Cleaning support system and cleaning support method

Abstract

To provide a cleaning support system and a cleaning support method capable of returning a feedback to a user, about a cleaning action.SOLUTION: A cleaning support system 1 comprises: a storage part 32 storing a cleaning request being a request related to a cleaning action; a cleaning action acquiring part 10 for acquiring a motion of a floor mop 2 used for cleaning; a learned learning tool 35 which has performed machine learning for estimating a cleaning action of a user, on the basis of the motion of the floor mop 2 acquired by the cleaning action acquiring part 10 on the basis of, teacher data including the motion of the floor mop 2, and the cleaning action of the user corresponding to the motion of the floor mop; a cleaning motion estimating part 31b for estimating a cleaning action of the user on the basis of the motion of the floor mop 2 acquired by the cleaning action acquiring part 10, by using the learning tool 35; and a feedback part 31d for returning a feedback to the user, by collating the estimated cleaning action of the user with the cleaning request.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a cleaning support system and a cleaning support method. [Background technology]

[0002] Patent Document 1 discloses an information processing system that generates a performance screen showing the cleaning performance using a cleaning tool based on the movement of the cleaning tool detected by a movement detection unit provided on the cleaning tool. In this information processing system, cleaning distance, cleaning time, and cleaning frequency are tallied as cleaning performance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-192167 Summary of the Invention [Problem to be solved by the invention]

[0004] The information processing system of Patent Document 1 merely provides the user (cleaner) with a record of cleaning performance, but does not provide any suggestion as to whether the cleaning is sufficient or insufficient. Meanwhile, against a recent social backdrop, there is an increasing demand for maintaining clean living environments (e.g., homes, offices, stores) through appropriate cleaning, for example, to prevent the spread of infectious diseases. In other words, there is room for improvement in providing the user with suggestions for appropriate cleaning behavior by returning feedback on their cleaning behavior to the user.

[0005] An object of the present invention is to provide a cleaning assistance system and a cleaning assistance method that can provide feedback to a user about their cleaning behavior. [Means for solving the problem]

[0006] One aspect of the present invention is a cleaning request storage unit that stores cleaning requests that are requests regarding cleaning actions; a cleaning action acquisition unit that acquires the action of the cleaning tool being used for cleaning; a learning device that has undergone machine learning based on training data including cleaning tool movements and corresponding cleaning actions of the user to estimate the cleaning actions of the user from the cleaning tool movements acquired by the cleaning action acquisition unit; a cleaning behavior estimation unit that estimates the cleaning behavior of the user based on the cleaning tool behavior acquired by the cleaning behavior acquisition unit using the learning device; and a feedback unit that compares the estimated cleaning behavior of the user with the cleaning request and returns feedback to the user.

[0007] Another aspect of the present invention is to store cleaning requests, which are requests for cleaning actions; Obtain the behavior of the cleaning tools used for cleaning, using a learning device that has undergone machine learning to estimate the cleaning behavior of the user from the acquired cleaning tool movements based on training data including cleaning tool movements and corresponding user cleaning behaviors; A cleaning assistance method is provided in which the estimated cleaning behavior of the user is compared with the cleaning request and feedback is returned to the user. [Effects of the Invention]

[0008] According to the present invention, feedback on cleaning behavior is returned to the user, and suggestions on appropriate cleaning behavior can be given to the user. As a result, appropriate cleaning behavior by the user is supported, making it easier to maintain a clean living environment. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a diagram showing a schematic configuration of a cleaning support system according to a first embodiment of the present invention. [Figure 2] 2 is a flowchart showing the flow of cleaning support by the cleaning support system of FIG. 1; [Figure 3] FIG. 1 is a plan view of an exemplary room to be cleaned. [Figure 4] FIG. 10 is a diagram showing a schematic configuration of a cleaning support system according to a second embodiment of the present invention. [Figure 5] 5 is a flowchart showing the flow of cleaning support by the cleaning support system of FIG. 4. [Figure 6] FIG. 10 is a plan view of a room to be cleaned according to another example. [Figure 7] FIG. 10 is a diagram showing a schematic configuration of a cleaning support system according to a third embodiment of the present invention. [Figure 8] 8 is a flowchart showing the flow of cleaning support by the cleaning support system of FIG. 7. [Figure 9] A floor plan showing the cleaning results for the room to be cleaned in Figure 6. DETAILED DESCRIPTION OF THE INVENTION

[0010] A cleaning support system and a cleaning support method according to an embodiment of the present invention will be described below with reference to the accompanying drawings. Note that the following description is merely exemplary in nature and is not intended to limit the present invention, its applications, or its uses.

[0011] [First embodiment] 1 shows a schematic configuration of a cleaning support system 1 according to a first embodiment of the present invention. The cleaning support system 1 is configured to provide feedback to a user (cleaner) 90 regarding the cleaning behavior of the user 90 with respect to a room 100 to be cleaned. In this embodiment, an example will be described in which the user 90 wipes and cleans a floor surface 101 of the room 100 to be cleaned using a floor mop (cleaning tool) 2. The floor mop 2 has a long, rod-shaped handle 2a that is held by the user 90 and a horizontally long wiping part 2b attached to the bottom end of the handle 2a.

[0012] The cleaning support system 1 includes a cleaning action acquisition unit 10 that acquires the cleaning actions of a user 90, a server 30 that evaluates the acquired cleaning actions and generates feedback, and a speaker 3 that notifies the user 90 of the feedback.

[0013] The cleaning action acquisition unit 10 is attached to the handle 2a of the floor mop 2. Specifically, the cleaning action acquisition unit 10 is located at the center of gravity of the floor mop 2. The cleaning action acquisition unit 10 has an acceleration sensor 11, a data transmission unit 12, a start switch 13, and an end switch 14.

[0014] In this embodiment, the acceleration sensor 11 is a triaxial acceleration sensor that can measure acceleration in a first direction along the longitudinal direction of the handle 2a, a second direction perpendicular to the first direction and parallel to the longitudinal direction of the wiping part 2b, and a third direction perpendicular to the first and second directions and parallel to the lateral direction of the wiping part 2b when the floor mop 2 is in a specific posture (for example, the handle 2a extends parallel to the vertical direction). Note that although an acceleration sensor capable of measuring acceleration in one or two axial directions may be used as the acceleration sensor 11, a triaxial acceleration sensor is preferable for accurately obtaining data on the cleaning operation.

