Abnormality detection system for water-related space

Abstract

To enable appropriate detection of an abnormality in a water-related space.SOLUTION: An abnormality detection system according to an embodiment includes: three or more washstands each having a bowl portion, a sensor unit, and a water-related device configured to control discharge to the bowl portion according to an output value of the sensor unit; and a determination unit configured to determine that an abnormality has occurred in one washstand among the three or more washstands when an output value of the sensor unit of the one washstand deviates from output values of the sensor units of the other washstands.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The disclosed embodiment relates to an anomaly detection system for a wet space. [Background technology]

[0002] Conventionally, there have been provided technologies relating to the management of water-related spaces where water-related equipment such as faucets that discharge water are installed. For example, in a case where multiple hand wash basins are installed in a washroom space, which is an example of a water-related space, there has been provided a technology that detects an abnormality when the usage rate of some of the hand wash basins becomes low and sends a warning message (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-025310 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is room for improvement in the above-mentioned conventional technology. For example, there may be variations in the usage rates of multiple hand washing basins under normal circumstances, making it difficult to properly detect abnormalities in the bathroom space simply by using the usage rates. Therefore, there is a need for a method for properly detecting abnormalities in the bathroom space.

[0005] An object of the disclosed embodiment is to provide a wet space anomaly detection system that can appropriately detect anomalies in a wet space. [Means for solving the problem]

[0006] One embodiment of an abnormality detection system for a bathroom space is characterized by comprising three or more hand wash basins each having a bowl portion, a sensor unit, and a bathroom device whose discharge into the bowl portion is controlled according to the output value of the sensor unit, and a judgment unit that determines that an abnormality has occurred in one of the hand wash basins when the output value of the sensor unit of one of the plurality of hand wash basins deviates from the output values of the sensor units of the basins other than the one.

[0007] According to one embodiment of the wet area anomaly detection system, when the output value of a sensor unit of one of a plurality of hand wash basins deviates from the output values of the sensor units of the other hand wash basins, the system determines that an abnormality has occurred in the one hand wash basin. This allows the wet area anomaly detection system to appropriately detect abnormalities in the wet area. For example, the wet area anomaly detection system compares the output values of the sensor units (e.g., light reception levels) of the sensor units used to control the water discharge of the plurality of hand wash basins, and determines that an abnormality has occurred if there is a difference between the output values (e.g., sensor reception levels). In this way, the wet area anomaly detection system compares the output values (e.g., sensor reception levels) in real time to determine an abnormality, enabling rapid abnormality detection. Furthermore, the wet area anomaly detection system uses existing sensors (used for discharge control) in wet area appliances such as automatic faucets, thereby enabling appropriate anomaly detection without the hassle or cost of additional sensors.

[0008] In one aspect of the embodiment, in the plumbing space abnormality detection system, the determination unit is characterized by comparing the output value of the sensor unit when the discharge of the plumbing equipment is stopped.

[0009] According to one aspect of the embodiment, a water-related space anomaly detection system can compare the output values of the sensor unit when the water-related appliance is stopped, thereby determining an anomaly based on a state that is less affected by the water discharge. Therefore, the water-related space anomaly detection system can appropriately detect anomalies in the water-related space. For example, because the water flow affects the water discharge, the output value (e.g., the amount of sensor reception) fluctuates significantly, making it difficult to distinguish whether the difference in the output value (e.g., the amount of sensor reception) is due to the water flow or an anomaly in the hand basin. Therefore, the water-related space anomaly detection system can reduce the possibility of erroneous determination by comparing the output values (e.g., the amount of sensor reception) when the water discharge is stopped (e.g., when the water is stopped) and the output value is stable.

[0010] In one aspect of the embodiment, the sensor unit has a transmitter and a receiver, and the transmitter and receiver are opposite a portion of the bowl portion.

[0011] In one embodiment of the wet space anomaly detection system, the transmitter and receiver face a portion of the bowl portion, and the sensor unit's detection direction faces the bowl portion, so that the signal emitted from the sensor unit is reflected by the bowl portion, and the output value (sensor reception amount, etc.) reflects the state of the bowl portion. This allows the wet space anomaly detection system to make anomaly determinations based on a comparison of the state of the bowl portion, enabling more accurate anomaly determinations. Therefore, the wet space anomaly detection system can appropriately detect anomalies in the wet space.

[0012] In one aspect of the embodiment of the abnormality detection system for a bathroom space, the multiple hand washing basins are characterized in that they are installed in the same bathroom space.

[0013] According to one aspect of the embodiment, the wet space anomaly detection system can compare output values (sensor reception amount, etc.) by comparing hand wash basins in the same space (environment), excluding influences that occur in the entire space (for example, reduced lighting during energy saving), thereby enabling more accurate anomaly determination. Therefore, the wet space anomaly detection system can appropriately detect anomalies in the wet space.

[0014] In one aspect of the embodiment, in the abnormality detection system for a bathroom space, the judgment unit is characterized in that it judges that an abnormality has occurred in the one hand wash basin when the output value of the sensor unit of the one hand wash basin deviates by more than a first threshold value from the output values of the sensor units of any of the multiple hand wash basins other than the one hand wash basin.

[0015] According to one aspect of the embodiment, a water-related space anomaly detection system determines an anomaly in a water-related space based on the deviation of the output value from other water-related basins, thereby enabling more accurate anomaly detection for the water-related space in which the water-related basin is located. Therefore, the water-related space anomaly detection system can appropriately detect anomalies in the water-related space. For example, as a first step in the anomaly detection flow, the water-related space anomaly detection system can appropriately detect anomalies in the water-related space by determining whether there are other water-related devices with output values similar to those of the target.

[0016] In one aspect of the embodiment, in the abnormality detection system for bathroom spaces, the judgment unit is characterized in that it judges that an abnormality has occurred in the one hand wash basin when the output value of the sensor unit of the one hand wash basin deviates from the average output value of the sensor units of the other hand wash basins among the plurality of hand wash basins by more than a second threshold value.

[0017] According to one embodiment of the water-related space anomaly detection system, by determining an abnormality in a particular water-related space based on the deviation of its output value from the average value of other water-related devices, more accurate abnormality detection is possible for the water-related space in which the particular water-related device is located. Therefore, the water-related space anomaly detection system can appropriately detect abnormalities in the water-related space. For example, as the second step of the anomaly detection flow, the water-related space anomaly detection system determines an abnormality in a particular water-related space based on the deviation (difference) between the output value of the target water-related device and the average value of other water-related devices, thereby enabling more accurate abnormality detection for the water-related space in which the particular water-related device is located. For example, depending on the condition of each water-related device, such as the sensor unit or the accumulation of dirt in the bowl, the output value (e.g., sensor reception amount) may not necessarily fall within the specified value. Therefore, by comparing the output value with the average value of other water-related devices, the water-related space anomaly detection system can reduce the possibility of erroneous determination and enable more accurate abnormality detection.

[0018] In one aspect of the embodiment, in the system for detecting abnormalities in a water-related space, the judgment unit is characterized in that it excludes from the targets for abnormality judgment a hand washing basin that has been judged to be abnormal a predetermined number of times in succession.

[0019] For example, if the data of a hand wash basin determined to be abnormal is left as a target for judgment, it may be impossible to accurately judge the abnormality. Therefore, according to one aspect of the embodiment, the water-related space anomaly detection system can improve the accuracy of anomaly judgment for other hand wash basins by excluding the data related to the hand wash basin determined to be abnormal from the comparison target. Therefore, the water-related space anomaly detection system can appropriately detect anomalies in the water-related space.

[0020] In the wet space anomaly detection system according to one aspect of the embodiment, at least one of the first threshold value and the second threshold value is changeable.

[0021] For example, when performing anomaly detection under the same conditions, the more hand wash basins there are, the higher the detection accuracy tends to be, and the fewer the hand wash basins there are, the lower the accuracy tends to be. Therefore, according to one aspect of the embodiment, the water-related space anomaly detection system allows the administrator to set thresholds according to the environment, such as the number of hand wash basins, so that the standard for anomaly determination can be set appropriately for each water-related space. Therefore, the water-related space anomaly detection system can appropriately detect anomalies in the water-related space. [Effects of the Invention]

[0022] According to one aspect of the embodiment, it is possible to appropriately detect abnormalities in a wet space. [Brief explanation of the drawings]

[0023] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a plumbing space anomaly detection system according to the first embodiment. [Figure 2] FIG. 2 is a schematic diagram showing an automatic faucet device as an example of plumbing equipment according to the first embodiment. [Figure 3] FIG. 3 is a flowchart showing an example of processing according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the abnormality determination process. [Figure 5] FIG. 5 is a diagram illustrating an example of the abnormality determination process. [Figure 6] FIG. 6 is a diagram showing an example of setting thresholds according to scale. [Figure 7] FIG. 7 is a diagram showing an example of an abnormality pattern. [Figure 8] FIG. 8 is a diagram illustrating an example of the configuration of a wet space anomaly detection system according to the second embodiment. [Figure 9] FIG. 9 is a flowchart showing an example of processing according to the second embodiment. [Figure 10] FIG. 10 is a flowchart showing an example of processing according to the second embodiment. [Figure 11]FIG. 11 is a flowchart showing an example of processing according to the second embodiment. [Figure 12] FIG. 12 is a flowchart showing an example of processing according to the second embodiment. [Figure 13] FIG. 13 is a diagram showing another example of the configuration of the wet space anomaly detection system according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0024] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the wet space anomaly detection system disclosed in the present application will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the following embodiments.

[0025] <1. First embodiment> First, the configuration of an anomaly detection system 1, which is an anomaly detection system for a wet space according to the first embodiment, will be described, and then an example of processing executed by the anomaly detection system 1 will be described.

[0026] In the following, a space where a washroom is provided (also referred to as a "washroom space") will be described as an example of a wet space, but the wet space is not limited to a washroom space and may be any space (place) where water is used. For example, the wet space is not limited to a space where only a washroom is provided (washroom space), but may be any space (place) where the processing described below can be applied, such as a dressing room or toilet that has the function of a washroom.

[0027] In the following, an automatic flushing device 2, which is an automatic faucet, will be described as an example of a plumbing device, but the plumbing device is not limited to the automatic flushing device 2 and may be various devices to which the process can be applied. For example, the plumbing device may be an automatic liquid soap dispenser 3, a hand dryer (such as the hand dryer 4 in FIG. 13), an electric water heater, etc.

[0028] A plumbing device such as an automatic flushing device 2 discharges liquid at a hand washing basin 5. For example, the plumbing device whose information is used in the processing according to the first embodiment may be any device, as long as it discharges liquid liquid toward the bowl portion 20 described below. For example, the plumbing device may be an automatic liquid soap device 3 that discharges liquid liquid toward the bowl portion 20, a hand dryer that discharges air toward the bowl portion 20, an electric water heater that discharges hot water toward the bowl portion 20, or the like. For example, when a hand dryer is the target, the anomaly detection system 1 may include the hand dryer. For example, when an electric water heater is the target, the anomaly detection system 1 may include the electric water heater.

[0029] <1-1. Configuration of anomaly detection system> The configuration of an anomaly detection system 1 according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration of the anomaly detection system according to the first embodiment.

[0030] As shown in FIG. 1, anomaly detection system 1 has hand basins 51, 52, and 53, cloud 100, output unit 200, and GW (gateway device) 300. When describing hand basins such as hand basin 51, hand basin 52, and hand basin 53 without distinguishing between them, they may be referred to as "hand basins 5." In this way, anomaly detection system 1 has three or more hand basins 5. Furthermore, hand basins 51, 52, and 53 are provided in the same water-related space. For example, hand basins 51, 52, and 53 are three hand basins 5 arranged side by side in a single washroom space.

[0031] 1 is merely an example, and the anomaly detection system 1 may include four or more hand wash basins 5, multiple clouds 100, multiple output units 200, and multiple GWs 300. The anomaly detection system 1 does not have to include a GW 300. In this case, plumbing equipment such as an automatic flushing device 2 described below may communicate with an external device such as the cloud 100 via the network N.

[0032] The hand washing basin 5 has a bowl portion 20, a sensor unit, and a plumbing device whose discharge into the bowl portion 20 is controlled according to the output value of the sensor unit. In Fig. 1, the hand washing basin 51 has the bowl portion 20, a sensor unit such as sensor 101, an automatic flushing device 21 that discharges (spits) water into the bowl portion 20 according to the output value of sensor 101, and an automatic liquid soap device 31 that discharges liquid soap into the bowl portion 20 according to the output value of the sensor unit (for example, sensor 10A in Fig. 8). In other words, discharge is performed into the bowl portion 20 of the hand washing basin 51 from the automatic flushing device 21, the automatic liquid soap device 31, etc.

