Anomaly detection system

Abstract

To determine properly an abnormality in a use object for a toilet.SOLUTION: An abnormality detection system according to an embodiment has an acquisition part for acquiring information for showing a selection tendency which is such a tendency that one use object is selected by a user of a toilet, from the same kind of use object group which is an object of use by the user, and a determination part for determining that an abnormality in the one use object occurs, when the selection tendency of the one use object is deviated from a past selection tendency of the one use object.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The disclosed embodiments relate to an abnormality detection system.

Background Art

[0002] Conventionally, systems for detecting abnormalities in toilet usage targets such as toilet booths have been known (for example, Patent Documents 1 and 2). For example, in the system described in Patent Document 1, when the usage frequency of domestic living infrastructure such as toilets falls below a predetermined value, it is determined as abnormal. For example, in the system described in Patent Document 2, when the usage frequency of one private room is clearly less than or absent compared to other private rooms, it is determined as abnormal.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, there is room for improvement in the above technologies. For example, in the systems of the prior art described above, if the decrease in the usage frequency of toilets and the like is not abnormal but due to factors such as nighttime or holidays, it may be misjudged as abnormal, or if the toilet booth usually has a low usage frequency, it may be misjudged as abnormal even when it is not. Thus, there is room for improvement in the abnormality determination by the above systems. Therefore, it is desired to appropriately determine abnormalities related to toilet usage targets such as toilet booths where toilets are installed.

[0005] In view of the above points, it becomes an issue to appropriately determine abnormalities related to toilet usage targets.

[0006] The embodiments of the disclosure aim to provide an abnormality detection system that can appropriately determine abnormalities related to the user of a toilet. [Means for solving the problem]

[0007] An abnormality detection system according to one embodiment of the system is characterized by comprising: an acquisition unit that acquires information indicating a selection tendency in which one of a group of identical types of objects that are subject to use by a toilet user tends to be selected by the user; and a determination unit that determines that an abnormality has occurred with respect to the one object when the selection tendency of the one object deviates from the past selection tendency of the one object.

[0008] According to one embodiment of the anomaly detection system, if the selection tendency of one of several users of the same type deviates from the past selection tendency of that user, it can be estimated that something has happened to that user, and by determining (identifying) it as an anomaly, the anomaly can be detected early. For example, unlike conventional systems, the anomaly detection system can reduce the possibility of misidentifying a decrease in the total number of toilet users used over a certain period, such as at night or on holidays, or a toilet user located in a location that is not used frequently, as an anomaly, and can detect anomalies with high accuracy. Therefore, the anomaly detection system can appropriately determine anomalies related to toilet users.

[0009] For example, if a system determines an abnormality when the number of times a device is used decreases over a certain period and falls below a predetermined number, it may mistakenly identify a device as abnormal if the decrease in usage is simply due to regular cleaning, nighttime use, or emergency response. Also, if a system determines an abnormality when the number of uses is low compared to other similar devices, it may mistakenly detect an abnormality in devices that are normally used infrequently. On the other hand, according to one embodiment of the abnormality detection system, for toilet users such as toilet booths, abnormalities can be appropriately determined by using the proportion of each type of user selected among users of the same type, regardless of the usual number of uses.

[0010] In an abnormality detection system according to one embodiment, the same type is characterized by being one of the following types: toilet booth, washbasin, urinal, toilet, or toilet space.

[0011] According to an abnormality detection system in one embodiment, by processing multiple usage targets of any one type among toilet booths, washbasins, urinals, toilets, and toilet spaces, it is possible to determine (distinguish) that an abnormality has occurred with respect to one usage target based on the selection trend among the same type among toilet booths, washbasins, urinals, toilets, and toilet spaces.

[0012] An abnormality detection system according to one embodiment of the system is characterized in that the acquisition unit acquires information indicating the selection tendency of one toilet booth from among the group of toilet booths of the same type that are the target group for use, and the determination unit determines that an abnormality has occurred in the one toilet booth if the selection tendency of the one toilet booth deviates from the past selection tendency of the one toilet booth.

[0013] According to one embodiment of the anomaly detection system, if the selection trend of one toilet booth in a group of toilet booths deviates from the past selection trend of that toilet booth, it can be estimated that something has happened to that toilet booth, and by determining (identifying) it as an anomaly, an anomaly can be detected early. For example, unlike conventional systems, the anomaly detection system can reduce the possibility of misidentifying a decrease in the overall number of toilet booth uses over a certain period, such as at night or on holidays, or a toilet booth located in a location that is not usually used frequently, as an anomaly, and can detect anomalies with high accuracy. Therefore, the anomaly detection system can appropriately determine anomalies related to the use of toilets.

[0014] In an abnormality detection system according to one embodiment, the determination unit determines that an abnormality has occurred with respect to the first object of use if the first object of use is not selected consecutively for a predetermined number of times or more.

[0015] According to one embodiment of the anomaly detection system, if a particular usage target is not selected consecutively for a predetermined number of times or more, it is determined that an anomaly has occurred with respect to that usage target, thereby enabling early detection of an anomaly related to the toilet. Therefore, the anomaly detection system can appropriately determine an anomaly related to the usage target of the toilet. For example, according to the anomaly detection system, even when there are toilet users but only a particular usage target is not being used, this control can accurately detect the anomaly.

[0016] In an abnormality detection system according to one embodiment, the determination unit is characterized in that it determines whether or not an abnormality has occurred with respect to the one target of use, using the selection probability of the one target of use within the group of targets of use over a predetermined period or number of times.

[0017] According to one embodiment of the anomaly detection system, an anomaly related to a toilet can be detected using appropriate information by determining whether or not an anomaly has occurred with respect to a particular toilet by using the selection probability of a particular toilet within a predetermined period or number of uses. For example, the anomaly detection system can make a determination that better reflects the situation at the time of determination by setting the information collection period used to calculate the selection probability to a predetermined period (the most recent week, one month, etc.) from the time of determination. For example, the anomaly detection system can appropriately respond to changes in usage conditions due to seasons, etc., even when toilets or other toilets are used for extended periods, and can detect anomalies accordingly. Therefore, the anomaly detection system can appropriately detect anomalies related to toilets. For example, according to the anomaly detection system, even when there are toilet users but only a particular toilet is not being used, this control allows for accurate anomaly detection. The selection probability may be the usage ratio obtained by dividing the number of uses (or cumulative usage time) within a certain number of uses (or period) by the total number of uses (or total cumulative usage time).

[0018] In an abnormality detection system according to one embodiment, the determination unit determines whether or not an abnormality has occurred with respect to the one target for use by using the selection probability, which represents the ratio of the number of times the one target for use is selected during the predetermined period or number of times, to the total number of times the target group for use is selected during the predetermined period or number of times.

[0019] According to one embodiment of the anomaly detection system, an anomaly related to a toilet can be determined using appropriate information by using the selection probability of a single user, calculated using the total number of selections of a group of users and the number of selections of a single user, collected over a predetermined period or number of times, to determine whether an anomaly has occurred with respect to a single user. For example, the anomaly detection system can make a determination that better reflects the situation at the time of determination by setting the information collection period used to calculate the selection probability to a predetermined period (the most recent week, one month, etc.) from the time of determination. For example, the anomaly detection system can appropriately respond to changes in usage conditions, such as seasonal changes, when a toilet booth or other user is used for a long period of time, and can determine an anomaly. Therefore, the anomaly detection system can appropriately determine an anomaly related to a toilet user. [Effects of the Invention]

[0020] According to one embodiment, abnormalities related to the object being used by the toilet can be appropriately determined. [Brief explanation of the drawing]

[0021] [Figure 1] Figure 1 shows an example of an anomaly detection process according to an embodiment. [Figure 2] Figure 2 shows an example of the target users of the toilet according to the embodiment. [Figure 3] Figure 3 shows an example of the configuration of an anomaly detection system according to the embodiment. [Figure 4] Figure 4 is a block diagram showing an example of the configuration of the analysis apparatus according to this embodiment. [Figure 5]Figure 5 shows an example of a placement information storage unit according to the embodiment. [Figure 6] Figure 6 is a flowchart showing an example of the steps performed by the anomaly detection system. [Figure 7] Figure 7 shows an example of the usage ratio of the same type of product. [Figure 8] Figure 8 shows an example of a processing flow related to anomaly detection. [Figure 9] Figure 9 shows an example of the usage rate of the target product. [Figure 10] Figure 10 shows an example of anomaly detection processing. [Modes for carrying out the invention]

[0022] The embodiments of the anomaly detection system disclosed herein will be described in detail below with reference to the attached drawings. However, the present invention is not limited to the embodiments described below.

