Notification device and notification method

Abstract

To provide a notification device capable of allowing a user to easily comprehend the improving method of operating equipment.SOLUTION: The notification device includes: a separation unit 103 that separates an evaluation value indicating the performance of a monitored device 2 into a factor degree corresponding to multiple explanatory variables based on the acquired operating information, based on the operation information of the monitored device 2; and an output processing unit 106 that outputs the factor degree. The factor level is the degree of impact that the operation of a user has on the performance of the monitored device 2. The separation unit 103 outputs a direct factor degree resulting from sensing of a user's operation on the monitored device 2 and the degree of indirect factor caused by the secondary consequences of the actions of the user as the factor degree.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a notification device and a notification method. [Background technology]

[0002] Home appliances that are used daily in individual households include refrigerators, washer-dryers, vacuum cleaners, air conditioners, water heaters, etc. These products operate constantly or periodically, and as they are used, their evaluation values, such as the amount of power consumed and the degree of deterioration of the product, change for various reasons. For example, if the refrigerator door is opened and closed frequently, the temperature inside the refrigerator rises, and power consumption increases. Also, if the filter for the drying operation of a washer-dryer becomes dirty, the resistance of the air passage increases, and power consumption also increases.

[0003] In other words, power consumption increases due to user behavior and performance changes that progress with repeated use. These can be improved by reducing door opening and closing and regularly cleaning the filters, but because the usage and maintenance of home appliances are up to the user, presenting concrete benefits is an effective way to encourage behavior. However, there is a challenge in that it is difficult to clarify the correspondence between user behavior and the improvement in the evaluation value.

[0004] In response to this, Patent Document 1 discloses an energy-saving action evaluation device and method which "includes an equipment information measurement unit which takes in equipment information measurement data which indicates the power consumption of the equipment, the intensity of use of the equipment, and the frequency of use of the equipment, an equipment information measurement value database which records the equipment information measurement value data, an equipment information aggregation unit which aggregates the recorded equipment information measurement value data by evaluation unit, an equipment information database which records the aggregated data, an energy-saving action evaluation unit which obtains from the equipment information database the intensity and frequency of use of the equipment and the power consumption for the period to be evaluated and the intensity and frequency of use and the power consumption of the equipment for the period to be compared, calculates the difference and ratio between them, and evaluates the degree of implementation and the effects of the energy-saving action, an evaluation result database which records the results of evaluating the degree of implementation and the effects of the energy-saving action, and an information display unit which displays the evaluation results of the recorded energy-saving actions" (see abstract). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2010-146387 A Summary of the Invention [Problem to be solved by the invention]

[0006] In the technology described in Patent Document 1, actions that can be directly sensed as parameters of frequency of use are assumed, and changes in power consumption according to the actions are reflected in the intensity of use. For example, when opening and closing a refrigerator door, opening the door and putting in hot food and putting in cold drinking water are both considered to be the same frequency of use, once. Therefore, changes in power consumption caused by differences in food temperature are converted into the same change in intensity of use.

[0007] Here, the intensity of use includes the amount of power consumption caused by the performance of the device itself, which is not related to the user's actions. Therefore, with the technology described in Patent Document 1, it is difficult to clearly present the change in the amount of power consumption caused by storing high-temperature food. In other words, the technology described in Patent Document 1 has a problem in that it is difficult to quantify the degree of impact of events that occur as a by-product of the user's actions.

[0008] The present invention has been made in view of the above background, and an object of the present invention is to enable a user to easily grasp a method for improving the operation of a device. [Means for solving the problem]

[0009] In order to solve the above-mentioned problems, the present invention comprises a separation unit that separates, based on operation information about a monitored device, an evaluation value indicating the performance of the monitored device into factor degrees corresponding to a plurality of explanatory variables based on the acquired operation information, and an output processing unit that outputs the factor degrees, wherein the factor degrees are the degree of effect that a user's operation has on the performance of the monitored device. Other solutions will be described in the embodiments as appropriate. Effect of the Invention

[0010] According to the present invention, it is possible to easily grasp how to improve the operation of a device. [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram showing a configuration of a notification device according to a first embodiment. [Diagram 2] 1 is a diagram illustrating a hardware configuration of a notification device according to an embodiment of the present invention. [Diagram 3] 4 is a flowchart showing a procedure of a process performed by the notification device according to the first embodiment. [Figure 4] FIG. 13 is a diagram showing the results of separating evaluation values ​​by factors. [Diagram 5] FIG. 4 is a diagram showing an example of a display screen that displays a result performed in the first embodiment. [Figure 6] FIG. 11 is a diagram illustrating a configuration of a notification device according to a second embodiment. [Figure 7] 10 is a flowchart showing a procedure of a process performed by a notification device according to the second embodiment. [Figure 8] The results of cooling anomalies separated by cause are shown below. [Figure 9] FIG. 11 is a diagram showing an example of a display screen that displays a result obtained in the second embodiment. [Figure 10] FIG. 13 is a diagram illustrating a configuration of a notification device in a third embodiment. [Figure 11] 13 is a flowchart showing a procedure of a process performed by a notification device according to the third embodiment. [Figure 12] The results show that the drying time, which is the evaluation value, is separated by factor. [Figure 13] FIG. 13 is a diagram showing an example of a display screen that displays a result obtained in the third embodiment. [Figure 14] FIG. 13 is a diagram showing the configuration of a notification device according to a fourth embodiment. [Figure 15]13 is a flowchart showing a procedure of a process performed by a notification device according to a fourth embodiment. [Figure 16] The results show that the power consumption, which is the evaluation value, is separated by factor. [Figure 17] FIG. 13 is a diagram (part 1) showing an example of a display screen that displays a result performed in the fourth embodiment. [Figure 18] FIG. 13 is a diagram (part 2) showing an example of a display screen for displaying results in the fourth embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Next, a mode for carrying out the present invention (referred to as an "embodiment") will be described in detail with reference to the drawings as appropriate.

[0013] [First embodiment] First, a first embodiment of the present invention will be described with reference to Figures 1 to 5. In the first embodiment, the concept of a notification device 1 will be described.

[0014] 1 is a diagram showing the configuration of a notification device 1 according to the first embodiment. In the first embodiment, the notification device 1 is shown which diagnoses the normality of a monitored device 2, separates the diagnosis results by cause, and notifies a user. The notification device 1 shown in Fig. 1 monitors a monitoring target device 2, and is composed of an output unit 101, a storage unit 102, an evaluation unit 104, a separation unit 103, a communication unit 105, and an output processing unit 106 (cost calculation unit). The notification device 1 can also transmit and receive information to and from a terminal 3. The terminal 3 is a smartphone, a tablet terminal, a PC (Personal Computer), or the like.

[0015] (Monitored device 2) The monitored device 2 is a device that is to be monitored by the notification device 1. Specific examples of the monitored device 2 will be shown in the second and subsequent embodiments. The monitored device 2 is provided with a plurality of sensors 251 for acquiring information on the external and internal environments of the monitored device 2. Specific examples of the information on the external and internal environments will be shown in the second and subsequent embodiments.

[0016] (Output unit 101) The output unit 101 is provided in the notification device 1, and outputs the result of the evaluation value calculation by the evaluation unit 104, the cause separation result by the separation unit 103, etc. However, the output unit 101 is not an essential component.

[0017] (Storage unit 102) The storage unit 102 records an evaluation value of the monitored device 2 calculated by the evaluation unit 104. The storage unit 102 also records control information 201 obtained from the control device 21 and information obtained from the sensor 251 as operation information. The control information 201 includes control information for the drive source. The storage unit 102 can also record information sent from the terminal 3 as operation information.

[0018] (Separation part 103) The separation unit 103 executes a process of dividing the evaluation value into factor degrees corresponding to the explanatory variables using the operation information accumulated in the storage unit 102. The factor degrees indicate the degree of influence that the user's operation has on the performance of the monitored device 2. The separation unit 103 and the evaluation unit 104 may be executed by the same computer.

[0019] (Evaluation unit 104) The evaluation unit 104 calculates an evaluation value of the monitored device 2 using the control information 201 of the monitored device 2 generated by the control device 21, the control information of the drive source of the monitored device 2 acquired by the sensor 251, and the like. Specific examples of the evaluation value will be shown in the second and subsequent embodiments.

[0020] (Communication unit 105) The communication unit 105 acquires information from the terminal 3 and records it in the storage unit 102 , and transmits display screen information generated by the output processing unit 106 to the terminal 3 .

[0021] (Output processing unit 106) The output processing unit 106 generates display screen information to be output to the terminal 3 or the output unit 101. Furthermore, the output processing unit 106 calculates the cost and the like to be displayed on the display screen 300 (see FIG. 5). The cost is the cost required for the user to execute the improvement action plan.

[0022] <Hardware configuration> Fig. 2 is a diagram showing a hardware configuration of the notification device 1 according to this embodiment, with reference to Fig. 1 as necessary. The notification device 1 has a memory 111 such as a random access memory (RAM). Furthermore, the notification device 1 has a calculation device 112 configured with a central processing unit (CPU), a graphic processing unit (GPU), etc. The notification device 1 also has a storage device 113 configured with a hard disk drive (HDD), a solid state drive (SSD), etc. The notification device 1 also has a communication device 114 and an output device 115 configured with a display, etc. The notification device 1 also has an input device 116 configured with a keyboard, a mouse, a touch panel display, etc.

[0023] Furthermore, a program is stored in the storage device 113, and this program is loaded into the memory 111. The loaded program is then executed by the calculation device 112, whereby the separation unit 103, the evaluation unit 104, and the output processing unit 106 are realized.

[0024] 1 corresponds to the storage device 113, and the communication unit 105 corresponds to the communication device 14. Moreover, the output device 115 corresponds to the output unit 101 in FIG.