[0015] The data transmission unit 12 transmits the acceleration data acquired by the acceleration sensor 11 to the server 30. Communication between the data transmission unit 12 and the server 30 is performed via wireless communication means such as Bluetooth (registered trademark) or Wi-Fi (registered trademark), or via the Internet. Alternatively, communication between the data transmission unit 12 and the server 30 may be performed via a wired connection, and various communication connection means can be used.

[0016] The start switch 13 is operated by the user 90 when starting measurement by the acceleration sensor 11. The end switch 14 is operated by the user 90 when ending measurement by the acceleration sensor 11.

[0017] The server 30 is a well-known computer including an arithmetic processing unit 31 (CPU), a storage unit 32, a memory, a data receiving unit 33, and a data transmitting unit 34. A learning device 35 and an input unit 36 ​​are connected to the server 30. Note that the learning device 35 and / or the input unit 36 ​​may be integrated into the server 30.

[0018] Various functions are configured by the installed programs in the calculation processing unit 31. Specifically, the calculation processing unit 31 has a cleaning request generation unit 31a, a cleaning behavior estimation unit 31b, a cleaning behavior evaluation unit 31c, and a feedback unit 31d.

[0019] The cleaning request generator 31a generates a cleaning request that serves as an index for properly cleaning the room 100 based on information about cleaning of the room 100 input via the input unit 36. The cleaning request includes cleaning actions, including a cleaning method and cleaning time, required for each cleaning tool. For example, when using a floor mop 2 as the cleaning tool, the cleaning request generator 31a assigns a first cleaning action of pushing the mop straight to wipe areas of the floor 101 of the room 100 excluding the fixtures, a second cleaning action of inserting the mop into the gaps and areas underneath the fixtures, and a third cleaning action of sweeping like a broom to other areas such as around the fixtures. The generated cleaning requests are stored in the memory unit 32.

[0020] The cleaning behavior estimation unit 31b estimates the cleaning behavior of the user 90 using the learning device 35 based on the acceleration data received by the data receiving unit 33. Specifically, the learning device 35 classifies the cleaning behavior of the user 90 into one of the first to third cleaning behaviors. The estimated cleaning behavior of the user 90 is stored in the storage unit 32.

[0021] The cleaning behavior evaluation unit 31c evaluates the cleaning behaviors from the memory unit 32 and generates a cleaning record including a first actual cleaning time for the first cleaning behavior, a second actual cleaning time for the second cleaning behavior, a third actual cleaning time for the third cleaning behavior, and a total actual cleaning time obtained by adding up the first to third actual cleaning times. The generated cleaning records are stored in the memory unit 32.

[0022] The feedback unit 31d reads the cleaning request and the cleaning record from the memory unit 32, evaluates the cleaning record in light of the cleaning request, and generates feedback. Specifically, it evaluates whether the cleaning record satisfies the cleaning request and / or to what extent the cleaning request is satisfied. For example, if the cleaning request requires the first cleaning action to be performed for 5 minutes or more and the estimated first cleaning record time is 4 minutes, the feedback unit 31d generates feedback that the cleaning request is not satisfied and that the satisfaction level with the cleaning request is 80%.

[0023] Next, the feedback unit 31d transmits the feedback to the speaker 3 via the data transmission unit 34. The feedback is notified to the user via the speaker 3.

[0024] As described above, the memory unit 32 stores the cleaning request generated by the cleaning request generation unit 31a. Therefore, the memory unit 32 constitutes a cleaning request storage unit according to the present invention. Furthermore, the memory unit 32 stores the cleaning behavior of the user 90 estimated by the cleaning behavior estimation unit 31b and the cleaning performance evaluated by the cleaning behavior evaluation unit 31c.

[0025] The data receiving unit 33 receives the acceleration data transmitted by the data transmitting unit 12 of the cleaning operation acquiring unit 10. The received acceleration data is stored in the memory unit 32. The data transmitting unit 34 transmits the evaluation results stored in the memory unit 32 to the speaker 3.

[0026] When a large amount of input data is input, the learner 35 learns the relationships between the input data by machine learning such as supervised learning, unsupervised learning, or reinforcement learning, and generates a trained model that represents the relationships using parameters. In other words, the learner 35 learns features contained in the input data and generates a model that estimates results from new input. Specifically, the learner 35 is a neural network, an SVM (support vector machine), a decision tree, a combination of these, or the like.

[0027] In this embodiment, the learning device 35 estimates cleaning behavior based on the cleaning behavior of the user 90 acquired by the cleaning behavior acquisition unit 10. Specifically, the learning device 5 is a trained model (also referred to as cleaning AI) generated by a machine learning algorithm for estimating the cleaning behavior of the user from the acceleration data acquired by the cleaning behavior acquisition unit 10, based on teacher data including acceleration data acquired in advance by the cleaning behavior acquisition unit 10 and the cleaning behavior of the user 90 corresponding to the acceleration data.

[0028] The learning device 35 estimates the cleaning behavior of the floor mop 2, and classifies the acquired cleaning behavior based on the acceleration data into one of the following: "first cleaning behavior" where the mop is pushed straight to wipe, "second cleaning behavior" where the mop is inserted into gaps to wipe, and "third cleaning behavior" where the mop is swept like a broom. Therefore, the training data for the learning device 35 to perform machine learning includes the first to third cleaning behaviors and the acceleration data corresponding to each of them.

[0029] The input unit 36 ​​is an input device for inputting information regarding cleaning of the room 100 to be cleaned. The input information includes, for example, one or more of the following: the shape and size of the room 100; the type, shape, size, arrangement, presence or absence of gaps around and at the bottom of each piece of furniture arranged in the room 100; the location of the room 100 to be cleaned (floor, wall, etc.); and the cleaning tools to be used for cleaning. The input unit 36 ​​can be a device that allows manual input, such as a keyboard or tablet.

[0030] Next, with reference to the flowchart shown in Fig. 2, a flow of cleaning support by the cleaning support system 1 when cleaning an exemplary room 100 to be cleaned shown in Fig. 3 will be described. First, the room 100 will be described. For convenience of explanation, the left-right direction in Fig. 3 will be referred to as the X direction, the up-down direction as the Y direction, and more specifically, the right side will be referred to as X1, the left side as X2, the upper side as Y1, and the lower side as Y2.

