JP7600927B2 - Toilet System - Google Patents

Description

The disclosed embodiment relates to a toilet system.

Various technologies have been proposed for detecting abnormalities in devices such as toilet devices (see, for example, Patent Document 1). In the above-mentioned conventional technologies, an abnormality detection sensor such as an odor sensor is provided near the device such as the toilet device, and an abnormality in the device is detected based on the output of the sensor.

JP 2002-021148 A

However, the conventional technology was only able to detect specific abnormalities that could be detected by an abnormality detection sensor, leaving room for improvement. In other words, because there are a wide variety of types of abnormalities in equipment, there was a risk that the conventional technology described above would not be able to detect the abnormalities, leaving room for improvement.

One aspect of the embodiment aims to provide a toilet system that can detect a wide range of abnormalities in equipment.

The toilet system according to one embodiment of the present invention is characterized by comprising a device provided in a toilet room, an imaging unit capable of imaging a user using the device, a recognition unit that recognizes the user's behavioral pattern based on the image captured by the imaging unit, and an abnormality determination unit that determines an abnormality in the device based on the behavioral pattern recognized by the recognition unit.

This makes it possible to detect a wide range of abnormalities in equipment. That is, for example, when using an abnormality detection sensor like that of conventional technology, there is a risk that only specific abnormalities can be detected. However, by using the user's behavior pattern recognized based on the captured image, it becomes possible to detect abnormalities such as water not being discharged from an automatic faucet even when the user holds their hand near the spout, and therefore it is possible to detect a wide range of abnormalities in equipment.

The device is an automatic faucet, and the recognition unit recognizes the user's hand movement relative to the automatic faucet as the behavior pattern based on the captured image.

In this way, the abnormality determination unit can detect that an abnormality has occurred in the automatic faucet by using a behavioral pattern that indicates the user's hand movements relative to the automatic faucet.

The imaging unit is capable of capturing an image of the spout of the automatic faucet and the user's hands, and the recognition unit recognizes the change over time in the distance between the spout and the user's hands based on the captured image as the behavioral pattern, and the abnormality determination unit determines that an abnormality has occurred in the automatic faucet if the behavioral pattern including the change in distance is a specific pattern.

In this way, by performing an abnormality determination process using a behavioral pattern, including the change over time in the distance between the water outlet and the user's hand, and a specific pattern, it becomes possible to easily and accurately detect abnormalities in an automatic faucet.

The abnormality determination unit is also characterized in that it determines that an abnormality has occurred in the automatic faucet if the behavior pattern including the change in distance is the specific pattern that indicates a periodic change.

In this way, by performing an abnormality determination process using a behavioral pattern that includes changes over time in the distance between the water outlet and the user's hand, and a specific pattern that indicates periodic changes, it becomes possible to detect abnormalities in automatic faucets more easily and accurately.

The automatic faucet is also equipped with a water outlet sensor that detects the user's hand near the water outlet, and the abnormality determination unit determines an abnormality in the automatic faucet based on the behavior pattern and the detection result of the water outlet sensor.

This allows the abnormality determination unit to detect the occurrence of an abnormality in the automatic faucet and estimate the location in the automatic faucet where the abnormality has occurred.

The device is a urinal, and the recognition unit recognizes the movement of the user's head relative to the urinal as the behavior pattern based on the captured image.

In this way, by using the movement of the user's head relative to the urinal, it is possible to widely detect abnormalities in a wide range of equipment (urinals).

The recognition unit is also characterized in that it recognizes the trajectory of the user's movement relative to the device as the behavior pattern based on the captured image.

In this way, by using a behavior pattern including the trajectory of the user's movement relative to the device, it becomes possible to accurately detect that an abnormality has occurred in the device. That is, for example, if the behavior pattern including the trajectory of movement recognized by the recognition unit is a specific pattern such as immediately leaving the device and moving on, the abnormality determination unit can determine that an abnormality has occurred in the device, that is, it becomes possible to accurately detect that an abnormality has occurred in the device.

The device is also characterized in that a plurality of the devices are provided in series, the imaging unit is capable of capturing an image of the movement of the user between the plurality of the devices in series, the recognition unit recognizes the movement of the user between the plurality of the devices in series and the time the user uses the device as the behavioral pattern based on the captured image, and the abnormality determination unit determines that an abnormality has occurred in the device when at least one of the movement of the user between the plurality of the devices in series and the time the user uses the device, which are included in the behavioral pattern, is a specific pattern.

In this way, by using the movement of users between multiple devices and the amount of time the users spend using each device, it is possible to detect a wide range of abnormalities in devices in a toilet system in which multiple devices are installed in series.

According to one aspect of the embodiment, a wide range of abnormalities in equipment in a toilet system can be widely detected.

FIG. 1 is a diagram illustrating an overview of a toilet system according to a first embodiment. FIG. 2 is a block diagram showing an example of the configuration of the toilet system according to the first embodiment. FIG. 3 is a diagram showing an example of the specific pattern information. FIG. 4 is a diagram for explaining the specific pattern information. FIG. 5 is a flowchart showing a processing procedure executed by the anomaly detection device. FIG. 6 is a diagram showing an example of the configuration of a toilet system according to the second embodiment. FIG. 7 is a diagram for explaining specific pattern information according to the second embodiment. FIG. 8 is a diagram showing an example of the configuration of a toilet system according to the third embodiment.