[0025] <Processing> Next, the process (notification method) performed by the notification device 1 will be described with reference to FIG. 3 and FIG. Fig. 3 is a flowchart showing the procedure of the process performed by the notification device 1 according to the first embodiment. Fig. 4 is a diagram showing the results of the evaluation values ​​separated by factors. Fig. 1 will be referred to as appropriate. First, the evaluation unit 104 acquires operation information regarding the monitored device 2 (S101 in FIG. 3). The operation information is information that affects the operation of the monitored device 2. The evaluation unit 104 performs calculations based on the control information 201 as necessary, and stores the information in the storage unit 102. The separation unit 103 acquires the evaluation value and the control information 201, the first information 202, the second information 203, the third information 204, and the fourth information 205 included in the operation information from the storage unit 102. The control information 201 is information acquired directly from the monitored device 2, and is control information of a drive source that provides a predetermined function to the monitored device 2. In other words, the operation information includes at least the control information 201. The first information 202, the second information 203, the third information 204, and the fourth information 205 are information acquired from the sensor 251 and the terminal 3. The acquired operation information is stored in the storage unit 102. In the first embodiment, the first information 202 to the fourth information 205 are acquired, but it is sufficient that two or more pieces of information are acquired.

[0026] Then, based on the operation information, the separation unit 103 executes a separation process to separate the evaluation value indicating the performance of the monitored device 2 into a plurality of explanatory variable values ​​based on the acquired operation information (S102 in FIG. 3: separation step). The evaluation value may be obtained by calculation from the control information 201, or the control information 201 itself may be used as the evaluation value. In other words, the evaluation value is calculated based on the control information of the drive source. In this way, the evaluation value indicating the performance of the monitored device 2 can be quantitatively calculated.

[0027] In step S102, the separation unit 103 separates the evaluation value into the values ​​of the multiple explanatory variables by multiple regression analysis (separation process). Specifically, the separation unit 103 calculates a factor-specific coefficient related to each explanatory variable by multiple regression analysis using the operation information stored in the storage unit 102. At this time, the separation unit 103 executes the separation process using past operation information and operation information immediately after acquired in step S101. At this time, the values ​​of the first information 202, the second information 203, the third information 204, the fourth information 205, and so on shown in FIG. 4 are set as explanatory variables of the multiple regression analysis. In addition, an evaluation value is set as the objective variable of the multiple regression analysis. The evaluation value is the value of the control information 201 itself, an evaluation value calculated based on the control information 201, and so on. Specifically, the separation unit 103 calculates a factor-specific coefficient related to each explanatory variable by multiple regression analysis using the operation information stored in the storage unit 102 based on the following formula (1). Methods of multiple regression analysis that can be selected include optimization calculations such as gradient methods and machine learning using neural networks.

[0028] Evaluation score = 1st factor coefficient x 1st factor + 2nd factor coefficient x 2nd factor + 3rd factor coefficient x 3rd factor + 4th factor coefficient x 4th factor...(1)

[0029] In formula (1), the first factor is a factor (explanatory variable) based on the first information 202. The second factor is a factor based on the second information 203. The third factor is a factor based on the third information 204. And the fourth factor is a factor based on the fourth information 205. Specific examples of the first factor, the second factor, the third factor, and the fourth factor will be shown in the second embodiment and thereafter. In formula (1), the evaluation value is separated into four factors, the first factor to the fourth factor, but it may be separated into three or less factors, or five or more factors. The first factor to the fourth factor correspond to explanatory variables.

[0030] Then, the separating unit 103 calculates factor degrees such as the first factor degree F1, the second factor degree F2, the third factor degree F3, the fourth factor degree F4, etc., shown in Fig. 4 based on the result of the multiple regression analysis (S103). In this way, the separating unit 103 separates the evaluation value into factor degrees corresponding to a plurality of explanatory variables based on the acquired operation information, by the multiple regression analysis.

[0031] The first factor degree F1 corresponds to the first factor coefficient x the first factor (value of explanatory variable) in formula (1). Similarly, the second factor degree F2 corresponds to the second factor coefficient x the second factor (value of explanatory variable) in formula (1). The third factor degree F3 corresponds to the third factor coefficient x the third factor (value of explanatory variable) in formula (1). The fourth factor degree F4 corresponds to the fourth factor coefficient x the fourth factor (value of explanatory variable) in formula (1). The calculated factor degrees are not limited to four.

[0032] Furthermore, the output processing unit 106 calculates the cost based on the first factor degree F1 to the fourth factor degree F4 (S104 in FIG. 3). In step S104, the output processing unit 106 calculates the cost required to execute the improvement action plan based on the first factor degree F1 to the fourth factor degree F4. Then, the output processing unit 106 outputs the first factor degree F1, the second factor degree F2, the third factor degree F3, the fourth factor degree F4, and the cost calculated in step S104 (S105 in FIG. 3: output processing step). In step S105, the output processing unit 106 outputs the factor degrees of the abnormality of the monitored device 2 based on the values ​​of the respective explanatory variables.

[0033] In Fig. 4, the horizontal axis is time and the vertical axis is the first evaluation value. The first evaluation value is the evaluation value in formula (1). In the stacked line graph in Fig. 4, the first evaluation value is separated into the first factor degree F1, the second factor degree F2, the third factor degree F3, and the fourth factor degree F4, and is displayed as the accumulation of each factor degree value.

[0034] The separation unit 103 separates the second cause degree F2 to the fourth cause degree F4 into a direct cause degree and an indirect cause degree. The direct cause degree is caused by the result of sensing the operation of the user on the monitoring target device 2. The indirect cause degree is caused by the result that occurs secondarily from the user's behavior. In the first embodiment, the direct cause degree is the second cause degree F2, and the indirect cause degree is the third cause degree F3 and the fourth cause degree F4. Which cause degree is the direct cause degree and which cause degree is the indirect cause degree is set in advance by the user. However, the indirect cause degree does not necessarily have to be extracted. Similarly, the direct cause degree does not necessarily have to be output. Also, the first cause degree F1 is caused by the performance of the monitoring target device 2 itself. In this way, the separation unit 103 extracts the main body cause degree caused by the performance of the monitoring target device 2 itself from the cause degrees.

[0035] <Display screen 300> FIG. 5 is a diagram showing an example of a display screen 300 that displays the results performed in the first embodiment. An example of a notification by the output unit 101 based on the characteristics of the four factor levels shown in Fig. 4 will be described with reference to Fig. 5. Fig. 1 will be referred to as appropriate. 5 shows a case where a diagnosis result by the notification device 1 is notified on the display screen 300 of the terminal 3. Three types of information are output on the display screen 300: an evaluation value information column 310, an action comparison information column 320, and an action suggestion information column 330. The display screen 300 may be output to the output unit 101 in FIG. 1. Each piece of information displayed on the display screen 300 is generated and output by the output processing unit 106.

[0036] The evaluation value information column 310 shows, in a bar graph, the time series change of the second evaluation value, which is the result of accumulating the second factor degree F2, the third factor degree F3, and the fourth factor degree F4 in FIG. 4. The second evaluation value is obtained by subtracting the value of the first factor degree F1 from the first evaluation value shown in FIG. 4. That is, the output processing unit 106 outputs the factor degrees excluding the first factor degree F1, which is the main body factor degree, to the evaluation value information column 310. The reason why the first factor degree F1, which is the main body factor degree, is not displayed in the evaluation value information column 310 is that the main body factor degree is difficult to improve by the user's actions. According to the example shown in FIG. 5, the user can confirm that the second evaluation value has decreased from time A to time B. However, as in a fourth embodiment described later, the main body factor degree may be displayed in the evaluation value information column 310. In addition, in the evaluation value information column 310, the second factor degree F2, which is the direct factor degree, is shown by a white bar graph, and the third factor degree F3 and the fourth factor degree F4, which are indirect factor degrees, are shown by hatching. This allows the direct factor degree and the indirect factor degree to be displayed separately. In other words, in the evaluation value information column 310, the direct factor degree and the indirect factor degree are output. By extracting the indirect factors, it is possible to quantitatively evaluate the user's behavior and phenomena that cannot be directly measured.

[0037] The output processing unit 106 outputs information about the operation of the monitoring target device 2 by the user to the behavior comparison information column 320 based on the change over time of the cause degree.

[0038] Furthermore, the output processing unit 106 generates improvement action proposals that the user can take based on the factor degrees (first factor degree F1 to fourth factor degree F4), and outputs them to the action proposal information field 330. Specifically, the action proposal information field 330 displays information on specific actions the user can take to improve the evaluation value. Furthermore, the output processing unit 106 outputs the cost calculated in step S104 of FIG. 3 to the action proposal information field 330 (reference number 331).

[0039] According to the first embodiment, the performance of the monitored device 2 is estimated using operation information for a certain period of time, so that the degree of causation of monitoring can be quantified. As a result, the notification device 1 can extract the degree of causation of the use of the monitored device 2, which is a combination of the user's direct actions and secondary events, and notify the user of the degree of causation as a quantitative numerical value. In other words, the maximum possible improvement of the evaluation value that can be improved by actions can be clearly indicated. As a result, it is possible to present a more accurate value to the user and encourage the user to take action, compared to when notifications are made using only actions that can be directly sensed. In other words, it is possible for the user to easily understand how to improve the operation of the equipment (monitored device 2). As a result, it is possible to expect energy saving effects and a longer life for the monitored device 2.

[0040] According to the first embodiment, an evaluation value excluding the influence on the main body of the monitored device 2 (main body factor degree) is shown to the user of the monitored device 2. This makes it possible to clearly indicate the range of values ​​that can be improved by the user's actions, including not only the direct factor degree calculated by directly sensing the user's actions, but also the indirect factor degree that occurs secondarily from the user's actions. In addition, the evaluation value information column 310 outputs the second factor degree F2, which is the direct factor degree, and the third factor degree F3 and fourth factor degree F4, which are indirect factor degrees. This allows the user to compare the result of sensing the user's operation with the result that occurs secondarily from the user's actions. As a result, the user can easily formulate improvement actions. This can be expected to have an energy-saving effect and to extend the life of the monitored device 2.

[0041] Furthermore, information regarding the user's operation of the monitored device 2 is output to the behavior comparison information column 320 based on the change over time in the factor degree. This allows the user to easily and specifically grasp the change in each factor degree (i.e., the degree of the effect that the user's operation has on the performance of the monitored device 2).