[0023] <1. Embodiments> <1-1. Example of anomaly detection processing> First, an overview of the anomaly detection performed in the anomaly detection system 1 (see Figure 3) according to this embodiment will be explained with reference to Figure 1. Figure 1 is a diagram showing an example of the anomaly detection process according to this embodiment. Note that Figure 1 explains the overview of the anomaly determination process, and specific processing examples will be described in detail in Figures 7 to 10.

[0024] Figure 1 illustrates the case where the abnormality detection system 1 performs an abnormality detection on a toilet booth TB in which a toilet bowl 10 is located. Note that the target of abnormality detection is not limited to the toilet booth TB, but may also be the toilet bowl 10, or various other toilet-related objects such as the toilet space 2, urinal 20, and washbasin 30 shown in Figure 2, but further details will be described later.

[0025] The following describes an example of the process shown in Figure 1. Specifically, in Figure 1, the toilet bowl 10 11 Toilet booth TB1, toilet bowl 1012 Toilet booth TB2 and toilet bowl 10 are located in this area. 13 The abnormality detection system 1 performs an abnormality determination on the three toilet booths TB of toilet booth TB3 where the toilet booth TB3 is located. For example, toilet booths TB1 to TB3 are a group of the same type of toilets located in the toilet space 21 (see Figure 2), and may be selectively selected when a user of the toilet space 21 uses the toilet 10. Thus, Figure 1 illustrates an example in which the abnormality detection system 1 performs an abnormality determination on three toilet booths TB that are a group of the same type of toilets. For example, the analysis device 100 of the abnormality detection system 1 (see Figure 3) executes the process shown in Figure 1.

[0026] The analysis device 100 acquires information indicating the number of times each of the toilet booths TB1 to TB3 to be detected has been used. For example, for each of the toilet booths TB1 to TB3, the analysis device 100 acquires information indicating the time a user used (selected) that toilet booth TB. For example, for each of the toilet booths TB1 to TB3, the analysis device 100 acquires information indicating the time a user entered that toilet booth TB. For each of the same type of target object in the target object group, the analysis device 100 calculates the number of selections (also called "number of uses") by counting the number of times that target object has been selected (used).

[0027] In Figure 1, the analysis device 100 calculates the number of selections (number of uses) of toilet booth TB1 among toilet booths TB1 to TB3 by counting the number of users who entered toilet booth TB1. For example, the analysis device 100 calculates the number of selections of toilet booth TB1 (also called the "first selection count") by counting the number of users who entered toilet booth TB1 within a predetermined collection range, such as a predetermined period or number of times.

[0028] Furthermore, the analysis device 100 calculates the number of selections (number of uses), which is the number of times toilet booth TB2 was selected among toilet booths TB1 to TB3, by counting the number of users who entered toilet booth TB2. For example, the analysis device 100 calculates the number of selections of toilet booth TB2 (also called the "second selection count") by counting the number of users who entered toilet booth TB2 within a predetermined collection range, such as a predetermined period or number of times.

[0029] Furthermore, the analysis device 100 calculates the number of selections (number of uses), which is the number of times toilet booth TB3 was selected among toilet booths TB1 to TB3, by counting the number of users who entered toilet booth TB3. For example, the analysis device 100 calculates the number of selections of toilet booth TB3 (also called the "third selection count") by counting the number of users who entered toilet booth TB3 within a predetermined collection range, such as a predetermined period or number of times.

[0030] The analysis device 100 calculates the total number of selections for an entire group of the same type of target by summing the number of selections for each target within the same type of target group. In Figure 1, the analysis device 100 calculates the total number of selections for toilet booths TB1 to TB3 by summing the first selection count for toilet booth TB1, the second selection count for toilet booth TB2, and the third selection count for toilet booth TB3. Note that the above process is merely an example, and the analysis device 100 may acquire the information by any method as long as it is possible to obtain information indicating the first selection count for toilet booth TB1, the second selection count for toilet booth TB2, the third selection count for toilet booth TB3, and the total number of selections for toilet booths TB1 to TB3.

[0031] The analysis device 100 calculates the selection probability of each target by dividing the number of selections of each target by the total number of selections of the target group of the same type. In Figure 1, the analysis device 100 calculates the selection probability of toilet booth TB1 (also called the "first selection probability") by dividing the first selection count of toilet booth TB1 by the total number of selections of toilet booths TB1 to TB3. The analysis device 100 calculates the selection probability of toilet booth TB2 (also called the "second selection probability") by dividing the second selection count of toilet booth TB2 by the total number of selections of toilet booths TB1 to TB3. The analysis device 100 calculates the selection probability of toilet booth TB3 (also called the "third selection probability") by dividing the third selection count of toilet booth TB3 by the total number of selections of toilet booths TB1 to TB3.

[0032] Graph GR1 in Figure 1 shows information regarding the selection probability of each toilet booth TB1 to TB3 and a predetermined value (also called a "threshold") used for abnormality detection. The horizontal axis of Graph GR1 represents time, and the vertical axis represents the selection probability as a usage ratio. Moving in the positive (upward) direction on the vertical axis of Graph GR1 indicates a higher selection probability (usage ratio). Note that the usage ratio is not limited to the selection probability, but may also be the ratio of the time each target occupies to the total usage time of the entire usage ratio group (total usage time) (usage time ratio).

[0033] Line LN11 in graph GR1 represents the first selection probability for toilet booth TB1. Line LN12 in graph GR1 represents the second selection probability for toilet booth TB2. Line LN13 in graph GR1 represents the third selection probability for toilet booth TB3. The analysis device 100 calculates the first selection probability for toilet booth TB1, the second selection probability for toilet booth TB2, and the third selection probability for toilet booth TB3, which change over time as shown by lines LN11 to LN13.

[0034] The analysis device 100 determines whether or not an abnormality has occurred in toilet booths TB1 to TB3 using the selection probability and threshold for each of the toilet booths TB1 to TB3. In Figure 1, for the sake of simplicity, only line TH1, which indicates the threshold corresponding to toilet booth TB3, the target of abnormality detection, is shown, but thresholds are also set for the other toilet booths TB1 and TB2. Details of the thresholds will be described later. The analysis device 100 determines whether or not an abnormality has occurred in toilet booths TB1 to TB3 using the abnormality detection threshold (predetermined value) shown by line TH1 in graph GR1.

[0035] The analysis device 100 determines that an abnormality has occurred in a target when the selection probability of that target falls below a threshold. In Figure 1, the analysis device 100 determines that an abnormality has occurred in a toilet booth TB when the selection probability of the toilet booth TB falls below a threshold, that is, when the selection probability of the toilet booth TB falls below a threshold.