[0031] Referring to Figure 3, room 100 to be cleaned is a rectangular room that is long in the X direction and has a wooden floor 101 and is surrounded by walls 102. A door 103 is provided at the Y2 end of wall 102 on the X2 side of room 100. Room 100 is 16 tatami mats in size. Room 100 is a so-called living / dining / kitchen room that includes a living room 40 located on the X1 side, a kitchen room 60 located on the X2 side, and a dining room 50 located between living room 40 and kitchen room 60.

[0032] The living room 40 is rectangular and elongated in the Y direction, with an area of ​​approximately 6 tatami mats. A television stand 41 with a television 43 placed on it is disposed at the Y1 end of the living room 40, and a three-seater sofa 42 is disposed at the Y2 end. The television stand 41 and the sofa 42 extend horizontally in the X direction across the living room 40, with their back surfaces 41a and 42a on the Y direction sides abutting against a wall surface 102 of the room or separated by a small gap so small that cleaning is not necessary, while their ends 41b and 42b on the X1 side are disposed with a gap between them and the wall surface 102. The sofa 42 is placed on a floor surface 101 via multiple legs (not shown), and there is a space between the sofa 42 and the floor surface 101.

[0033] Therefore, the living room 40 includes the following cleaning areas: a rectangular first cleaning area R1 defined between the TV stand 41 and the sofa 42 and extending in the X and Y directions; a second cleaning area R2 defined between the TV stand 41 and the wall surface 102 on the X1 side and extending in the X direction; a third cleaning area R3 defined between the sofa 42 and the wall surface 102 on the X1 side and extending in the X direction; and a fourth cleaning area R4 defined below the sofa 42.

[0034] Dining room 50 is rectangular and long in the Y direction, with an area of ​​about 6 tatami mats. Dining room 50 has a shelf 51 disposed at the end on the Y1 side, a table 52 disposed in the center, and four chairs 53 disposed at the four corners of table 52. Shelf 51 is disposed so that its back surface 51a is substantially in contact with wall surface 102, while there is a gap between shelf 51 and TV stand 41. Table 52 and chair 53 are placed on floor surface 101 via legs (not shown), and there is a gap between them.

[0035] Therefore, the dining room 50 includes the following cleaning areas: a fifth cleaning area R5 defined between the shelf 51 on the X1 side and the TV stand 41; a sixth cleaning area R6 defined below the table 52; a seventh cleaning area R7 defined below each chair 53; an eighth cleaning area R8 located on the Y2 side of the table 52 and extending in the X direction from the sofa 42 to the kitchen room 60; and a ninth cleaning area R9 located on the Y1 side of the eighth cleaning area R8 and excluding the area where the shelf 51, table 52 and chairs 53 are located.

[0036] The kitchen room 60 is rectangular and elongated in the Y direction, with an area of ​​about 4 tatami mats. A kitchen counter 61 is located at the X1 end of the kitchen room 60, and a refrigerator 62 and a cupboard 63 are located at the X2 end. The kitchen counter 61 extends from the Y1-side wall 102 to the Y2 side, reaching the Y1-side end of the eighth cleaning area R8. The refrigerator 62 is located so that its X2-side back surface 62a and its Y1-side end 62b are in substantial contact with the wall 102. The cupboard 63 has a gap on the Y2 side of the refrigerator 62, while its X2-side back surface 63a is in contact with the wall 102 or is separated from it by a small gap so small that cleaning is not necessary.

[0037] Therefore, the kitchen room 60 includes, as cleaning areas, a tenth cleaning area R10 extending in the Y direction between the kitchen counter 61 and the refrigerator 62 and cupboard 63, and an eleventh cleaning area R11 extending in the X direction defined between the refrigerator 62 and the cupboard 63. As described above, the eighth cleaning area R8 extending from the dining room 50 is located on the Y2 side of the kitchen room 60.

[0038] Next, the flow of cleaning support by the cleaning support system 1 will be described with reference to Fig. 2. First, cleaning location information, which is information related to cleaning of the room 100 to be cleaned, is input to the server 30 via the input unit 36 ​​(step S101). For example, the cleaning location information includes one or more of the following: the shape and size of the room 100; the type, shape, size, arrangement, presence or absence of gaps around and below each fixture arranged in the room 100; the cleaning location in the room 100 (floor, wall, etc.); and the cleaning tools to be used for cleaning. This information may be input each time cleaning is performed, or information input at the time of initial setup may be read from the storage unit 32.

[0039] Next, the cleaning request generator 31a of the server 30 generates a cleaning request based on the cleaning location information (step S102). In this embodiment, the cleaning request generator 31a divides the room 100 into first to eleventh cleaning areas R1 to R11 based on the cleaning location information, and assigns cleaning actions suitable for each cleaning area to each of the first to eleventh cleaning areas R1 to R11, taking into account the size and shape of each area, for a time period according to the size of each area.

[0040] Specifically, the cleaning request generator 31a assigns the first cleaning action to the first cleaning area R1, the eighth cleaning area R8, and the tenth cleaning area R10, which have linearly extending portions with a predetermined width (for example, the width in the longitudinal direction of the wiping part 2b of the floor mop 2), for a time period corresponding to the size of each area. The cleaning request generator 31a assigns the second cleaning action to the second to seventh cleaning areas R2 to R7 and the eleventh cleaning area R11, which are the areas below the fixtures and the gap areas around the fixtures, for a time period corresponding to the size of each area. The cleaning request generator 31a assigns the third cleaning action to the ninth cleaning area R9, which is the other area (such as the area around the fixtures), for a time period corresponding to the size of the area.

[0041] Table 1 below illustrates an example of the cleaning actions and cleaning times assigned to each cleaning area for room 100.

[0042] [Table 1]

[0043] Finally, the cleaning request generator 31a calculates a first cleaning request time by adding up the cleaning times for the first cleaning action, a second cleaning request time by adding up the cleaning times for the second cleaning action, a third cleaning request time by adding up the cleaning times for the third cleaning action, and a total cleaning request time by adding up these, and generates these as cleaning requests. The memory 32 stores the generated cleaning requests (step S103).