[0042] The action suggestion information field 330 outputs improvement action proposals that the user can take based on the cause degrees (first cause degree F1 to fourth cause degree F4). This allows the user to confirm the action to be taken to improve the performance of the monitored target device 2. Furthermore, the action suggestion information field 330 displays the cost required to execute the improvement action proposal. This allows the user to understand the cost required to implement the improvement action proposal.

[0043] [Second embodiment] First, a second embodiment of the present invention will be described with reference to FIGS. Fig. 6 is a diagram showing the configuration of a notification device 1 according to the second embodiment. In the second embodiment, the notification device 1 is shown which diagnoses the normality of the cooling state of the refrigerator 400 which is the monitored device 2 in the first embodiment, and notifies a user of the degree of cooling abnormality for each cause. The degree of cooling abnormality is the degree of abnormality in the cooling performance of the refrigerator 400.

[0044] The notification device 1 according to the second embodiment is composed of an output unit 101, a storage unit 102, a separation unit 103, an evaluation unit 104, a communication unit 105, and an output processing unit 106. In FIG. 6, the refrigerator 400 and the control device 21 are shown as separate devices, but it is assumed that the refrigerator 400 and the control device 21 are actually configured as an integrated unit. It is assumed that the storage unit 102, the evaluation unit 104, the output unit 101, the separation unit 103, the communication unit 105, and the output processing unit 106 (that is, the notification device 1) are a computing system deployed in a cloud environment. Furthermore, it is assumed that the terminal 3 is a smartphone, a tablet terminal, a PC, or the like used by a user. However, the effect of the second embodiment is not related to the arrangement of each component. It is also possible to integrate the components of the refrigerator 400, the control device 21, and the notification device 1, and display the cooling abnormality degree from the output unit 101 installed on the operation panel of the refrigerator 400, or the like.

[0045] (Refrigerator 400) An internal space 401 of refrigerator 400 is separated from the outside by a heat-insulating box filled with heat insulating material 402. Internal space 401 is divided into a plurality of compartments such as a refrigerator compartment 401a and a freezer compartment 401b, and each compartment is provided with an openable and closable door 403. In the second embodiment, internal space 401 of refrigerator 400 is divided into a refrigerator compartment 401a, a freezer compartment 401b, and a vegetable compartment 401c.

[0046] In order to cool each compartment of the refrigerator 400, a compressor 410, an evaporator 411, a condenser 412, and an expansion valve 413 constituting a refrigeration cycle are arranged on the rear side of the refrigerator 400. The compressor 410 is a driving source. In order to deliver the cold air generated by the refrigeration cycle to the interior space 401, the refrigerator 400 is arranged with a blower fan 404 and an air duct damper 405. In order to deliver the cold air generated by the blower fan 404 to the vegetable compartment 401c, a ventilation duct (not shown) is provided. In addition, a return duct 406 is provided to return the cold air supplied to the refrigerator compartment 401a and the vegetable compartment 401c to the evaporator 411 again. In order to control the supply timing of the cold air, an opening / closing sensor 422 is provided on the door 403, an interior temperature sensor 420 is provided in the interior space 401, and an ambient temperature sensor 421 is provided on the top surface of the main body of the refrigerator 400. An inside temperature sensor 420, an ambient temperature sensor 421, and an opening / closing sensor 422 correspond to the sensor 251 in the first embodiment. The dashed arrows in Fig. 6 indicate the flow of air.

[0047] The refrigeration cycle is configured by sealing a refrigerant in an evaporator 411, a compressor 410, a condenser 412, and an expansion valve 413, which are connected in a ring shape, and operates by the refrigerant circulating through these. The evaporator 411 is a heat exchanger that absorbs heat from the interior space 401. The compressor 410 is driven according to the rotation speed 211 (see FIG. 7) and the operation time 212 (see FIG. 7) transmitted from the control device 21, and compresses the gas-phase refrigerant evaporated in the evaporator 411. The condenser 412 cools the gas-phase refrigerant compressed in the compressor 410 with air, thereby dissipating heat possessed by the gas-phase refrigerant. At this time, the gas-phase refrigerant is liquefied to become a liquid refrigerant. The expansion valve 413 expands the liquid refrigerant discharged from the condenser 412 to produce a low-pressure, low-temperature two-phase refrigerant, which is then sent to the evaporator 411.

[0048] The blower fan 404 blows low-temperature air generated by heat exchange in the evaporator 411 into the interior space 401. As described above, the interior space 401 is divided into the refrigerator compartment 401a, the freezer compartment 401b, and the vegetable compartment 401c. Therefore, the controller 21 controls the rotation speed of the blower fan 404 and the opening degree of the air duct damper 405 to adjust the amount of cold air sent to each of the refrigerator compartment 401a, the freezer compartment 401b, and the vegetable compartment 401c. This controls the interior temperature to be within a predetermined range. When the interior temperatures of the refrigerator compartment 401a, the freezer compartment 401b, and the vegetable compartment 401c drop to a predetermined value, the controller 21 controls the compressor 410 to stop. In addition, the controller 21 controls the compressor 410 to restart when the temperature of the interior space 401 gradually increases and reaches a predetermined temperature when the compressor 410 stops.

[0049] In refrigerator 400 that operates for a long time, moisture in interior space 401 condenses and solidifies in evaporator 411, forming frost, which adheres to evaporator 411. As frost adhesion progresses, the ventilation section of evaporator 411 becomes blocked, and the amount of cold air supplied decreases, thereby reducing cooling performance. For this reason, defrosting operation is performed at predetermined time intervals. During defrosting operation, control device 21 temporarily stops compressor 410 and operates a heater (not shown) to increase the temperature of evaporator 411 and melt the frost. Then, when the temperature of evaporator 411 reaches or exceeds a predetermined value, control device 21 starts compressor 410 and resumes cooling of interior space 401.

[0050] (Output unit 101) The output unit 101 is provided in the notification device 1, and displays the calculation result of the cooling abnormality degree by the evaluation unit 104 and the cause separation result by the separation unit 103. The output unit 101 is a display panel or the like installed on the surface of the refrigerator 400.

[0051] (Storage unit 102) In storage unit 102, the degree of cooling abnormality of refrigerator 400 calculated by evaluation unit 104, the detection values ​​of internal temperature sensor 420, ambient temperature sensor 421, and opening / closing sensor 422, etc. are recorded as operation information. In addition, installation information 215 (see FIG. 7) of refrigerator 400 photographed using the camera function of terminal 3 owned by the user of refrigerator 400 can be recorded as additional operation information via communication unit 105. The installation status refers to the distance between refrigerator 400 and objects placed around refrigerator 400 (walls, etc.).

[0052] (Separation part 103) The separating unit 103 executes a process of dividing the cooling abnormality degree into factor degrees corresponding to the explanatory variables by using the cooling abnormality degree and the operation information accumulated in the storage unit 102. As described above, the factor degree is the degree of influence that the user's operation has on the performance of the refrigerator 400. The separating unit 103 and the evaluating unit 104 may be executed by the same computer.

[0053] (Evaluation unit 104) Evaluation unit 104 calculates a cooling abnormality degree, which is the degree of abnormality in the cooling performance of refrigerator 400. At this time, rotation speed 211 (see FIG. 7) and operation time 212 (see FIG. 7) of compressor 410 generated by control device 21, temperature information acquired by internal temperature sensor 420, and the like are used. Specific calculation of the cooling abnormality degree will be described later.

[0054] (Communication unit 105) The communication unit 105 acquires information from the terminal 3 and records it in the storage unit 102 , and transmits display screen information generated by the output processing unit 106 to the terminal 3 .

[0055] (Output processing unit 106) The output processing unit 106 generates display screen information to be output to the terminal 3 and the output unit 101. Furthermore, the output processing unit 106 calculates the cost and the like to be displayed on the display screen 300A (see FIG. 9).

[0056] <Processing> Next, the process (notification method) performed by the notification device 1 will be described with reference to FIGS. Fig. 7 is a flowchart showing the procedure of the process performed by the notification device 1 according to the second embodiment. Fig. 8 shows the results of separating the cooling abnormality degree by cause. Fig. 6 will be referred to as appropriate. As shown in Fig. 7, the operation information used in the second embodiment includes the rotation speed 211 of the compressor 410, the operation time 212 of the compressor 410, the inside and outside temperatures (inside and outside temperatures 213), the number of times the door has been opened and closed 214 acquired by the opening and closing sensor 422, and the installation information 215 photographed by the terminal 3. Each piece of operation information is acquired at regular intervals such as one second to one hour (S101A in Fig. 7) and stored in the storage unit 102. The stored information is used for subsequent calculations in the separation unit 103. The acquisition interval of the operation information may be changed at the timing when the operation information changes.

[0057] The rotation speed 211 of the compressor 410 and the operation time 212 of the compressor 410 correspond to the control information 201 (control information of the drive source) in Fig. 3. The operation time 212 of the compressor 410, the inside and outside temperatures 213, the number of times the door is opened and closed 214, and the installation information 215 correspond to the first information 202 to the fourth information 205 in Fig. 3. The operation time 212 of the compressor 410 corresponds to the control information 201 as well as the first information 202 to the fourth information 205.

[0058] The evaluation unit 104 calculates the cooling abnormality degree, which is an evaluation value, by using three pieces of information, the rotation speed 211 of the compressor 410, the operation time 212 of the compressor 410, and the inside and outside temperature 213, among the operation information (S111A in FIG. 7). In this way, the cooling abnormality degree, which is an evaluation value, is calculated based on the control information of the drive source (the rotation speed 211 of the compressor 410, the operation time 212 of the compressor 410). In step S111A, the inside temperature measured by the inside temperature sensor 420 is used among the inside and outside temperatures 213. The cooling abnormality degree represents a thermal equilibrium state of the refrigerator 400. The cooling abnormality degree is obtained by normalizing the input energy amount and the inside temperature change amount, which are calculated based on the rotation speed 211 of the compressor 410 and the operation time 212 of the compressor 410, and then adding them together. Indices other than the above may be given as the cooling abnormality degree. The input energy amount is obtained by integrating the rotation speed 211 of the compressor 410 by the operation time 212 of the compressor 410. The change in temperature inside the cabinet is the change in the temperature inside the cabinet per unit time. Note that, because the dimensions of the input energy amount and the change in temperature inside the cabinet disappear due to normalization, there is no problem in taking the sum of the input energy amount and the change in temperature inside the cabinet. The degree of cooling abnormality is an index that quantitatively evaluates the following two-stage phenomenon, (A1) and (A2). (A1) When the amount of heat entering from ingredients or the outside of the cabinet increases relative to the cooling capacity that the evaporator 411 can supply, first the rotation speed 211 and operation time 212 of the compressor 410 increase, thereby increasing the amount of cooling heat supplied. (A2) When the amount of heat entering further increases, the amount of cooling heat that can be supplied reaches its limit, and the temperature inside the cabinet rises.