[0036] In Figure 1, the analysis device 100 determines that an abnormality has occurred in toilet booth TB3 because the line LN13, which indicates the third selection probability of toilet booth TB3, is below the line TH1, which indicates a predetermined value (threshold) for abnormality detection. For example, the analysis device 100 determines that an abnormality has occurred in toilet booth TB3 after the line LN13, which indicates the third selection probability of toilet booth TB3, has fallen below the line TH1, which indicates a predetermined value (threshold).

[0037] <1-1-1. Target of Anomaly Detection> In the example described above, the abnormality detection system 1 performed an abnormality detection using a toilet booth TB in which a toilet bowl 10 is located as an example. However, the target of abnormality detection is not limited to the toilet booth TB, but may also be various toilet users such as the toilet space 2, toilet bowl 10, urinal 20, and washbasin 30 as described in Figure 2.

[0038] For example, the abnormality detection system 1 may perform abnormality detection processing for a plurality of toilet spaces 2 that are of the same type of usage target group. For example, the abnormality detection system 1 may perform abnormality detection processing for a plurality of urinals 20 that are of the same type of usage target group. For example, the abnormality detection system 1 may perform abnormality detection processing for a plurality of washbasins 30 that are of the same type of usage target group.

[0039] For example, the abnormality detection system 1 may use the information of the three urinals 20 in the urinals 20 shown in FIG. 2 11 , urinal 20 12 , urinal 20 13 to perform abnormality detection of the urinal 20 11 , urinal 20 12 , urinal 20 13 . Further, the abnormality detection system 1 may use the information of the three washbasins 30 in the washbasin 30 shown in FIG. 2 11 , washbasin 30 12 , washbasin 30 13 to perform abnormality detection of the washbasin 30 11 , washbasin 30 12 , washbasin 30 13 . Note that even if the usage target to be determined is other than the toilet booth TB, the determination process and the like are the same as the process shown in FIG. 1, so detailed description is omitted.

[0040] <1-1-2. Information used in the determination process> In the example described above, the anomaly detection system 1 made an anomaly determination using the number of times an object was selected for use. However, the information used in the determination process can be various types of information. For example, the anomaly detection system 1 may make an anomaly determination using the proportion of time each object was selected and used (usage time) (usage time ratio). For example, the anomaly detection system 1 may make an anomaly determination using the proportion of usage time for each object within a predetermined period, such as one week. Furthermore, the anomaly detection system 1 may make an anomaly determination without using any information related to probability. For example, the analysis device 100 may determine that an anomaly has occurred with respect to an object if that object has not been selected for a predetermined number of consecutive times or more. For example, in Figure 1, the analysis device 100 may determine that an anomaly has occurred with respect to toilet booth TB2 if it has not been selected for 10 consecutive times. For example, the analysis device 100 may determine that an anomaly has occurred with respect to toilet booth TB2 if all 10 users who entered the toilet space 2 consecutively selected a toilet booth TB other than toilet booth TB2 (toilet booth TB1 or toilet booth TB3).

[0041] <1-2. Examples of people who can use the toilet> From here, we will explain examples of toilet usage targets using Figure 2. Figure 2 is a diagram showing an example of toilet usage targets according to the embodiment. Figure 2 conceptually shows the physical arrangement of toilet usage targets. Note that the toilet usage targets shown in Figure 2 are merely examples, and toilet usage targets are any elements related to a toilet that can be used by a user, and can be any elements that can be the target of anomaly detection.

[0042] As shown in Figure 2, the users of the toilet may include toilet spaces such as toilet space 21 and toilet space 22. Toilet space 21 shown in Figure 2 has toilet booths TB1 to TB3, which are toilet booths that form individual spaces, and a shared space CS1, which is a shared space other than the toilet booths. When describing toilet booths provided in toilet space 2 without distinguishing between toilet booths TB1, toilet booth TB2, toilet booth TB3, etc., they may be referred to as "toilet booth TB".

[0043] Note that when describing toilet spaces such as toilet space 21 and toilet space 22 without distinguishing between them, they may be referred to as "toilet space 2". In Figure 2, toilet space 2 is illustrated as two toilet spaces 21 and toilet space 22, but the target users of the toilet may include three or more toilet spaces 2. In Figure 2, the case where toilet space 2 has three toilet booths TB is explained as an example, but toilet space 2 may have two or fewer toilet booths TB or four or more toilet booths TB.

[0044] In other words, the toilet space 21 shown in Figure 2 is merely one example of a toilet space 2, and any configuration can be adopted for the toilet space 2. For example, the toilet space 2 may have a configuration having one toilet booth TB. The toilet space 2 may have any configuration as long as it includes at least one item that can be used as a toilet by a user. For example, the toilet space 2 may have any configuration as long as it has at least one of the following items (toilet equipment): a toilet bowl 10, a urinal 20, and a washbasin 30.

[0045] A toilet booth TB is a space (individual room) separated by a partition. Each toilet booth TB is equipped with a toilet 10 and functions as a place for a person to defecate. Each toilet booth TB is also equipped with an openable and closable door (not shown in the illustration) for entering the toilet booth TB. Note that the doors of each toilet booth TB are the same as those provided in a typical toilet booth TB, so a detailed explanation is omitted. Furthermore, a toilet booth TB does not have to be a space completely separated by a partition; there may be parts above or below the partition that are not separated from adjacent toilet booths TB or the common space.

[0046] Furthermore, when describing shared spaces within toilet space 2, such as shared space CS1, without distinguishing between them, they may be referred to simply as "shared space CS." Shared space CS is a shared area within toilet space 2 where multiple people can stay simultaneously. For example, shared space CS includes an entrance to toilet space 2, washbasins 30 and other washing facilities, and pathways to each toilet booth TB. Shared space CS functions as a place for entering toilet space 2, washing hands, and moving to each toilet booth TB. When toilet space 2 functions as a men's toilet space 2, urinals 20 are placed in its shared space CS. In other words, shared space CS may also function as a place where people perform excretory acts.

[0047] Next, we will explain the arrangement of each device in toilet space 2, using the toilet space 21 shown in Figure 2 as an example. Toilet bowl 10 11 It will be placed in toilet booth TB1. Also, toilet bowl 10 12 It will be placed in toilet booth TB2. Also, toilet bowl 10 13 It will be placed in toilet booth TB3. Also, toilet bowl 10 11 , urinal 10 12 , urinal 10 13 When describing toilets without distinguishing between them, they may be written as "Toilet 10".

[0048] Figure 2 shows an example where there are three toilets 10, but any number of toilets 10 can be used as long as the desired processing is possible, and the number of toilets 10 may be two or less or four or more. Details of the functions of the toilets 10 will be described later. Also, when describing a toilet 10 as the subject of information collection and judgment processing, the description may be read as the toilet booth TB in which the toilet 10 is located. Also, when describing a toilet booth TB as the subject of information collection and judgment processing, the description may be read as the toilet 10 located in that toilet booth TB. For example, toilet 10 11 This can also be read as toilet booth TB1, and toilet booth TB1 is a toilet bowl 10 11 It can also be interpreted as follows.

[0049] urinal 20 11 , urinal 20 12 , urinal 20 13 It will be placed in the shared space CS1. Urinal 20 11 , urinal 20 12 , urinal 20 13 When describing urinals placed in toilet space 2 without distinction, they may be referred to as "urinal 20". Figure 2 shows an example where there are three urinals 20, but any number of urinals 20 can be used as long as the desired processing is possible, and the number of urinals 20 may be two or less, or four or more. Details of the functions of the urinals 20 will be described later.

[0050] washbasin 30 11 , basin 30 12 , basin 30 13 It will be placed in the shared space CS1. Washbasin 30 11 , basin 30 12 , basin 30 13 When describing urinals located in toilet space 2 without distinguishing between them, they may be referred to as "washbasin 30". Figure 2 shows an example where there are three washbasins 30, but any number of washbasins 30 can be used as long as the desired treatment can be performed, and the number of washbasins 30 may be two or less, or four or more. Details regarding the function of washbasins 30 will be described later.