Below, an embodiment of the toilet system disclosed in the present application will be described in detail with reference to the attached drawings. Note that the present invention is not limited to the embodiment shown below. It should be noted that the drawings are schematic, and the dimensional relationships and ratios of each element may differ from reality. There may also be parts in which the dimensional relationships and ratios differ between the drawings.

(First embodiment)
<Toilet system overview>
First, an outline of the configuration of a toilet system according to a first embodiment will be described below with reference to Fig. 1. Fig. 1 is a diagram illustrating an outline of the toilet system according to the first embodiment.

As shown in FIG. 1, the toilet system 1 according to this embodiment includes a faucet 10, a water supply valve 14, an imaging unit 20, an equipment control device 30, and an anomaly detection device 60.

The faucet 10 is an automatic faucet that includes a water discharge sensor 11 and automatically discharges water when the hand H of a user U is detected by the water discharge sensor 11. For example, the faucet 10 is provided on the washbasin 2 of the toilet room 200. The faucet 10 is an example of a device.

In more detail, the water discharge sensor 11 of the faucet 10 is a human body detection sensor that detects, for example, the hand H of a user U. When the water discharge sensor 11 detects a hand H or the like, it outputs a detection signal indicating the detection to the equipment control device 30. Note that the water discharge sensor 11 is placed, for example, near the water outlet 12, but is not limited to this and can be placed in any position.

The faucet 10 is connected to a water supply valve 14 via a water supply pipe 13. The water supply valve 14 is, for example, an electromagnetic valve, and is connected to a water source such as a water pipe (not shown). The water supply valve 14 is controlled to open and close by an equipment control device 30.

For example, when a detection signal indicating that a hand H or the like has been detected is input from the water discharge sensor 11, the equipment control device 30 opens the water supply valve 14. As a result, water supplied from a water source (such as tap water) is discharged from the water outlet 12 via the water supply valve 14 and the water supply pipe 13 toward the bowl portion 3 of the sink 2.

However, if the above-mentioned toilet room 200 is, for example, a public toilet room, when an abnormality occurs in the equipment (here, the faucet 10), it is required to detect the abnormality as early as possible and return the equipment to a normal state.

However, there are many different types of abnormalities in equipment. Therefore, in a configuration using an abnormality detection sensor as in conventional technology, there was a risk that only specific abnormalities that can be detected by such a sensor could be detected.

Here, we will explain one example of an abnormality that can occur in the faucet 10, which is an appliance. In the faucet 10, the water discharge sensor 11 may not be able to properly detect the user U's hand H, etc., resulting in an abnormality where water is not discharged. The cause of such an abnormality is, for example, that dirty water from hand washing or soapy water is likely to splash onto the surface (detection surface) of the water discharge sensor 11, which makes it easy for calcium components and oil contained in dirt such as dirty water to adhere to it. As a result, the water discharge sensor 11 is unable to properly detect the user U's hand H, etc., due to the adhesion of calcium components and other substances, and as a result, an abnormality occurs in the faucet 10 where water is not discharged. It is difficult for the faucet 10 to detect such abnormalities by itself, and measures have been called for.

Therefore, in the toilet system 1 according to this embodiment, the imaging unit 20 and anomaly detection device 60 described above are provided, making it possible to widely detect anomalies in a wide range of equipment (here, the faucet 10).

The imaging unit 20 is a camera capable of capturing an image of a user U using the faucet 10. For example, the imaging unit 20 is configured to be able to capture an image of the water outlet 12 of the faucet 10 and the hand H of a user U using the faucet 10. Note that the imaging unit 20 does not need to capture an image of the water outlet 12 itself, but only needs to be able to capture an image of the vicinity of the water outlet 12 (in other words, the position equivalent to the water outlet 12).

The imaging unit 20 is installed at an appropriate position above the faucet 10 and the washbasin 2 (for example, on the ceiling of the toilet room 200). Note that, in the above description, the imaging unit 20 is installed above the faucet 10, etc., but this is not limited to this. In other words, the imaging unit 20 can be installed at any position, and for example, as shown by the imaginary lines in FIG. 1, the imaging unit 120 may be installed at a position to the side of the faucet 10 (for example, on the wall of the toilet room 200) to capture an image of a hand H, etc.

The imaging unit 20 outputs the captured image to the anomaly detection device 60. Note that the captured image is video data, but is not limited to this and may be still image data, etc.

The anomaly detection device 60 can execute an anomaly detection process to detect an anomaly in the faucet 10 (equipment). Before proceeding with the explanation of the anomaly detection process in the anomaly detection device 60, we will now explain the actions that the user U will take if an anomaly occurs in the faucet 10.

For example, as described above, if the water discharge sensor 11 has a deposit on it and is unable to properly detect the user U's hand H, causing an abnormality in which water is not discharged from the faucet 10, water will not be discharged from the faucet 10 even if the user U holds out their hand H near the water outlet 12 (in other words, near the water discharge sensor 11). At this time, the user U may perform an action that prompts the water discharge sensor 11 to make a detection, such as by moving their hand H back and forth, as shown by arrow A1.

In this embodiment, if the user U exhibits such a behavioral pattern, that is, for example, a behavioral pattern of moving the hand H in a forward/backward direction, it is determined that an abnormality has occurred in the faucet 10 (appliance), in other words, it is detected that an abnormality has occurred in the faucet 10. In this specification, expressions indicating directions such as "forward/backward" and "left/right" are used to mean "forward/backward" and "left/right" as seen from the user U when the user U is standing in front of the appliance (here, the faucet 10) to use it.