[0044] Table 2 below shows exemplary cleaning requirements for room 100.

[0045] [Table 2]

[0046] Next, the user 90 operates the start switch 13 of the cleaning operation acquisition unit 10 to start cleaning (step S104). When cleaning starts, the acceleration sensor 11 starts measuring acceleration (step S105), and the measured acceleration data is transmitted to the server 30 via the data transmission unit 12 (step S106).

[0047] Next, the server 30 receives the transmitted acceleration data via the data receiving unit 33 (step S107). The received acceleration data is processed by the server 30. Specifically, the cleaning behavior estimation unit 31b estimates which of the first to third cleaning behaviors the cleaning behavior of the user 90 corresponds to based on the received acceleration data using the learning device 35 (step S108). The storage unit 32 stores the estimated cleaning behavior (step S109).

[0048] Thereafter, steps S105 to S109 are repeatedly performed until the user 90 operates the end switch 14 of the cleaning action acquisition unit 10 to end the cleaning. Therefore, the memory unit 32 stores continuous data of the estimated cleaning behavior, i.e., the cleaning behavior at each time from the start to the end of cleaning.

[0049] Next, when the user 90 operates the end switch 14 to end cleaning (step S111), the cleaning behavior evaluation unit 31c of the server 30 evaluates the cleaning behavior read from the memory unit 32 and calculates cleaning performances from the estimated continuous data of cleaning behavior, including a first actual cleaning time which is the total time for which the first cleaning behavior was performed, a second actual cleaning time which is the total time for which the second cleaning behavior was performed, a third actual cleaning time which is the total time for which the third cleaning behavior was performed, and a total actual cleaning time which is the total of the first to third actual cleaning times (step S112). The calculated cleaning performances are stored in the memory unit 32.

[0050] Next, the feedback unit 31d reads out the cleaning request and the cleaning record from the memory unit 32, and compares the first to third requested cleaning times and the total requested cleaning time in the cleaning request with the first to third actual cleaning times and the total actual cleaning time in the cleaning record to evaluate the cleaning behavior of the user 90 (step S113).

[0051] For example, as shown in the following Table 3, the feedback unit 31d generates an evaluation result that evaluates the achievement rate based on each requested cleaning time and each actual cleaning time. Alternatively, each item may be expressed as a single index using a weighting function.

[0052] [Table 3]

[0053] Next, the feedback unit 31d generates feedback to be returned to the user 90 as voice data based on the evaluation result, and outputs the same to the speaker 3 via the data transmission unit 34 (step S113). For example, based on the evaluation result in Table 3 above, the achievement rates of all items may be generated as feedback, or feedback may be generated only for items with achievement rates below 100%.

[0054] The speaker 3 notifies the user of the feedback by voice (step S114). As a result, the user 90 reviews his / her cleaning behavior based on the feedback notified by the speaker 3, and, for example, performs additional cleaning behavior that has not yet reached 100% completion, etc., and thus the cleaning behavior is supported, which makes it easier to maintain a clean living environment.

[0055] The cleaning support system 1 according to the above embodiment provides the following effects.

[0056] (1) The cleaning support system 1 includes a memory unit 32 storing cleaning requests, which are requests regarding cleaning actions; a cleaning action acquisition unit 10 acquiring the actions of the floor mop 2 being used for cleaning; a trained learning device 35 that has performed machine learning to estimate the cleaning action of the user from the actions of the floor mop 2 acquired by the cleaning action acquisition unit 10 based on training data including the actions of the floor mop 2 and the corresponding cleaning actions of the user 90; a cleaning action estimation unit 31b that uses the learning device 35 to estimate the cleaning action of the user 90 based on the actions of the floor mop 2 acquired by the cleaning action acquisition unit 10; and a feedback unit 31d that returns feedback to the user 90 about the estimated cleaning action of the user 90 in light of the cleaning request. As a result, feedback on the cleaning behavior is returned to the user 90, and suggestions on appropriate cleaning behavior can be given to the user 90. As a result, the cleaning of the user 90 is supported, and it becomes easier to maintain a clean living environment.

[0057] (2) The cleaning request includes at least one or more cleaning actions and a cleaning time for each cleaning action, the cleaning action estimation unit 31b estimates the cleaning time for each cleaning action, and the feedback unit 31d further returns feedback regarding the cleaning time for each cleaning action. As a result, feedback on the cleaning time for each cleaning action is returned to the user 90, so that more detailed suggestions for the cleaning action can be given to the user 90.

[0058] (3) The cleaning action acquisition unit 10 is attached to the floor mop 2. As a result, the movement of the floor mop 2 is acquired directly from the floor mop 2, and therefore the movement of the floor mop 2 is acquired more accurately than when the floor mop 2 is attached to the user 90. This improves the accuracy of the estimation of the cleaning behavior by the cleaning behavior estimation unit 31b.

[0059] (4) The cleaning action acquisition unit 10 is attached to the center of gravity of the floor mop 2. As a result, even when the cleaning action acquisition unit 10 is attached to the floor mop 2, it is possible to suppress changes in the position of the center of gravity of the floor mop 2. Therefore, the influence of the cleaning action acquisition unit 10 attached to the floor mop 2 on the cleaning behavior of the user 90, such as ease of handling the floor mop 2, is suppressed.

[0060] [Second embodiment] 4 shows a schematic configuration of a cleaning support system 200 according to the second embodiment. The cleaning support system 200 differs from the cleaning support system 1 according to the first embodiment in that it is configured to further provide feedback to the user 90 for each cleaning area.

[0061] Specifically, cleaning support system 200 differs from cleaning support system 1 according to the first embodiment in that it additionally includes cleaning position acquisition unit 210 and beacon transmitter 220, and that it additionally includes cleaning position estimation unit 31e in arithmetic processing unit 31. It also differs from cleaning support system 1 in that cleaning request generation unit 31a generates a cleaning request for each cleaning area, and cleaning behavior evaluation unit 31c evaluates cleaning behavior for each cleaning area. In the following description, components common to the first embodiment are denoted by the same reference numerals, and description thereof will be omitted.