[0059] Then, evaluation unit 104 stores the calculated degree of cooling abnormality in storage unit 102. Inside and outside temperature 213 and number of door opening and closing times 214 are numerical time-series data. Installation information 215 is a value for the ease of heat dissipation in condenser 412, calculated by analyzing image data acquired by terminal 3. Specifically, the distance between refrigerator 400 and the surrounding objects (walls, etc.) is used as installation information 215. However, installation information 215 may be a value detected using a distance sensor (not shown) installed around refrigerator 400, or a value obtained by evaluating the heat retention state using a separate temperature sensor (not shown).

[0060] The separation unit 103 executes a process of separating the cooling anomaly levels using the information stored in the storage unit 102 (S102A: separation step). Specifically, the separation unit 103 calculates a factor-specific coefficient for each explanatory variable by multiple regression analysis using the past operation information stored in the storage unit 102. At this time, the inside temperature, the outside temperature, the door opening / closing count 214, and the installation information 215 are set as explanatory variables. In addition, the cooling anomaly level is set as a target variable. Specifically, the separation unit 103 calculates a factor-specific coefficient for each explanatory variable by multiple regression analysis using the past operation information and the like stored in the storage unit 102 based on the formula (11). As a method of the multiple regression analysis, an optimization calculation such as a gradient method, machine learning using a neural network, and the like can be selected.

[0061] Cooling abnormality level = 11th factor coefficient 11 × outside temperature + 12th factor coefficient × door opening and closing count 214 + 13th factor coefficient × inside temperature change + 14th factor coefficient × installation information 215 ... (11)

[0062] In addition, in formula (11), the outside temperature, the number of door opening and closing times 214, the amount of change in inside temperature, and the installation information 215 correspond to the first to fourth factors in formula (1). Thus, in the second embodiment, the explanatory variables include the outside temperature, the number of door opening and closing times 214, the amount of change in inside temperature, and the installation information 215.

[0063] Then, the separation unit 103 uses the calculated factor coefficients and explanatory variables to calculate four factor degrees (each factor degree), namely, the main body factor degree F11, the installation environment factor degree F12, the door opening / closing factor degree F13, and the internal load factor degree F14 shown in FIG. 8 (S103A). The main body factor degree F11 corresponds to the 11th factor coefficient of the formula (11)×the outside temperature. The installation environment factor degree F12 corresponds to the 14th factor coefficient×the installation information 215. The door opening / closing factor degree F13 corresponds to the 12th factor coefficient of the formula (11)×the number of times the door is opened / closed 214. Furthermore, the internal load factor degree F14 corresponds to the 13th factor coefficient×the amount of change in the temperature inside the cabinet. The amount of change in the temperature inside the cabinet is the change in the temperature inside the cabinet per unit time. Moreover, the main body factor degree F11, the installation environment factor degree F12, the door opening / closing factor degree F13, and the internal load factor degree F14 correspond to the first to fourth factor degrees F1 to F4 in the first embodiment.

[0064] That is, the factor degrees include a main body factor degree F11 based on the outside temperature, a door opening and closing factor degree F13 based on the number of door opening and closing times 214, an internal load factor degree F14 based on the amount of change in temperature inside the cabinet, and an installation environment factor degree F12 based on the installation information 215.

[0065] Incidentally, a plurality of inside-compartment temperature sensors 420 are provided as shown in Fig. 6. The amount of change in inside-compartment temperature in equation (11) may be the average value of the values ​​detected by each inside-compartment temperature sensor 420. Alternatively, the amount of change in inside-compartment temperature obtained by each inside-compartment temperature sensor 420 may be used as a separate explanatory variable in equation (11). In this way, it is possible to obtain the factorial degree related to the inside-compartment temperature of each of the refrigerator compartment 401a, the freezer compartment 401b, and the vegetable compartment 401c.

[0066] Similarly, a plurality of opening / closing sensors 422 are provided. For the door opening / closing count 214 in equation (11), an average value of the detection values ​​by the respective opening / closing sensors 422 may be used. Alternatively, the door opening / closing count 214 obtained by each opening / closing sensor 422 may be used as a separate explanatory variable in equation (11). In this way, it is possible to obtain a factor degree related to the door opening / closing count 214 for each of the refrigerator compartment 401a, the freezer compartment 401b, and the vegetable compartment 401c.

[0067] Main body factor degree F11 is a numerical representation of an influence of an increase in input energy to compressor 410 due to a decrease in heat dissipation by condenser 412 caused by an increase in the temperature outside the refrigerator and an increase in heat penetration into the refrigerator. In other words, main body factor degree F11 can be regarded as a basic performance of refrigerator 400 main body. In this manner, separating unit 103 extracts main body factor degree F11 caused by the performance of refrigerator 400 itself from the factor degrees.

[0068] The installation environment factor level F12 is a numerical representation of the effect of an increase in the input energy of the compressor 410 as a result of the following (B1) and (B2). (B1) Because heat accumulates around refrigerator 400, it becomes difficult for condenser 412 to dissipate heat. (B2) Furthermore, the amount of heat entering the interior of refrigerator 400 increases. Heat may accumulate when the refrigerator 400 is installed in a small space or when an obstacle is placed around the heat dissipation section (not shown).

[0069] Door opening / closing factor degree F13 represents the degree of influence when the user of refrigerator 400 frequently opens door 403, causing the cold air inside the refrigerator to be exchanged with the warm air outside the refrigerator, resulting in an increase in the energy input to compressor 410 to cool the inside of the refrigerator.

[0070] Internal load factor level F14 represents the degree of influence when the temperature inside the refrigerator becomes difficult to decrease after the user of refrigerator 400 opens and closes door 403, and the input energy to compressor 410 increases. Factors that make it difficult to decrease the temperature after opening and closing door 403 include the user storing high-temperature ingredients, storing heavy ingredients, a foreign object being caught between door 403 and the main body causing a temporary decrease in the degree of sealing, etc. In other words, this applies to any temporary increase in the load on compressor 410, not limited to ingredients.

[0071] Furthermore, the output processing unit 106 calculates the cost required to execute the improvement action plan using the values ​​of the installation environment factor degree F12, the door opening / closing factor degree F13, and the internal load factor degree F14 (S104A in FIG. 7). Thereafter, the output processing unit 106 outputs the installation environment factor level F12, the door opening / closing factor level F13, the internal load factor level F14 (each factor level), and the calculated cost (S105A in FIG. 7: output processing step).

[0072] Incidentally, steps S101A to S105A in FIG. 7 correspond to steps S101 to S105 in the first embodiment.

[0073] In FIG. 8, the horizontal axis indicates the date, and the vertical axis indicates the cooling abnormality degree calculated by evaluation unit 104. FIG. 8 shows a range from the latest evaluation implementation date to three weeks ago. The stacked line graph in FIG. 8 shows the cooling abnormality degree separated into refrigerator body factor degree F11, installation environment factor degree F12, door opening / closing factor degree F13, and internal load factor degree F14, which are stacked. In FIG. 8, time A is the time when the cooling abnormality degree is the highest when the four factor degrees are totaled. The cooling abnormality degree shown in FIG. 8 corresponds to the first evaluation value in the first embodiment. Door opening / closing factor degree F13 and internal load factor degree F14 are associated with use, and are therefore also factor degrees of use.

[0074] In the example of Figure 8, the degree of cooling abnormality was highest at time A, one week before time B, which indicates the date of the most recent evaluation, and exceeded the threshold value indicated by the dashed line L1. The internal load factor degree F14 is particularly high at time A. This means that an increase in the temperature inside the refrigerator caused the degree of cooling abnormality to exceed the threshold value even when the rotation speed 211 (see Figure 7) and operation time 212 (see Figure 7) of the compressor 410 were at their maximums. On the other hand, at time B, which indicates the date of the most recent evaluation, the degree of cooling abnormality is low, and it can be seen that the cooling condition has improved.

[0075] Of the four factor levels, namely, main unit factor level F11, installation environment factor level F12, door opening / closing factor level F13, and internal load factor level F14, door opening / closing factor level F13 and internal load factor level F14 change depending on the user's actions. Therefore, door opening / closing factor level F13 and internal load factor level F14 can be classified as user factor levels. Of these, door opening / closing factor level F13 is classified as a direct factor level that senses and quantifies the user's actions themselves. In contrast, internal load factor level F14 is classified as an indirect factor level that quantifies a secondary phenomenon caused by the user's actions.

[0076] On the other hand, main body factor degree F11 and installation environment factor degree F12 are not events that change with the use of refrigerator 400, and therefore can be classified as factor degrees of refrigerator 400 itself. However, installation environment factor degree F12 can be improved by changing the installation location of refrigerator 400 or removing obstacles around condenser 412, and therefore can be improved by a user, a cleaning company, a maintenance company, or the like.

[0077] <Display screen 300A> Fig. 9 is a diagram showing an example of a display screen 300A that displays the results performed in the second embodiment. Fig. 6 and Fig. 8 are referenced as appropriate. Each piece of information displayed on the display screen 300A is generated by the output processing unit 106 and output to the terminal 3. 9 shows an example in which a diagnosis result is displayed on a display screen 300A of a terminal 3 owned by a user. Three types of information are output on the display screen 300A in an evaluation value information column 310A, an action comparison information column 320A, and an action suggestion information column 330A. The evaluation value information column 310A, the action comparison information column 320A, and the action suggestion information column 330A correspond to the evaluation value information column 310, the action comparison information column 320, and the action suggestion information column 330 in the first embodiment, respectively.