[0033] The hand washing basin 52 also has a bowl portion 20, a sensor unit such as sensor 102, an automatic flushing device 22 that discharges (spits) water into the bowl portion 20 according to the output value of the sensor 102, and an automatic liquid soap device 32 that discharges liquid soap into the bowl portion 20 according to the output value of the sensor unit (for example, sensor 10A in FIG. 8). In other words, discharges are made into the bowl portion 20 of the hand washing basin 52 from the automatic flushing device 22, the automatic liquid soap device 32, etc.

[0034] Further, hand washing basin 53 has bowl portion 20, a sensor unit such as sensor 103, an automatic flushing device 23 that discharges (spits) water into bowl portion 20 according to the output value of sensor 103, and an automatic liquid soap device 33 that discharges liquid soap into bowl portion 20 according to the output value of the sensor unit (for example, sensor 10A in FIG. 8). That is, discharge is performed into bowl portion 20 of hand washing basin 53 from automatic flushing device 23, automatic liquid soap device 33, etc.

[0035] When describing automatic flushing devices such as automatic flushing device 21, automatic flushing device 22, and automatic flushing device 23 without distinction, they may be referred to as "automatic flushing device 2." When describing automatic flushing devices such as automatic liquid soap device 31, automatic liquid soap device 32, and automatic liquid soap device 33 without distinction, they may be referred to as "automatic liquid soap device 3." When describing sensor units such as sensor 101, sensor 102, and sensor 103 without distinction, they may be referred to as "sensor 10." As shown in FIG. 1, each hand washing basin 5 has a bowl portion 20, a sensor 10, and an automatic flushing device 2 whose discharge into the bowl portion 20 is controlled according to the output value of sensor 10. The hand washing basin 5 may or may not have an automatic liquid soap device 3.

[0036] Here, the configuration related to the water discharge of an automatic flushing device 2, which is an example of plumbing equipment, will be described with reference to Fig. 2. Fig. 2 is a schematic diagram showing an automatic faucet device, which is an example of plumbing equipment according to the first embodiment. Specifically, Fig. 2 is a schematic diagram showing a cross section of a bowl portion 20 and the like, as seen from the side.

[0037] As shown in Figure 2, an automatic flushing device 2 is placed in a bathroom space provided with a bowl section 20. For example, the bowl section 20 is a washbasin. The bowl section 20 is used for washing people's hands, etc., and functions as a water receiving section that receives water from the water discharge section 30 of the automatic flushing device 2.

[0038] Bowl portion 20 has drain outlet 21 that communicates with drain channel 22, and water released into bowl portion 20, water accumulated in bowl portion 20, etc. is discharged from drain outlet 21 to drain channel 22. Bowl portion 20 is provided on the underside of washbasin counter 23. A water discharge portion 30 (discharge portion) that constitutes a spout for discharging water onto bowl surface 20a of bowl portion 20 is provided above washbasin counter 23.

[0039] Sensor 10 is a sensor unit that acquires information used to control automatic flushing device 2 (plumbing equipment). For example, sensor 10 has a sensor device (detection device) such as an infrared light emitting / receiving optical sensor (photoelectric sensor). For example, sensor 10 has an optical sensor that detects the internal state of bowl portion 20, including bowl surface 20a of bowl portion 20. Note that the optical sensor is merely an example, and sensor 10 is not limited to an optical sensor, and any type of sensor may be used as long as anomaly detection system 1 can perform the desired processing.

[0040] The detection area of the sensor 10 is a predetermined area such as the inside of the bowl portion 20. The sensor 10 projects (radiates) infrared light toward the bowl portion 20 and receives light that includes reflected light from the bowl portion 20. As shown in FIG. 2, the sensor 10 is disposed above the bowl portion 20 and faces downward.

[0041] The sensor 10 includes a light-projecting unit 11 and a light-receiving unit 12. For example, the sensor 10 includes the light-projecting unit 11 and the light-receiving unit 12 that receives light that includes reflected light emitted from the light-projecting unit 11, and detects the amount of light received by the light-receiving unit 12.

[0042] Light-projecting unit 11 functions as a transmitter that emits a predetermined signal such as an electromagnetic wave. Light-projecting unit 11, which is a transmitter, faces a part of bowl unit 20. Light-projecting unit 11 emits electromagnetic waves (light) of a predetermined wavelength. For example, light-projecting unit 11 has a light-emitting element such as an LED (Light Emitting Diode) that outputs light of a predetermined wavelength. Note that light-projecting unit 11 is not limited to an LED and may have any configuration that outputs light of a predetermined wavelength.

[0043] The light-projecting unit 11 emits infrared light. The light-projecting unit 11 irradiates the infrared light toward the bowl unit 20. The light-projecting unit 11 irradiates the infrared light in a detection direction corresponding to the detection range of the sensor 10.

[0044] The light receiving unit 12 functions as a receiving unit that receives a predetermined signal such as an electromagnetic wave. The light receiving unit 12, which is a receiving unit, faces a part of the bowl portion 20. The light receiving unit 12 receives electromagnetic waves (light) of a predetermined wavelength. For example, the light receiving unit 12 has a light receiving element that receives light of a predetermined wavelength. The light receiving unit 12 receives infrared light. For example, the light receiving unit 12 has a light receiving element that receives infrared light. For example, the light receiving unit 12 has a light receiving element that receives light that is incident on the light receiving surface. The light receiving unit 12 receives light that includes reflected light from the bowl portion 20. The light receiving unit 12 receives light that includes reflected light from a detection direction that corresponds to the detection range of the sensor 10.

[0045] The sensor 10 outputs an output value (also referred to as a "sensor output value") of the sensor 10 in response to detection. The sensor 10 generates a sensor output value corresponding to light received by the light receiving unit 12. For example, the output value (sensor output value) of the sensor 10 is a value based on the amount of light received by the sensor 10, which is an example of a sensor reception amount (also simply referred to as a "reception amount"), which is the amount of light received by the sensor unit. For example, the output value (sensor output value) of the sensor 10 is the amount of light received by the light receiving unit 12 (also referred to as a "sensor received light amount"). In this case, the sensor output value may be interpreted as the sensor received light amount, and the sensor received light amount may be interpreted as the sensor output value. Note that the sensor output value is not limited to the sensor received light amount itself, and may be various values based on the sensor received light amount. For example, the sensor output value may be a value derived (calculated) based on the sensor received light amount using a predetermined function or the like.

[0046] For example, the sensor 10 is connected to the control unit 50 by a wire. In FIG. 2, the sensor 10 is connected to the control unit 50 by a connection cable 13. In this case, the sensor 10 is supplied with a power supply voltage from the control unit 50 via the connection cable 13 and is controlled by the control unit 50. The sensor 10 also transmits various types of information to the control unit 50. For example, the sensor 10 transmits information acquired by detection (also referred to as "detection information") to the control unit 50. For example, the detection information includes various types of information acquired by the sensor 10. For example, the sensor 10 may transmit detection information such as a sensor output value to the control unit 50 via the connection cable 13.

[0047] The sensor 10 may be communicably connected to the control unit 50 using a predetermined wireless communication function such as Bluetooth (registered trademark) or Wi-Fi (registered trademark). That is, the control unit 50 and the sensor 10 may be connected in any manner as long as they are capable of transmitting and receiving information, and may be connected to each other via a wired or wireless connection.

[0048] Sensor 10 may be arranged in any manner as long as it can perform the desired detection. For example, sensor 10 is not limited to being arranged at water outlet 31, but may be arranged at any location on water discharge unit 30, such as somewhere along the water discharge pipe. Sensor 10 may also be arranged with light-emitting unit 11 and light-receiving unit 12 separated from each other. Sensor 10 is not limited to being an infrared sensor, and any sensor can be used as long as it can control water discharge using the process described below.

[0049] The automatic flushing device 2 is a plumbing device that discharges water toward the bowl section 20. The automatic flushing device 2 is an automatic faucet that automatically discharges (spouts) water in response to detection by a sensor, etc. The automatic flushing device 2 has a water discharge section 30, a solenoid valve 40, and a control section 50. In this way, the automatic flushing device 2 has the solenoid valve 40 and discharges water from the water discharge section 30 into the bowl section 20.

[0050] Water discharger 30 has a water discharge port 31 provided at the tip end of the water discharge pipe, and discharges (spouts) water from water discharge port 31. In this way, water discharger 30 has water discharge port 31 that discharges water, and is arranged so that the water discharged from water discharge port 31 is discharged into bowl surface 20a of bowl portion 20.

[0051] The water discharged from the water discharger 30 through the water outlet 31 is supplied by a water supply passage 32. The water supply passage 32 guides water supplied from a water supply source such as a water pipe to the water outlet 31. In the example of FIGS. 1 and 2, the water discharger 30 discharges water in response to control of the solenoid valve 40 by the control unit 50. The water discharger 30 may be able to change the flow rate of water in response to control of the solenoid valve 40.

[0052] The solenoid valve 40 functions as a valve for discharging water (raw water, etc.) from the water discharger 30. The solenoid valve 40 is connected to the control unit 50, and in response to control by the control unit 50, it is placed in a water discharge state (valve open state) in which water is discharged from the water discharger 30, or in a water stop state (valve closed state) in which water being discharged from the water discharger 30 is stopped. For example, the state of the solenoid valve 40 is controlled by the control unit 50 in response to detection by the sensor 10.

[0053] In Figure 2, solenoid valve 40 is provided in water supply passage 32 and opens and closes water supply passage 32. Solenoid valve 40 is connected to and driven by control section 50. Solenoid valve 40 is electrically controlled in accordance with a control signal from control section 50 and opens and closes water supply passage 32. In this way, solenoid valve 40 functions as a water supply valve that opens and closes water supply passage 32 for water to be discharged from water outlet 31.

[0054] For example, the solenoid valve 40 may be a self-holding solenoid valve (latching solenoid valve) known as a latching solenoid valve. In this case, the solenoid valve 40 operates from a closed state to an open state (opening operation) when current is applied to the solenoid coil in one direction, maintains the open state even when current is subsequently cut off to the solenoid coil, and operates from the open state to a closed state (closing operation) when current is applied to the solenoid coil in the other direction. The solenoid valve 40 maintains the closed state even when current is cut off to the solenoid coil. Note that the opening and closing of the water supply passage 32 is not limited to the solenoid valve 40, and may be performed by another on-off valve mechanism capable of opening and closing the water supply passage 32 under the control of the control unit 50.

[0055] The control unit 50 is a control device (information processing device) used for various processes such as controlling plumbing equipment. The control unit 50 controls the discharge from the automatic flushing device 2 (plumbing equipment) to the bowl unit 20 according to the output value of the sensor 10.

[0056] The control unit 50 has a communication function (communication unit) for transmitting and receiving information to and from other devices. The communication function (communication unit) of the control unit 50 is realized by a communication device, a communication circuit, etc. The control unit 50 is connected to an arbitrary network by wire or wirelessly using the communication function, and transmits and receives information to and from an external information processing device such as the GW 300.

[0057] The control unit 50 is connected to the sensor 10 via a wire. In FIG. 2, the control unit 50 is connected to the sensor 10 via a connection cable 13. The control unit 50 is also connected to the sensor 10 so as to be able to communicate information. The control unit 50 transmits and receives information to and from the sensor 10. Note that the control unit 50 may be connected to the sensor 10 in any manner as long as it is possible to transmit and receive information, and may also be connected so as to be able to communicate wirelessly.

[0058] The control unit 50 switches the opening and closing of the solenoid valve 40. The control unit 50 sends a control signal to the solenoid valve 40, and switches the opening and closing of the solenoid valve 40, thereby switching the water discharge state of the water discharge unit 30. The control unit 50 controls the solenoid valve 40 in accordance with a signal from the sensor 10. For example, when the amount of received light exceeds a set value that serves as a reference for discharging water, the control unit 50 opens the solenoid valve 40 and causes the automatic flushing device 2 to discharge water.

[0059] The control unit 50 acquires information. The control unit 50 acquires detection information detected by the sensor 10. The control unit 50 receives the detection information acquired by the sensor 10 from the sensor 10. The control unit 50 may store the acquired various pieces of information in a memory unit (such as a storage device) within the control unit 50.