[0062] The cleaning position acquisition unit 210 is attached to the handle 2a of the floor mop 2. Preferably, the attachment position of the cleaning position acquisition unit 210 is adjusted together with the cleaning action acquisition unit 10 so that the center of gravity of the floor mop 2 does not change. The cleaning position acquisition unit 210 has a beacon receiver 211 and a data transmission unit 212. A plurality of beacon transmitters 220 are provided in the room to be cleaned, and the beacon receiver 211 receives beacon signals emitted by each beacon transmitter 220 and transmits the beacon signal reception results to the server 30 via the data transmission unit 212.

[0063] In addition, while a beacon transmitter is attached to the floor mop 2, multiple beacon receivers and data transmission units may be provided on the room side, and the beacon signals received by each of the multiple beacon receivers may be transmitted to the server 30 via the respective data transmission units.

[0064] Next, the processing unit 31 will be described. The cleaning request generator 31a generates a cleaning request including a cleaning action and cleaning time for each cleaning area. That is, a cleaning request is generated for each cleaning area as shown in Table 1 described in the first embodiment.

[0065] The cleaning position estimation unit 31e detects the position of the floor mop 2 based on the beacon signal received from the cleaning position acquisition unit 210, and classifies each cleaning action into a cleaning area. As a result, the cleaning performance evaluated by the cleaning action evaluation unit 31c is associated with the cleaning area and stored in the storage unit 32.

[0066] The cleaning action evaluation unit 31c evaluates the cleaning actions read from the memory unit 32, and generates a cleaning record for each cleaning area, including a first actual cleaning time for the first cleaning action, a second actual cleaning time for the second cleaning action, a third actual cleaning time for the third cleaning action, and a total actual cleaning time obtained by adding up the first to third actual cleaning times. The generated cleaning records are stored in the memory unit 32.

[0067] The feedback unit 31d reads the cleaning request and cleaning record from the memory unit 32, evaluates the cleaning record in light of the cleaning request for each cleaning area, and generates feedback. Specifically, for each cleaning area, it evaluates whether the cleaning record satisfies the cleaning request and / or to what extent the cleaning request is satisfied. For example, in a certain cleaning area, if the cleaning request requests that the first cleaning action be performed for 5 minutes or more and the estimated first cleaning record time is 4 minutes, the feedback unit 31d generates feedback that the cleaning request is not satisfied and that the satisfaction level with the cleaning request is 80%.

[0068] Next, the feedback unit 31d transmits the feedback to the speaker 3 via the data transmission unit 34. The feedback is notified to the user via the speaker 3.

[0069] Next, with reference to the flowchart shown in Fig. 5, a flow of cleaning support by the cleaning support system 200 when cleaning an exemplary room 230 to be cleaned shown in Fig. 6 will be described. First, the room 230 will be described. For convenience of explanation, the left-right direction in Fig. 6 will be referred to as the X direction, the up-down direction as the Y direction, and more specifically, the right side will be referred to as X1, the left side as X2, the upper side as Y1, and the lower side as Y2.

[0070] Referring to Figure 6, room 230 to be cleaned is a substantially square room with floor 231 made of wood and surrounded by wall 232. Door 233 is provided at the end of the wall on the X2 side of room 230 on the Y1 side. Room 230 is an 8-tatami mat room. Room 230 is a bedroom.

[0071] In the room 230, a desk 235 and a chair 236 are arranged on the Y1 side, and a bed 237 is arranged on the Y2 side. The desk 235 is arranged so as to be in approximate contact with the wall surfaces 232 on the X1 and Y2 sides, and extends horizontally in the Y direction. The chair 236 is arranged on the X2 side of the desk 235. The bed 237 is arranged so as to be in approximate contact with the wall surfaces 232 on the X1 and Y2 sides so as to have a gap in the X direction from the desk 235, and extends horizontally in the X direction. On the other hand, the bed 237 is spaced apart from the wall surface 232 on the X2 side. The desk 235, chair 236, and bed 237 are placed on a floor surface 231 via a plurality of legs (not shown), and there is a space between them and the floor surface 231.

[0072] Therefore, room 230 includes the following cleaning areas: a first cleaning area S1 which is approximately rectangular and defined on the X2 side of desk 235 and chair 236 and on the Y1 side of bed 237; a second cleaning area S2 which is defined between desk 235 and bed 237 and extends in the Y direction; a third cleaning area S3 which is approximately rectangular and defined between bed 237 and wall surface 232 on the X2 side; a fourth cleaning area S4 which is defined below desk 235; a fifth cleaning area S5 which is defined below chair 236; and a sixth cleaning area S6 which is defined below bed 237.

[0073] Next, the flow of cleaning support by the cleaning support system 200 will be described with reference to Fig. 5. First, cleaning location information, which is information related to cleaning of the room 230 to be cleaned, is input to the server 30 via the input unit 36 ​​(S201). For example, the cleaning location information includes one or more of the shape and size of the room 230, the type, shape, size, arrangement, presence or absence of gaps around and at the bottom of each piece of furniture arranged in the room 230, the cleaning location in the room 230 (floor, wall, etc.), and the cleaning tools to be used for cleaning.

[0074] Next, the cleaning request generator 31a of the server 30 generates a cleaning request based on the cleaning location information (step S202). In this embodiment, the cleaning request generator 31a divides the room 230 into first to sixth cleaning areas S1 to S6 based on the cleaning location information, and assigns cleaning actions to each of the first to sixth cleaning areas S1 to S6 for a time period according to the size of the area.

[0075] Specifically, the cleaning request generator 31a assigns the first cleaning action to the first cleaning area S1, which has a linear portion extending across a predetermined width (for example, the width in the longitudinal direction of the wiping part 2b of the floor mop 2), for a time period corresponding to the size of the area. The cleaning request generator 31a assigns the second cleaning action to the fourth to sixth cleaning areas S4 to S6, which are the areas below the desk 235, chair 236, and bed 237, for a time period corresponding to the size of each area. The cleaning request generator 31a assigns the third cleaning action to the second and third cleaning areas S2 and S3, which are the areas around each piece of furniture, for a time period corresponding to the size of each area.

[0076] Table 4 below shows examples of cleaning requests generated by the cleaning request generator 31a for the room 230, i.e., cleaning actions and cleaning times for each cleaning area, and the total requested cleaning time obtained by adding up each cleaning time. The memory unit 32 stores the generated cleaning requests (step S203).