[0078] The evaluation value information column 310A shows bar graphs of the time series changes of the installation environment factor degree F12, the door opening / closing factor degree F13, and the internal load factor degree F14 in FIG. 8. The main body factor degree F11 is not displayed on the display screen 300A because it is difficult to improve it by the user's actions. In addition, the time series conversion shown in the evaluation value information column 310A has the horizontal axis representing the date and the vertical axis representing the cooling performance (evaluation value). That is, the evaluation value information column 310A outputs the time change of the factor degree. The cooling performance is the cooling abnormality degree shown in formula (11) minus the main body factor degree F11. That is, the output processing unit 106 outputs the factor degree excluding the main body factor degree F11 from among the factor degrees to the evaluation value information column 310A. In this way, the user can consider only the factor degree caused by the user's actions.

[0079] On the vertical axis of time series change, the cooling performance decreases (deteriorates) as it goes up, and increases (improves) as it goes down. The cooling performance corresponds to the second evaluation value in the first embodiment. In this way, the cooling performance indicates the degree of abnormality of refrigerator 400. In evaluation value information field 310A, information on the degree of abnormality of refrigerator 400 is output based on the change in cooling performance over time (change in factor degree over time).

[0080] In the evaluation value information column 310A, the names of the installation environment factor degree F12, the door opening / closing factor degree F13, and the internal load factor degree F14 have been changed to "installation space," "door opening / closing," and "food load" for users. In the evaluation value information column 310A, it can be seen that the cooling performance improves from time A to time B, just like in FIG. 8. The improvement in the cooling performance from time A to time B can be quantitatively confirmed as a numerical value "V." In addition, it is shown that the numerical value of the internal load factor degree F14 is "Y1" at time B, which is the latest evaluation implementation date. Furthermore, it is shown that the numerical value of the factor degree obtained by adding up the internal load factor degree F14 and the door opening / closing factor degree F13 is "Y2."

[0081] The behavior comparison information field 320A displays a notification that compares the past and latest cooling abnormality degrees (cooling performance) and mentions changes in behavior. Specifically, the output processing unit 106 generates information on the user's operation of the refrigerator 400, such as "the number of times the door was opened and closed was reduced, and the temperature inside the refrigerator was kept good," and outputs this information to the behavior comparison information field 320A. Such information is generated by the output processing unit 106 based on the changes over time in the door opening / closing factor degree F13 and the internal load factor degree F14 (based on the changes over time in the factor degrees).

[0082] 9, the door opening / closing factor degree F13 and the internal load factor degree F14 were high at time A, but improved by a numerical value "V" pt (points) at time B. As a result, the behavior comparison information column 320A displays the improvement in cooling performance due to the behavior change together with the quantitative numerical value.

[0083] In the example shown in Fig. 9, the internal load factor degree F14 and the door opening / closing factor degree F13 drop significantly from time A to time B, so that the number of times the door was opened and closed has decreased and the temperature inside the refrigerator has been kept good is displayed as the inferred behavior. The display of such inferred behavior is generated based on the decrease in the door opening / closing factor degree F13 and the internal load factor degree F14. Moreover, in the behavior comparison information column 320A, the improvement in the cooling state is displayed as the amount of savings in the electricity bill, "W" yen.

[0084] In the action proposal information field 330A, as indicated by reference numeral 331A, a notification is displayed that refers to a specific action that the user should take to improve the cooling performance. In the example shown in Fig. 9, a notification is displayed regarding a reduction in the internal load factor level F14 as a proposed action for the evaluation result at time B, which is the most recent evaluation date. In addition to the proposed action, the action proposal information field 330A displays the expected improvement in cooling performance and the amount of power saving effect.

[0085] In the example shown in FIG. 9, it is displayed that cooling performance can be improved by a maximum of "Y1" pts and "Z1" yen can be saved by lowering the temperature of ingredients before putting them into internal space 401 of refrigerator 400. "Y1" pts is the cooling performance value of internal load factor degree F14 in evaluation value information column 310A. Furthermore, "Z1" yen is the fee corresponding to cooling performance value "Y1". When the temperature of ingredients is high, the internal temperature of refrigerator 400 rises. Then, power is consumed to lower the raised internal temperature. By lowering the temperature of ingredients before putting ingredients into internal space 401, power consumption can be suppressed.

[0086] Also, in the example shown in FIG. 9, it is displayed that by lowering the temperature of ingredients and reducing the opening and closing of door 403, the cooling performance can be improved by a maximum of "Y2" pts, and "Z2" yen can be saved. "Y2" pts is the total value of the cooling performance related to the internal load factor degree F14 and the door opening and closing factor degree F13 in the evaluation value information column 310A. Also, "Z2" yen is the fee corresponding to the cooling performance value "Y2". Lowering the temperature of ingredients and reducing the opening and closing of door 403 are improvement actions that the user can take, generated based on the factor degrees.

[0087] Fees such as "Z1" and "Z2" can be set by storing a correspondence table between cooling performance and fees in the memory unit 102 as a fee table (not shown). "Y1" and "Y2" are evaluation values ​​that are improved by the improvement action proposal. In this way, the output processing unit 106 calculates and outputs the evaluation value (cooling performance) that is improved by the improvement action proposal. Also, "Z1" and "Z2" are costs. In this way, the output processing unit 106 outputs the costs calculated in step S104A of FIG. 7 to the action proposal information column 330A.

[0088] The contents displayed in the action comparison information column 320A and the action suggestion information column 330A are generated by the output processing unit 106 using a generation AI (Artificial Intelligence) or the like based on the time-series changes in the factor degrees constituting the cooling abnormality degree (cooling performance).

[0089] Incidentally, the evaluation value information column 310A, the behavior comparison information column 320A, and the behavior suggestion information column 330A correspond to the evaluation value information column 310, the behavior comparison information column 320, and the behavior suggestion information column 330 in the first embodiment, respectively.

[0090] According to the second embodiment, the cooling performance excluding the influence of the refrigerator 400 main body (main body factor degree F11) is shown to the user of the refrigerator 400. This makes it possible to clearly indicate the range of the cooling abnormality degree (cooling performance) that can be improved by the user's behavior, including not only the direct factor degree calculated by directly sensing the user's behavior but also the indirect factor degree that occurs secondarily with the user's behavior as the starting point.

[0091] Also, the degree of cooling abnormality (cooling performance) that can be improved by the user's actions is displayed separately as a door opening / closing factor degree F13 (direct factor degree) and an internal load factor degree F14 (indirect factor degree). This makes it possible to increase the specificity of the effect of the improvement action plan. Furthermore, in the second embodiment, the factor degree of cooling abnormality in the refrigerator 400 is separated into a main body factor degree F11 that is difficult for the user to improve by himself / herself and an installation environment factor degree F12 that can be improved, and among these, the installation environment factor degree F12 is displayed together with the door opening / closing factor degree F13 and the internal load factor degree F14. This makes it possible to show the possibility of performance improvement by removing obstacles, performing maintenance, or cleaning.

[0092] Furthermore, in the second embodiment, the evaluation value is the degree of cooling abnormality (cooling performance). Information on compressor 410, which is the drive source of refrigerator 400 body, is used to calculate the degree of cooling abnormality. Therefore, as shown in action proposal information column 330A, it is possible to associate the improvement amount of cooling abnormality (cooling performance) with the amount of power saving. By converting the degree of cooling abnormality into the amount of power saving and notifying it to the user, the value of the action can be known as economic value, and the value of the improvement action proposal can be compared with the value of other actions.

[0093] In the display screen 300A, the change in factor degree over time is displayed as a history in the evaluation value information column 310A. This allows the user to look back on the results of past actions. In addition, the action comparison information column 320A displays the action that is estimated as the factor degree at time B, which is the latest evaluation implementation date. The estimated action is the action "reduced the number of times the door was opened and closed, and maintained a good temperature inside the refrigerator" in the evaluation value information column 310A. This allows the user to look back on his or her own actions in detail. Furthermore, in the action comparison information column 320A, the cooling performance at time A in the past and the cooling performance at time B are compared by factor and displayed ("Y1", "Y2"). This allows the user to quantitatively realize the value of the improvement action plan that he or she took.

[0094] Then, the action proposal information field 330A displays an improvement action plan for reducing the cooling abnormality degree. This allows the user to recognize two pieces of information: the maximum possible improvement amount of the factor degree (Vpt in the example of FIG. 9) and the improvement action plan. In addition, the action proposal information field 330A outputs the cost required to execute the improvement action plan. This allows the user to judge the improvement action plan by comparing it with his / her own value standard (savings amount). His / her own value standard is a standard such as whether or not to implement the improvement action plan if the saving amount is about this amount. Furthermore, the action proposal information field 330A displays the cooling performance (cooling abnormality degree) that can be reduced by a specific improvement action plan, rather than the maximum possible improvement amount of the factor degree. The cooling performance that can be reduced is Y1pt or Y2pt in the example shown in FIG. 9. In this way, the improvement action plan and the expected improvement amount of the cooling performance are clarified, so that the user can obtain effective information for judgment.

[0095] As described above, according to the second embodiment, it is possible to clearly show to the user of the refrigerator 400 how the cooling performance changes depending on the usage method. This makes it possible to encourage the user to take improvement actions.

[0096] [Third embodiment] Next, a third embodiment of the present invention will be described with reference to FIGS. 10 is a diagram showing the configuration of a notification device 1 in the third embodiment. In the third embodiment, the normality of the drying time of a washer / dryer 500 is diagnosed, and the diagnosis result is notified to a user for each cause. The washer / dryer 500 corresponds to the monitored device 2 in the first embodiment.

[0097] Notification device 1 of washer / dryer 500 is composed of memory unit 102, evaluation unit 104, separation unit 103, communication unit 105, and output processing unit 106. Notification device 1 and washer / dryer 500 are connected via control device 21 that controls washer / dryer 500. Washer / dryer 500 and control device 21 are integrated. Notification device 1 is connected to terminal 3 via communication unit 105. Terminal 3 is a smartphone, tablet terminal, PC, etc. Memory unit 102, evaluation unit 104, output unit 101, separation unit 103, and communication unit 105 (i.e., notification device 1) are assumed to be a computing system deployed in a cloud environment. However, the effect of the third embodiment is not related to the arrangement of memory unit 102 to communication unit 105. Washer / dryer 500, control device 21, and notification device 1 may be integrated. In this embodiment, the output processing unit 106 functions to display the display screen 300B (see FIG. 13) on the terminal 3, but the results separated by cause may also be displayed from the output unit 101 (see FIG. 1) installed on the operation panel of the washer / dryer 500.