The anomaly detection process will now be described in detail. First, the anomaly detection device 60 acquires an image captured by the imaging unit 20 (step S1).

Next, the anomaly detection device 60 recognizes the behavioral pattern of the user U based on the captured image (step S2). For example, the anomaly detection device 60 analyzes the captured image and extracts the part of the user U's hand H in the image. Then, the anomaly detection device 60 recognizes the movement of the extracted part of the hand H as the behavioral pattern of the user U.

Next, an abnormality in the faucet 10 (apparatus) is determined based on the recognized behavioral pattern of the user U (step S3). For example, the abnormality detection device 60 stores in advance in the storage unit 80 (see FIG. 2) information indicating a specific behavioral pattern (hereinafter sometimes referred to as a "specific pattern") that the user U may take when an abnormality occurs in the faucet 10, such as a behavioral pattern of moving the hand H back and forth, and compares the recognized behavioral pattern with the stored specific pattern.

Then, if the recognized behavioral pattern matches or nearly matches a specific pattern, in other words, if the behavioral pattern of the user U is a specific pattern, the anomaly detection device 60 determines that an abnormality has occurred in the faucet 10, i.e., detects that an abnormality has occurred in the faucet 10.

As a result, this embodiment can detect abnormalities in a wide range of devices (in this case, the faucet 10).

When an abnormality in the faucet 10 is detected, the abnormality detection device 60 may notify the administrator who manages the faucet 10 of information indicating that an abnormality has been detected. This allows the administrator to take action such as repairing the faucet 10 in which an abnormality has occurred, thereby enabling the faucet 10 to be quickly returned (restored) to a normal state.

<Toilet system configuration>
Next, the configuration of the toilet system 1 according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the toilet system 1 according to the first embodiment. Note that in the block diagram of Fig. 2, only components necessary for explaining the features of this embodiment are shown as functional blocks, and descriptions of general components are omitted.

In other words, each component shown in the block diagram of FIG. 2 is a functional concept, and does not necessarily have to be physically configured as shown. For example, the specific form of distribution and integration of each functional block is not limited to that shown, and all or part of it can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

As shown in FIG. 2, the toilet system 1 includes the water discharge sensor 11, the water supply valve 14, the imaging unit 20, the equipment control device 30, and the anomaly detection device 60.

The water discharge sensor 11 is a sensor that detects, for example, the hand H of the user U near the water discharge port 12 (see FIG. 1). The water discharge sensor 11 can output a detection signal indicating that the hand H of the user U has been detected to the equipment control device 30. Note that the water discharge sensor 11 can be, but is not limited to, a photoelectric sensor or a radio wave sensor.

The water supply valve 14 is an electromagnetic valve that supplies or stops water to the faucet 10. For example, the water supply valve 14 opens and closes in response to a control signal input from the equipment control device 30, supplying or stopping water to the faucet 10. Note that the "water" is not limited to room temperature water such as tap water (tap water), but may be, for example, hot water, a mixture of hot and cold water, or cold water.

The imaging unit 20 is a camera (e.g., an AI camera) capable of capturing an image of a user U using the faucet 10. The imaging unit 20 outputs the captured image to the anomaly detection device 60. The imaging unit 20 includes, for example, a lens and an imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), but is not limited to this.

The device control device 30 controls the faucet 10, which is an example of a device. Specifically, the device control device 30 includes a control unit 40 and a memory unit 50. The control unit 40 is realized by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing a program stored in the memory unit 50 using a RAM (Random Access Memory) as a working area. The control unit 40 can also be realized by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The memory unit 50 has volatile and non-volatile memory, and is realized by, for example, semiconductor memory elements such as RAM and flash memory.

Specifically, the control unit 40 includes an acquisition unit 41 and an equipment control unit 42. The acquisition unit 41 acquires a detection signal output from the water discharge sensor 11, which indicates that the hand H of the user U has been detected. When the acquisition unit 41 acquires a detection signal from the water discharge sensor 11, it outputs information indicating that the detection signal has been acquired to the equipment control unit 42 and the abnormality detection device 60. In this way, the acquisition unit 41 can output the presence or absence of a detection signal output from the water discharge sensor 11 to the equipment control unit 42 and the abnormality detection device 60 as the detection result of the water discharge sensor 11.

The device control unit 42 controls the opening and closing of the water supply valve 14 and the like. For example, when information indicating that a detection signal from the water discharge sensor 11 has been acquired is input, the device control unit 42 opens the water supply valve 14. This causes water to be discharged from the water outlet 12 in the faucet 10.

The abnormality detection device 60 detects an abnormality in the faucet 10, which is an example of a device. Specifically, the abnormality detection device 60 includes a control unit 70 and a memory unit 80. The control unit 70 is realized, for example, by a CPU or MPU, executing a program stored in the memory unit 80 using RAM as a working area. The control unit 70 can also be realized, for example, by hardware such as an ASIC or FPGA. The memory unit 80 has volatile and non-volatile memory, and is realized, for example, by semiconductor memory elements such as RAM or flash memory.

Specifically, the control unit 70 includes an acquisition unit 71, a recognition unit 72, an abnormality determination unit 73, and a notification unit 74. The storage unit 80 stores specific pattern information 81 and the like.