[0051] <1-3. Toilet Space Layout> Let me briefly explain the placement of toilet space 2. Toilet space 2 can be installed in any location as long as there is space in which it can be physically placed. In other words, any location can be chosen for toilet space 2. For example, toilet space 2 may be installed in a commercial facility such as a department store. Furthermore, the location of toilet space 2 is not limited to commercial facilities such as shops, but can be various types of facilities. For example, toilet space 2 may be installed in an amusement park, a stadium, or an office building. For example, toilet space 2 may be installed in a toilet in a tourist area. For example, toilet space 2 may be installed in a park or parking lot. In other words, toilets may be installed outdoors in parks, parking lots, etc. Thus, toilet space 2 can be installed in any location as long as there is space in which it can be installed.

[0052] <1-4. Configuration of the Anomaly Detection System> Next, the configuration of the anomaly detection system 1 will be described with reference to Figure 3. Figure 3 is a diagram showing an example configuration of the anomaly detection system according to the embodiment. Specifically, Figure 3 shows the configuration of the anomaly detection system 1.

[0053] Anomaly detection system 1 includes an information processing device (analysis device 100 in Figure 3) that performs anomaly detection processing. Anomaly detection system 1 also includes an information processing device (collection device 50 in Figure 3) that collects information used for anomaly detection processing. The anomaly detection system 1 shown in Figure 3 includes an analysis device 100, a collection device 50, a detection unit 21, a detection unit 101, a detection unit 201, and a detection unit 301. Note that anomaly detection system 1 may include multiple analysis devices 100, multiple collection devices 50, multiple detection units 21, multiple detection units 101, multiple detection units 201, multiple detection units 301, etc.

[0054] In Figure 3, the toilet users targeted for abnormality detection by the abnormality detection system 1 include at least one of the following: toilet space 2, toilet bowl 10, urinal 20, and washbasin 30. Note that, as shown in Figure 2, the toilet users targeted for abnormality detection by the abnormality detection system 1 may include multiple toilet spaces 2, multiple toilet bowls 10, multiple urinals 20, multiple washbasins 30, etc.

[0055] Toilet space 2 is a space provided within a facility or other space where users enter and exit to use the toilet. For example, toilet space 2 has toilet booths TB, a shared space CS, etc., and is a space where toilet-related structures are arranged. For example, toilet space 21 shown in Figure 2 has multiple toilets 10 11 , 10 12 , 10 13 Toilet booths TB1-TB3 and multiple urinals 20 are arranged in separate toilet booths TB1-TB3. 11 , 20 12 , 20 13 , and multiple washbasins 30 11 , 30 12 , 30 13 It has a shared space CS1 where the equipment is located. Note that the toilet space 2 does not necessarily have to be included in the abnormality detection system 1.

[0056] The detection unit 21 in the toilet space 2 performs detections related to the toilet space 2. The detection unit 21 functions as a detection unit that detects the entry and exit of people (users) into the toilet space 2. The detection unit 21 may be implemented by various sensors; for example, the detection unit 21 may be various sensors such as a human body detection sensor, an action detection sensor, or a presence detection sensor. The detection unit 21 may also be an image sensor (action detection sensor) that captures images of the entrance and exit of the toilet space 2. Note that the above is just an example, and the detection unit 21 may be implemented by any means that can detect the entry and exit of users into the toilet space 2.

[0057] The detection unit 21 of the toilet space 2 includes, for example, a communication unit (e.g., a communication circuit) having wireless communication capabilities, and is wirelessly connected to the collection device 50. For example, when the detection unit 21 detects a person entering or leaving the toilet space 2, it transmits detection information, such as the date and time of the detection, to the collection device 50 along with identification information (such as an ID) that identifies the toilet space 2. For example, the detection unit 21 transmits detection information, such as the time from when a person entered the toilet space 2 until they left (usage time), to the collection device 50 along with identification information (such as an ID) that identifies the toilet space 2. Note that if the toilet space 2 is not included in the abnormality detection targets of the abnormality detection system 1, the detection unit 21 may not be included in the abnormality detection system 1.

[0058] The toilet bowl 10 is the object of use in the toilet, used by users to excrete feces or urine. The toilet bowl 10 is located in a toilet booth TB provided within the toilet space 2. For example, the toilet bowl 10 has a toilet seat device that includes a toilet seat on which the user sits, and an operating unit for operating a nozzle for dispensing water for flushing. In addition to the toilet seat device, the toilet bowl 10 has various components such as a toilet lid and toilet bowl to realize the function of a toilet, but a detailed explanation is omitted. Note that the toilet bowl 10 does not necessarily have to be included in the abnormality detection system 1.

[0059] The detection unit 101 of the toilet 10 performs detections related to the toilet 10. The detection unit 101 of the toilet 10 performs detections related to the toilet booth TB in which the toilet 10 is located. The detection unit 101 functions as a detection unit that detects the entry and exit of a person (user) to and from the toilet booth TB in which the toilet 10 is located. The detection unit 101 may be implemented by various sensors; for example, the detection unit 101 may be various sensors such as a human body detection sensor, an action detection sensor, or a seating detection sensor. The detection unit 101 may be a door sensor installed on the door of the toilet booth TB to detect the opening and closing of the door of the toilet booth TB. The detection unit 101 may be an image sensor that captures images of the area around the door of the toilet booth TB. The detection unit 101 may be a seating sensor that detects when a user sits on the toilet 10. Note that the above is just an example, and the detection unit 101 may be implemented by any means that can detect the entry and exit of a user to and from the toilet 10. The toilet bowl 10 and the detection unit 101 may be integrated. In this case, the toilet bowl 10 having the detection unit 101 may be included in the abnormality detection system 1. The placement of the detection unit 101 is not particularly limited and may be installed on the ceiling, wall, floor, etc. of the toilet booth TB, or mounted on the toilet seat of the toilet bowl 10.

[0060] The detection unit 101 of the toilet bowl 10 includes, for example, a communication unit (e.g., a communication circuit) having a wireless communication function, and is wirelessly connected to the collection device 50. For example, when the detection unit 101 detects a person entering or leaving a toilet booth TB in which the toilet bowl 10 is located, it transmits detection information, such as the date and time of the detection, to the collection device 50 along with identification information (such as an ID) that identifies the toilet bowl 10 or the toilet booth TB in which the toilet bowl 10 is located. For example, the detection unit 21 transmits detection information, such as the time (usage time) from when a person enters the toilet booth TB in which the toilet bowl 10 is located until they leave, to the collection device 50 along with identification information (such as an ID) that identifies the toilet bowl 10 or the toilet booth TB in which the toilet bowl 10 is located. Note that if the toilet bowl 10 is not included in the abnormality detection targets of the abnormality detection system 1, the detection unit 101 may not be included in the abnormality detection system 1.

[0061] The urinal 20 is located in the men's toilet space 2 and is intended for use by toilet users when they need to urinate. The urinal 20 is located in the shared space CS within the toilet space 2. Note that the urinal 20 does not necessarily have to be included in the abnormality detection system 1.

[0062] The detection unit 201 of the urinal 20 performs detections related to the urinal 20. The detection unit 201 functions as a detection unit that detects the use of the urinal 20 by a person (user). The detection unit 201 may be implemented by various sensors; for example, the detection unit 201 may be a human body detection sensor, an action detection sensor, an existence detection sensor, or other types of sensors. The detection unit 201 may also be a proximity sensor that detects the proximity of a person to the urinal 20. The above is just an example, and the detection unit 201 may be implemented by any means that can detect the use of the urinal 20 by a user. The urinal 20 and the detection unit 201 may be integrated. In this case, the urinal 20 having the detection unit 201 may be included in the abnormality detection system 1. For example, the detection unit 201 is provided on the front of the urinal 20 and detects a person located in front of the urinal 20. The placement of the detection unit 201 is not particularly limited and may be installed on the ceiling, wall, floor, etc., of the shared space CS.