[0077] [Table 4]

[0078] Next, the user 90 operates the start switch 13 of the cleaning operation acquisition unit 10 to start cleaning (step S204). When cleaning starts, the acceleration sensor 11 starts measuring acceleration and the beacon receiver 211 starts receiving beacon signals (signals for position measurement) (step S205), and the measured acceleration and the signal reception results of the beacon signals are transmitted to the server 30 via the data transmission units 12 and 212, respectively (step S206).

[0079] Next, the server 30 receives the transmitted acceleration data and signal reception results via the data receiving unit 33 (step S207). The received acceleration data and signal reception results are processed by the server 30. Specifically, the cleaning behavior estimation unit 31b estimates which of the first to third cleaning behaviors the cleaning behavior of the user 90 corresponds to based on the received acceleration data using the learning device 35. Furthermore, the cleaning position estimation unit 31e estimates which of the first to sixth areas is being cleaned based on the received signal results (step S208). The storage unit 32 stores the estimated cleaning behavior and cleaning area (step S209).

[0080] Thereafter, steps S205 to S209 are repeatedly performed until the user 90 operates the end switch 14 of the cleaning action acquisition unit 10 to end the cleaning. Therefore, the memory unit 32 stores continuous data of the estimated cleaning behavior and cleaning area, i.e., the cleaning behavior and cleaning area at each time from the start to the end of cleaning.

[0081] Next, when the user 90 operates the end switch 14 to end the cleaning (step S211), the cleaning action evaluation unit 31c of the server 30 reads out the cleaning action and cleaning area from the memory unit 32, and calculates, for each cleaning area, a first actual cleaning time which is the total time for which the first cleaning action was performed, a second actual cleaning time which is the total time for which the second cleaning action was performed, and a third actual cleaning time which is the total time for which the third cleaning action was performed, based on the continuous data of the estimated cleaning action and cleaning area, and calculates a cleaning action for each cleaning area, including a total actual cleaning time which is the total of the first to third actual cleaning times (step S212). The calculated cleaning actions are stored in the memory unit 32.

[0082] Next, the feedback unit 31d reads out the cleaning request and the cleaning record from the storage unit 32, compares the cleaning request with the cleaning record for each cleaning area, and generates an evaluation result that evaluates the cleaning behavior of the user 90 (step S212).

[0083] For example, the feedback unit 31d evaluates the achievement rate for the first cleaning area S1 based on the cleaning request and cleaning performance, as shown in the following Table 5. Although not explained further, the feedback unit 31d similarly evaluates the other cleaning areas.

[0084] [Table 5]

[0085] Next, the feedback unit 31d generates feedback to be returned to the user 90 based on the evaluation result, and outputs the feedback to the speaker 3 via the data transmission unit 34 (step S213). For example, the feedback may be generated for each cleaning area, or may be generated collectively for the room 230. The speaker 3 notifies the user of the feedback by voice (step S214).

[0086] In the above embodiment, the position of the floor mop 2 is detected using a beacon signal. However, GPS may also be used. The position of the floor mop 2 may also be detected based on still images and / or video captured by an imaging device such as the camera 4 shown by the two-dot chain line in FIG. 4 . Alternatively, light detection and ranging (LiDAR) may be used for detection and ranging. For example, a LiDAR installed in the room 230 may irradiate the floor mop 2 with light and observe the light reflected from the floor mop 2, thereby detecting the position of the floor mop 2. Detection and ranging based on Wi-Fi (registered trademark) signal strength (RSSI and / or CSI) may also be used. For example, a Wi-Fi transmitter and receiver may be installed in the room 230, and when the floor mop 2 is cleaning between them, AI trained on machine learning based on the position of the floor mop 2 and the waveform received by the receiver may be used to detect the position of the floor mop 2 based on the waveform received by the receiver. Alternatively, for example, a Wi-Fi transmitter may be provided in room 230 and a Wi-Fi receiver may be provided in floor mop 2, and the position of floor mop 2 in room 230 may be detected based on the reception strength of the Wi-Fi signal at floor mop 2.

[0087] The cleaning support system 200 of this embodiment further includes a cleaning position acquisition unit 210 that acquires the position of the floor mop 2, and a cleaning position estimation unit 31e that estimates the cleaning position by the user 90 based on the position of the floor mop 2 acquired by the cleaning position acquisition unit 210, and the cleaning request further includes a request regarding the cleaning area, and the feedback unit 31d further returns feedback regarding the cleaning area. As a result, feedback regarding the cleaning area is also returned to the user 90, and suggestions regarding the cleaning area can be given to the user 90, thereby suppressing the occurrence of areas that are left uncleaned. [Third embodiment]

[0088] 7 shows a schematic configuration of a cleaning support system 300 according to the third embodiment. The cleaning support system 300 differs from the cleaning support system 200 according to the second embodiment in that it projects an image onto a cleaned area.

[0089] Specifically, the cleaning support system 300 differs from the cleaning support system 200 according to the second embodiment in that it additionally includes a projector 310 and a cleaned location display unit 31f that outputs a signal to the projector 310 to cause the processor 31 to project an image on the cleaned location. The cleaned location display unit 31f outputs a signal to the projector 310 to display a color for the cleaned location read from the memory unit 32.

[0090] 8, the flow of cleaning support by cleaning support system 300 will be described. Steps S301 to S309 and S311 to S314 are the same as steps S201 to S209 and S211 to S214 in cleaning support system 200, and therefore will not be described again. In step S310, cleaned location display unit 31f reads out the locations wiped by floor mop 2 from memory unit 32, and outputs to projector 310 so that colors are projected sequentially onto the locations.

[0091] 9 conceptually illustrates a state in which colors are projected from projector 310 onto an area wiped by user 90 using floor mop 2. As indicated by hatching in FIG. 9, images are projected sequentially (in real time) onto first cleaning area S1 as user 90 wipes with floor mop 2. For example, the color projected from projector 310 may be changed depending on the number of wipes, such as projecting red for the first wipe, yellow for the second wipe, and blue for the third wipe. Alternatively, instead of projecting colors from projector 310, an image including a pattern may be projected.