[0098] (Washer / dryer 500) Washer / dryer 500 includes washing tub 501, door 502, motor 503, drain outlet 506, and water inlet 507 for performing a laundry washing operation. Washer / dryer 500 also includes blower 504, duct 505 (duct inlet 505a), filter 508, and heat pump unit 510 for performing a drying operation. Filter 508 is detachable so that the user can remove dirt by himself. Heat pump unit 510 is configured such that a refrigerant is sealed in a flow path in which compressor 511, evaporator 512, condenser 513, and expansion valve 514 are connected in a ring shape. Arrows shown on duct 505 indicate the flow of air.

[0099] The operating state of the washing / drying machine 500 is monitored by a load sensor 522 attached to the motor 503 , and a temperature sensor 520 and a pressure sensor 521 provided in the duct 505 .

[0100] Further, a post-heating temperature sensor 520a and an evaporator temperature sensor 520b are provided as the temperature sensor 520. The post-heating temperature sensor 520a measures the temperature of the air that has passed through the condenser 513 and flowed out of the heat pump unit 510. The evaporator temperature sensor 520b is provided in the evaporator 512 and measures the temperature of the evaporator 512.

[0101] Pressure sensor 521 measures the air pressure in duct 505. In addition, a post-filter pressure sensor 521a, a pre-filter pressure sensor 521b, and a duct inlet pressure sensor 521c are provided as pressure sensors 521. Post-filter pressure sensor 521a measures the air pressure at the rear stage of filter 508. Pre-filter pressure sensor 521b measures the air pressure at the front stage of filter 508. In addition, duct inlet pressure sensor 521c measures the air pressure at the outlet of washing tub 501 (i.e., duct inlet 505a).

[0102] It should be noted that a temperature sensor 520, a pressure sensor 521, and a load sensor 522 correspond to the sensor 251 in the first embodiment.

[0103] When using washer / dryer 500, a user opens door 502, puts clothes into washing tub 501, closes door 502, and holds the clothes in washing tub 501. When a user gives an instruction to start operation, water is supplied from water supply port 507, and motor 503 rotates washing tub 501 to perform the washing, spin-drying, and drying cycles. The water supplied in the washing cycle is drained through drain outlet 506. In the drying cycle, blower 504 (drive source) and a heat pump cycle are activated to perform the drying operation of the clothes.

[0104] When the heat pump cycle is in operation, the compressor 511 and the expansion valve 514 operate to generate a flow of refrigerant circulating through the evaporator 512, the compressor 511, the condenser 513, and the expansion valve 514. The evaporator 512 is a heat exchanger that cools the air in the duct 505 that flows into the heat pump. The compressor 511 compresses the gas-phase refrigerant evaporated in the evaporator 512. The condenser 513 performs heat exchange by releasing the heat of the high-temperature gas-phase refrigerant compressed in the compressor 511 to the air in the duct 505. In the condenser 513, the gas-phase refrigerant is liquefied to become a liquid refrigerant. The expansion valve 514 expands the liquid refrigerant discharged from the condenser 513 to become a low-pressure, low-temperature two-phase refrigerant, and sends it to the evaporator 512.

[0105] The operation of blower 504 generates an air flow that circulates in the order of washing tub 501, heat pump unit 510, duct 505, filter 508, and blower 504. The moisture contained in the wet clothes mixes with the air in washing tub 501, becomes highly humid, flows into heat pump unit 510, and passes through evaporator 512 and condenser 513 in that order. In evaporator 512, the temperature of the air is reduced and dehumidified, and in the subsequent condenser 513, the dehumidified low-temperature air is heated to a high temperature and flows back into washing tub 501 through duct 505 and blower 504. As this continues, the moisture in the clothes held in washing tub 501 is removed and the clothes are dried. When the value of temperature sensor 520, which detects the temperature of the air flowing into evaporator 512, has risen sufficiently, control device 21 stops the rotation of blower 504, and the drying operation ends.

[0106] Rotation speed 221 (see FIG. 11) of blower 504 is determined according to a command value generated by control device 21. Increasing rotation speed 221 of blower 504 can increase the volume of air flowing through duct 505. However, if a user repeats the drying operation, dust gradually adheres to duct 505 and filter 508, and as a result, the volume of air decreases even if rotation speed 221 remains the same.

[0107] (Storage unit 102) In the storage unit 102, the drying time calculated by the evaluation unit 104, the collected detection values ​​of the temperature sensor 520, the pressure sensor 521, and the load sensor 522, and the like are recorded as operation information.

[0108] (Separation part 103) Separation unit 103 executes a process of dividing the drying time into factor degrees corresponding to the explanatory variables using the drying time and operation information stored in storage unit 102. As described above, the factor degree is the degree of influence that the user's operation has on the performance of washer / dryer 500. Note that separation unit 103 and evaluation unit 104 may be executed by the same computer.

[0109] (Evaluation unit 104) The evaluation unit 104 evaluates the drying time, which is an evaluation value, using the rotation speed 221 (see FIG. 11) of the blower 504 generated by the control device 21 and the operation time 222 of the blower 504. Specifically, the time from the start to the stop of the blower 504 is calculated.

[0110] (Communication unit 105) The communication unit 105 transmits the display screen information generated by the output processing unit 106 to the terminal 3 .

[0111] (Output processing unit 106) The output processing unit 106 generates display screen information to be output to the terminal 3 and the output unit 101. Furthermore, the output processing unit 106 calculates the cost and the like to be displayed on the display screen 300B (see FIG. 13).

[0112] <Processing> Next, the process (notification method) performed by the notification device 1 will be described with reference to FIGS. Fig. 11 is a flowchart showing the procedure of the process performed by the notification device 1 according to the third embodiment. Fig. 12 shows the results of separating the drying time, which is an evaluation value, by factor. Fig. 10 will be referred to as appropriate. The operation information used in the third embodiment includes the rotation speed 221 of the blower 504, the operation time 222 of the blower 504, the post-heating temperature 223, and the evaporator temperature 224. Furthermore, the operation information includes the load 225 of the washing tub 501, the duct inlet pressure 226, the pre-filter pressure 227, and the post-filter pressure 228. Each piece of operation information is acquired at a fixed period such as one second to one hour (S101B in FIG. 11), and stored in the storage unit 102. Note that the acquisition period of the operation information may be changed at the timing when the operation information changes.

[0113] Incidentally, post-heating temperature 223 is a detection value of post-heating temperature sensor 520a, and evaporator temperature 224 is a detection value of evaporator temperature sensor 520b. Furthermore, load 225 of washing tub 501 is a detection value of load sensor 522, and duct inlet pressure 226 is a detection value of duct inlet pressure sensor 521c. Furthermore, pre-filter pressure 227 is a detection value of pre-filter pressure sensor 521b, and post-filter pressure 228 is a detection value of post-filter pressure sensor 521a.

[0114] Note that the rotation speed 221 of the blower 504 and the operation time 222 of the blower 504 correspond to the control information 201 (control information of the driving source) of the first embodiment. Also, the post-heating temperature 223, the evaporator temperature 224, the load 225, the duct inlet pressure 226, the pre-filter pressure 227, and the post-filter pressure 228 correspond to the first information 202 to the fourth information 205 of the first embodiment.

[0115] The evaluation unit 104 calculates the drying time, which is an evaluation value, using the rotation speed 221 of the blower 504 and the operation time 222 of the blower 504 from the operation information (S111B in FIG. 11). In this way, the evaluation unit 104 calculates the drying time calculated based on the rotation speed 221 of the blower 504 and the operation time 222 of the blower 504. In other words, the drying time, which is an evaluation value, is calculated based on the control information of the drive source (the rotation speed 221 of the blower 504 and the operation time 222 of the blower 504).

[0116] Then, the evaluation unit 104 stores the calculated drying time in the storage unit 102.

[0117] The separation unit 103 executes a process of separating the drying times using the information stored in the storage unit 102 (S102B: separation step). Specifically, the separation unit 103 calculates factor-specific coefficients for each explanatory variable by multiple regression analysis using the past operation information stored in the storage unit 102. The multiple regression analysis is performed using the following formula (21).

[0118] Drying time = 21st factor coefficient x (1 / (post-heating temperature 223-evaporator temperature 224)) + 22nd factor coefficient x load of washing tub 501 225 + 23rd factor coefficient x (duct inlet pressure 226-pre-filter pressure 227) + 24th factor coefficient x (pre-filter pressure 227-post-filter pressure 228) ... (21)

[0119] At this time, the reciprocal of (heated temperature 223-evaporator temperature 224) (1 / (heated temperature 223-evaporator temperature 224)), the load 225 of the washing tub 501, the duct inlet pressure 226-pre-filter pressure 227, and the pre-filter pressure 227-post-filter pressure 228 are set as explanatory variables. That is, the reciprocal of (heated temperature 223-evaporator temperature 224), the load 225 of the washing tub 501, the duct inlet pressure 226-pre-filter pressure 227, and the pre-filter pressure 227-post-filter pressure 228 correspond to the first factor to the fourth factor of the first embodiment. Thus, in the third embodiment, the explanatory variables include the reciprocal of (heated temperature 223-evaporator temperature 224), the load 225 of the washing tub 501, the duct inlet pressure 226-pre-filter pressure 227, and the pre-filter pressure 227-post-filter pressure 228.

[0120] In addition, the drying time is set as the objective variable. Specifically, in step S102B, the separation unit 103 calculates the factor-specific coefficients for each explanatory variable based on the formula (21) by multiple regression analysis using the past operation information and the like stored in the storage unit 102. As the method of the multiple regression analysis, an optimization calculation such as a gradient method, machine learning using a neural network, and the like can be selected.