[0060] The control unit 50 executes an output process to output various types of information. The control unit 50 functions as a transmission unit to transmit various types of information. The control unit 50 transmits information to an external information processing device such as the GW 300. For example, the control unit 50 transmits detection information detected by the sensor 10 to the GW 300 together with information identifying the plumbing device (such as the automatic flushing device 2) that the sensor 10 is detecting.

[0061] In addition, when a plumbing device communicates with the cloud 100 without going through the GW300, the control unit 50 transmits the detection information detected by the sensor 10 to the cloud 100 via the network N, together with information identifying the plumbing device (such as the automatic flushing device 2) that the sensor 10 is detecting.

[0062] Furthermore, the control unit 50 may be located in any location. For example, the control unit 50 may be located inside the automatic flushing device 2. The device configuration and location of the control unit 50 may be any configuration as long as it is possible to realize switching control of the solenoid valve 40, communication with the sensor 10, and processing. The control unit 50 may be located outside the automatic flushing device 2, rather than inside it.

[0063] Here, an example of automatic water discharge (automatic water ejection) by the automatic flushing device 2 will be described. When an object such as a human hand is present in the bowl portion 20, as shown in Figure 2, the distance L1 from when the light emitted from the sensor 10 (detection light) is reflected by the object is shorter than the distance L2 from when the detection light is reflected by the bowl portion 20.

[0064] Therefore, when an object such as a human hand is present inside bowl portion 20, light (detection light) emitted from sensor 10 is reflected from an object closer than the inner surface of bowl portion 20 (bowl surface 20a), increasing the amount of reflected light returning to sensor 10. In other words, when light-receiving portion 12 of sensor 10 receives light reflected from bowl portion 20, the amount of light received is smaller than when an object such as a human hand is present inside bowl portion 20. Therefore, by appropriately setting the setting value that serves as the standard for performing water discharge and which is compared with the amount of received light, automatic flushing device 2 can perform automatic water discharge, appropriately discharging water in response to the placement of a person's hand, etc.

[0065] 1, an automatic liquid soap dispenser 3 is placed in the bathroom space where the bowl portion 20 is provided. The automatic liquid soap dispenser 3 is a bathroom device that dispenses liquid soap toward the bowl portion 20. The automatic liquid soap dispenser 3 is an automatic liquid soap device (also simply referred to as "automatic liquid soap") that automatically dispenses (spouts) liquid soap in response to detection by a sensor, etc. For example, the automatic liquid soap dispenser 3 has a tank for storing liquid soap, a discharge pipe for discharging liquid soap toward the bowl portion 20, a pump (e.g., pump 40A in FIG. 8) for supplying the liquid soap stored in the tank to the discharge pipe, etc. Similarly to the automatic flushing device 2, the automatic liquid soap dispenser 3 has a sensor (e.g., sensor 10A in FIG. 8) for automatic discharging, a control unit (e.g., control unit 50A in FIG. 8), etc. In this way, the automatic liquid soap dispenser 3 may have the same configuration as the automatic liquid soap dispenser 3 shown in FIG. 8. Note that the automatic liquid soap dispenser 3 can have any configuration as long as it is capable of automatic discharging.

[0066] The cloud 100 is a computer (information processing device) that provides cloud services. For example, the cloud 100 is a server device managed by a service provider that provides services related to abnormality determination in wet spaces. Note that abnormality determination in wet spaces is not limited to abnormality determination of the entire wet space, but may be any determination that targets at least a portion of the configuration of the wet space, such as abnormality determination of wet equipment placed in the wet space, or abnormality determination of structures such as floors and walls that constitute the wet space. Details of the cloud 100 will be described later.

[0067] The output unit 200 is an information processing device (computer) for outputting various types of information. The output unit 200 also functions as a notification unit for notifying information. For example, the output unit 200 has a display device such as a display, and displays the various types of information received from the cloud 100 on the display device. Note that the output unit 200 may output the various types of information received from the cloud 100 as audio using an audio output device such as a speaker.

[0068] The output unit 200 may be a terminal device (computer) used by a predetermined user. For example, the output unit 200 may be any device such as a smartphone, a mobile phone, a PDA (Personal Digital Assistant), a tablet terminal, a notebook PC (Personal Computer), or a desktop PC. For example, the output unit 200 may be a computer such as a PC (Personal Computer) such as a notebook PC used by the administrator of the anomaly detection system 1. Furthermore, for example, the output unit 200 may be a portable device such as a smartphone carried by a cleaner (also simply referred to as a "cleaner") of the wet area.

[0069] The output unit 200 outputs information indicating whether or not there is an abnormality in the water-related space based on the abnormality determination process. The output unit 200 outputs the determination result by the determination unit 120 that executes the abnormality determination.

[0070] For example, the output unit 200 displays the determination result by the determination unit 120. For example, the output unit 200 outputs the determination result by the determination unit 120 as sound.

[0071] The output unit 200 outputs information indicating that an abnormality has occurred in the wet space (also referred to as "abnormality detection information"). For example, the output unit 200 displays the abnormality detection information in the wet space. For example, the output unit 200 outputs the abnormality detection information in the wet space as sound.

[0072] The GW300 is a computer (information processing device) that also functions as a gateway. The GW300 collects information related to the wet space and provides the information to other devices via the network N. The GW300 may be located outside the wet space or inside the wet space. For example, the GW300 may be a device located in a facility that has a wet space.

[0073] For example, the GW300 may be communicably connected to a plumbing device such as the automatic flushing device 2 using a predetermined wireless communication function such as Wi-Fi or Bluetooth. Note that the GW300 and a plumbing device such as the automatic flushing device 2 may be connected in any manner as long as they are capable of transmitting and receiving information, and may be connected to each other via a wired or wireless connection. For example, the GW300 may be communicably connected to a control device (e.g., the control unit 50) of a plumbing device (e.g., the automatic flushing device 2) via a wired or wireless connection.

[0074] The GW300 receives various types of information and transmits the received information to the cloud 100. The GW300 collects information acquired about plumbing appliances such as the automatic flushing appliance 2 and transmits the collected information about the plumbing space to the cloud 100. For example, the GW300 communicates with the control unit 50 of the automatic flushing appliance 2 and transmits the information about the automatic flushing appliance 2 received from the automatic flushing appliance 2 to the cloud 100.

[0075] <1-2. Cloud Configuration> Next, an example of the configuration of the cloud 100 will be described. As shown in FIG. 1, the cloud 100 has a first storage unit 1101, a second storage unit 1102, a third storage unit 1103, and a determination unit 120. Note that the cloud 100 may have a configuration having various functions, not limited to those described above. Furthermore, the configuration of the storage units shown in FIG. 1 is merely an example, and the first storage unit 1101, the second storage unit 1102, and the third storage unit 1103 may be configured as a single storage unit. For example, the cloud 100 may have a single storage unit that stores the information to be stored in the first storage unit 1101, the second storage unit 1102, and the third storage unit 1103.

[0076] The first memory unit 1101 stores information collected about the hand wash basin 51. For example, the first memory unit 1101 stores information detected by the sensor 101. The second memory unit 1102 stores information collected about the hand wash basin 52. For example, the second memory unit 1102 stores information detected by the sensor 102. The third memory unit 1103 stores information collected about the hand wash basin 53. For example, the third memory unit 1103 stores information detected by the sensor 103. When describing the memory units such as the first memory unit 1101, the second memory unit 1102, and the third memory unit 1103 without distinguishing between them, they may be referred to as "memory unit 110".

[0077] The storage unit 110 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. For example, the storage unit 110 is a computer-readable recording medium that non-temporarily records data used by various information processing programs and the like.

[0078] The storage unit 110 stores various information necessary for processing. The storage unit 110 stores information collected about plumbing equipment such as the automatic flushing equipment 2. The storage unit 110 stores various information used in various information processing such as determining abnormalities in the plumbing space. For example, the storage unit 110 stores information used in processing including various values such as thresholds and setting values.

[0079] The memory unit 110 stores the output values of the sensor units. The memory unit 110 stores information identifying a plumbing device in association with the output values of the sensor units used to control that plumbing device. For example, the memory unit 110 stores information identifying an automatic flushing device 2 in association with the output value of the sensor 10 used to control that automatic flushing device 2. For example, the memory unit 110 stores the output value of the plumbing device when the discharge of the plumbing device is stopped. The memory unit 110 stores information identifying a plumbing device in association with the output value of the sensor unit when the discharge of the plumbing device is stopped.

[0080] The determination unit 120 and the like (sometimes collectively referred to as "information processing unit") are realized by, for example, an MPU (Micro Processing Unit) or a CPU (Central Processing Unit) executing a program (for example, various information processing programs related to the present disclosure) stored inside the cloud 100 using a RAM or the like as a working area. The information processing unit may also be realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0081] The determination unit 120 determines whether there is an abnormality in the plumbing space. The determination unit 120 determines whether there is an abnormality in the plumbing equipment based on the output value (also simply referred to as the "output value") of the sensor unit when the plumbing equipment stops discharging.

[0082] The determination unit 120 determines that an abnormality has occurred in one of the plurality of hand wash basins 5 when the output value of the sensor unit of one of the hand wash basins 5 deviates from the output values of the sensor units other than the one of the hand wash basins 5. The determination unit 120 compares the output values of the sensor units when the discharge of the plumbing equipment is stopped.

[0083] The determination unit 120 determines that an abnormality has occurred in one of the multiple hand wash basins 5 when the output value of the sensor 10 of one of the hand wash basins 5 deviates from the output values of the sensors 10 of the hand wash basins 5 other than the one of the hand wash basins 5. The determination unit 120 compares the output values of the sensors 10 when the discharge of the automatic flushing device 2 is stopped. For example, the determination unit 120 determines that an abnormality has occurred in the hand wash basin 51 when the output value of the sensor 101 of the hand wash basin 51 among the hand wash basins 51, 52, and 53 deviates from the output value of the sensor 102 of the hand wash basin 52 and the output value of the sensor 103 of the hand wash basin 53.

[0084] The determination unit 120 determines that an abnormality has occurred in one hand wash basin 5 when the output value of the sensor unit of one hand wash basin 5 deviates by a first threshold value or more from all output values of the sensor units of hand wash basins 5 other than the one hand wash basin 5 among the multiple hand wash basins 5. For example, the determination unit 120 determines that an abnormality has occurred in the hand wash basin 51 when the output value of the sensor 101 of the hand wash basin 51 among the hand wash basins 51, 52, and 53 deviates by a first threshold value or more from all of the output value of the sensor 102 of the hand wash basin 52 and the output value of the sensor 103 of the hand wash basin 53.

[0085] The determination unit 120 determines that an abnormality has occurred in one hand wash basin 5 when the output value of the sensor unit of one hand wash basin 5 deviates by a second threshold value or more from the average value of the output values of the sensor units of hand wash basins 5 other than the one hand wash basin 5 among the multiple hand wash basins 5. For example, the determination unit 120 determines that an abnormality has occurred in the hand wash basin 51 when the output value of the sensor 101 of the hand wash basin 51 among the hand wash basins 51, 52, and 53 deviates by a second threshold value or more from the average value of the output value of the sensor 102 of the hand wash basin 52 and the output value of the sensor 103 of the hand wash basin 53.

[0086] The determination unit 120 excludes a hand wash basin 5 that has been determined to be abnormal a predetermined number of times in succession from the objects to be determined for abnormality determination. For example, if the hand wash basin 51 of the hand wash basins 51, 52, and 53 has been determined to be abnormal a predetermined number of times in succession, the determination unit 120 excludes the hand wash basin 51 from the objects to be determined for abnormality determination and performs the determination process.

[0087] Note that the configuration shown in Fig. 1 is only a part of the configuration, and the cloud 100 may have various configurations other than the configuration shown in Fig. 1. For example, the cloud 100 may have an acquisition unit that acquires various pieces of information from the storage unit 110 or the like. The acquisition unit acquires various pieces of information from other information processing devices via a communication unit described later. Furthermore, for example, the cloud 100 may have a provision unit that transmits (provides) information to other information processing devices via a communication unit described later.

[0088] For example, the cloud 100 has a communication unit realized by a communication device, a communication circuit, etc. The communication unit enables the cloud 100 to communicate with external information processing devices such as the output unit 200 and the GW 300. The cloud 100 is connected via a network N by wire or wirelessly, and transmits and receives information to and from the external information processing devices.