[0092] The cleaning assistance system 300 according to this embodiment includes a projector that projects colors and / or images onto the cleaned area. As a result, the user 90 is given feedback on the areas that have been cleaned, making it easy to visually identify areas that remain uncleaned. This allows the user 90 to be given a hint as to areas that have not yet been cleaned and need to be cleaned, further reducing the occurrence of uncleaned areas. Furthermore, by changing the projected color depending on the number of wipes, it becomes easier to reliably perform multiple wipes even in areas that require multiple wipes, thereby supporting the cleaning behavior of the user 90.

[0093] In the above embodiments, a floor mop 2 is used as an example of a cleaning tool, and cleaning actions using the floor mop 2 are classified into first to third cleaning actions. However, other cleaning actions using the floor mop 2 may be added. For example, when wiping in an S-shape using the floor mop 2, cleaning actions such as sweeping may be included. Furthermore, the present invention can be applied to various cleaning tools other than the floor mop 2 (for example, a handy mop, a broom, an electric vacuum cleaner, etc.). Even in this case, cleaning actions corresponding to each cleaning tool may be set.

[0094] Furthermore, multiple cleaning actions may be assigned to one cleaning area, such as a first cleaning action, a second cleaning action, etc. In this case, for example, the total cleaning performance time obtained by adding up the cleaning performance of the multiple cleaning actions may be evaluated in light of the cleaning request.

[0095] Furthermore, although the above embodiments have been described using an example in which only one type of cleaning tool (floor mop 2) is used, the present invention can also be applied to cases in which multiple cleaning tools are used for cleaning. In this case, the acceleration data (and position data) detected for each cleaning tool can be sent to server 30 along with a signal that can identify each cleaning tool, and server 30 can analyze the cleaning motion (and position) for each cleaning tool to estimate the cleaning behavior. In this case, cleaning requests can be set for each cleaning tool, and evaluation and feedback of cleaning behavior can be provided for each cleaning tool.

[0096] That is, the cleaning request may include at least one or more cleaning tools and the cleaning time for each cleaning tool, and the cleaning behavior estimation unit 31b may be configured to estimate the cleaning time for each cleaning tool, and the feedback unit 31d may be configured to further return feedback regarding the cleaning time for each cleaning tool. As a result, feedback is returned to the user 90 regarding the cleaning time for each cleaning tool, so that more detailed suggestions regarding cleaning behavior can be given to the user 90.

[0097] In addition, in the above embodiments, the feedback unit 31d evaluates each cleaning action in terms of cleaning time. However, instead of or in addition to this, the feedback unit 31d may be configured to evaluate the quality of the cleaning. For example, the appropriateness of the cleaning may be determined based on acceleration data. For example, if the acceleration exceeds a predetermined threshold, it may be assumed that the cleaning is undesirable, such as if dust is being kicked up. In addition, if an impactful acceleration is detected, it may be assumed that the cleaning tool is colliding with a fixture or the like. In this case, the feedback unit 31d may be configured to prompt the user 90 to wipe and clean at an appropriate speed or to provide feedback to be careful not to collide with fixtures.

[0098] That is, the cleaning request includes at least one or more cleaning actions and the cleaning quality for each cleaning action, the cleaning action estimation unit 31b estimates the cleaning quality for each cleaning action, and the feedback unit 31d may further return feedback regarding the cleaning quality. As a result, feedback on the quality of the cleaning is returned to the user 90, and it is possible to give the user 90 suggestions for higher quality cleaning behavior.

[0099] In addition, in the above-described embodiments, the cleaning action acquisition unit 10 is attached to a cleaning tool, but this is not limiting. For example, an acceleration sensor or the like mounted on a smart device such as a smartphone worn by the user 90 may be used instead.

[0100] In addition, in each of the above embodiments, an example has been described in which the acceleration sensor 11 is used as the cleaning action acquisition unit 10, but instead of or in addition to the acceleration sensor 11, a gyro sensor may be used to measure angular velocity, and the cleaning action may be estimated based on the angular velocity.

[0101] 1, 4, and 7, the cleaning behavior may be estimated based on still images and / or videos captured by an image capturing unit such as a camera 4 instead of or in addition to the acceleration sensor 11. In this case, the learner 35 may be machine-learned from training data including angular acceleration data or still images and / or videos and corresponding cleaning behaviors, and may be modeled to estimate the cleaning behavior based on the angular acceleration data or still images and / or videos.

[0102] In addition, in each of the above embodiments, the feedback is provided via the speaker 3. However, instead of or in addition to the speaker 3, the feedback may be provided by displaying an image on the screen of a smart device worn by the user 90, or by generating vibrations from the cleaning tool or the smart device. When the feedback is provided by vibration, for example, when a cleaning request is satisfied, the cleaning tool or the smart device may vibrate. As a result, feedback on cleaning can be easily given to the user 90.

[0103] In addition, in each of the above embodiments, the server 30 is configured as a computer separate from the cleaning action acquisition unit 10, but they may be integrated. For example, the cleaning action acquisition unit 10 and the above functions of the server 30 may be implemented in a smart device worn by the user 90. Furthermore, the cleaning action acquisition unit 10 may be attached to a cleaning tool or implemented in a smart device worn by the user 90, while the above functions of the server 30 may be implemented in a smart device worn by the user 90 that is separate from the cleaning action acquisition unit 10.

[0104] In each of the above embodiments, the cleaning request generator 31a is configured to generate a cleaning request based on information input about the room to be cleaned, but this is not limited to this. The user 90 may also set a cleaning request. [Explanation of symbols]

[0105] 1. Cleaning support system 2 Cleaning equipment (floor mop) 3 speakers 4. Camera 10 Cleaning operation acquisition section 11 Acceleration sensor 12 Data transmission unit 30 servers 31 Processing unit 31a Cleaning request generator 31b Cleaning behavior estimation part 31c Cleaning Behavior Evaluation Department 31d Feedback section 31e Cleaning position estimation part 31F Cleaned area display 32 Storage section 35 Learning Units 36 Input section 90 users (cleaners) 200 Cleaning Support System 210 Cleaning position acquisition section 211 Beacon Receiver 220 Beacon Transmitter 300 Cleaning Support System 310 Projector

Claims

1. A cleaning request storage unit in which cleaning requests, which are requests regarding cleaning actions, are stored; a cleaning action acquisition unit that acquires the action of the cleaning tool being used for cleaning; a learning device that has undergone machine learning based on training data including cleaning tool movements and corresponding cleaning actions of the user to estimate the cleaning actions of the user from the cleaning tool movements acquired by the cleaning action acquisition unit; a cleaning behavior estimation unit that estimates the cleaning behavior of the user based on the cleaning tool behavior acquired by the cleaning behavior acquisition unit using the learning device; a feedback unit that provides feedback to the user about the estimated cleaning behavior of the user in light of the cleaning request, The cleaning request includes at least one or more cleaning actions and a cleaning time for each of the cleaning actions; the cleaning behavior estimation unit estimates a cleaning time for each cleaning behavior; The feedback unit returns feedback regarding cleaning time for each cleaning action.

