth embodiment periodically acquires pressure data detected and output by the pressure sensor 402, and detects whether or not the pet is sick based on the acquired pressure data. Note that the configuration of the state management device 1 is the same as the configuration according to Embodiment 1 shown in FIG. 4, so a description thereof will be omitted.

[Status detection processing]
The first time data extraction unit 12 of the state management device 1 extracts first time data, which is the cumulative sleeping time in the set period A, from the pressure data from the pressure sensor 402 acquired by the data acquisition unit 11. Further, the frequency data extraction unit 13 extracts frequency data indicating the number of times the position changes during sleep from the pressure data.

The method of extracting the first time data and the number of times data from the pressure data is the same as in the first embodiment. That is, the time from the time when the pressure data per unit time increases by the first set amount or more to the time when the pressure data decreases by the first set amount or more becomes the first time data indicating the sleeping time. In addition, the value counted when the pressure data per unit time changes by a second set amount or more and a third set amount or less in the first time data determined as sleep time becomes the number of posture changes. Then, as in the first embodiment, the condition management device 1 detects whether the pet is sick or not based on the extracted first time data and frequency data.

As described above, in the pet health management system 400 according to the fourth embodiment, the pressure sensor 402 that detects the pressure exerted by the pet as a physical quantity and outputs the detected pressure as state data is used as the state detection sensor 2. . Thereby, it is possible to detect whether a pet is sick from the pressure exerted by the pet.

Embodiment 5.
Next, Embodiment 5 will be described. The pet health management system according to Embodiment 5 is different from Embodiments 1 to 4 in that the pet health management system further uses posture maintenance time, which is the time for maintaining the same posture at the same place, to detect the health condition of the pet. differ. In the fifth embodiment, parts common to those in the first to fourth embodiments are denoted by the same reference numerals, and detailed description thereof will be omitted.

[Configuration of pet health management system 500]
FIG. 15 is a block diagram showing an example of the configuration of a pet health management system according to the fifth embodiment. As shown in FIG. 15, the pet health management system 500 includes a state management device 501, a state detection sensor 2, and an output device 3. The state management device 501 and the state detection sensor 2 are connected wirelessly or by wire.

In the fifth embodiment, the state detection sensor 2 detects a physical quantity indicating the state of the pet that comes into contact with the state detection sensor 2, as in the first embodiment. The state detection sensor 2 outputs the detected physical quantity as state data. Note that in the fifth embodiment, any one of the temperature sensor 202, weight sensor 302, and pressure sensor 402 described in the second to fourth embodiments may be used as the state detection sensor 2.

Similarly to the condition management device 1 according to the first embodiment, the condition management device 501 periodically acquires condition data detected and output by the condition detection sensor 2, and based on the acquired condition data, determines whether the pet is sick or not. Detect.

FIG. 16 is a functional block diagram showing an example of the configuration of the state management device shown in FIG. 15. As shown in FIG. 16, the state management device 501 includes a data acquisition section 11, a first time data extraction section 12, a frequency data extraction section 13, a second time data extraction section 516, a comparison judgment section 14, and a storage section 15. ing. The state management device 501 realizes various functions by executing software on an arithmetic device such as a microcomputer, or is configured with hardware such as a circuit device that realizes various functions.

The second time data extractor 516 extracts second time data based on the first time data extracted by the first time data extractor 12 and the number data extracted by the number data extractor 13. The second time data indicates posture maintenance time, which is the time during which the same posture was maintained during sleep.

In addition to the various comparisons and judgments described in Embodiments 1 to 4, the comparison/judgment unit 14 compares the second time data with a preset normal range to maintain the pet's posture within the set period A. Determine whether the time is within the normal range.

[Detection of pre-symptomatic disease]
Next, a method for detecting pre-symptomatic diseases using the pet health management system 500 according to the fifth embodiment will be described. As the pet's health deteriorates, the time between one change in position changes. For example, if a pet is not feeling well, it will toss and turn more frequently, and the time from one change of position to the next change of position will be shorter. Furthermore, when it becomes difficult to move the body, it becomes difficult to turn over in bed, and it takes a long time to change the position from one time to the next.

Therefore, in the fifth embodiment, in addition to the first time data and the number of times data, second time data, which is the posture maintenance time, is used to detect whether the pet is sick.