[0089] The cloud 100 receives information about plumbing equipment such as the automatic flushing appliance 2 from the GW 300 via the communication unit. In this way, the cloud 100 acquires information about plumbing equipment such as the automatic flushing appliance 2. Note that the cloud 100 may be able to communicate directly with plumbing equipment such as the automatic flushing appliance 2 without going through the GW 300. In this case, the cloud 100 may receive information about the plumbing equipment from the plumbing equipment such as the automatic flushing appliance 2 via the network N. The cloud 100 transmits information to be output by the output unit 200 via the network N.

[0090] <1-3. Processing example> Based on the above-mentioned premise, the following describes the processing executed by the anomaly detection system 1 according to the first embodiment. Note that, although the following description will be made with the anomaly detection system 1 as the processing subject, each processing step may be performed by any device capable of executing that processing, depending on the device configuration included in the anomaly detection system 1.

[0091] The determination process shown in Fig. 3 will be described below. Fig. 3 is a flowchart showing an example of the process according to the first embodiment. Specifically, Fig. 3 is a flowchart showing the process of abnormality determination executed by the abnormality detection system 1.

[0092] The anomaly detection system 1 acquires data on each automatic faucet when the water is stopped (step S101). For example, the cloud 100 acquires detection information from the sensor 10 used to control each automatic flushing device 2 when the water is stopped for the automatic flushing devices 2 of each hand washing basin 5. For example, the cloud 100 acquires detection information from the sensors 101, 102, and 103 of the corresponding automatic flushing devices 21, 22, and 23 when the water is stopped.

[0093] The anomaly detection system 1 selects an automatic faucet to be subjected to the determination (Step S102). For example, the cloud 100 selects, from among the automatic flushing devices 2 of each hand washing basin 5, an automatic flushing device 2 to be subjected to the determination.

[0094] The anomaly detection system 1 excludes data for automatic faucets that were previously determined to be abnormal and that are still abnormal from the comparison (step S103). For example, the cloud 100 does not use data for automatic flushing devices 2 of each hand basin 5 that were previously determined to be abnormal and are still abnormal in the processing from step S104 onwards. For example, if one automatic flushing device 2 of each hand basin 5 is still abnormal, the cloud 100 excludes that one automatic flushing device 2 and performs the processing from step S104 onwards.

[0095] The anomaly detection system 1 executes a first determination process to determine whether there are any other automatic faucets with a reception volume similar to that of the determination target (step S104). For example, the cloud 100 compares the reception volume of the one automatic flushing appliance 2 that is the determination target with the reception volume of the other automatic flushing appliances 2, and determines whether there are any other automatic flushing appliances 2 with a reception volume similar to that of the one automatic flushing appliance 2.

[0096] For example, the cloud 100 executes a first determination process using a first threshold. For example, if the difference between the output value of the sensor 10 of one automatic flushing appliance 2 and the output value of the sensor 10 of at least one of the other automatic flushing appliances 2 is less than the first threshold, the cloud 100 determines that there is another automatic flushing appliance 2 with an output value of the sensor 10 similar to that of the object of determination. For example, if the difference between the output value of the sensor 10 of one automatic flushing appliance 2 and the output values of the sensors 10 of all the other automatic flushing appliances 2 is equal to or greater than the first threshold, the cloud 100 determines that there is no other automatic flushing appliance 2 with an output value of the sensor 10 similar to that of the object of determination.

[0097] If the anomaly detection system 1 determines that there is another automatic faucet with a reception volume similar to that of the subject of the determination (step S104: Yes), it determines that there is no anomaly (step S105). For example, if the cloud 100 determines that there is another automatic flushing device 2 with an output value of the sensor 10 similar to that of the one automatic flushing device 2 that is the subject of the determination, it determines that there is no anomaly in the hand basin 5 of the one automatic flushing device 2.

[0098] If the anomaly detection system 1 determines that there are no other automatic faucets with a reception volume similar to that of the one automatic flushing appliance 2 being the object of determination (step S104: No), it calculates the average value of the data of the automatic faucets other than the one automatic flushing appliance 2 being the object of determination (step S106). For example, if the cloud 100 determines that there are no other automatic flushing appliances 2 with output values of the sensors 10 similar to that of the one automatic flushing appliance 2 being the object of determination, it calculates the average value of the output values of the sensors 10 of the automatic flushing appliances 2 other than the one automatic flushing appliance 2.

[0099] The anomaly detection system 1 executes a second determination process to determine whether the difference between the reception amount of the object to be determined and the average value is equal to or greater than a second threshold (step S107). For example, the cloud 100 compares the reception amount of the automatic flushing appliance 2 that is the object to be determined with the average value of the reception amounts of the other automatic flushing appliances 2, and determines whether the difference between the reception amount of the object to be determined and the average value is equal to or greater than the second threshold.

[0100] In this way, the cloud 100 executes the second determination process using the second threshold. For example, the cloud 100 compares the difference between the output value of the sensor 10 of one automatic flushing appliance 2 and the average value of the output values of the sensors 10 of the other automatic flushing appliances 2 with the second threshold, and determines whether the difference between the amount of reception of one automatic flushing appliance 2 and the average value is equal to or greater than the second threshold.

[0101] If the difference in the received amount between the target and average values is not equal to or greater than the second threshold (step S107: No), the anomaly detection system 1 determines that there is no anomaly (step S105). For example, if the cloud 100 determines that the difference between the output value of the sensor 10 of the target automatic flushing appliance 2 and the average value of the output values of the sensors 10 of the other automatic flushing appliances 2 is not equal to or greater than the second threshold, it determines that there is no anomaly in the hand basin 5 of the target automatic flushing appliance 2.

[0102] If the difference in the received amount between the target and average value is equal to or greater than the second threshold (step S107: Yes), the anomaly detection system 1 determines that an anomaly has occurred (step S108). For example, if the cloud 100 determines that the difference between the output value of the sensor 10 of the target automatic flushing appliance 2 and the average value of the output values of the sensors 10 of the other automatic flushing appliances 2 is equal to or greater than the second threshold, it determines that an anomaly has occurred in the hand basin 5 of the target automatic flushing appliance 2.

[0103] Through the above-described processing, the anomaly detection system 1 can appropriately determine an abnormality in the wet space through a two-stage determination process consisting of a first determination process and a second determination process. For example, the anomaly detection system 1 can more accurately determine an abnormality in the wet space through a two-stage determination process that combines the first determination process and the second determination process as described above. Note that the anomaly detection system 1 may perform an abnormality determination only through the first determination process, or may perform an abnormality determination only through the second determination process.

[0104] For example, in order to improve the cleaning efficiency of water-related spaces such as public restrooms, there is a need for managers to immediately detect the occurrence of abnormalities. Conventional methods compare the usage rates of each hand wash basin and determine that a hand wash basin with a low usage rate is abnormal. However, because the abnormality is indirectly estimated by comparing usage rates, the following issues arise: For example, with conventional methods, it takes some time for a difference in usage rate to appear after the occurrence of an abnormality, making it difficult to quickly estimate the abnormality. For example, with conventional methods, there is a problem that usage rates vary depending on the installation layout of the hand wash basins, resulting in a lack of accuracy in determining the abnormality. For example, with conventional methods, there is a problem that in order to refer to the operation history of water-related equipment such as individual faucets, it is necessary to count the number of times the water is dispensed.

[0105] Therefore, the abnormality detection system 1 compares the state of the hand basin 5 (bowl surface 20a, etc.) in real time, for example, by comparing the sensor reception amounts of multiple hand basins 5. In this way, the abnormality detection system 1 can determine abnormalities in the wet area space in real time by detecting abnormalities in the wet area space based on the output values of each sensor 10. Furthermore, the abnormality detection system 1 uses existing sensors (discharge control sensors) to perform abnormality determination more quickly and accurately. In this way, the abnormality detection system 1 can appropriately determine abnormalities in the wet area space without using dedicated sensors for abnormality determination.

[0106] <1-4. Example of abnormality detection processing> An example of the abnormality determination process will now be described with reference to Fig. 4 and Fig. 5. Fig. 4 and Fig. 5 are diagrams showing an example of the abnormality determination process. First, the example of the determination process shown in Fig. 4 will be described.

[0107] Figure 4 illustrates an example in which three hand basins 5 are arranged in the same bathroom space: one with automatic faucet A, one with automatic faucet B, and one with automatic faucet C. For example, automatic faucet A in Figure 4 is automatic flushing device 21 of hand basin 51 in Figure 1, automatic faucet B in Figure 4 is automatic flushing device 22 of hand basin 52 in Figure 1, and automatic faucet C in Figure 4 is automatic flushing device 23 of hand basin 53 in Figure 1. When automatic faucet A, automatic faucet B, and automatic faucet C are described without any particular distinction, they will all be referred to as automatic faucets.

[0108] Stage #1 in Figure 4 corresponds to the stage of acquiring information detected by the sensor units corresponding to each automatic faucet. Stage #1 shows an example of the amount of reception (e.g., output value) at the sensor units corresponding to each automatic faucet. In Figure 4, the anomaly detection system 1 acquires information indicating that the amount of reception from automatic faucet A is "20," the amount of reception from automatic faucet B is "20," and the amount of reception from automatic faucet C is "60."

[0109] For example, the received amount from automatic faucet A is the output value of sensor 101 targeting automatic flushing device 21 of hand basin 51, the received amount from automatic faucet B is the output value of sensor 102 targeting automatic flushing device 22 of hand basin 52, and the received amount from automatic faucet C is the output value of sensor 103 targeting automatic flushing device 23 of hand basin 53. In this way, anomaly detection system 1 makes an anomaly determination using detection information from sensor units targeting the same plumbing equipment.

[0110] Stage #2 in Figure 4 corresponds to the stage of executing a process to determine whether another automatic faucet has a reception volume similar to that of the target for determination. Stage #2 shows an example of processing using a first threshold value of "3." Note that the first threshold value shown in Figure 4 is merely an example, and the first threshold value may be set to various values rather than being limited to the value shown in Figure 4, but this point will be discussed later. In Figure 4, the reception volume of automatic faucet A is "20", and there is a reception volume of automatic faucet B of "20", the difference of which is less than the first threshold value of "3", so the anomaly detection system 1 determines that there is an automatic faucet B with a reception volume similar to that of automatic faucet A.

[0111] Similarly, the anomaly detection system 1 determines that there is an automatic faucet A with a similar reception volume to that of automatic faucet B. On the other hand, since there is no other automatic faucet with a reception volume of "60" that differs from the first threshold value of "3" by no other automatic faucet, the anomaly detection system 1 determines that there is no other automatic faucet with a similar reception volume to that of automatic faucet C.

[0112] Stage #3 in Figure 4 corresponds to the stage at which an abnormality determination (first determination process) is performed based on the processing in Stage #2. Stage #3 shows an example of the provisional determination result of whether or not there is an abnormality for each automatic faucet based on the determination in Stage #2. In Figure 4, automatic faucet A and automatic faucet B have other automatic faucets with similar reception volumes, so the abnormality detection system 1 determines that there is no abnormality for automatic faucet A and automatic faucet B. On the other hand, automatic faucet C does not have other automatic faucets with similar reception volumes, so the abnormality detection system 1 provisionally determines that there is an abnormality for automatic faucet C.

[0113] Therefore, the abnormality detection system 1 performs the processing of steps #4 to #5 in Fig. 4 (second determination processing) with automatic faucet C, which was determined to have an abnormality in step #3, as the determination target. The abnormality detection system 1 excludes automatic faucet A and automatic faucet B, which were determined to have no abnormality in step #3, from the determination targets in the processing of steps #4 to #5 in Fig. 4 (second determination processing).

[0114] Stage #4 in Figure 4 corresponds to the stage of calculating the average value of automatic faucets other than the one being judged (comparison target). Stage #4 shows an example of processing in which only the automatic faucet (automatic faucet C) judged to have an abnormality in Stage #3 is the judgment target. In Figure 4, the abnormality detection system 1 calculates the average value "20 (= (20 + 20) / 2)" of the reception volume of automatic faucets A and B, which are automatic faucets other than automatic faucet C (comparison target).

[0115] Stage #5 in Figure 4 corresponds to the stage of calculating the difference between the judgment target and the average value, and executing a process of comparing the calculated difference with a threshold value. Stage #5 shows an example of processing using a second threshold value of "10." Note that the second threshold value shown in Figure 4 is merely an example, and the second threshold value may be set to various values other than the value shown in Figure 4, which will be discussed later. In Figure 4, the anomaly detection system 1 calculates the difference "40 (= |60-20|)" between the reception amount of automatic faucet C "60" and the average value of the comparison target automatic faucet C "20," and compares the calculated difference "40" with the second threshold value "10."