[0063] The detection unit 201 of the urinal 20 includes, for example, a communication unit (e.g., a communication circuit) having wireless communication capabilities, and is wirelessly connected to the collection device 50. For example, when the detection unit 201 detects that a person is using the urinal 20, it transmits detection information, such as the date and time of the detection, to the collection device 50 along with identification information (such as an ID) that identifies the urinal 20. For example, the detection unit 21 transmits detection information, such as the time (usage time) while a person is using the urinal (e.g., from when a person approaches the urinal 20 until they leave), to the collection device 50 along with identification information (such as an ID) that identifies the urinal 20. Note that if the urinal 20 is not included in the abnormality detection targets of the abnormality detection system 1, the detection unit 201 may not be included in the abnormality detection system 1.

[0064] The washbasin 30 is a toilet-use item used by toilet users to wash their hands, wash their face, or look in the mirror. The washbasin 30 is located in the shared space CS provided within the toilet space 2. For example, the washbasin 30 has a faucet with an automatic faucet function and a bowl that receives water from the faucet. Note that the washbasin 30 does not necessarily have to be included in the abnormality detection system 1.

[0065] The detection unit 301 of the washbasin 30 performs detections related to the washbasin 30. The detection unit 301 functions as a detection unit that detects the use of the washbasin 30 by a person (user). The detection unit 301 may be implemented by various sensors; for example, the detection unit 301 may be various sensors such as a human body detection sensor, an action detection sensor, or a presence detection sensor. The detection unit 301 may also be a proximity sensor that detects the proximity of a person's body to the washbasin 30. Note that the above is just an example, and the detection unit 301 may be implemented by any means that can detect the use of the washbasin 30 by a user. Note that the washbasin 30 and the detection unit 301 may be integrated. In this case, the washbasin 30 having the detection unit 301 may be included in the abnormality detection system 1. For example, the detection unit 301 may be installed near the faucet of the washbasin 30 and detect the proximity of a person's hand to the faucet of the washbasin 30. The placement of the detection unit 301 is not particularly limited and may be installed on the ceiling, walls, floor, etc., of the shared space CS.

[0066] The detection unit 301 of the washbasin 30 includes, for example, a communication unit (e.g., a communication circuit) having wireless communication capabilities, and is wirelessly connected to the data collection device 50. For example, when the detection unit 301 detects that a person is using the washbasin 30, it transmits detection information, such as the date and time of the detection, to the data collection device 50 along with identification information (such as an ID) that identifies the washbasin 30. For example, the detection unit 21 transmits detection information, such as the time (usage time) while a person is using the washbasin (e.g., while the faucet of the washbasin 30 is dispensing water), to the data collection device 50 along with identification information (such as an ID) that identifies the washbasin 30. Note that if the washbasin 30 is not included in the abnormality detection targets of the abnormality detection system 1, the detection unit 301 may not be included in the abnormality detection system 1.

[0067] The data collection device 50 collects various types of information. For example, the data collection device 50 may be a gateway device or the like. The data collection device 50 is a device that collects detection information, such as sensor data detected for each object of use. The data collection device 50 collects information from other devices. The data collection device 50 collects detection information, which is information detected about the toilet space 2, toilet bowl 10, urinal 20, and washbasin 30, etc. The data collection device 50 receives detection information from devices that detect information about the toilet space 2, toilet bowl 10, urinal 20, and washbasin 30, etc.

[0068] The data collection device 50 receives detection results for the toilet space 2 from the detection unit 21 of the toilet space 2. The data collection device 50 receives detection information for the toilet bowl 10 from the detection unit 101 of the toilet bowl 10. The data collection device 50 receives detection information for the urinal 20 from the detection unit 201 of the urinal 20. The data collection device 50 receives detection information for the washbasin 30 from the detection unit 301 of the washbasin 30. The data collection device 50 is wirelessly connected to the detection unit 21 of the toilet space 2, the detection unit 101 of the toilet bowl 10, the detection unit 201 of the urinal 20, and the detection unit 301 of the washbasin 30. The data collection device 50 may be connected to the devices that detect information such as the toilet space 2, toilet bowl 10, urinal 20, and washbasin 30 in any way as long as it is capable of transmitting and receiving information, and may also be connected via wired communication.

[0069] The data collection device 50 may be located outside the toilet space 2 or inside the toilet space 2. For example, the data collection device 50 may be a server device located in a facility where the toilet space 2 is provided. The above is merely an example, and any configuration and placement of the data collection device 50 is possible as long as it can communicate with the detection units 21, 101, 201 and 301 to collect information and transmit the collected information to the analysis device 100. The data collection device 50 may also be integrated with the analysis device 100. In this case, the analysis device 100 has the functions of the data collection device 50, and the analysis device 100 collects information on the toilet space 2, toilets 10, urinals 20, and washbasins 30, etc.

[0070] The analysis device 100 is an information processing device (computer) that performs a determination process to determine whether or not an abnormality has occurred regarding the object of toilet use. For example, the analysis device 100 may be a cloud server or the like. The analysis device 100 is connected to the collection device 50 wirelessly or wired via a predetermined network N such as the Internet. The analysis device 100 may be connected to the collection device 50 in any way as long as it is capable of sending and receiving information, and may be connected wirelessly or wired.

[0071] The analysis device 100 uses the information collected by the collection device 50 to perform a determination process to determine whether or not an abnormality has occurred with respect to the toilet user. The analysis device 100 uses the collected information, which is the information received from the collection device 50, to perform a determination process to determine whether or not an abnormality has occurred with respect to the toilet user. The analysis device 100 uses the collected information to determine whether or not an abnormality has occurred with respect to the toilet space 2. The analysis device 100 uses the collected information to determine whether or not an abnormality has occurred with respect to the toilet bowl 10. The analysis device 100 uses the collected information to determine whether or not an abnormality has occurred with respect to the urinal 20. The analysis device 100 uses the collected information to determine whether or not an abnormality has occurred with respect to the washbasin 30.

[0072] <1-5. Functional Configuration of the Analysis Device> The functional configuration of the analysis device will be described below with reference to Figure 4. Figure 4 is a block diagram showing an example of the configuration of the analysis device according to this embodiment.

[0073] As shown in Figure 4, the analysis device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The analysis device 100 may also include an input unit (e.g., a keyboard or mouse) for receiving various operations from the administrator of the analysis device 100, and a display unit (e.g., a liquid crystal display) for displaying various information.

[0074] The communication unit 110 is implemented, for example, by a communication circuit. The communication unit 110 is connected to a predetermined network N (see Figure 3) by wire or wireless connection and transmits and receives information with an external information processing device. For example, the communication unit 110 is connected to a predetermined network N (see Figure 3) by wire or wireless connection and transmits and receives information with other devices such as the data collection device 50.

[0075] The storage unit 120 is implemented by, for example, a semiconductor memory element such as RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or optical disc. For example, the storage unit 120 is a computer-readable recording medium that non-temporarily records data used by abnormality detection programs such as judgment processing.

[0076] As shown in Figure 4, the storage unit 120 according to this embodiment includes a placement information storage unit 121 and a collection information storage unit 122. The storage unit 120 stores various information necessary for processing, not limited to the placement information storage unit 121 and the collection information storage unit 122. For example, the storage unit 120 stores various information used in the determination process (e.g., information related to thresholds). For example, the storage unit 120 stores a function for calculating thresholds used in the determination process, and information about the calculated function.