The posture maintenance time can be determined from the time between a plurality of times when the posture is determined to have changed in the sleeping time extracted as the first time data. Specifically, the second time data extraction unit 516 starts measuring time when the number of times data extraction unit 13 detects a change in position during sleep. Further, the second time data extraction unit 516 ends the time measurement when the next position change is detected by the number of times data extraction unit 13. Then, the second time data extraction unit 516 stores the time from the start of time measurement to the end of time measurement as a posture maintenance time.

When detecting whether a pet is sick using the posture maintenance time, a normal range for determining that the pet is in good health is set in advance for the posture maintenance time. If the posture maintenance time is within the normal range, it is determined that the pet is in good health.

Next, when the posture maintenance time falls outside the normal range, from that point on, the number of times the posture maintenance time consecutively falls outside the normal range is counted. Then, when the number of times the posture maintenance time is outside the normal range exceeds a preset number of times, it is determined that the pet is not sick. This is to prevent the pet's health condition from being erroneously determined to be disease-free if the posture maintenance time falls outside the normal range only once due to erroneous sensor detection or the like.

As described above, in the pet health management system 500 according to the fifth embodiment, based on the first time data that is the sleeping time, the number of times data that is the number of posture changes, and the second time data that is the posture maintenance time, It is determined whether the pet is disease-free or not. In this way, in the fifth embodiment, the health condition of the pet is determined using the second time data in addition to the first time data and the number of times data, so the health condition of the pet can be determined more appropriately. be able to.

Embodiment 6.
Next, Embodiment 6 will be described. The pet health management system according to Embodiment 6 differs from Embodiments 1 to 5 in that the sleeping time is determined in consideration of the weather. In the sixth embodiment, parts common to those in the first to fifth embodiments are given the same reference numerals, and detailed explanations are omitted.

[Configuration of pet health management system 600]
FIG. 17 is a block diagram showing an example of the configuration of a pet health management system according to the sixth embodiment. As shown in FIG. 17, the pet health management system 600 includes a state management device 601, a state detection sensor 2, and an output device 3. The state management device 601 and the state detection sensor 2 are connected wirelessly or by wire. Furthermore, in the sixth embodiment, the state detection sensor 2 may be any one of the temperature sensor 202, weight sensor 302, and pressure sensor 402 described in the second to fourth embodiments.

Similarly to the condition management device 1 according to the first embodiment, the condition management device 601 periodically acquires condition data detected and output by the condition detection sensor 2, and based on the acquired condition data, determines whether the pet is sick or not. Detect. Further, the state management device 601 obtains weather information from the outside and corrects the sleeping time included in the obtained state data based on the obtained weather information.

FIG. 18 is a functional block diagram showing an example of the configuration of the state management device shown in FIG. 17. As shown in FIG. 18, the state management device 601 includes a data acquisition section 11, a first time data extraction section 12, a frequency data extraction section 13, a sleep time correction section 616, a comparison judgment section 14, and a storage section 15. . The state management device 601 realizes various functions by executing software on an arithmetic device such as a microcomputer, or is configured with hardware such as a circuit device that realizes various functions.

The data acquisition unit 11 periodically acquires state data, which is a physical quantity detected by the state detection sensor 2, similarly to the data acquisition unit 11 according to the first embodiment. Further, in the sixth embodiment, the data acquisition unit 11 acquires weather information from the outside.

The sleep time correction unit 616 corrects the first time data extracted by the first time data extraction unit 12 based on the weather information acquired by the data acquisition unit 11. Specifically, the sleep time correction unit 616 multiplies the first time data by a weather coefficient that is preset according to weather conditions such as sunny or cloudy. The weather coefficient is a correction coefficient depending on the weather, and is stored in advance in the storage unit 15 in association with the weather state.

[Correction of first time data]
Next, correction of the first time data, which is sleep time, will be explained. It is known that the sleeping hours of pets such as cats vary depending on the weather. For example, if the weather is "sunny" as a standard, if the weather is "cloudy" the sleeping time will be longer. On the other hand, when the weather is too rough, such as a thunderstorm or a typhoon, it becomes difficult for pets to sleep peacefully, resulting in shorter sleep times.

Such changes in sleep time due to weather have no relation to the pet's health condition, so when determining the pet's health condition based on sleep time, it is necessary to remove changes in sleep time due to weather. Therefore, in the sixth embodiment, the first time data is corrected by multiplying the first time data indicating the sleeping time extracted from the time series data of the state data by a weather coefficient indicating a correction coefficient depending on the weather. be done.