[0116] Stage #6 in Figure 4 corresponds to the stage at which an abnormality determination (second determination process) is performed based on the processing in stage #5. Stage #6 shows an example of the final determination result of whether or not there is an abnormality for each automatic faucet based on the comparison in stage #5. In Figure 4, the difference "40" between automatic faucet C, which is the target of determination, and the average value is greater than or equal to the second threshold value "10," so the abnormality detection system 1 determines that there is an abnormality for automatic faucet C. Note that automatic faucet A and automatic faucet B are excluded from the targets of determination in stages #4 to #5, so the abnormality detection system 1 determines that there is no abnormality for automatic faucet A and automatic faucet B.

[0117] Thus, in FIG. 4, the abnormality detection system 1 determines that there is no abnormality in automatic faucet A and automatic faucet B, and determines that there is an abnormality in automatic faucet C.

[0118] Next, an example of the determination process shown in Fig. 5 will be described. Note that explanations of the same points as in Fig. 4 will be omitted as appropriate. For example, stages #1 to #6 in Fig. 5 are the same as stages #1 to #6 in Fig. 4, and therefore explanations will be omitted except for points where there are differences in content.

[0119] At stage #1 in Figure 5, the abnormality detection system 1 acquires information indicating that the reception volume of automatic faucet A is "24", the reception volume of automatic faucet B is "20", and the reception volume of automatic faucet C is "10".

[0120] In stage #2 in Figure 5, the reception volume of automatic faucet A is "24", and there are no reception volumes of other automatic faucets with a difference less than the first threshold value "3", so the anomaly detection system 1 determines that there are no other automatic faucets with a similar reception volume for automatic faucet A. Similarly, the reception volume of automatic faucet B is "20", and there are no reception volumes of other automatic faucets with a difference less than the first threshold value "3", so the anomaly detection system 1 determines that there are no other automatic faucets with a similar reception volume for automatic faucet B. The reception volume of automatic faucet C is "10", and there are no reception volumes of other automatic faucets with a difference less than the first threshold value "3", so the anomaly detection system 1 determines that there are no other automatic faucets with a similar reception volume for automatic faucet C.

[0121] In stage #3 in Figure 5, because there are no other automatic faucets with similar reception volumes for automatic faucet A, automatic faucet B, and automatic faucet C, the anomaly detection system 1 provisionally determines that there is an abnormality for automatic faucet A, automatic faucet B, and automatic faucet C. Therefore, in Figure 5, the anomaly detection system 1 performs the processing of stages #4 to #5 in Figure 5 (second determination processing) with all of automatic faucet A, automatic faucet B, and automatic faucet C as the determination targets.

[0122] In stage #4 in Figure 5, the anomaly detection system 1 calculates, for automatic faucet A, the average value of the reception volume of automatic faucets B and C, which are automatic faucets other than automatic faucet A (comparison objects), "15 (= (20 + 10) / 2)". Similarly, for automatic faucet B, the anomaly detection system 1 calculates the average value of the reception volume of automatic faucets A and C, which are automatic faucets other than automatic faucet B (comparison objects), "17 (= (24 + 10) / 2)". For automatic faucet C, the anomaly detection system 1 calculates the average value of the reception volume of automatic faucet A and automatic faucet B, which are automatic faucets other than automatic faucet C (comparison objects), "22 (= (24 + 20) / 2)".

[0123] In stage #5 in FIG. 5, the anomaly detection system 1 calculates the difference "9" (=|24-15|) between the reception volume "24" of automatic faucet A and the average value "15" of the comparison object for automatic faucet A, and compares the calculated difference "9" with the second threshold value "10." Similarly, the anomaly detection system 1 calculates the difference "3" (=|20-17|) between the reception volume "20" of automatic faucet B and the average value "17" of the comparison object for automatic faucet B, and compares the calculated difference "3" with the second threshold value "10." The anomaly detection system 1 calculates the difference "12" (=|10-22|) between the reception volume "10" of automatic faucet C and the average value "22" of the comparison object for automatic faucet C, and compares the calculated difference "12" with the second threshold value "10."

[0124] At stage #6 in Figure 5, the difference "9" between the automatic faucet A being judged and the average value is less than the second threshold value "10," so the anomaly detection system 1 judges that there is no abnormality with automatic faucet A. The difference "3" between the automatic faucet B being judged and the average value is less than the second threshold value "10," so the anomaly detection system 1 judges that there is no abnormality with automatic faucet B. On the other hand, the difference "22" between the automatic faucet C being judged and the average value is greater than or equal to the second threshold value "10," so the anomaly detection system 1 judges that there is an abnormality with automatic faucet C.

[0125] Thus, in FIG. 5, the abnormality detection system 1 determines that there is no abnormality in automatic faucet A and automatic faucet B, and determines that there is an abnormality in automatic faucet C.

[0126] The above-described processing is merely an example, and the anomaly detection system 1 may perform processing using various information. For example, the anomaly detection system 1 may change the threshold value depending on the size of the bathroom space where anomaly detection is performed. For example, at least one of the first threshold value and the second threshold value may be changeable. An example of this point will be described with reference to FIG. 6. FIG. 6 is a diagram showing an example of setting threshold values depending on the size.

[0127] FIG. 6 shows a case where the first threshold and the second threshold are set according to the number of target units (e.g., the number of hand washing basins 5). FIG. 6 shows a case where the threshold values are set so that the greater the number of target units, the smaller the threshold values. For example, if the number of target units is 100, the first threshold is set to "3" and the second threshold is set to "10." The number of target units may also be specified as a range; for example, if the number of target units is 100 or more, the first threshold may be set to "3" and the second threshold to "10."

[0128] For example, if the number of target vehicles is 50, the first threshold value is set to "4" and the second threshold value is set to "12." The number of target vehicles may be specified as a range, for example, if the number of target vehicles is 50 to 99, the first threshold value may be set to "4" and the second threshold value to "12." For example, if the number of target vehicles is 10, the first threshold value may be set to "5" and the second threshold value to "14." The number of target vehicles may be specified as a range, for example, if the number of target vehicles is 3 to 49, the first threshold value may be set to "5" and the second threshold value to "14."

[0129] For example, the anomaly detection system 1 may set the first threshold and the second threshold using the threshold setting table shown in Fig. 6. The first threshold and the second threshold may be automatically updated by a device (such as the cloud 100) of the anomaly detection system 1 depending on the number of devices to be determined, or may be specified by an administrator of the anomaly detection system 1 based on the threshold setting table shown in Fig. 6.

[0130] An example of an actual abnormality pattern will now be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of an abnormality pattern. Note that explanations of points similar to those described above will be omitted as appropriate.

[0131] 7 shows a state where there is no abnormality (normal state). For example, pattern PT1 shows a case where the sensor output value of sensor 10 is "20" when there is no abnormality.

[0132] 7 shows a state in which paper has been discarded (first abnormal pattern). For example, abnormal pattern CS1 shows a case in which the sensor output value of sensor 10 rises to "40" in an abnormal state in which paper has been discarded (first abnormal pattern).

[0133] Abnormal pattern CS2 in Figure 7 indicates a state in which a drainage blockage has occurred (second abnormal pattern). For example, abnormal pattern CS2 indicates a case in which the sensor output value of sensor 10 decreases to "10" in an abnormal pattern in which the clogged water is transparent (second abnormal pattern). Abnormal pattern CS2 indicates a case in which the sensor output value of sensor 10 increases to "40" in an abnormal pattern in which the clogged water is murky (second abnormal pattern).

[0134] 7 shows a state (third abnormal pattern) in which the light-emitting element is broken and unable to emit light. For example, abnormal pattern CS3 shows a case in which the sensor output value of sensor 10 decreases to "0" in an abnormal state (third abnormal pattern) in which the light-emitting element is broken and unable to emit light.

[0135] Abnormal pattern CS4 in Figure 7 shows a state in which the sensor 10 is blocked by gum (fourth abnormal pattern). For example, abnormal pattern CS4 shows a case in which the sensor output value of sensor 10 decreases to "5" in an abnormal pattern (fourth abnormal pattern) in which the light receiving side is blocked by gum (for example, when only the light receiving portion 12 of sensor 10 is blocked). Abnormal pattern CS4 shows a case in which the sensor output value of sensor 10 increases to "50" in an abnormal pattern (fourth abnormal pattern) in which the entire light emitting and receiving side is blocked by gum (for example, when the entire sensor 10 is blocked).

[0136] In this way, the anomaly detection system 1 detects anomalies in the wet area by comparing information from multiple hand basins 5 (detection information from sensors 10) based on the sensor output values (received amount, etc.) that change according to each anomaly pattern as described above. Note that the anomaly patterns and sensor output values shown in Figure 7 are merely examples, and the anomaly patterns are not limited to those shown below, and any pattern related to various anomalies that can occur in the wet area may be used.

[0137] 2. Second embodiment The anomaly detection described in the first embodiment is merely an example, and the anomaly detection system may detect an anomaly in the wet area by appropriately using various information. For example, the anomaly detection system may detect an anomaly in the wet area based on the behavioral patterns of users of the wet area. This will be described below as a second embodiment.

[0138] <2-1. Configuration of anomaly detection system> First, the configuration of an anomaly detection system 1A according to the second embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram showing an example of the configuration of an anomaly detection system for a plumbing space according to the second embodiment. Note that in the anomaly detection system 1A according to the second embodiment, the same components as those in the anomaly detection system 1 according to the first embodiment will be denoted by the same reference numerals and will not be described in detail. For example, in the anomaly detection system 1A according to the second embodiment, the automatic faucet device 2 has the same configuration as the automatic faucet device 2 according to the first embodiment, and therefore detailed description thereof will be omitted.

[0139] As shown in Fig. 8, anomaly detection system 1A has a hand wash basin 5A, a cloud 100A, an output unit 200, and a GW 300. In this way, anomaly detection system 1A has a cloud 100A instead of a cloud 100. Note that the configuration shown in Fig. 8 is merely an example, and anomaly detection system 1A may have multiple hand wash basins 5A.

[0140] The hand washing basin 5A has a bowl portion 20, a sensor 10, an automatic flushing device 2 whose discharge into the bowl portion 20 is controlled according to the output value of the sensor 10, a sensor 10A, and an automatic liquid soap device 3 whose discharge into the bowl portion 20 is controlled according to the output value of the sensor 10A. For example, the sensor 10 is a detection means that detects the use of the automatic faucet device 2. For example, the sensor 10A is a detection means that detects the use of the automatic liquid soap device 3. In this way, the sensors 10 and 10A are detection means that detect the use of plumbing equipment. The hand washing basin 5A may have a configuration similar to that of the hand washing basin 5 according to the first embodiment.

[0141] The sensor 10A is a sensor unit that acquires information used to control the automatic liquid soap dispenser 3 (plumbing equipment). For example, the sensor 10A has a sensor device (detection device) such as an infrared light emitting / receiving optical sensor (photoelectric sensor). Note that the sensor 10A is similar to the sensor 10 except that the plumbing equipment to be controlled is the automatic liquid soap dispenser 3, and therefore a detailed description thereof will be omitted.

[0142] The sensor 10A has a predetermined area such as inside the bowl portion 20 (for example, below the discharge port of the automatic liquid soap dispenser 3) as its detection area. The sensor 10A projects (emits) infrared light toward the bowl portion 20 and receives light that includes reflected light from the bowl portion 20. The sensor 10A includes a light projecting unit 11A and a light receiving unit 12A. For example, the sensor 10A has the light projecting unit 11A and the light receiving unit 12A that receives light that includes reflected light of the light emitted from the light projecting unit 11A, and detects the amount of light received by the light receiving unit 12A.

[0143] The light-projecting unit 11A has the same configuration as the light-projecting unit 11, and functions as a transmitting unit that transmits a predetermined signal such as an electromagnetic wave. The light-receiving unit 12A has the same configuration as the light-receiving unit 12, and functions as a receiving unit that receives a predetermined signal such as an electromagnetic wave.

[0144] Similar to the sensor 10, the sensor 10A outputs an output value of the sensor 10A (sensor output value) in response to detection. For example, the sensor 10A is connected to the control unit 50A in the same manner as the connection between the sensor 10 and the control unit 50. The sensor 10A is controlled by the control unit 50A. The sensor 10A also transmits various types of information to the control unit 50A. For example, the sensor 10A transmits information acquired by detection (detection information) to the control unit 50A.