[0077] The arrangement information storage unit according to the embodiment stores various information regarding the arrangement of various objects to be detected by the anomaly detection system 1. For example, the arrangement information storage unit stores information regarding the arrangement of the toilet space 2, toilet bowl 10, urinal 20, and washbasin 30. Figure 5 is a diagram showing an example of the arrangement information storage unit according to the embodiment. The arrangement information storage unit shown in Figure 5 includes items such as "facility," "toilet space," "location," and "location element."

[0078] "Facility" refers to the location where a toilet space is located. Note that "Facility" is not limited to structures; various locations where a toilet space is located can be registered. "Toilet Space" refers to identification information used to identify each toilet space. In Figure 5, for illustrative purposes, codes such as "21" and "22" are shown for each toilet space, but each "Toilet Space" stores information that allows it to be identified (e.g., a toilet space ID).

[0079] The "location" field indicates identification information for identifying each location. In Figure 5, for illustrative purposes, the "location" field is shown with codes such as "TB1," "TB2," "TB3," and "CS1" assigned to each component of toilet space 2. However, the "location" field stores information that allows for the identification of the location (e.g., location ID).

[0080] "Placement element" indicates identification information for identifying the user of the toilet placed at the corresponding placement location. For example, "Placement element" indicates identification information for identifying the user of the toilet, such as a toilet bowl 10, a urinal 20, and a washbasin 30, placed at the corresponding placement location. In Figure 5, for illustrative purposes, "10" is shown in "Placement element". 11 "10 12 "10 13 "20 11 "20 12 "20 13 "30 11 "30 12 "30 13 The symbols attached to each component, such as "[...]", are shown in the diagram, but any information may be registered in the "[arrangement element]" as long as it is possible to identify each target of use.

[0081] For example, the "placement element" stores information that can identify a toilet placed in the corresponding location (e.g., toilet ID), identification information for identifying a urinal (e.g., urinal ID), or identification information for identifying a washbasin (e.g., washbasin ID).

[0082] In the example in Figure 5, the toilet space 21 identified by "21" includes toilet booths TB1 identified by "TB1", toilet booth TB2 identified by "TB2", and toilet booth TB3 identified by "TB3". It also indicates that the toilet space 21 includes a shared space CS1 identified by "CS1".

[0083] The toilet booth TB1, which is identified by "TB1", has "10 11 Toilet 10 identified by " 11 This indicates that it will be located. Also, the toilet booth TB2, which is the location identified by "TB2", will have "10 12 Toilet 10 identified by " 12 This indicates that it will be located. Also, the toilet booth TB3, which is the location identified by "TB3", will have "10 13 Toilet 10 identified by " 13 This indicates that it will be placed.

[0084] Furthermore, the shared space CS1, which is the location identified by "CS1", contains "20 11 Urinal 20 identified by " 11 "20 12 Urinal 20 identified by " 12 "20 13 Urinal 20 identified by " 13 This indicates that it will be placed in the shared space CS1. 11 Washbasin 30 identified by " 11 "30 12 Washbasin 30 identified by " 12 "30 13 Washbasin 30 identified by " 13 This indicates that it will be placed.

[0085] The placement information storage unit 121 is not limited to the above and may store various types of information depending on the purpose. For example, the placement information storage unit 121 may store information (e.g., sensor ID, etc.) that identifies the sensor (detection unit) that detects the sensor data (detection information) of each target being detected, in association with the target being detected by that sensor.

[0086] Information indicating the specific location of each toilet's user (e.g., latitude and longitude information) may be stored in association with each toilet's user. For example, information indicating the specific location of each toilet space 2 (e.g., latitude and longitude information) may be stored in association with each toilet space 2. For example, information indicating the specific location of each toilet bowl 10 (e.g., latitude and longitude information) may be stored in association with each toilet bowl 10. For example, the location information storage unit 121 may store information detected by each urinal 20 and information indicating the specific location of each urinal 20 (e.g., latitude and longitude information) in association with each urinal 20. For example, the location information storage unit 121 may store information detected by each washbasin 30 and information indicating the specific location of each washbasin 30 (e.g., latitude and longitude information) in association with each washbasin 30.

[0087] The collected information storage unit 122 stores detection information, such as sensor data, collected for each toilet user. The collected information storage unit 122 stores information received from the collection device 50. The collected information storage unit 122 stores detection information detected by the detection unit 21, detection unit 101, detection unit 201, and detection unit 301. For example, the collected information storage unit 122 stores the usage history of each toilet user, associating it with information that identifies each toilet user.

[0088] For example, the collected information storage unit 122 stores the date and time each toilet's user was used, associating it with information that identifies each toilet's user. The collected information storage unit 122 stores the date and time each toilet bowl 10 was used, associating it with information that identifies each toilet bowl 10. The collected information storage unit 122 stores the date and time each toilet booth TB in which each toilet bowl 10 is located was used, associating it with information that identifies each toilet booth TB. The collected information storage unit 122 stores the date and time each urinal 20 was used, associating it with information that identifies each urinal 20. The collected information storage unit 122 stores the date and time each washbasin 30 was used, associating it with information that identifies each washbasin 30. The collected information storage unit 122 stores the date and time each toilet space 2 was used, associating it with information that identifies each toilet space 2.

[0089] For example, the collected information storage unit 122 stores the date and time each toilet was used, and the duration of use, in association with information that identifies each toilet. The collected information storage unit 122 stores the date and time each toilet bowl 10 was used, and the duration of use, in association with information that identifies each toilet bowl 10. The collected information storage unit 122 stores the date and time each toilet booth TB in which each toilet bowl 10 is located was used, and the duration of use, in association with information that identifies each toilet booth TB. The collected information storage unit 122 stores the date and time each urinal 20 was used, and the duration of use, in association with information that identifies each urinal 20. The collected information storage unit 122 stores the date and time each washbasin 30 was used, and the duration of use, in association with information that identifies each washbasin 30. The collected information storage unit 122 stores the date and time each toilet space 2 was used, and the duration of use, in association with information that identifies each toilet space 2.

[0090] The above is merely one example; the collected information storage unit 122 stores various types of collected information.

[0091] Returning to Figure 4, let's continue the explanation. The control unit 130 is implemented by a CPU (Central Processing Unit) or GPU (Graphics Processing Unit), for example, by executing a program stored inside the analysis device 100 (for example, an anomaly detection program such as the judgment process related to this disclosure) using RAM or the like as a working area. The control unit 130 is also a controller and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array).

[0092] As shown in Figure 4, the control unit 130 includes an acquisition unit 131, a calculation unit 132, a determination unit 133, and a transmission unit 134, and realizes or executes the information processing functions and operations described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Figure 4, and other configurations are also acceptable as long as they perform the information processing described later.

[0093] The acquisition unit 131 acquires various types of information. The acquisition unit 131 acquires various types of information from the storage unit 120. The acquisition unit 131 receives information from other devices. The acquisition unit 131 receives information (detection information, etc.) collected by the collection device 50 from the collection device 50. The acquisition unit 131 receives detection information detected regarding the toilet user from the collection device 50. The acquisition unit 131 receives detection information detected by sensors, etc., corresponding to the toilet user from the collection device 50.

[0094] The acquisition unit 131 receives detection information from the detection unit 21 regarding the toilet space 2 from the detection unit 50. The acquisition unit 131 receives detection information from the detection unit 101 regarding the toilet bowl 10 from the detection unit 50. The acquisition unit 131 receives detection information from the detection unit 101 regarding the toilet booth TB in which the toilet bowl 10 is located from the detection unit 50. The acquisition unit 131 receives detection information from the detection unit 201 regarding the urinal 20 from the detection unit 50. The acquisition unit 131 receives detection information from the detection unit 301 regarding the washbasin 30 from the detection unit 50.