The weather coefficient is a coefficient determined in advance according to the weather. The weather coefficient is set to a value of "1" as a reference value when the weather is "sunny". Weather coefficients are then set for weather conditions such as "cloudy" and "stormy weather." For example, if the weather is "cloudy" and your pet's sleeping time is longer than when it is "sunny", the weather coefficient for "cloudy" weather is set to a value smaller than the standard value "1" (for example, the value "0.7") is set. In addition, when the weather is "stormy" such as thunderstorms or typhoons, and the sleeping time is shorter than when the weather is "sunny," the weather coefficient for "stormy" weather is set to a value larger than the standard value "1." (For example, the value "1.4") is set.

When the first time data extraction unit 12 extracts the first time data, the sleep time correction unit 616 reads out the corresponding weather coefficient from the storage unit 15 based on the weather information. Then, the sleep time correction unit 616 multiplies the extracted first time data by the read weather coefficient.

As a result, the pet's sleeping time extracted as the first time data is converted to the time when the weather is "sunny" as a reference, with changes in sleeping time due to weather removed. Therefore, since the sleeping time of the pet can be obtained regardless of the weather condition, it is possible to prevent false detection of the pet's pre-symptomatic condition and appropriately detect the pre-symptomatic condition of the pet.

Note that in the case of weather conditions that make it difficult to estimate how a pet's sleeping time will change, if the sleeping time is extracted using this status data, the accuracy of the data may be impaired. Therefore, if it is difficult to set the weather coefficient, the value of the weather coefficient may be set to "0" and the first hour data at this time may be excluded from the data used for detecting Mibyo. .

As described above, in the pet health management system 600 according to the sixth embodiment, the first time data is corrected based on weather information. This removes the influence of the weather on the pet's sleeping time, making it possible to extract more accurate sleeping time, thereby making it possible to more appropriately detect whether the pet is pre-sick.

In the pet health management system 600, when correcting the first time data, the first time data is multiplied by a weather coefficient that is preset according to the weather condition indicated by the weather information. Thereby, the first time data can be easily corrected.

Embodiment 7.
Next, Embodiment 7 will be described. The pet health management system according to Embodiment 7 detects whether each pet is sick or not, without being aware of the multiple pets, even if the user owns multiple pets. In the seventh embodiment, parts common to those in the first to sixth embodiments are denoted by the same reference numerals, and detailed description thereof will be omitted.

Generally, if you have multiple pets, each pet has its own preferred temperature. Therefore, each pet tends to stay in a location that has its preferred temperature. Therefore, in the seventh embodiment, the temperature of a specific place is controlled and the specific pet is guided so that the specific pet stays in a specific place.

[Configuration of pet health management system 700]
FIG. 19 is a block diagram showing an example of the configuration of a pet health management system according to the seventh embodiment. As shown in FIG. 19, the pet health management system 700 includes a state management device 701, a plurality of state detection devices 710, and an output device 3. The state management device 701 and each state detection device 710 are connected wirelessly or by wire. Further, the state management device 701 and the output device 3 are connected wirelessly or by wire.

Each of the plurality of state detection devices 710 includes the state detection sensor 2 and the heating device 4 described in the first embodiment. The heating device 4 is installed near the state detection sensor 2 and heats a specific place where the pet stays. Heating by the heating device 4 is controlled by the state management device 701.

The state detection sensor 2 detects a physical quantity indicating the state of the pet that comes into contact with the state detection sensor 2, as in the first embodiment. The state detection sensor 2 outputs the detected physical quantity as state data. Note that in the seventh embodiment, any one of the temperature sensor 202, weight sensor 302, and pressure sensor 402 described in the second to fourth embodiments may be used as the state detection sensor 2.

Like the state management device 1 according to the first embodiment, the state management device 701 periodically acquires state data detected and output by each of the state detection sensors 2 provided in the plurality of state detection devices 710. , detects whether multiple pets are sick based on the acquired status data. Further, in the seventh embodiment, the condition management device 701 is provided in the plurality of condition detection devices 710 so that the temperature of a specific place is in accordance with the preference of the target pet among the plurality of pets. Each heating device 4 is controlled.

In this way, in the pet health management system 700 according to the seventh embodiment, the heating device 4 is installed near the condition detection sensor 2, and heats the specific place so that the temperature becomes the temperature preferred by the specific pet. Device 4 is controlled. This allows you to guide a specific pet to the location where the condition detection sensor 2 is installed, so even if you have multiple pets, you can check the health status of each pet without having to identify each pet. Can be detected.