The specific pattern information 81 is information indicating a specific behavior pattern (specific pattern) that a user U may take when, for example, an abnormality occurs in a device (here, the faucet 10). Here, the specific pattern information 81 will be explained using FIG. 3. FIG. 3 is a diagram showing an example of the specific pattern information 81. As shown in FIG. 3, the specific pattern information 81 includes items such as "pattern ID" and "specific pattern," and each item is associated with each other.

"Pattern ID" is identification information that identifies a specific pattern. "Specific pattern" is information that indicates a specific pattern. Note that in the example shown in FIG. 3, for convenience, the "specific pattern" is described abstractly as "specific pattern C01", but specific information is stored in "specific pattern C01".

In the example shown in Figure 3, the data of the specific pattern identified by the pattern ID "B01" indicates that the specific pattern is "specific pattern C01."

Here, the information contained in the "specific pattern" of the specific pattern information 81 will be specifically explained with reference to FIG. 4. FIG. 4 is a diagram for explaining the specific pattern information, and is a diagram of the faucet 10, user U, etc., as viewed from above. Note that here, the specific patterns that the user U may take when an abnormality occurs, for example, when the user U holds out his/her hand H near the water outlet 12 and water is not dispensed from the faucet 10, are explained, but the type of abnormality is not limited to this.

As shown in FIG. 4, if water does not come out from the faucet 10 even when the user U holds out their hand H near the water outlet 12, it is estimated that the user U may adopt a behavior pattern of moving their hand H back and forth, as indicated by the arrow A1. Therefore, such a behavior pattern is included in the "specific pattern" of the specific pattern information 81.

In this way, the "specific pattern" of the specific pattern information 81 includes information indicating the movement of the user U's hand H relative to the faucet 10 when an abnormality occurs in the faucet 10. As an example of such hand H movement, the user U may adopt a behavioral pattern of moving the hand H back and forth multiple times (in other words, moving it back and forth) when an abnormality occurs in the faucet 10. At this time, the distance L between the water outlet 12 and the hand H changes periodically over time; more specifically, the distance L becomes longer and shorter over time. Therefore, a behavioral pattern in which the change over time in the distance L between the water outlet 12 and the hand H indicates a periodic change is included in the "specific pattern" of the specific pattern information 81.

Note that, although FIG. 4 shows an example in which the distance L is from the water outlet 12 to the tip of the hand H, this is not limited to this, and for example, the distance from the water outlet 12 to the center of gravity of the hand H (more precisely, the center of gravity of a part of the hand H in the captured image) may also be used.

In the above, the behavioral pattern of moving the hand H in a forward and backward direction is included in the specific pattern information 81, but this is not limited thereto, and other behavioral patterns may be included in the specific pattern information 81, such as a behavioral pattern of moving the hand H in a left-right direction near the water outlet 12 (see arrow A2).

Although not shown in the figure, the movement trajectory of the user U relative to the faucet 10 may be included in the specific pattern information 81. In other words, if the user U holds out his/her hand H near the water outlet 12 and water does not come out of the faucet 10, it is estimated that the user U may adopt a behavior pattern in which he/she immediately moves away from the faucet 10. Therefore, a behavior pattern indicating a movement trajectory in which the user immediately moves away from the faucet 10 may be included in the specific pattern information 81.

Returning to the explanation of FIG. 2, the acquisition unit 71 of the control unit 70 acquires an image from the imaging unit 20. The acquisition unit 71 outputs the acquired image to the recognition unit 72. The acquisition unit 71 also acquires the detection result of the water discharge sensor 11, and outputs the acquired detection result to the abnormality determination unit 73.

The recognition unit 72 recognizes the behavioral pattern of the user U based on the captured image captured by the imaging unit 20. For example, the recognition unit 72 analyzes the captured image and extracts a part of the user U's hand H in the image. Next, the recognition unit 72 recognizes (detects) the movement of the extracted part of the hand H over time as the behavioral pattern of the user U. Note that any analysis method can be used to analyze the captured image described above.

More specifically, the recognition unit 72 recognizes the movement of the user U's hand H relative to the faucet 10 as a behavior pattern based on the captured image. In this way, the recognition unit 72 recognizes the movement of the user U's hand H relative to the faucet 10 as a behavior pattern, making it possible to detect that an abnormality has occurred in the faucet 10, as described below.

For example, the recognition unit 72 may recognize a change over time in the distance L (see FIG. 4) between the water outlet 12 and the hand H of the user U based on the captured image as a behavioral pattern. As an example, the recognition unit 72 may calculate the distance L between the water outlet 12 and a part of the hand H extracted by analyzing the captured image, and recognize the change over time (e.g., the amount of change or rate of change) of the calculated distance L (see FIG. 4) as a behavioral pattern.

The recognition unit 72 may also recognize the trajectory of the user U's movement relative to the faucet 10 as a behavior pattern based on the captured image. For example, the recognition unit 72 may analyze the captured image and extract parts of the user U's body in the image. The recognition unit 72 may then take the trajectory of the extracted body parts as the trajectory of the user U's movement and recognize this trajectory as the behavior pattern of the user U. In this way, the recognition unit 72 recognizes the trajectory of the user U's movement relative to the faucet 10, which is an example of a device, as a behavior pattern, making it possible to accurately detect the occurrence of an abnormality in the device, as described below.

The recognition unit 72 outputs the behavioral pattern recognized (detected) as described above to the abnormality determination unit 73.