[0095] The acquisition unit 131 acquires information to be used for processing from the storage unit 120. The acquisition unit 131 acquires information to be used for judgment processing from the collected information storage unit 122. The acquisition unit 131 acquires information corresponding to the toilets to be judged from the collected information storage unit 122. The acquisition unit 131 acquires information to be used for calculating statistics from the collected information storage unit 122. The acquisition unit 131 acquires information to be used for calculating thresholds from the collected information storage unit 122.

[0096] The acquisition unit 131 acquires information indicating a selection tendency, where a user tends to select one particular object from a group of objects of the same type that are the target of use by toilet users. The acquisition unit 131 acquires information indicating a selection tendency, where a user tends to select one particular object from a group of objects of the same type that are one of the following: toilet booths, washbasins, urinals, toilets, or toilet spaces. The acquisition unit 131 acquires information indicating a selection tendency for one toilet booth from a group of toilet booths that are a group of objects of the same type. The acquisition unit 131 acquires information indicating the number of times each of the objects of the same type has been used.

[0097] The calculation unit 132 performs calculation processing. The calculation unit 132 performs calculation processing using various information stored in the storage unit 120. The calculation unit 132 performs calculation processing using various information acquired by the acquisition unit 131.

[0098] The calculation unit 132 calculates various values ​​used in the judgment process. The calculation unit 132 calculates thresholds used in the judgment process. The calculation unit 132 calculates statistics related to the target of judgment.

[0099] The calculation unit 132 calculates statistics related to the object of use. The calculation unit 132 calculates statistics on the amount of time the object of use is used by the user over a predetermined period or number of times.

[0100] The calculation unit 132 calculates information indicating the tendency for a user to select one particular object from a group of objects of the same type that are intended for use by toilet users. The calculation unit 132 calculates the selection probability that a user tends to select one particular object from a group of objects of the same type. The calculation unit 132 calculates the selection probability that a user selects one particular object from a group of objects of the same type that consists of one of the following: toilet booth, washbasin, urinal, toilet bowl, or toilet space. The calculation unit 132 calculates the selection probability of one toilet booth from a group of toilet booths that are a group of objects of the same type. The calculation unit 132 calculates the selection probability of one particular object from a group of objects over a predetermined period or number of times. The calculation unit 132 calculates the selection probability of one particular object by dividing the number of times one particular object is selected over a predetermined period or number of times by the total number of times the group of objects is selected over a predetermined period or number of times.

[0101] The determination unit 133 performs a determination process. The determination unit 133 performs a determination process using various information stored in the storage unit 120. The determination unit 133 performs a determination process using various information acquired by the acquisition unit 131. The determination unit 133 performs a determination process using various values ​​(information) calculated by the calculation unit 132.

[0102] The determination unit 133 performs a determination process using the threshold calculated by the calculation unit 132. The determination unit 133 performs a determination process using the statistics related to the object of use calculated by the calculation unit 132. The determination unit 133 uses the statistics related to one object of use to determine whether or not an abnormality has occurred with respect to that object of use.

[0103] The determination unit 133 determines that an abnormality has occurred with respect to a particular object if the selection tendency of that object deviates from the past selection tendency of that object. The determination unit 133 determines that an abnormality has occurred with a particular toilet booth if the selection tendency of a particular toilet booth deviates from the past selection tendency of that toilet booth. The determination unit 133 determines that an abnormality has occurred with respect to a particular object if that object is not selected for a predetermined number of consecutive times or more. The determination unit 133 determines whether an abnormality has occurred with respect to a particular object using the selection probability of that object within a predetermined period or number of times within a group of objects. The determination unit 133 determines whether an abnormality has occurred with respect to a particular object using the selection probability that represents the proportion of the number of selections of a particular object within a predetermined period or number of times to the total number of selections of the group of objects within a predetermined period or number of times within a predetermined period or number of times.

[0104] The transmitting unit 134 transmits information to an external information processing device. The transmitting unit 134 transmits information regarding the determination result by the determination unit 133 to an administrator device used by the administrator who manages the abnormality detection system 1. If the determination unit 133 indicates an abnormality, the transmitting unit 134 transmits information regarding the toilet user that was determined to be abnormal. If the determination unit 133 indicates an abnormality, the transmitting unit 134 transmits information identifying the toilet user that was determined to be abnormal to the administrator device. If the determination unit 133 indicates an abnormality, the transmitting unit 134 transmits information indicating the location where the toilet user that was determined to be abnormal is located to the administrator device. The transmitting unit 134 may also transmit information to the collection device 50. For example, the transmitting unit 134 transmits information to the information collection device 50 requesting information collected by the collection device 50.

[0105] <1-6. Processing Flow> From here, we will explain the processing flow of the anomaly detection process using Figure 6. Figure 6 is a flowchart showing an example of the steps of the process executed by the anomaly detection system.

[0106] The anomaly detection system 1 acquires information indicating a selection tendency, where a user tends to select one particular toilet from a group of toilets of the same type that are intended for use by toilet users (step S101). For example, the analysis device 100 of the anomaly detection system 1 acquires information indicating a selection tendency for one toilet booth TB from a group of toilet booths TB in which toilet bowls 10 are each located.

[0107] The anomaly detection system 1 determines that an anomaly has occurred with respect to a particular user if the selection trend of that user deviates from the past selection trend of that user (step S102). For example, the analysis device 100 of the anomaly detection system 1 determines that an anomaly has occurred with a particular toilet booth TB if the selection trend of a particular toilet booth TB where a toilet 10 is located deviates from the past selection trend of that toilet booth TB.

[0108] <1-7. Examples of Anomaly Detection> From here, we will explain specific processing examples related to anomaly detection using Figures 7 to 10. Note that explanations of points similar to those mentioned above will be omitted as appropriate. First, we will explain the calculation of selection probability, which is an example of usage ratio, using Figure 7. Figure 7 is a diagram showing an example of the usage ratio of objects of the same type.

[0109] In Figure 7, the analysis device 100 calculates the selection probability of each toilet booth TB (also simply called "booth") using calculation formula FC1. In calculation formula FC1, "m" corresponds to the number of booths and "i" corresponds to the booth number. Specifically, Figure 7 shows the case where the analysis device 100 has 3 booths (m=3) as an example. Booth #1 with "i=1" corresponds to, for example, toilet booth TB1. Booth #2 with "i=2" corresponds to, for example, toilet booth TB2. Booth #3 with "i=3" corresponds to, for example, toilet booth TB3. As shown in calculation formula FC1, the analysis device 100 calculates the selection probability, which is the usage rate of one booth, by dividing the number of users (number of uses) of one booth by the total number of users (number of uses) of multiple booths.

[0110] Figure 7 shows a case where the total number of users of all booths during a certain period (also called the "collection target range") is 10 people. It also shows that booth #1 was used 3 times during the collection target range. It also shows that booth #2 was used 2 times during the collection target range. It also shows that booth #3 was used 5 times during the collection target range. The collection target range can be set to any range, such as 1 day or 1 week. Alternatively, the collection target range may be defined by a number of times, such as 100 times (100 users).

[0111] In Figure 7, the analysis device 100 uses calculation formula FC1 to calculate the selection probability p1 of booth #1 within the target collection range as "3 / 10", which is expressed as "30% (=3 / 10*100)". The analysis device 100 also uses calculation formula FC1 to calculate the selection probability p2 of booth #2 within the target collection range as "2 / 10", which is expressed as "20% (=2 / 10*100)". The analysis device 100 also uses calculation formula FC1 to calculate the selection probability p3 of booth #3 within the target collection range as "5 / 10", which is expressed as "50% (=5 / 10*100)". In this way, the analysis device 100 calculates the selection probability corresponding to the target collection range for each target in the target group.