Embodiment 8.
Next, Embodiment 8 will be described. The pet health management system according to the eighth embodiment is similar to the seventh embodiment in that even if you have multiple pets, you can detect whether each pet is sick without being aware of the multiple pets. . Furthermore, the pet health management system according to the eighth embodiment differs from the seventh embodiment in that a specific pet is guided by the pet's own scent. In the eighth embodiment, parts common to those in the first to seventh embodiments are denoted by the same reference numerals, and detailed description thereof will be omitted.

In general, pets often choose areas that have their own scent as their territory. Therefore, pets perceive a place where their own scent is present as a place where they can feel safe, and they tend to stay there. Therefore, in the eighth embodiment, a specific pet is guided by adsorbing a scent to a specific place so that the specific pet stays in a specific place.

[Configuration of pet health management system 800]
FIG. 20 is a block diagram showing an example of the configuration of a pet health management system according to the eighth embodiment. As shown in FIG. 20, the pet health management system 800 includes a state management device 801, a plurality of state detection devices 810, and an output device 3. The state management device 801 and each state detection device 810 are connected wirelessly or by wire. Furthermore, the state management device 801 and the output device 3 are connected wirelessly or by wire.

Each of the plurality of state detection devices 810 includes the state detection sensor 2 and the odor adsorption member 5 described in the first embodiment. The odor adsorption member 5 is installed near the state detection sensor 2 and adsorbs the pet's own odor.

The state detection sensor 2 detects a physical quantity indicating the state of the pet that comes into contact with the state detection sensor 2, as in the first embodiment. The state detection sensor 2 outputs the detected physical quantity as state data. Note that in the eighth embodiment, any one of the temperature sensor 202, weight sensor 302, and pressure sensor 402 described in the second to fourth embodiments may be used as the state detection sensor 2.

Like the state management device 1 according to the first embodiment, the state management device 801 periodically acquires state data detected and output by each of the state detection sensors 2 provided in the plurality of state detection devices 810. , detects whether multiple pets are sick based on the acquired status data.

In this manner, in the pet health management system 800 according to the eighth embodiment, the odor adsorption member 5 is installed near the state detection sensor 2, and the odor of a specific pet itself is adsorbed at a specific location. This allows you to guide a specific pet to the location where the condition detection sensor 2 is installed, so even if you have multiple pets, you can check the health status of each pet without having to identify each pet. Can be detected.

Embodiment 9.
Next, Embodiment 9 will be described. The pet health management system according to Embodiment 9 is similar to Embodiments 7 and 8 in that even if you have multiple pets, you can detect whether each pet is sick without being aware of the multiple pets. Common. Furthermore, the pet health management system according to the ninth embodiment is different from the seventh and eighth embodiments in that a plurality of pets are identified using a microchip embedded in the pet. In Embodiment 9, parts common to Embodiments 1 to 8 are denoted by the same reference numerals, and detailed description thereof will be omitted.

Recently, it has been proposed and put into practical use that a microchip is embedded in a pet and identification information stored in advance in the microchip is used to identify each of a plurality of pets. Therefore, in the present embodiment 9, when multiple pets are kept, each pet is identified using identification information from a microchip, and the health condition of each identified pet is detected.

[Configuration of pet health management system 900]
FIG. 21 is a block diagram showing an example of the configuration of a pet health management system according to the ninth embodiment. As shown in FIG. 21, the pet health management system 900 includes a state management device 901, a plurality of state detection devices 910, and an output device 3. The state management device 901 and each state detection device 910 are connected wirelessly or by wire. Further, the state management device 901 and the output device 3 are connected wirelessly or by wire.

Each of the plurality of state detection devices 910 includes the state detection sensor 2 and the communication device 6 described in the first embodiment. The communication device 6 receives the pet's own identification information from a microchip embedded in the pet. Furthermore, the communication device 6 transmits the received identification information to the state management device 901. The identification information is information unique to the pet in which the microchip is embedded, and includes various information such as the pet's type, gender, and age.

The state detection sensor 2 detects a physical quantity indicating the state of the pet that comes into contact with the state detection sensor 2, as in the first embodiment. The state detection sensor 2 outputs the detected physical quantity as state data. Note that in the eighth embodiment, any one of the temperature sensor 202, weight sensor 302, and pressure sensor 402 described in the second to fourth embodiments may be used as the state detection sensor 2.

The condition management device 901 periodically acquires condition data detected and output by each of the condition detection sensors 2 provided in the plurality of condition detection devices 910, and based on the acquired condition data, determines whether the plurality of pets are sick or not. Detect. Here, if there are multiple pets, the state management device 901 receives identification information from the communication device 6, and classifies the acquired state data based on the received identification information. The condition management device 901 then detects the health condition of each pet individually based on the classified condition data.