[0145] The sensor 10A may be placed in any manner as long as it can perform the desired detection. For example, the sensor 10A is not limited to being placed at the outlet of the automatic liquid soap dispenser 3, but may be placed at any location, such as somewhere along the water discharge pipe of the automatic liquid soap dispenser 3.

[0146] The automatic liquid soap dispenser 3 is a plumbing device that dispenses liquid soap toward the bowl section 20. The automatic liquid soap dispenser 3 is an automatic liquid soap dispenser that automatically dispenses liquid soap in response to detection by a sensor, etc. The automatic liquid soap dispenser 3 has a configuration that allows it to automatically dispense liquid soap appropriately in response to a person's hand movements, etc. For example, the automatic liquid soap dispenser 3 has a pump 40A and a control unit 50A. In this way, the automatic liquid soap dispenser 3 has the pump 40A and dispenses liquid soap into the bowl section 20 from the discharge port of the discharge pipe.

[0147] In addition to the pump 40A and the control unit 50A, the automatic liquid soap dispenser 3 also has components such as a tank for storing liquid soap and a discharge pipe for discharging the liquid soap toward the bowl portion 20. For example, the pump 40A supplies the liquid soap stored in the tank to the discharge pipe, and the liquid soap supplied by the pump 40A is discharged from the discharge port of the discharge pipe. The automatic liquid soap dispenser 3 can be configured in any way as long as it is capable of discharging liquid soap into the bowl portion 20.

[0148] The control unit 50A is a control device (information processing device) used for various processes such as controlling plumbing equipment, similar to the control unit 50. The control unit 50A controls the discharge from the automatic liquid soap device 3 (plumbing equipment) to the bowl unit 20 according to the output value of the sensor 10A.

[0149] The control unit 50A switches the pump 40A ON / OFF. The control unit 50A sends a control signal to the pump 40A, switching the pump 40A ON / OFF, thereby discharging liquid soap from the automatic liquid soap dispenser 3. For example, when discharging liquid soap, the control unit 50A turns the pump 40A ON to discharge liquid soap from the discharge port of the automatic liquid soap dispenser 3. For example, when discharging liquid soap, the control unit 50A turns the pump 40A OFF to stop the discharge of liquid soap from the discharge port of the automatic liquid soap dispenser 3. This is the same as the automatic discharge of liquid soap in a normal automatic machine, so a detailed explanation will be omitted.

[0150] The control unit 50A acquires information. The control unit 50A acquires detection information detected by the sensor 10A. The control unit 50A receives the detection information acquired by the sensor 10A from the sensor 10A. The control unit 50A may store the acquired various pieces of information in a memory unit (such as a storage device) within the control unit 50A.

[0151] The control unit 50A transmits information to an external information processing device such as the GW 300. For example, the control unit 50A transmits the detection information detected by the sensor 10A to the GW 300 together with information identifying the plumbing device (such as the automatic liquid soap device 3) that the sensor 10A is detecting.

[0152] In addition, when a plumbing device communicates with the cloud 100A without going through the GW300, the control unit 50A transmits the detection information detected by the sensor 10A to the cloud 100A via the network N, together with information identifying the plumbing device (such as the automatic water soap device 3) that the sensor 10A is detecting.

[0153] Like the cloud 100, the cloud 100A is a computer (information processing device) that provides cloud services. As shown in Fig. 8, the cloud 100A includes a storage unit 110A and a determination unit 120A. Note that the cloud 100A is not limited to the above and may have a configuration having various functions.

[0154] The storage unit 110A is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, or a storage device such as a hard disk or an optical disk. For example, the storage unit 110A is a computer-readable recording medium that non-temporarily records data used by various information processing programs. The storage unit 110A stores various pieces of information necessary for processing, similar to the storage unit 110.

[0155] The memory unit 110A stores the detection results of the detection means. For example, the memory unit 110A stores information on the use of a plumbing device in response to detection (detection) by the detection means, in association with the time of use. For example, when the automatic flushing device 2 discharges in response to detection by the sensor 10, the memory unit 110A stores the date and time of the discharge as the date and time of the automatic flushing device 2 being used. For example, when the automatic liquid soap device 3 discharges in response to detection by the sensor 10A, the memory unit 110A stores the date and time of the discharge as the date and time of the automatic liquid soap device 3 being used. For example, the memory unit 110A stores identification information (e.g., a device ID, etc.) that identifies the plumbing device corresponding to the sensor that performed the detection, in association with the date and time of the discharge of the plumbing device.

[0156] The storage unit 110A stores usage model patterns corresponding to predetermined behavioral patterns for using the wet area. The storage unit 110A stores, as usage model patterns, information indicating predetermined behavioral patterns that are expected to be taken by users when normally using the wet area. For example, the storage unit 110A stores a plurality of usage model patterns. For example, the storage unit 110A stores a list of usage model patterns. For example, the usage model pattern is information indicating a usage pattern that is normally expected when using the wet area (for example, a series of steps for using wet area equipment).

[0157] For example, the usage pattern may include a pattern of use in the wet area with water (use of the automatic flushing device 2), liquid soap (use of the automatic liquid soap device 3), and the flow (order) of water. For example, the usage pattern may include a pattern of use in the wet area with liquid soap and the flow (order) of water. For example, the usage pattern may include a pattern of use in the wet area with the flow (order) of water and a hand dryer (use of the hand dryer 4). For example, the usage pattern may include a pattern of use in the wet area with the flow (order) of liquid soap, water, and a hand dryer. For example, the usage pattern may include a pattern of use in the wet area with the flow (order) of liquid soap, water, and a hand dryer. Note that the above is merely an example, and any usage pattern can be adopted depending on, for example, the configuration and arrangement of the wet area (devices, etc.)

[0158] The storage unit 110A may store a usage model pattern for each usage situation. For example, the storage unit 110A may store a usage model pattern (non-crowded model parameters) used for abnormality detection when the number of people located in the bathroom space is less than a predetermined value, i.e., when the space is not crowded. For example, the storage unit 110A may store a usage model pattern (crowded model parameters) used for abnormality detection when the number of people located in the bathroom space is equal to or greater than a predetermined value, i.e., when the space is crowded.

[0159] For example, the storage unit 110A may store a usage model pattern (first time zone model parameters) used for abnormality detection in the morning. For example, the storage unit 110A may store a usage model pattern (second time zone model parameters) used for abnormality detection in the afternoon.

[0160] The memory unit 110A stores the number of times that the determination unit 120A has determined an abnormality. The memory unit 110A stores the number of times that the determination unit 120A has determined an abnormality in association with a list of the dates on which each determination was made. The memory unit 110A stores the number of times that the determination unit 120A has determined an abnormality in association with information identifying the plumbing space that was the subject of the determination. The memory unit 110A stores the number of times that the determination unit 120A has determined an abnormality in association with information identifying the plumbing appliance that was the subject of the determination.

[0161] The information processing unit such as the determination unit 120A is realized by, for example, an MPU, a CPU, etc. executing a program stored in the cloud 100A (for example, various information processing programs related to the present disclosure) using a RAM, etc. as a work area. The information processing unit may also be realized by, for example, an integrated circuit such as an ASIC or an FPGA.

[0162] The determination unit 120A functions as an estimation unit that estimates the usage state of the wet area space based on the detection result. If the usage state deviates from the usage model pattern, the determination unit 120A estimates that an abnormality has occurred in the wet area space. The usage model pattern and the usage state include at least one of the usage ratio, usage order, and usage count of the wet area appliances.

[0163] When the number of people located in the wet area is less than a predetermined value, the determination unit 120A may perform an abnormality determination (abnormality estimation) of the wet area using the non-crowded model parameters stored in the storage unit 110A. When the number of people located in the wet area is equal to or greater than a predetermined value, the determination unit 120A may perform an abnormality determination (abnormality estimation) of the wet area using the crowded model parameters stored in the storage unit 110A.

[0164] When performing processing on the state of the wet area space in the morning, the determination unit 120A may perform an abnormality determination (abnormality estimation) on the wet area space using the first time period model parameters stored in the memory unit 110A. When performing processing on the state of the wet area space in the afternoon, the determination unit 120A may perform an abnormality determination (abnormality estimation) on the wet area space using the second time period model parameters stored in the memory unit 110A. In this way, the usage model pattern may be changed depending on at least one of the number of users of the wet area space or the time period.

[0165] The determination unit 120A determines that an abnormality has occurred in the wet space when an abnormality in the wet space is estimated a predetermined number of times or more. The determination unit 120A determines that an abnormality has occurred in the wet space when an abnormality in the wet space is estimated a predetermined number of times in succession. The determination unit 120A determines that an abnormality has occurred in the wet space when an abnormality in the wet space is estimated at a predetermined rate or more.

[0166] Note that the configuration shown in Fig. 8 is only a part of the configuration, and the cloud 100A may have various configurations other than the configuration shown in Fig. 8. For example, the cloud 100A may have an acquisition unit that acquires each piece of information from the storage unit 110A, etc. For example, the cloud 100A, like the cloud 100, has a communication unit realized by a communication device, a communication circuit, etc.

[0167] The output unit 200 according to the second embodiment outputs various types of information in the same manner as the output unit 200 according to the first embodiment. The output unit 200 outputs the determination result by the determination unit 120A. For example, the output unit 200 displays the determination result by the determination unit 120A. For example, the output unit 200 outputs the determination result by the determination unit 120A as sound.

[0168] The output unit 200 functions as a notification unit that notifies the state of the wet area space based on the estimation result of the determination unit 120A. The output unit 200 notifies an abnormality in the wet area space based on the estimation result of the determination unit 120A. The output unit 200 notifies an abnormality in the wet area space when the determination unit 120A estimates an abnormality in the wet area space a predetermined number of times or more. The output unit 200 notifies an abnormality in the wet area space when the determination unit 120A estimates an abnormality in the wet area space a predetermined number of times in succession. The output unit 200 notifies an abnormality in the wet area space when the determination unit 120A estimates an abnormality in the wet area space at a predetermined rate or more.

[0169] <2-2. Processing example> Next, the processing executed by the anomaly detection system 1A according to the second embodiment will be described. Below, first to fourth processes will be shown as examples of the processing executed by the anomaly detection system 1A. Note that the processing executed by the anomaly detection system 1A is not limited to the first to fourth processes, and may be processing other than the first to fourth processes, or may be processing that combines the first to fourth processes, etc.

[0170] In the following, the anomaly detection system 1A will be described as the processing subject, but each process may be performed by any device capable of executing that process, depending on the device configuration included in the anomaly detection system 1A. Furthermore, among the processes performed by the anomaly detection system 1A, descriptions of those that are similar to the processes performed by the anomaly detection system 1 according to the first embodiment will be omitted as appropriate.

[0171] First, the first processing will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of processing according to the second embodiment. Specifically, Fig. 9 is a flowchart showing the first processing related to anomaly determination (anomaly detection) executed by the anomaly detection system 1A. Note that a variable T used in the processing described below is set to an arbitrary initial value (for example, -1).

[0172] The anomaly detection system 1A acquires information on a series of detected hand-washing actions (step S201). For example, the cloud 100A acquires information on the use of plumbing equipment within a predetermined period of time in the target plumbing space as information on the detected series of hand-washing actions. Note that the series of hand-washing actions referred to here may be any action as long as it is the use of plumbing equipment in the plumbing space, and for example, it does not have to include the use (spouting) of the automatic flushing device 2.

[0173] For example, anomaly detection system 1A acquires the use of plumbing devices performed within a predetermined period (e.g., within 30 seconds) in the same plumbing space as a series of hand-washing actions. For example, anomaly detection system 1A acquires the use of a plumbing device, a single hand basin 5, and a hand dryer 4 located in the same plumbing space as that hand basin 5, performed within a predetermined period (e.g., within one minute) as a series of hand-washing actions.

[0174] For example, cloud 100A acquires information indicating the use of the automatic faucet device 2, the automatic liquid soap device 3, and the hand dryer 4 of the same hand washing basin 5 within a predetermined period as information indicating a series of hand washing actions. In this case, the series of hand washing actions is the use of the plumbing devices in the order of water, liquid soap, and hand dryer. For example, cloud 100A acquires detection information from memory unit 110A indicating the use of the plumbing devices in the order of water, liquid soap, and hand dryer. Cloud 100A estimates the series of hand washing actions in the order of water, liquid soap, and hand dryer indicated by the detection information acquired from memory unit 110A as the usage state of the plumbing space.