The abnormality determination unit 73 determines an abnormality in the device (the faucet 10 in this case) based on the behavioral pattern recognized by the recognition unit 72. For example, the abnormality determination unit 73 reads out the specific pattern information 81 from the memory unit 80 and compares the recognized behavioral pattern with the specific pattern included in the specific pattern information 81.

Then, if the recognized behavioral pattern matches or approximately matches a specific pattern (for example, a specific behavioral pattern such as moving the hand H back and forth), in other words, if the behavioral pattern of the user U is a specific pattern, the abnormality determination unit 73 determines that an abnormality has occurred in the faucet 10, i.e., detects that an abnormality has occurred in the faucet 10.

As a result, in this embodiment, it is possible to widely detect abnormalities in a wide variety of devices (faucets 10). That is, when using an abnormality detection sensor such as that used in the prior art, there is a risk that only specific abnormalities can be detected. However, in this embodiment, by using the behavioral patterns of the user U recognized based on the captured image, it is possible to detect abnormalities such as water not being discharged from the faucet 10 even when the user U holds out his/her hand H near the water outlet 12, and therefore it is possible to widely detect abnormalities in a wide variety of devices (faucets 10).

The abnormality determination unit 73 also determines an abnormality in the faucet 10 based on a behavior pattern indicating the movement of the user U's hand H relative to the faucet 10 recognized by the recognition unit 72. In this way, the abnormality determination unit 73 is able to detect that an abnormality has occurred in the faucet 10 by using a behavior pattern indicating the movement of the user U's hand H relative to the faucet 10.

In addition, when a change over time in the distance L (see FIG. 4) between the water outlet 12 and the hand H of the user U is recognized as a behavioral pattern and input, the abnormality determination unit 73 can determine an abnormality in the faucet 10 based on the behavioral pattern that includes the change in the distance L.

For example, the abnormality determination unit 73 reads out the specific pattern information 81 from the memory unit 80 and compares the behavior pattern including the change in distance L with the specific pattern included in the specific pattern information 81. If the behavior pattern including the change in distance L is a specific pattern, the abnormality determination unit 73 determines that an abnormality has occurred in the faucet 10.

In this way, by performing an abnormality determination process using a behavioral pattern that includes the change over time in the distance L between the water outlet 12 and the user U's hand H, and a specific pattern, it becomes possible to easily and accurately detect abnormalities in the faucet 10.

Specifically, the abnormality determination unit 73 determines that an abnormality has occurred in the faucet 10 when the behavior pattern including the change in the distance L is a specific pattern that indicates a periodic change. As an example, the abnormality determination unit 73 determines that an abnormality has occurred in the faucet 10 when the distance L periodically changes, becoming longer or shorter, over time.

In this way, by performing an abnormality determination process using a behavioral pattern that includes changes over time in the distance L between the water outlet 12 and the user U's hand H, and a specific pattern that indicates periodic changes, it becomes possible to detect abnormalities in the faucet 10 more easily and accurately.

In addition, in this embodiment, as described above, by storing the specific pattern in advance in the storage unit 80 as specific pattern information 81, it becomes possible to detect an abnormality in the device (faucet 10) with a simple configuration. That is, for example, if the behavioral patterns that the user U may take when an abnormality occurs in the faucet 10 were calculated using machine learning or the like, a large amount of training data would be required, which would increase the processing load and may lead to the complication of the abnormality detection process. In contrast, in this embodiment, by preparing a specific pattern with simple content as described above in advance as specific pattern information 81, it is possible to reduce the processing load in the abnormality detection process, and as a result, it becomes possible to detect an abnormality in the device (faucet 10) with a simple configuration.

In addition, when the movement trajectory of the user U is recognized and input as a behavioral pattern, the abnormality determination unit 73 can determine an abnormality in the device (faucet 10) based on the behavioral pattern including the movement trajectory.

For example, the abnormality determination unit 73 reads out the specific pattern information 81 from the memory unit 80 and compares the behavior pattern including the trajectory of the user U's movement with the specific pattern included in the specific pattern information 81. Then, if the behavior pattern including the trajectory of movement is, for example, a specific pattern in which the user immediately leaves the appliance (faucet 10) and moves on, the abnormality determination unit 73 determines that an abnormality has occurred in the appliance, that is, detects that an abnormality has occurred in the appliance. In this way, by using a behavior pattern including the trajectory of the user U's movement relative to the appliance (faucet 10), the abnormality determination unit 73 is able to accurately detect that an abnormality has occurred in the appliance.

The abnormality determination unit 73 may also determine an abnormality in the faucet 10 based on the behavioral pattern and the detection results of the water discharge sensor 11. This allows the abnormality determination unit 73 to detect the occurrence of an abnormality in the faucet 10 and estimate the location in the faucet 10 where the abnormality has occurred.

For example, as described above, when the behavior pattern of the user U is a specific pattern of moving the hand H back and forth, the abnormality determination unit 73 determines that an abnormality has occurred in the faucet 10, i.e., can detect that an abnormality has occurred in the faucet 10.

At this time, the abnormality determination unit 73 estimates the location where the abnormality has occurred in the faucet 10 based on the detection result of the water discharge sensor 11. For example, if the detection result of the water discharge sensor 11 includes information indicating that the hand H of the user U has not been detected (i.e., no detection signal is being output from the water discharge sensor 11), this means that even though the hand H is held out near the water outlet 12 and moving back and forth, the water discharge sensor 11 is unable to detect the hand H. Therefore, if the detection result of the water discharge sensor 11 includes information indicating that the hand H has not been detected, the abnormality determination unit 73 can estimate that the location where the abnormality has occurred is the water discharge sensor 11.