In this way, in the pet health management system 900 according to the ninth embodiment, the acquired status data is classified based on the identification information received from the status detection device 910, and whether or not the pet is pre-symptomatic is individually determined. be judged. As a result, even if you have multiple pets, the health status of each pet can be detected without having to identify each pet.

1, 501, 601, 701, 801, 901 state management device, 2 state detection sensor, 3 output device, 4 heating device, 5 odor adsorption member, 6 communication device, 11 data acquisition section, 12 first time data extraction section, 13 count data extraction section, 14 comparison judgment section, 15 storage section, 31 processing circuit, 41 processor, 42 memory, 50 pet sheet, 50A step section, 60 box body, 100, 200, 300, 400, 500, 600, 700 , 800, 900 pet health management system, 151 time data database, 152 frequency data database, 202 temperature sensor, 302 weight sensor, 402 pressure sensor, 516 second time data extraction section, 616 sleep time correction section, 710, 810, 910 Condition detection device.

Claims (12)

a state detection sensor that periodically detects the state of the pet and outputs a physical quantity indicating the state of the pet as state data;
and a condition management device that detects the health condition of the pet based on the condition data,
The state management device includes:
a first time data extraction unit that extracts first time data indicating the sleeping time of the pet from time series data of the state data periodically detected and output by the state detection sensor;
a frequency data extraction unit that extracts frequency data indicating the number of times the pet's position changes during sleep from the first time data;
a determination unit that determines whether or not the pet is pre-symptomatic based on the extracted first time data and the number of times data ;
The judgment unit is
Comparing the first time data and the number of times data with a preset normal range,
When the first time data or the number of times data falls outside the normal range, and the number of times that the first time data or the number of times falls outside the normal range exceeds a set number of consecutive times, it is determined that the pet is not sick. A pet health management system. The first time data extraction unit includes:
determining the time from the time when the state data increases by a preset first amount or more to the time when the state data decreases by more than a first preset amount as the sleeping time of the pet;
The pet health management system according to claim 1, wherein the sleeping time is extracted as the first time data. The number of times data extraction unit includes:
The pet health management system according to claim 1 or 2, wherein the number of times the state data changes by a second set amount or more and a third set amount or less per unit time in the first time data is extracted as the number of times data. The state management device includes:
further comprising a second time data extraction unit that extracts second time data indicating a time period during which the pet maintains its posture while sleeping;
The second time data extraction unit includes:
In the sleep time extracted as the first time data, when a change in posture is detected by the frequency data extraction section, time measurement is started, and when the next change in posture is detected by the frequency data extraction section, the time measurement is started. The time measurement is ended and the time from the start to the end of the time measurement is extracted as the second time data,
The judgment unit is
The extracted second time data is compared with a preset normal range, and if the second time data is outside the normal range, it is determined that the pet is not sick. The pet health management system according to any one of 1 to 3 . The state management device includes:
The pet health management system according to any one of claims 1 to 4 , further comprising a sleep time correction section that corrects the first time data based on weather information. The sleep time correction unit includes:
The pet health management system according to claim 5 , wherein the first time data is multiplied by a weather coefficient that is preset according to the weather condition indicated by the weather information. the state detection sensor;
Further comprising a plurality of condition detection devices each having a heating device installed near the condition detection sensor and heating a specific location,
The state management device includes:
The pet health management system according to any one of claims 1 to 6 , wherein a plurality of said heating devices are controlled. the state detection sensor;
The pet health management system according to any one of claims 1 to 6 , further comprising a plurality of condition detection devices each having an odor adsorption member installed near the condition detection sensor and adsorbing odor. the state detection sensor;
further comprising a plurality of state detection devices each having a communication device installed near the state detection sensor and transmitting and receiving identification information,
The state management device includes:
classifying the status data based on the received identification information;
The pet health management system according to any one of claims 1 to 6 , wherein the pet health management system individually determines whether or not a plurality of pets are pre-symptomatic. The state detection sensor is
The pet health management system according to any one of claims 1 to 9 , wherein a surface temperature of the pet is detected as the physical quantity, and the detected surface temperature is output as the state data. The state detection sensor is
The pet health management system according to any one of claims 1 to 9 , wherein the weight of the pet is detected as the physical quantity, and the detected weight is output as the state data. The state detection sensor is
The pet health management system according to any one of claims 1 to 9 , wherein pressure caused by the pet is detected as the physical quantity, and the detected pressure is output as the state data.

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