[0175] The anomaly detection system 1A determines whether or not the pattern corresponds to a usage model pattern (step S202). For example, the cloud 100A determines whether or not a list of usage model patterns used for the determination, among the usage model patterns stored in the storage unit 110A, includes a series of hand-washing actions indicated by the information on a series of hand-washing action detections. For example, the cloud 100A determines that the pattern corresponds to a usage model pattern if the list of usage model patterns used for the determination includes a series of hand-washing actions indicated by the information on a series of hand-washing action detections. For example, the cloud 100A determines that the pattern does not correspond to a usage model pattern if the list of usage model patterns used for the determination does not include a series of hand-washing actions indicated by the information on a series of hand-washing action detections.

[0176] If it corresponds to the usage model pattern (step S202: Yes), the anomaly detection system 1A determines that it is normal use (step S203). For example, if the cloud 100A determines that the series of hand-washing actions indicated by the information on the detection of a series of hand-washing actions corresponds to the usage model pattern, it determines that the series of hand-washing actions is normal use and presumes that no abnormality has occurred. Then, the anomaly detection system 1A sets the variable T to "-1" (step S204). For example, if the value of the variable T is 0 or greater, the anomaly detection system 1A changes it to "-1."

[0177] If it does not correspond to the usage model pattern (step S202: No), the anomaly detection system 1A counts it as a mismatch and increments the value of the variable T by 1 (step S205). For example, if the cloud 100A determines that the series of hand-washing actions indicated by the information on the detection of a series of hand-washing actions does not correspond to the usage model pattern, it increments the value of the variable T for counting mismatch. Then, the anomaly detection system 1A estimates that an abnormality has occurred in the wet area space being evaluated, and issues an alert (step S206). For example, the cloud 100A determines that the series of hand-washing actions is not for normal use, estimates that an abnormality has occurred in the wet area space being evaluated, and issues an alert of information indicating an abnormality in the wet area space being evaluated.

[0178] Next, the second processing will be described with reference to Fig. 10. Fig. 10 is a flowchart showing an example of processing according to the second embodiment. Specifically, Fig. 10 is a flowchart showing the second processing related to anomaly determination (anomaly detection) executed by the anomaly detection system 1A. Note that explanations of points similar to the above-described processing such as the first processing will be omitted as appropriate.

[0179] The anomaly detection system 1A acquires information on a series of hand-washing action detections (step S301), and determines whether or not the information corresponds to a usage model pattern (step S302).

[0180] If the usage model pattern corresponds to the usage model pattern (step S302: Yes), the anomaly detection system 1A determines that the usage is normal (step S303).Then, the anomaly detection system 1A sets the variable T to "-1" (step S304).

[0181] If the usage model pattern does not apply (step S302: No), the anomaly detection system 1A counts it as a mismatch and increments the value of the variable T by 1 (step S305).

[0182] Then, the anomaly detection system 1A determines whether the number of consecutive times that the usage model pattern does not match is equal to or greater than a predetermined number (step S306). For example, the cloud 100A determines whether the number of consecutive times that the usage model pattern does not match is equal to or greater than a predetermined number (for example, an arbitrary number of times such as 3 times, 10 times, etc.).

[0183] If the number of consecutive times that the usage model pattern does not match is not a predetermined number or more (step S306: No), the anomaly detection system 1A keeps the value of the non-matching count variable T and processes this as an event that occurred by chance (step S307). For example, when the cloud 100A determines that the number of consecutive times that the usage model pattern does not match is not a predetermined number or more, it presumes that this is an event that occurred by chance and ends the processing without changing the value of the non-matching count variable T or issuing an alert.

[0184] If the number of consecutive times that do not match the usage model pattern is equal to or greater than a predetermined number (step S306: Yes), the anomaly detection system 1A estimates that an abnormality has occurred in the target wet area space and issues a notification (step S308). For example, the cloud 100A estimates that an abnormality has occurred in the target wet area space where a series of consecutive hand-washing actions that do not match the usage model pattern have been performed, and issues a notification of information indicating an abnormality in the target wet area space.

[0185] Next, the third processing will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an example of processing according to the second embodiment. Specifically, Fig. 11 is a flowchart showing the third processing related to anomaly determination (anomaly detection) executed by the anomaly detection system 1A. Note that explanations of points similar to the above-described processing such as the first processing and the second processing will be omitted as appropriate.

[0186] The anomaly detection system 1A acquires information on a series of hand-washing action detections (step S401), and determines whether or not the information corresponds to a usage model pattern (step S402).

[0187] If the usage model pattern corresponds to the usage model pattern (step S402: Yes), the anomaly detection system 1A determines that the usage is normal (step S403).Then, the anomaly detection system 1A sets the variable T to "-1" (step S404).

[0188] If the usage model pattern does not apply (step S402: No), the anomaly detection system 1A counts it as a mismatch and increments the value of the variable T by 1 (step S405).

[0189] Then, the anomaly detection system 1A determines whether the number of times that the usage model pattern did not match per unit time is equal to or greater than a predetermined rate (step S406). For example, the cloud 100A determines whether the number of times that the usage model pattern did not match per unit time (any unit time, such as 30 minutes, 2 hours, etc.) is equal to or greater than a predetermined rate (any rate, such as 10%, 30%, etc.).

[0190] If the number of times per unit time that did not match the usage model pattern is not equal to or greater than a predetermined rate (step S406: No), the anomaly detection system 1A keeps the value of the non-matching count variable T and processes this as an event that occurred by chance (step S407). For example, when the cloud 100A determines that the number of times per unit time that did not match the usage model pattern is not equal to or greater than a predetermined rate, it presumes that this is an event that occurred by chance and ends the process without changing the value of the non-matching count variable T or issuing an alert.

[0191] If the number of times per unit time that does not match the usage model pattern is equal to or greater than a predetermined rate (step S406: Yes), the anomaly detection system 1A estimates that an abnormality has occurred in the target wet room space and issues a notification (step S408). For example, the cloud 100A estimates that an abnormality has occurred in the target wet room space for which the number of times per unit time that does not match the usage model pattern is equal to or greater than a predetermined rate, and issues a notification of information indicating an abnormality in the target wet room space.

[0192] Next, the fourth process will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of the process according to the second embodiment. Specifically, Fig. 12 is a flowchart showing the fourth process related to anomaly determination (anomaly detection) executed by the anomaly detection system 1A. Note that the description of the same points as those in the first to third processes described above will be omitted as appropriate.

[0193] The anomaly detection system 1A acquires information on a series of hand-washing action detections (step S501), and determines whether or not the information corresponds to a usage model pattern (step S502).

[0194] If the usage model pattern corresponds to the usage model pattern (step S502: Yes), the anomaly detection system 1A determines that the usage is normal (step S503).Then, the anomaly detection system 1A sets the variable T to "-1" (step S504).

[0195] If the usage model pattern does not apply (step S502: No), the anomaly detection system 1A counts it as a mismatch and increments the value of the variable T by 1 (step S505).

[0196] Then, the anomaly detection system 1A determines whether the number of consecutive times that the usage model pattern does not match is equal to or exceeds a predetermined number (step S506).

[0197] If the number of consecutive times that do not match the usage model pattern is equal to or greater than a predetermined number (step S506: Yes), the anomaly detection system 1A estimates that an abnormality has occurred in the target wet area space and issues a notification (step S507). For example, the cloud 100A estimates that an abnormality has occurred in the target wet area space where a series of consecutive hand-washing actions that do not match the usage model pattern have been performed, and issues a notification of information indicating an abnormality in the target wet area space.

[0198] If the number of consecutive times that the usage model pattern did not match is not equal to or greater than a predetermined number (step S506: No), the anomaly detection system 1A determines whether the number of times that the usage model pattern did not match per unit time is equal to or greater than a predetermined rate (step S508). For example, if the cloud 100A determines that the number of consecutive times that the usage model pattern did not match is not equal to or greater than a predetermined number, it determines whether the number of times that the usage model pattern did not match per unit time is equal to or greater than a predetermined rate.

[0199] If the number of times per unit time that does not match the usage model pattern is equal to or greater than a predetermined rate (step S508: Yes), the anomaly detection system 1A executes the process of S507. For example, the cloud 100A estimates that an abnormality has occurred in the wet area space to be evaluated, for which the number of times per unit time that does not match the usage model pattern is equal to or greater than a predetermined rate, and notifies information indicating the abnormality in the wet area space to be evaluated.

[0200] If the number of times per unit time that did not match the usage model pattern is not equal to or greater than a predetermined rate (step S508: No), the anomaly detection system 1A keeps the value of the non-matching count variable T and processes this as an event that occurred by chance (step S509). For example, if the cloud 100A determines that the number of times per unit time that did not match the usage model pattern is not equal to or greater than a predetermined rate, it presumes that this is an event that occurred by chance and ends the process without changing the value of the non-matching count variable T or issuing an alert.

[0201] By performing the above-described anomaly detection processes, such as the first to fourth processes, the anomaly detection system 1A can appropriately detect an anomaly in the target wet area and can appropriately notify the user of the anomaly as necessary. For example, the anomaly detection system 1A may determine (estimate) that an anomaly has occurred in the wet area where the automatic liquid soap dispenser 3 is located if the automatic liquid soap dispenser 3 is not being used very often. For example, the anomaly detection system 1A may determine (estimate) that an anomaly has occurred in the wet area where the automatic liquid soap dispenser 3 is located if hand washing is completed with liquid soap (use of the automatic liquid soap dispenser 3). For example, the anomaly detection system 1A may determine (estimate) that an anomaly has occurred in the wet area where the hand dryer 4 is located if the hand dryer 4 is not being used very often.

[0202] For example, in order to improve the cleaning efficiency of public restrooms and other water-related spaces, there is a need for managers to immediately grasp the occurrence of abnormalities. Conventional methods compare the usage rates of each hand wash basin and determine that a hand wash basin with a low usage rate is abnormal. Therefore, with conventional methods, when the usage rate of a certain device is low, it is difficult to determine whether the space is empty or there is an abnormality in the device. Furthermore, with conventional methods, when the usage rate of a certain device is high, it is difficult to determine whether the space is full of users or there is an abnormality in the device. Because conventional methods have difficulty in making such determinations, there are limits to the accuracy of determining abnormalities in individual fixtures using conventional methods.

[0203] Therefore, the abnormality detection system 1A estimates whether or not there is an abnormality in the entire wet area (such as a bathroom space) by comparing the usage pattern of each wet area device during normal use with the actual usage state. As a result, the abnormality detection system 1A can appropriately determine whether there is an abnormality in the wet area space by detecting an abnormality in the wet area space according to the usage pattern of the wet area devices in the wet area. Furthermore, the abnormality detection system 1A can acquire information indicating the usage state of the wet area space using an existing sensor (a discharge control sensor). In this way, the abnormality detection system 1A can appropriately determine whether there is an abnormality in the wet area space without using a dedicated sensor for abnormality detection.

[0204] <2-3. Other configurations of the anomaly detection system> The configuration of the anomaly detection system according to the second embodiment is not limited to the configuration shown in the anomaly detection system 1A in Fig. 8, and any configuration to which the processing can be applied can be adopted. For example, the anomaly detection system according to the second embodiment can adopt any configuration as long as it includes at least two or more plumbing devices.

[0205] For example, the anomaly detection system according to the second embodiment may have any configuration as long as the two or more plumbing devices include at least two types of plumbing devices from among an automatic faucet (e.g., automatic faucet device 2, etc.), an automatic liquid soap device (e.g., automatic liquid soap device 3, etc.), a hand dryer (e.g., hand dryer 4, etc.), and an electric water heater. In this way, the two or more plumbing devices included in the anomaly detection system according to the second embodiment may have any configuration as long as they include at least two types of plumbing devices from among an automatic faucet, automatic liquid soap device, hand dryer, and electric water heater.

[0206] Anomaly detection system 1B, which is another example of the configuration of the anomaly detection system according to the second embodiment, will be described with reference to FIG. 13. FIG. 13 is a diagram showing another example of the configuration of the anomaly detection system for a wet space according to the second embodiment. Note that in anomaly detection system 1B, which is another example of the configuration of the anomaly detection system for a wet space according to the second embodiment, similar components to those in anomaly detection system 1 or anomaly detection system 1A will be denoted by similar reference numerals and will not be described again as appropriate. For example, anomaly detection system 1B has the same configuration as anomaly detection system 1 or anomaly detection system 1A except that it includes a hand dryer 4, and therefore detailed description thereof will be omitted as appropriate.