On the other hand, for example, when the detection result of the water discharge sensor 11 includes information indicating that the hand H of the user U has been detected (i.e., when a detection signal is being output from the water discharge sensor 11), it can be estimated that the location where the abnormality has occurred is a location other than the water discharge sensor 11. In other words, even when the water discharge sensor 11 detects a hand H or the like, if the user U's behavior pattern is a specific pattern, for example, of moving the hand H back and forth, this means that water is not being discharged due to an abnormality in the water supply side part, such as the water supply valve 14 or the equipment control device 30. Therefore, when the detection result of the water discharge sensor 11 includes information indicating that a hand H or the like has been detected, the abnormality determination unit 73 can estimate that the location where the abnormality has occurred is a water supply side part, such as the water supply valve 14.

In this way, by using the behavioral pattern and the detection results of the water discharge sensor 11, the abnormality determination unit 73 can detect the occurrence of an abnormality in the faucet 10 and estimate the location in the faucet 10 where the abnormality has occurred.

As described above, when the abnormality determination unit 73 detects that an abnormality has occurred in the device (faucet 10), it outputs abnormality occurrence information indicating that an abnormality has occurred to the notification unit 74. The abnormality occurrence information may also include information indicating the location where the abnormality has occurred.

When abnormality information is input from the abnormality determination unit 73, the notification unit 74 notifies the administrator who manages the device (faucet 10) of the information. For example, the notification unit 74 notifies a terminal device (not shown) used by the administrator of the abnormality information via a communication network such as the Internet. Based on the abnormality information notified to the terminal device, the administrator can take action such as repairing the device (here, the faucet 10) in which the abnormality is occurring, thereby enabling the faucet 10 to be quickly returned (restored) to a normal state.

<Control process of the abnormality detection device according to the first embodiment>
Next, a specific processing procedure in the anomaly detection device 60 will be described with reference to Fig. 5. Fig. 5 is a flowchart showing the processing procedure executed by the anomaly detection device 60.

As shown in FIG. 5, the control unit 70 of the anomaly detection device 60 acquires a captured image from the imaging unit 20 (step S10). Next, the control unit 70 recognizes the behavior pattern of the user U based on the captured image (step S11).

Next, the control unit 70 determines whether the recognized behavior pattern is a specific pattern (step S12). If the control unit 70 determines that the recognized behavior pattern is a specific pattern (step S12, Yes), it determines that an abnormality has occurred in the device (e.g., the faucet 10) (step S13).

On the other hand, if the control unit 70 determines that the recognized behavior pattern is not a specific pattern (step S12, No), it skips the processing of step S13 and ends the processing.

As described above, the toilet system 1 according to the first embodiment includes a device (faucet 10), an imaging unit 20, a recognition unit 72, and an abnormality determination unit 73. The device is provided in a toilet room 200. The imaging unit 20 is configured to be able to capture an image of a user using the device. The recognition unit 72 recognizes the behavioral pattern of the user based on the captured image captured by the imaging unit 20. The abnormality determination unit 73 determines an abnormality in the device based on the behavioral pattern recognized by the recognition unit 72. This makes it possible to widely detect a wide range of abnormalities in the devices.

In the above, as shown in FIG. 2, an example has been given in which the equipment control device 30 and the anomaly detection device 60 are separate devices, but this is merely an example and is not limiting. In other words, for example, one device may have all of the functions of the equipment control device 30 and the anomaly detection device 60, or three or more devices may have the functions of the equipment control device 30 and the anomaly detection device 60 in a distributed manner.

Second Embodiment
Next, a second embodiment will be described. In the following description, the same reference numerals as those already described will be used to designate the same parts as those already described, and duplicated descriptions may be omitted.

Figure 6 is a diagram showing an example of the configuration of a toilet system 1 according to a second embodiment. Note that Figure 6 is a front view of the equipment (faucet 10) and other components included in the toilet system 1 according to the second embodiment.

As shown in FIG. 6, the toilet system 1 according to the second embodiment includes a plurality of faucets 10 (three in this example), which are an example of equipment. In the following, the faucet 10a located on the left side of FIG. 6 may be referred to as the "first faucet 10a," the faucet 10b located in the center as the "second faucet 10b," and the faucet 10c located on the right side as the "third faucet 10c," but when there is no need to distinguish between them, they will be referred to as "faucets 10."

The first, second and third faucets 10a, 10b and 10c are arranged in series in the left-right direction. Note that although FIG. 6 shows an example in which there are three faucets 10, the present invention is not limited to this and may, for example, be two or four or more faucets.

The imaging units 20 are installed at appropriate positions (e.g., on the ceiling of the toilet room 200) above the first, second, and third faucets 10a, 10b, and 10c, respectively. Each of the multiple imaging units 20 (three in this case) is configured to be able to capture an image of the water outlet 12 of the corresponding faucet 10 and the hand H of a user U (not shown).

The imaging unit 20 according to the second embodiment is configured to be able to capture an image of the movement of the user U between multiple adjacent faucets 10.

In the above description, the imaging unit 20 is installed above the faucet 10, but this is not limited to the above. The imaging unit 20 can be installed in any position. For example, as shown by the imaginary lines in FIG. 6, the imaging unit 220 may be installed on the wall of the toilet room 200 corresponding to the space between multiple adjacent faucets 10, or may be installed to the side of the faucet 10 like the imaging unit 120 shown in FIG. 1, to capture images of the user U's hands H and the movements of the user U.