2. A cleaning request storage unit in which cleaning requests, which are requests regarding cleaning actions, are stored; a cleaning action acquisition unit that acquires the action of the cleaning tool being used for cleaning; a learning device that has undergone machine learning based on training data including cleaning tool movements and corresponding cleaning actions of the user to estimate the cleaning actions of the user from the cleaning tool movements acquired by the cleaning action acquisition unit; a cleaning behavior estimation unit that estimates the cleaning behavior of the user based on the cleaning tool behavior acquired by the cleaning behavior acquisition unit using the learning device; a feedback unit that provides feedback to the user about the estimated cleaning behavior of the user in light of the cleaning request, The cleaning request includes at least one or more cleaning tools and a cleaning time for each of the cleaning tools; the cleaning behavior estimation unit estimates a cleaning time using the cleaning tool; The feedback unit returns feedback regarding cleaning time using the cleaning tool.

3. A cleaning request storage unit in which cleaning requests, which are requests regarding cleaning actions, are stored; a cleaning action acquisition unit that acquires the action of the cleaning tool being used for cleaning; a learning device that has undergone machine learning based on training data including cleaning tool movements and corresponding cleaning actions of the user to estimate the cleaning actions of the user from the cleaning tool movements acquired by the cleaning action acquisition unit; a cleaning behavior estimation unit that estimates the cleaning behavior of the user based on the cleaning tool behavior acquired by the cleaning behavior acquisition unit using the learning device; a feedback unit that provides feedback to the user about the estimated cleaning behavior of the user in light of the cleaning request, The cleaning request includes at least one or more of the cleaning actions and a cleaning quality for each of the cleaning actions; the cleaning behavior estimation unit estimates a cleaning quality for each cleaning behavior; The feedback unit returns feedback regarding the quality of the cleaning.

4. a cleaning position acquisition unit that acquires the position of the cleaning tool; a cleaning position estimation unit that estimates a cleaning position to be performed by the user based on the position of the cleaning tool acquired by the cleaning position acquisition unit; Furthermore, The cleaning request further includes a request for a cleaning area; The feedback unit further provides feedback about the cleaning area. The cleaning support system according to any one of claims 1 to 3.

5. the cleaning action acquisition unit includes an acceleration sensor capable of measuring the acceleration of the cleaning tool and / or a gyro sensor capable of measuring the angular velocity of the cleaning tool, the cleaning action acquisition unit acquires the measured acceleration and / or angular velocity of the cleaning tool as a value representing the action of the cleaning tool; the learning device estimates the cleaning behavior of the user based on the acceleration and / or angular velocity of the cleaning tool measured by the cleaning behavior acquisition unit; The cleaning support system according to any one of claims 1 to 4.

6. The cleaning action acquisition unit is attached to the cleaning tool. The cleaning support system according to any one of claims 1 to 5.

7. The cleaning action acquisition unit is attached to the center of gravity of the cleaning tool. The cleaning support system according to claim 6.

8. the cleaning operation acquisition unit includes an image capture unit capable of capturing still images and / or videos of the cleaning tool, the cleaning action acquisition unit acquires a still image and / or a video of the captured cleaning tool as an image representing the action of the cleaning tool; the learning device estimates the cleaning behavior of the user based on a still image and / or a video of the cleaning tool captured by the cleaning behavior acquisition unit; The cleaning support system according to any one of claims 1 to 7.

9. the feedback unit returns the feedback by sound, image display, and / or vibration. The cleaning support system according to any one of claims 1 to 8.

10. a projector that projects colors and / or images onto the cleaned area; A cleaning support system according to claim 4 and any one of claims 5 to 9 dependent on claim 4.

11. storing a cleaning request, which is a request for a cleaning action; Obtain the behavior of the cleaning tools used for cleaning, using a learning device that has undergone machine learning to estimate the cleaning behavior of the user from the acquired cleaning tool movements based on training data including cleaning tool movements and corresponding user cleaning behaviors; providing feedback to the user based on the estimated cleaning behavior of the user in light of the cleaning request; The cleaning request includes at least one or more cleaning actions and a cleaning time for each of the cleaning actions; estimating the cleaning behavior includes estimating a cleaning time for each of the cleaning behaviors; The cleaning assistance method, wherein the feedback includes feedback regarding cleaning time for each cleaning action.

12. Storing cleaning requests, which are requests regarding cleaning actions; Obtain the behavior of the cleaning tools used for cleaning, using a learning device that has undergone machine learning to estimate the cleaning behavior of the user from the acquired cleaning tool movements based on training data including cleaning tool movements and corresponding user cleaning behaviors; providing feedback to the user based on the estimated cleaning behavior of the user in light of the cleaning request; The cleaning request includes at least one or more cleaning tools and a cleaning time for each of the cleaning tools; estimating the cleaning behavior includes estimating a cleaning time using the cleaning tool; The cleaning assistance method, wherein the feedback includes feedback regarding cleaning time using the cleaning tool.

13. Storing cleaning requests, which are requests regarding cleaning actions; Obtain the behavior of the cleaning tools used for cleaning, using a learning device that has undergone machine learning to estimate the cleaning behavior of the user from the acquired cleaning tool movements based on training data including cleaning tool movements and corresponding user cleaning behaviors; providing feedback to the user based on the estimated cleaning behavior of the user in light of the cleaning request; The cleaning request includes at least one or more of the cleaning actions and a cleaning quality for each of the cleaning actions; estimating the cleaning behavior includes estimating a cleaning quality for each of the cleaning behaviors; The cleaning assistance method, wherein the feedback includes feedback regarding the quality of the cleaning.

Images