[0207] As shown in FIG. 13, anomaly detection system 1B has a hand dryer 4, hand wash basins 5B1 and 5B2, cloud 100B, output unit 200, and GW 300. In this way, anomaly detection system 1B has a hand dryer 4. When describing hand wash basins such as hand wash basin 5B1 and hand wash basin 5B2 without distinguishing between them, they may be referred to as "hand wash basin 5B." The configuration shown in FIG. 13 is merely an example, and anomaly detection system 1B may have one hand wash basin 5B or three or more hand wash basins 5B.

[0208] Furthermore, hand washing basin 5B1 has bowl portion 20, sensor 101, automatic flushing device 21, sensor 10A1, and automatic liquid soap device 31. Further, hand washing basin 5B2 has bowl portion 20, sensor 102, automatic flushing device 22, sensor 10A2, and automatic liquid soap device 32.

[0209] Note that hand washing basin 5B has the same configuration as hand washing basin 5A, and therefore detailed description thereof will be omitted. Also, when describing sensor units such as sensor 10A1 and sensor 10A2 without distinction, they may be referred to as "sensor 10A."

[0210] Sensor 10B is a sensor unit that acquires information used to control hand dryer 4 (a plumbing device). For example, sensor 10B is a detection means that detects the use of hand dryer 4. For example, sensor 10B has a sensor device (detection device) such as an infrared light emitting / receiving optical sensor (photoelectric sensor). Note that sensor 10B is similar to sensor 10 or sensor 10A except that the plumbing device to be controlled is hand dryer 4, and therefore a detailed description thereof will be omitted.

[0211] Sensor 10B may have a predetermined area (for example, in front of the outlet of hand dryer 4) as its detection area. When hand dryer 4 discharges air into bowl portion 20, sensor 10B may have a predetermined area (for example, below the outlet of hand dryer 4) as its detection area.

[0212] Sensor 10B projects (emits) infrared light toward bowl portion 20 and receives light that includes reflected light from bowl portion 20. Sensor 10B includes light-projecting portion 11B and light-receiving portion 12B. For example, sensor 10B has light-projecting portion 11B and light-receiving portion 12B that receives light that includes reflected light of the light emitted from light-projecting portion 11B, and detects the amount of light received by light-receiving portion 12B.

[0213] Light-projecting unit 11B has the same configuration as light-projecting unit 11, and functions as a transmitter that transmits a predetermined signal such as an electromagnetic wave. Light-receiving unit 12B has the same configuration as light-receiving unit 12, and functions as a receiver that receives a predetermined signal such as an electromagnetic wave.

[0214] Similar to the sensor 10, the sensor 10B outputs an output value of the sensor 10B (sensor output value) in response to detection. For example, the sensor 10B is connected to the control unit 50B in the same manner as the connection between the sensor 10 and the control unit 50. The sensor 10B is controlled by the control unit 50B. The sensor 10B also transmits various types of information to the control unit 50B. For example, the sensor 10B transmits information acquired by detection (detection information) to the control unit 50B.

[0215] Hand dryer 4 is a plumbing device that discharges air. Hand dryer 4 is an automatic hand dryer (automatic fan) that automatically discharges air in response to sensor detection or other factors. Hand dryer 4 has a configuration that allows it to automatically discharge air appropriately in response to a person's hand movements or other factors. For example, hand dryer 4 has fan 40B and control unit 50B. As described above, hand dryer 4 has fan 40B and discharges air from an outlet (air outlet, etc.). Note that hand dryer 4 can be configured in any way as long as it is capable of discharging (blowing) air.

[0216] Similar to control unit 50 or control unit 50A, control unit 50B is a control device (information processing device) used for various processes such as controlling plumbing equipment. Control unit 50B controls the air blowing (discharge) of hand dryer 4 (plumbing equipment) according to the output value of sensor 10B.

[0217] Control unit 50B switches fan 40B ON / OFF. Control unit 50B sends a control signal to fan 40B, switching fan 40B ON / OFF, thereby causing air to be discharged from the outlet of hand dryer 4. For example, when control unit 50B causes air to be discharged, control unit 50B turns fan 40B ON to cause air to be discharged from the outlet of hand dryer 4. For example, when control unit 50B does not cause air to be discharged, control unit 50B turns fan 40B OFF to stop air from being discharged from the outlet of hand dryer 4. This is the same as automatic air discharge in a normal hand dryer, so a detailed description will be omitted.

[0218] The control unit 50B acquires information. The control unit 50B acquires detection information detected by the sensor 10B. The control unit 50B receives the detection information acquired by the sensor 10B from the sensor 10B. The control unit 50B may store the acquired various pieces of information in a memory unit (such as a storage device) within the control unit 50B.

[0219] The control unit 50B transmits information to an external information processing device such as the GW 300. For example, the control unit 50B transmits the detection information detected by the sensor 10B to the GW 300 together with information identifying the plumbing appliance (such as the hand dryer 4) that is the detection target of the sensor 10B.

[0220] In addition, when a bathroom equipment communicates with cloud 100B without going through GW300, control unit 50B transmits the detection information detected by sensor 10B to cloud 100B via network N, together with information identifying the bathroom equipment (hand dryer 4, etc.) that sensor 10B is detecting.

[0221] Similar to the cloud 100A, the cloud 100B is a computer (information processing device) that provides cloud services. As shown in Fig. 13, the cloud 100B includes a storage unit 110B and a determination unit 120B. Note that the cloud 100B has a configuration having various functions, not limited to those described above.

[0222] Storage unit 110B is realized by, for example, a semiconductor memory element such as RAM or flash memory, or a storage device such as a hard disk or optical disk. For example, storage unit 110B is a computer-readable recording medium that non-temporarily records data used by various information processing programs. Like storage unit 110A, storage unit 110B stores various information necessary for processing. Since it is similar to storage unit 110A, detailed description will be omitted. For example, when hand dryer 4 discharges in response to detection by sensor 10B, storage unit 110B stores the date and time of the discharge as the date and time that hand dryer 4 was used.

[0223] The information processing unit such as the determination unit 120B is realized by, for example, an MPU, a CPU, etc. executing a program stored in the cloud 100B (for example, various information processing programs related to the present disclosure) using a RAM, etc. as a work area. The information processing unit may also be realized by, for example, an integrated circuit such as an ASIC or an FPGA.

[0224] The determination unit 120B functions as an estimation unit that estimates the usage state of the wet space based on the detection result. Similar to the determination unit 120A, the determination unit 120B performs processing related to abnormality detection based on the usage state and usage model pattern of the wet space, but since it is similar to the determination unit 120A, detailed description thereof will be omitted.

[0225] The above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0226] When the goal is to improve the cleaning efficiency of wet spaces such as public restrooms (public toilets), there is a demand (challenge) for detecting anomalies as accurately as possible in order to reduce the possibility of, for example, erroneously determining a pattern abnormality and resulting in unnecessary inspections, etc. To meet such demands (challenges), the anomaly detection system can detect anomalies more accurately by combining the processes described above in the first and second embodiments.

[0227] For example, the anomaly detection system may perform both the anomaly determination in the first embodiment and the anomaly determination in the second embodiment described above. In this case, the anomaly detection system (also referred to as "an anomaly detection system according to the third embodiment") may perform an anomaly determination using both the anomaly determination in the first embodiment and the anomaly determination in the second embodiment. For example, the anomaly detection system according to the third embodiment may determine that an anomaly has occurred in a water-related space that has been determined to be an anomaly by both the anomaly determination in the first embodiment and the anomaly determination in the second embodiment.

[0228] For example, the anomaly detection system according to the third embodiment may determine that an abnormality has occurred in a hand wash basin that has been determined to be abnormal in both the anomaly determination in the first embodiment and the anomaly determination in the second embodiment. For example, the anomaly detection system according to the third embodiment may determine that an abnormality has occurred in a plumbing appliance that has been determined to be abnormal in both the anomaly determination in the first embodiment and the anomaly determination in the second embodiment.

[0229] Further advantages and modifications will 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 their equivalents.

[0230] The above-described embodiments and modifications may have the following configurations, but are not limited to these. (1) Three or more hand washing basins each having a bowl portion, a sensor unit, and a plumbing device whose discharge into the bowl portion is controlled according to the output value of the sensor unit; A determination unit that determines that an abnormality has occurred in one of the plurality of hand wash basins when the output value of the sensor unit of one of the hand wash basins deviates from the output values of the sensor units other than the one of the hand wash basins; An abnormality detection system for a water-related space, comprising: (2) The determination unit compares the output value of the sensor unit when the discharge of the plumbing equipment is stopped. The abnormality detection system for a water-related space according to (1) is characterized in that: (3) the sensor unit has a transmitting unit and a receiving unit, The transmitting portion and the receiving portion face a part of the bowl portion. The water-related space abnormality detection system according to (1) or (2) is characterized in that: (4) The plurality of hand washing basins are provided in the same water space. The water-related space abnormality detection system according to any one of (1) to (3). (5) The determination unit determines that an abnormality has occurred in the one hand wash basin when the output value of the sensor unit of the one hand wash basin deviates by a first threshold value or more from any of the output values of the sensor units of the hand wash basins other than the one hand wash basin among the plurality of hand wash basins. The water-related space abnormality detection system according to any one of (1) to (4). (6) The determination unit determines that an abnormality has occurred in the one hand wash basin when the output value of the sensor unit of the one hand wash basin deviates by a second threshold value or more from the average value of the output values of the sensor units of the hand wash basins other than the one hand wash basin among the plurality of hand wash basins. The water-related space abnormality detection system according to any one of (1) to (5). (7) The determination unit excludes a hand washing basin that has been determined to be abnormal a predetermined number of times in succession from the objects to be determined to be abnormal. The water-related space abnormality detection system according to any one of (1) to (6). (8) At least one of the first threshold and the second threshold is changeable. The water-related space abnormality detection system according to any one of (1) to (7). [Explanation of symbols]

[0231] 1, 1A, 1B Abnormality detection system (abnormality detection system for wet spaces) 2 Automatic flushing equipment (water-related equipment) 3 Automatic water soap machine (water equipment) 4. Hand dryers (water-related equipment) 5, 5A, 5B Hand wash basin 10, 10A, 10B Sensor (Sensor Unit) 11, 11A, 11B Light projecting section 12, 12A, 12B light receiving section 20 Bowl section 30 Water outlet 31 Outlet 40 Solenoid valve 40A pump 40B fan 50, 50A, 50B Control unit (control device) 100, 100A, 100B Cloud 110, 110A, 110B storage section 120, 120A, 120B judgment section 200 Output section 300 GW (gateway device)

Claims

1. Three or more hand washing basins each having a bowl portion, a sensor unit, and a plumbing device whose discharge into the bowl portion is controlled according to an output value of the sensor unit; A determination unit that determines that an abnormality has occurred in one of the plurality of hand wash basins when the output value of the sensor unit of one of the hand wash basins deviates from the output values of the sensor units other than the one of the hand wash basins; An abnormality detection system for a water-related space, comprising:

2. The determination unit compares the output value of the sensor unit when the discharge of the plumbing equipment is stopped.

2. The water-related space abnormality detection system according to claim 1.

3. the sensor unit has a transmitting unit and a receiving unit, The transmitting portion and the receiving portion face a part of the bowl portion.

3. The water-related space abnormality detection system according to claim 2.

4. The plurality of hand washing basins are provided in the same water space.

4. The water-related space abnormality detection system according to claim 3.

5. The determination unit determines that an abnormality has occurred in the one hand wash basin when the output value of the sensor unit of the one hand wash basin deviates by a first threshold value or more from any of the output values of the sensor units of the hand wash basins other than the one hand wash basin among the plurality of hand wash basins.

5. The water-related space abnormality detection system according to claim 4.

6. The determination unit determines that an abnormality has occurred in the one hand wash basin when the output value of the sensor unit of the one hand wash basin deviates by a second threshold value or more from the average value of the output values of the sensor units of the hand wash basins other than the one hand wash basin among the plurality of hand wash basins.

5. The water-related space abnormality detection system according to claim 4.

7. The determination unit excludes a hand washing basin that has been determined to be abnormal a predetermined number of times in succession from the objects to be determined to be abnormal.

7. The water-related space abnormality detection system according to claim 5 or 6.

8. At least one of the first threshold and the second threshold is variable.

7. The water-related space abnormality detection system according to claim 5 or 6.

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