In addition, in the toilet room 200, mirrors 4 are provided at positions corresponding to the first, second and third faucets 10a, 10b and 10c, respectively, but are not limited to this.

In the second embodiment, the toilet system 1, in which multiple devices (faucets 10) as described above are installed in series, is configured to be able to widely detect abnormalities in a wide range of devices (in this case, the faucets 10).

To explain in more detail below, in the second embodiment, when an abnormality occurs in one or more of the multiple devices (faucets 10) installed in series, a specific behavior pattern that the user U can take is preset as specific pattern information 81 (see Figures 2 and 3).

Here, the information contained in the "specific pattern" of the specific pattern information 81 according to the second embodiment will be specifically described with reference to FIG. 7. FIG. 7 is a diagram for explaining the specific pattern information according to the second embodiment, and is a diagram of the faucet 10, the user U, etc., as viewed from above. Note that here, specific patterns that the user U may take when an abnormality occurs, for example, when the user U places his/her hand H near the water outlet 12 of the first faucet 10a and water is not discharged from the first faucet 10a, are illustrated as examples, but the types of abnormalities are not limited to these.

As shown in FIG. 7, if the user U holds out his/her hand H near the water outlet 12 of the first faucet 10a but water does not come out from the first faucet 10a, it is estimated that the user U may adopt a behavioral pattern of moving to the adjacent washbasin 2 and using the second faucet 10b, as indicated by arrow A3. Therefore, such a behavioral pattern is included in the "specific pattern" of the specific pattern information 81. In other words, the "specific pattern" of the specific pattern information 81 according to the second embodiment includes information indicating the movement of the user U between multiple connected devices when an abnormality occurs in the faucet 10. Note that in FIG. 7, the user U who moves to use the second faucet 10b is indicated by a dashed line.

In addition, if water does not come out from the first faucet 10a even when hand H is held out near the water outlet 12 of the first faucet 10a, it is estimated that the user U may have a behavioral pattern in which the time the user U uses the first faucet 10a (more precisely, the time they stand in front of the first faucet 10a) is relatively short. Therefore, such a behavioral pattern is included in the "specific pattern" of the specific pattern information 81. In other words, the "specific pattern" of the specific pattern information 81 according to the second embodiment includes information indicating the time the user used the device when an abnormality occurred in the faucet 10.

In the anomaly detection process according to the second embodiment, the recognition unit 72 recognizes the movement of the user U between multiple connected devices (faucets 10) and the user's usage time for the devices as a behavioral pattern based on the captured images captured by the imaging unit 20.

Next, if the movement of the user U among multiple connected devices (faucets 10) included in the behavioral pattern is a specific pattern, more specifically, if the movement is a specific pattern of moving to an adjacent washbasin 2 and using another faucet 10, the abnormality determination unit 73 determines that an abnormality has occurred in the device before the movement (here, the first faucet 10a).

The abnormality determination unit 73 also determines that an abnormality has occurred in the device (here, the first faucet 10a) if the user U's usage time for the device, which is included in the behavioral pattern, is a specific pattern, more specifically, a specific pattern that is a relatively short time.

In this way, in the second embodiment, by using the movement of the user U between multiple devices and the time the user U uses each device, it is possible to widely detect abnormalities in a wide range of devices (faucets 10) in a toilet system 1 in which multiple devices (faucets 10) are installed in series.

In the above, an example is shown in which both the movement of user U between multiple devices and the usage time of user U on a device are used, but it is also possible to configure the system to use only one of them.

Third Embodiment
Next, a third embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram showing a configuration example of a toilet system 1 according to the third embodiment. As shown in Fig. 8, in the third embodiment, the equipment provided in a toilet room 200 is a urinal 110. Note that Fig. 8 is a diagram showing the equipment (urinal 110) and the like included in the toilet system 1 according to the third embodiment as viewed from above.

As shown in FIG. 8, the toilet system 1 according to the third embodiment includes a plurality of urinals 110 (three in this example), which are an example of equipment. In the following, the urinal 110 provided on the left side in FIG. 8 may be referred to as the "first urinal 110a," the urinal 110 provided in the center as the "second faucet 10b," and the urinal 110 provided on the right side as the "second urinal 110b," but when there is no need to distinguish between them, they will be referred to as "urinals 110."

The first, second and third urinals 110a, 110b, 110c are arranged in series in the left-right direction. Although FIG. 8 shows an example in which there are three urinals 110, the number of urinals is not limited to this, and may be, for example, two or four or more. The urinal 110 is also equipped with, for example, a sensor that detects a user U, and can automatically discharge flushing water in response to the detection result of the sensor, but is not limited to this.

Although not shown, the imaging units 20 are installed at appropriate positions (e.g., on the ceiling of the toilet room 200) above the first, second and third urinals 110a, 110b, 110c, respectively, as in the first and second embodiments. Each of the multiple imaging units 20 (three in this case) is configured to be able to capture an image of the movement of a user U using the corresponding urinal 110, the head D of the user U, etc.

In the third embodiment, the toilet system 1, in which multiple devices (urinals 110) as described above are arranged in series, is configured to be able to widely detect abnormalities in a wide range of devices (here, the urinals 110).

To explain in more detail below, in the third embodiment, when an abnormality occurs in one or more of the devices (urinals 110) that are installed in a multiple-unit arrangement, a specific behavioral pattern that a user U may adopt is preset as specific pattern information 81 (see Figures 2 and 3).