[0112] Next, Figures 8 to 10 will be used to explain the processing flow and specific examples of the processing related to anomaly detection. Figure 8 is a diagram showing an example of the processing flow related to anomaly detection. Specifically, Figure 8 shows an example in which anomaly detection is performed when the Nth person (where N is any number) enters a booth. Figure 9 is a diagram showing an example of the usage rate of the target. Figure 10 is a diagram showing an example of the processing for anomaly detection.

[0113] First, in Figure 8, the Nth user enters a booth (step S201). Then, the analysis device 100 counts the number of users n1 to n3, which are the number of users (also called "users") in booths #1 to #3 from Na to Nth users (step S202). In Figure 9, as shown in the collected information LG1, the case where "a=10" is shown as an example, and the analysis device 100 counts the number of users n1 to n3 in booths #1 to #3 from N-10 to Nth users. As shown in the processing information PS1 in Figure 9, the analysis device 100 counts the number of users n1 in booth #1 as "2", the number of users n2 in booth #2 as "3", and the number of users n3 in booth #3 as "5" for users from N-10 to Nth users.

[0114] Then, the analysis device 100 calculates the usage rate of each booth (step S203). In Figure 9, the analysis device 100 calculates the selection probabilities p1 to p3, which are the usage rates of booths #1 to #3 for N-10 to N users. As shown in the processing information PS2 in Figure 9, the analysis device 100 calculates the selection probability p1, which is the usage rate of booth #1, as "0.2", the selection probability p2, which is the usage rate of booth #2, as "0.3", and the selection probability p3, which is the usage rate of booth #3, as "0.5" for N-10 to N users.

[0115] The analysis device 100 determines whether the usage rate of each booth is below the threshold corresponding to each booth (step S204). The analysis device 100 makes this determination using the selection probabilities p1 to p3, which are the usage rates of booths #1 to #3, and the thresholds T1 to T3 for booths #1 to #3. In Figure 10, the analysis device 100 calculates the thresholds T1 to T3 for booths #1 to #3 using information from the learning period D, which corresponds to a predetermined period LP1, which corresponds to the horizontal axis. Figure 10 shows an example where an anomaly that occurred at time point pd1 is detected at time point pd2.

[0116] In Figure 10, the analysis device 100 calculates a threshold for each target using the mean and variance of the usage rate during the learning period D. For booth #1, the analysis device 100 calculates the threshold T1 for booth #1 using the mean μ1 and variance σ1 of the selection probability p1 during the learning period D. Similarly, for booth #2, the analysis device 100 calculates the threshold T2 for booth #2 using the mean μ2 and variance σ2 of the selection probability p2 during the learning period D. Furthermore, for booth #3, the analysis device 100 calculates the threshold T3 for booth #2 using the mean μ3 and variance σ3 of the selection probability p3 during the learning period D.

[0117] For example, the analysis device 100 calculates the threshold T1 for booth #1 using the formula "T1 = μ1 - 3σ1". In this way, the analysis device 100 calculates the threshold T1 by subtracting the value obtained by multiplying the variance σ1 of the selection probability p1 by 3 from the mean μ1 of the selection probability p1. The analysis device 100 calculates the threshold T2 for booth #2 and the threshold T3 for booth #3 by performing a similar process.

[0118] If the analysis device 100 determines that the usage rate of any of the booths is not below the corresponding threshold (step S204; No), it returns to step S201 and repeats the process in response to the next user entering one of the booths.

[0119] On the other hand, if the analysis device 100 determines that the usage rate of one booth is below a corresponding threshold (step S204; Yes), it determines that an abnormality has occurred in that booth (step S205). The analysis device 100 then performs processing related to the booth in which the abnormality was detected. For example, the analysis device 100 notifies (transmits) information indicating the booth in which the abnormality was detected to an administrator device used by the administrator. After step S205, the analysis device 100 may terminate the abnormality detection process, or it may return to step S201 and repeat the process.

[0120] As described above, the analysis device 100 calculates the usage rate of the booths for each use over a certain period, and determines that an abnormality exists if the trend changes significantly.

[0121] As described above, the analysis device 100 calculates a threshold corresponding to each target of use. The analysis device 100 may also calculate the threshold based on the past selection probabilities of toilet booths TB1 to TB3. For example, in Figure 1, the analysis device 100 may calculate the anomaly detection threshold for each of toilet booths TB1 to TB3 using data from the learning period D.

[0122] For example, the analysis device 100 calculates the threshold (first threshold) for toilet booth TB1 using the first mean, which is the average of the first selection probabilities during the learning period D, and the first variance, which is the variance of the first selection probabilities during the learning period D. For example, the analysis device 100 calculates the threshold (second threshold) for toilet booth TB2 using the second mean, which is the average of the second selection probabilities during the learning period D, and the second variance, which is the variance of the second selection probabilities during the learning period D. For example, the analysis device 100 calculates the threshold (third threshold) for toilet booth TB3 using the third mean, which is the average of the third selection probabilities during the learning period D, and the third variance, which is the variance of the third selection probabilities during the learning period D.

[0123] The analysis device 100 calculates the third threshold by subtracting the value obtained by multiplying the third variance of the third selection probability by 3 from the third mean of the third selection probability. The analysis device 100 calculates the first threshold for toilet booth TB1 and the second threshold for toilet booth TB2 by similar processing. Note that the thresholds described above are merely examples, and various thresholds may be set as appropriate. For example, the analysis device 100 may set the threshold for a particular user to a value obtained by lowering the average of the past selection probabilities of that user (for example, half of the average (50%)).

[0124] Further effects and modifications can be readily derived by those skilled in the art. Therefore, broader aspects of the present invention are not limited to the specific details and representative embodiments expressed and described above. Accordingly, various modifications are possible without departing from the spirit or scope of the overall concept of the invention as defined by the appended claims and their equivalents. [Explanation of Symbols]

[0125] 1. Anomaly detection system 2 Toilet space 10 Toilet bowl 20 Urinal 30 wash basin 50 Collection device 100 Analyzer 110 Communications Department 120 Storage section 121 Location information storage unit 122 Information Collection and Storage Unit 130 Control Unit 131 Acquisition Department 132 Calculation Section 133 Judgment section 134 Transmitter CS shared space TB toilet booth

Claims

1. An acquisition unit that acquires information indicating a selection tendency, where one type of target is more likely to be selected by a user compared to other targets, among a group of targets of the same type that are intended for use by toilet users. A determination unit determines that an abnormality has occurred with respect to the first object of use if the selection trend of the first object of use deviates from the past selection trend of the first object of use. An anomaly detection system characterized by having the following features.

2. The aforementioned same type is, It is one of the following types: toilet booth, washbasin, urinal, toilet bowl, or toilet space. The anomaly detection system according to feature 1.

3. The acquisition unit is, From the group of toilet booths of the same type that are the target group for use, information indicating the selection tendency of one toilet booth is obtained. The determination unit, If the selection tendency of the first toilet booth deviates from the past selection tendency of the first toilet booth, it is determined that the abnormality has occurred in the first toilet booth. An anomaly detection system according to claim 1 or 2, characterized by the above.

4. The determination unit, If the aforementioned target of use is not selected for a predetermined number of consecutive times or more, it is determined that an abnormality has occurred with respect to the aforementioned target of use. An anomaly detection system according to any one of claims 1 to 3.

5. The determination unit, Using the selection probability, which is the tendency to select one of the target users within the aforementioned target group over a predetermined period or number of times, it is determined whether or not an abnormality has occurred with respect to the first target user. An anomaly detection system according to any one of claims 1 to 3.

6. The determination unit, Using the selection probability, which represents the ratio of the number of times a particular target is selected during a predetermined period or number of times to the total number of times the target group is selected during the predetermined period or number of times, it is determined whether or not an abnormality has occurred with respect to the particular target. The anomaly detection system according to feature 5.

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