[0121] As a result, the separating unit 103 uses the calculated factor-specific coefficients and explanatory variables to calculate four factor degrees shown in Fig. 12, namely, main body factor degree F21, clothing amount factor degree F22, duct dirt factor degree F23, and filter dirt factor degree F24 (S103B). The main body factor degree F21, clothing amount factor degree F22, duct dirt factor degree F23, and filter dirt factor degree F24 will be described later. The main body factor degree F21, clothing amount factor degree F22, duct dirt factor degree F23, and filter dirt factor degree F24 correspond to the first factor degree F1 to the fourth factor degree F4 in the first embodiment.

[0122] Furthermore, the output processing unit 106 calculates the cost using the values ​​of the clothing amount factor degree F22, the duct dirt factor degree F23, and the filter dirt factor degree F24 (S104B in FIG. 11). Thereafter, the output processing unit 106 outputs the clothing amount factor degree F22, the duct soiling factor degree F23, the filter soiling factor degree F24 (each factor degree), and the calculated cost (S105B in FIG. 11: output processing step).

[0123] 11. Steps S101B to S105B in FIG. 11 correspond to steps S101 to S105 in FIG.

[0124] Next, the main body factor degree F21, the clothing amount factor degree F22, the duct soiling factor degree F23, and the filter soiling factor degree F24 will be described with reference to FIG. In FIG. 12, the horizontal axis indicates the date. In the example shown in FIG. 12, a range of three months from the evaluation date is displayed. In addition, in FIG. 12, the vertical axis indicates the drying time. The line graph shown in FIG. 12 shows the drying time separated into the main body factor degree F21, the clothing amount factor degree F22, the duct dirt factor degree F23, and the filter dirt factor degree F24 of the washer-dryer 500, which are stacked up. That is, the total value of the main body factor degree F21 to the filter dirt factor degree F24 is the drying time calculated by the evaluation unit 104. FIG. 12 shows that the drying time, which increased in May, improved once, then started to increase again and reached a maximum.

[0125] The relationship between the drying time and the four factor levels is described by the relationship in formula (21) in which the factor-specific coefficients and the operation information stored in the storage unit 102 are used as explanatory variables. The separation unit 103 determines the factor-specific coefficients by performing multiple regression analysis using the operation information and drying time stored in the storage unit 102. The method of calculating the factor-specific coefficients is omitted since it is the same as in the second embodiment.

[0126] The four factor degrees constituting the drying time in FIG. 12 correspond to each term in the formula (21). Specifically, the main body factor degree F21 corresponds to the inverse of the 21st factor coefficient × (post-heating temperature 223-evaporator temperature 224) (based on the inverse of (post-heating temperature 223-evaporator temperature 224)). Also, the clothes amount factor degree F22 corresponds to the 22nd factor coefficient × load 225 of the washing tub 501 (based on the load 225 of the washing tub 501). And, the duct dirt factor degree F23 corresponds to the 23rd factor coefficient × (duct inlet pressure 226-pre-filter pressure 227) (based on the duct inlet pressure 226-pre-filter pressure 227). And, the filter dirt factor degree F24 corresponds to the 24th factor coefficient × (pre-filter pressure 227-post-filter pressure 228) (based on the pre-filter pressure 227-post-filter pressure 228).

[0127] Main body factor degree F21 is the difference between the temperature of the heated air after passing through heat pump unit 510 and the temperature of evaporator 512. When the temperature difference is large, main body factor degree F21 indicates a state in which sufficient energy is being supplied to dry the clothes. Conversely, when the temperature difference is small, main body factor degree F21 indicates that dehumidification or air heating is insufficient. In other words, main body factor degree F21 changes according to the deterioration state of the heating performance of the main body of washer / dryer 500. In other words, the main body factor degree F21 has a large value if there is an abnormality.

[0128] The clothing amount factor degree F22 represents the influence of the clothing amount on the drying time, and is proportional to the value obtained by the load sensor 522 attached to the motor 503. This factor degree is used to quantify the tendency for the drying time to be longer when there is a lot of clothing.

[0129] Duct dirt factor degree F23 is proportional to the difference between the value of duct inlet pressure sensor 521c installed at duct inlet 505a and the value of pre-filter pressure sensor 521b installed at the inlet of filter 508. When the air passage from duct inlet 505a to the inlet of filter 508 is blocked by foreign matter such as dust, the ventilation resistance increases when the same amount of air flows. Therefore, the differential pressure between duct inlet 505a and the inlet of filter 508 increases. Therefore, duct dirt factor degree F23 means a value obtained by converting the dirt of the air passage from duct inlet 505a to the inlet of filter 508 into a drying time.

[0130] The filter dirt factor degree F24 is proportional to the difference between the value of the pre-filter pressure sensor 521b installed at the inlet portion of the filter 508 and the value of the post-filter pressure sensor 521a installed at the outlet portion of the filter 508. When dust or the like adheres to the filter 508 and blocks the air passage, the ventilation resistance increases due to the same principle as the duct dirt factor degree F23. Therefore, the differential pressure between the inlet portion and the outlet portion of the filter 508 increases. Therefore, the filter dirt factor degree F24 means a value obtained by converting the degree of dirt of the filter 508 into a drying time.

[0131] In the example shown in Fig. 12, the main body factor degree F21 is almost constant. The clothing amount factor degree F22 and the filter dirt factor degree F24 fluctuate irregularly. And the duct dirt factor degree F23 gradually increases. This represents the following situations (C1) to (C3). (C1) Since the main body factor degree F21 is almost constant, the heating performance of the heat pump unit 510 is stable and there are no problems. (C2) The laundry amount factor degree F22 and the filter dirt factor degree F24 fluctuate irregularly, so that the amount of laundry and the timing of cleaning the filter 508 when the user performs the washing and drying operation are irregular. (C3) The duct fouling factor F23 is gradually increasing, which means that the fouling of duct 505 is gradually increasing.

[0132] These four factor degrees can be classified as factor degrees of the washer / dryer 500 and factor degrees attributable to usage as follows. First, main body factor degree F21 is a value that depends on the heating performance of heat pump unit 510, and is a parameter whose performance cannot be improved by the user's actions. In other words, main body factor degree F21 is a factor degree attributable to the washer / dryer 500 itself. Clothes amount factor degree F22 is a value that changes depending on the amount of clothes that the user puts into washing tub 501, and is therefore a factor degree attributable to usage by the user. As a result, clothes amount factor degree F22 is classified as a direct factor degree because it has a direct correspondence with the user's own operation.

[0133] The duct soiling factor degree F23 is a value that increases as the user repeats the washer / dry cycle and is a secondary phenomenon that occurs as a result of the user's use of the washing machine. Therefore, the duct soiling factor degree F23 is classified as an indirect factor degree among the factor degrees. The filter soiling factor degree F24 is also classified as an indirect factor degree because it is a secondary phenomenon that occurs as a result of the user repeating the washer / dry cycle.

[0134] <Display screen 300B> Fig. 13 is a diagram showing an example of a display screen 300B that displays the results performed in the third embodiment. Figs. 10 and 12 are referenced as appropriate. Each piece of information displayed on the display screen 300B is generated by the output processing unit 106 and output to the terminal 3. 13 shows a case where the diagnosis result is notified on the display screen 300B of the terminal 3 owned by the user. Three types of information are notified on the display screen 300B in an evaluation value information column 310B, a past evaluation information column 320B, and an action suggestion information column 330B. The evaluation value information column 310B, the past evaluation information column 320B, and the action suggestion information column 330B correspond to the evaluation value information column 310, the action comparison information column 320, and the action suggestion information column 330 in the first embodiment, respectively.

[0135] In the evaluation value information column 310B, the time series change of the drying time (clothing amount factor degree F22, duct dirt factor degree F23, filter dirt factor degree F24) in FIG. 12 is displayed as a bar graph. In the bar graph shown in FIG. 13, the horizontal axis is the date, and the vertical axis is the drying time. However, the drying time displayed in the evaluation value information column 310B of FIG. 13 is obtained by excluding the main body factor degree F21 from the formula (21). This is because the main body factor degree F21 is not improved by the user's actions. In other words, the output processing unit 106 outputs the factor degrees excluding the main body factor degree F21 from among the factor degrees to the evaluation value information column 310B. In this way, the user can consider only the factor degrees caused by the user's actions. In addition, the drying time indicates the degree of abnormality of the washer-dryer 500.

[0136] In the example shown in FIG. 13, the clothing amount factor degree F22, the duct dirt factor degree F23, and the filter dirt factor degree F24 are displayed as names of "clothing amount", "duct dirt", and "filter dirt", respectively. In the example shown in FIG. 13, the bar graph displayed in the evaluation value information column 310B displays the range of the latter half of July from the results shown in FIG. 13. It can be seen from the evaluation value information column 310B shown in FIG. 13 that the drying time (degree of abnormality) caused by the time change (time change of factor degree) of the duct dirt factor degree F23 and the filter dirt factor degree F24 is increasing. In this way, the output processing unit 106 outputs information regarding the degree of abnormality of the washer / dryer 500 based on the time change of the factor degree.

[0137] The drying time shown in FIG. 12 corresponds to the first evaluation value in the first embodiment, and the drying time shown in the evaluation value information column 310B in FIG. 13 corresponds to the second evaluation value in the first embodiment.

[0138] In the past evaluation information column 320B, past actions that can be read from the evaluation value information column 310B and comments regarding changes in drying time are described. In the example shown in FIG. 13, since the filter dirt cause degree F24 has increased significantly (change in the cause degree over time), a past action that the filter 508 has not been cleaned (information regarding the operation of the washer-dryer 500) is displayed. Note that it may be inferred from the value of the filter dirt cause degree F24 that the filter 508 has not been cleaned. For example, since the drying time has decreased in the middle of May in FIG. 12, the output processing unit 106 determines that the filter has been cleaned. In this way, the output processing unit 106 outputs information regarding the operation of the washer-dryer 500 by the user to the past evaluation information column 320B based on the change in the cause degree over time.

[0139] In addition, a sensor (not shown) may detect whether the lid of the filter storage section (not shown) has been opened or closed, and whether filter 508 has been inserted or removed, and based on the detection by the sensor, it may be determined whether filter 508 has not been cleaned.