In the third embodiment, the equipment abnormality is an abnormality such as clogging or excessive dirt in the urinal 110. In the following, specific patterns that the user U may take when an abnormality such as clogging occurs in the first urinal 110a are described as examples, but the types of abnormality are not limited to these.

As shown in FIG. 8, if the first urinal 110a is clogged, it is estimated that the user U may adopt a behavior pattern of moving to the adjacent second urinal 110b to use the second urinal 110b, as shown by arrow A4. Therefore, such a behavior pattern is included in the "specific pattern" of the specific pattern information 81. In other words, the "specific pattern" of the specific pattern information 81 according to the third embodiment includes information indicating the movement of the user U between multiple connected devices when an abnormality occurs in the urinal 110. Note that in FIG. 8, the user U who moves to use the second urinal 110b is shown by a dashed line.

In addition, if the first urinal 110a is clogged, it is estimated that the user U may adopt a behavior pattern of moving his/her head D forward and peering into the first urinal 110a, as shown by arrow A5. Therefore, such a behavior pattern is included in the "specific pattern" of the specific pattern information 81. That is, the "specific pattern" of the specific pattern information 81 according to the third embodiment is configured to include information indicating the movement of the user U's head D relative to the urinal 110 when an abnormality occurs in the urinal 110. Note that in FIG. 8, the user U who moves his/her head D forward and peers into the first urinal 110a is shown by a two-dot chain line.

In the abnormality detection process according to the third embodiment, the recognition unit 72 recognizes the movement of the user U between multiple adjacent devices (urinals 110) and the movement of the user U's head D relative to the urinal 110 as a behavioral pattern based on the captured images captured by the imaging unit 20.

Next, if the movement of the user U between multiple adjacent devices (urinals 110) included in the behavior pattern is a specific pattern, more specifically, if the movement is a specific pattern of moving to an adjacent urinal 110 and using that urinal 110, the abnormality determination unit 73 determines that an abnormality such as a blockage has occurred in the device before the movement (here, the first urinal 110a).

The abnormality determination unit 73 also determines that an abnormality has occurred in the device (here, the first urinal 110a) when the movement of the user U's head D relative to the urinal 110, which is included in the behavioral pattern, is a specific pattern, specifically, when the head D is moved forward and peers into the urinal 110.

In this way, in the third embodiment, by using the movement of the user U between multiple devices and the movement of the user U's head D relative to the urinal 110, it is possible to widely detect abnormalities in a wide range of devices (urinals 110) in a toilet system 1 in which multiple devices (urinals 110) are installed in series.

In the above, an example is shown in which both the movement of the user U between multiple devices and the head D of the user U relative to the urinal 110 are used, but the configuration may be such that only one of them is used.

In the above-described embodiments, the faucet 10 and the urinal 110 are used as examples of equipment, but the equipment is not limited to these and may be other types of equipment provided in the toilet room 200, such as a hand dryer or a toilet bowl.

Further advantages and modifications may readily occur to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and equivalents thereof.

1 Toilet system 10 Faucet 20 Imaging unit 72 Recognition unit 73 Abnormality determination unit

Claims (8)

Equipment provided in the toilet room;
an imaging unit capable of imaging a user who uses the device;
a recognition unit that recognizes a behavior pattern of a user based on an image captured by the imaging unit;
and an abnormality determination unit that determines an abnormality in the device based on the behavior pattern recognized by the recognition unit. The device is an automatic faucet,
The recognition unit is
The toilet system according to claim 1 , wherein the action pattern is recognized based on the captured image, the action pattern being a hand movement of the user relative to the automatic faucet. The imaging unit includes:
The image captured can include a water outlet of the automatic faucet and a user's hands,
The recognition unit is
Based on the captured image, a change over time in the distance between the water outlet and the user's hand is recognized as the behavior pattern;
The abnormality determination unit is
The toilet system according to claim 2 , wherein when the behavior pattern including the change in distance is a specific pattern, it is determined that an abnormality has occurred in the automatic faucet. The abnormality determination unit is
The toilet system according to claim 3 , wherein when the behavior pattern including the change in distance is the specific pattern that indicates a periodic change, it is determined that an abnormality has occurred in the automatic faucet. The automatic faucet is
A water discharge sensor is provided to detect the user's hand near the water discharge port.
The abnormality determination unit is
The toilet system according to any one of claims 2 to 4, wherein an abnormality in the automatic faucet is determined based on the behavior pattern and the detection result of the water discharge sensor. the device is a urinal,
The recognition unit is
The toilet system according to claim 1 , wherein a movement of the user's head relative to the urinal is recognized as the behavior pattern based on the captured image. The recognition unit is
7. The toilet system according to claim 1, wherein a trajectory of the user's movement relative to the device is recognized as the behavior pattern based on the captured images. The device is provided in a plurality of parallel arrangements,
The imaging unit includes:
A movement of a user between a plurality of the devices connected in series can be captured as the captured image,
The recognition unit is
Recognizing, based on the captured images, the movement of the user among the plurality of simultaneous devices and the time the user spends using the devices as the behavior pattern;
The abnormality determination unit is
The toilet system according to any one of claims 1 to 7, characterized in that when at least one of the user's movement between multiple simultaneous devices included in the behavioral pattern and the user's usage time for the device is a specific pattern, it is determined that an abnormality has occurred in the device.

Images