[0140] In addition, the past evaluation information column 320B shows the increase in drying time as an evaluation of past actions. In the example shown in Fig. 13, it is shown that the drying time has increased by "X". The value of "X" is the difference in drying time from the oldest date in the evaluation value information column 310B to the latest evaluation date.

[0141] The content displayed in the past evaluation information column 320B is generated by the output processing unit 106 using a generation AI or the like, based on the factor degrees constituting the drying time according to formula (21).

[0142] The action proposal information field 330B displays action proposals for improving the drying time. In the example shown in Fig. 13, filter cleaning and on-site cleaning are displayed as two improvement action proposals. Such improvement action proposals are generated by the output processing unit 106 based on the filter dirt cause degree F24 and output to the action proposal information field 330B.

[0143] As additional information for each proposed action, improvement amount information 331B and consumption cost information 332B, which is information related to costs, are displayed. The improvement amount information 331B is an expected value of the drying time that can be improved by the action, and is an evaluation value of the improvement by the proposed improvement action. In this way, the output processing unit 106 calculates the evaluation value of the improvement by the proposed improvement action, and outputs it to the proposed action information column 330B.

[0144] The consumption cost information 332B is the amount of money and the work time required to complete the proposed action (the cost required to execute the proposed improvement action). In the example shown in FIG. 13, the proposed actions are notified in ascending order of the amount of money required for improvement. Furthermore, when selecting on-site cleaning as the proposed action, the user can press the selection key 333B to reserve the cleaning company. In this way, the output processing unit 106 calculates the cost required to execute the proposed improvement action, and outputs the cost to the action proposal information column 330B.

[0145] According to the third embodiment, the user of the washer / dryer 500 is notified of the cause of the increase in the drying time and the numerical value of the factor degree. This allows the user to quantitatively know the degree of influence of dirt on the filter 508 caused by the washing / drying operation. In particular, since the evaluation value is notified to the user as the drying time, the user can intuitively understand the evaluation value. Also, since the evaluation value is notified to the user as the drying time, the user can compare the evaluation value with the action schedule when performing other actions and make a decision on the execution of an improvement action or a loss evaluation. In other words, the user can judge how much leeway the user will have for the action by shortening his / her own drying time. Also, according to the third embodiment, the past action content and the evaluation value (drying time) are clearly shown through the past evaluation information column 320B, so that the degree of loss can be clearly conveyed to the user.

[0146] Furthermore, in the action proposal information field 330B, the improvement action plan and the expected improvement amount information 331B are displayed, so that the user can decide whether to carry out the improvement action based on his / her own value criteria. The user's own value criteria is a criterion for judging whether or not the user will carry out the improvement action for a certain amount of money. In addition, in the action proposal information field 330B, the consumption cost information 332B is displayed, so that the user can consider carrying out the action plan based on his / her economic situation and schedule. In addition, in the action proposal information field 330B, the action plans are displayed in descending order of consumption cost, so that the user can easily select a more efficient action plan. Then, by pressing the selection key 333B when carrying out the action plan, the user can smoothly solve the part that cannot be improved by the user's own work through the service of a specialized business operator. In addition, the user can specifically grasp the action such as arranging for maintenance.

[0147] As described above, according to the third embodiment, a plurality of proposals for improving the drying time can be quantitatively and clearly presented along with the consumption costs to the user of the washer-dryer 500. This can encourage the user to take improvement action.

[0148] In the third embodiment, an example of notification of the drying time is described, but it is also possible to convert the increase in the drying time into a monetary amount and notify it by separately tabulating the relationship between the operation time 222 and the power consumption. In addition, although filter cleaning and on-site cleaning are shown as improvement proposals in the action proposal information column 330B, selection keys 333B for executing specific control such as automatic filter washing may also be provided. Furthermore, the magnitude of noise and vibration may be set as the objective variable for the evaluation of the washing operation. In this case, the vibration of the washing tub 501 by a vibration sensor (not shown), the amount of clothes, and the control information 201 of the motor 503 may be set as explanatory variables. This makes it possible to separate the magnitude of noise and vibration into uneven clothes (direct use factor), deterioration of the vibration damper (device factor), and resonance state (indirect use factor) and notify them.

[0149] [Fourth embodiment] Next, a fourth embodiment will be described with reference to FIGS. FIG. 14 is a diagram showing the configuration of a notification device 1 according to the fourth embodiment. In the fourth embodiment, a notification device 1 is shown that diagnoses the normality of a heat pump water heater 600, which is a monitored device 2, and outputs the diagnosis result to a user.

[0150] The notification device 1 of the heat pump water heater 600 is composed of a memory unit 102, a separation unit 103, an evaluation unit 104, a communication unit 105, and an output processing unit 106. The heat pump water heater 600 and the notification device 1 are connected via a control device 21. The heat pump water heater 600 and the control device 21 are integrated into one structure. The notification device 1 (memory unit 102, evaluation unit 104, output unit 101, separation unit 103) is assumed to be a computing system deployed in a cloud environment. However, the effect of the fourth embodiment is not related to the arrangement of each component. The heat pump water heater 600, the control device 21, and the notification device 1 are integrated into one structure, and the results separated by cause may be displayed from the output unit 101 installed on the operation panel of the heat pump water heater 600.

[0151] (Heat pump water heater 600) The heat pump unit 610 is equipped with a heat pump cycle that heats cold water to boil it into hot water during boiling operation. The heat pump cycle has a configuration in which a refrigerant is sealed in a flow path that connects a compressor 611, which is a driving source, a water / refrigerant heat exchanger 612, an expansion valve 613, and an evaporator 614 in a ring shape. In FIG. 14, the flow of the refrigerant in the heat pump unit 610 is indicated by thick arrows. The evaporator 614 is equipped with a blower fan 615. The heat pump unit 610 is also equipped with an ambient temperature sensor 630 for measuring the ambient temperature.

[0152] The hot water storage unit 620 is equipped with a water-side cycle that operates during boiling operation and a hot water supply flow path group that operates during hot water supply. In FIG. 14, the flow of water in the hot water storage unit 620 is indicated by thick arrows. The water-side cycle has a configuration in which a hot water storage container 621, a boiling circulation pump 622, and a water / refrigerant heat exchanger 612 are connected in a ring shape. The hot water supply flow path group is composed of a flow path in which a water pipe 624, a hot water storage container 621, and a hot water supply port 623 are connected in series, and a bypass pipe 625 that directly connects the water pipe 624 and the inlet of the hot water supply port 623. A pressure reducing valve 627 is installed between the water pipe 624 and the hot water storage container 621, and a valve 626 is installed in the middle of the bypass pipe 625. A water temperature sensor 631 and a flow rate sensor 632 for detecting temperature and flow rate are installed in the water-side cycle and the hot water supply flow path group.

[0153] The water temperature sensors 631 include a water / refrigerant heat exchanger inlet water temperature sensor 631a, a water / refrigerant heat exchanger outlet water temperature sensor 631b, a tap water temperature sensor 631c, and a hot water inlet water temperature sensor 631d. The water / refrigerant heat exchanger inlet water temperature sensor 631a measures the water temperature at the inlet of the water / refrigerant heat exchanger 612. The water / refrigerant heat exchanger outlet water temperature sensor 631b measures the water temperature at the outlet of the water / refrigerant heat exchanger 612. The tap water temperature sensor 631c measures the water temperature in the water pipe 624. The hot water inlet water temperature sensor 631d measures the water temperature at the hot water inlet 623.

[0154] The flow rate sensor 632 includes a boiling flow rate sensor 632a and a hot water supply flow rate sensor 632b. The boiling flow rate sensor 632a measures the flow rate at the inlet of the water / refrigerant heat exchanger 612. The hot water supply flow rate sensor 632b measures the flow rate at the hot water supply port 623.

[0155] Incidentally, a water temperature sensor 631 and a flow rate sensor 632 correspond to the sensor 251 in the first embodiment.

[0156] The operation of the heat pump water heater 600 will now be briefly described. When hot water is generated using the heat pump unit 610, the rotation speed 231 (see FIG. 15) of the compressor 611 and the opening degree of the expansion valve 613 are adjusted by a control signal sent from the control device 21. As a result, the refrigerant discharged from the compressor 611 is compressed to a high temperature and high pressure state. In the water / refrigerant heat exchanger 612, the high temperature refrigerant heats the cold water sent from the hot water storage container 621 by the boiling circulation pump 622, and in exchange, the refrigerant releases its own heat. Then, after the refrigerant passes through the expansion valve 613 and becomes a low temperature and low pressure state, the evaporator 614 receives heat from the outside air sent by the blower fan 615. After that, the refrigerant flows back into the compressor 611. In the water / refrigerant heat exchanger 612, the water and the refrigerant flow in opposite directions. The hot water heated by the refrigerant and increased in temperature is returned to the top of the hot water storage container 621.

[0157] When supplying hot water, hot water flows from the top of hot water storage container 621 to hot water outlet 623, and at the same time, tap water is supplied from water pipe 624 to hot water outlet 623 via bypass pipe 625. After mixing with the hot water, the hot water passes through a location where water temperature sensor 631 and flow rate sensor 632 are installed, and flows out from hot water outlet 623. Hot water storage container 621, whose pressure has decreased due to the flow of hot water, is replenished with tap water from water pipe 624 via pressure reducing valve 627.

[0158] (Storage unit 102) The memory unit 102 stores the power consumption amount calculated by the evaluation unit 104, and records the detected values ​​of the water temperature sensor 631 and the flow rate sensor 632 as operation information.

[0159] (Separation part 103) The separation unit 103 executes a process of dividing the power consumption into factors corresponding to the explanatory variables using the power consumption and operation information stored in the storage unit 102. As described above, the factor degree is the degree of influence that the user's operation has on the performance of the heat pump water heater 600. The separation unit 103 and the evaluation unit 104 may be executed by the same computer.

[0160] (Evaluation unit 104) The evaluation unit 104 calculates the amount of power consumption used to generate hot water using the rotation speed 231 (see FIG. 15) and operation time 232 (see FIG. 15) of the compressor 611 generated by the control device 21, the opening degree of the expansion valve 613, and operation information detected by the ambient temperature sensor 630. For example, the longer the rotation speed 231 (see FIG. 15) and operation time 232 (see FIG. 15) of the compressor 611, the smaller the opening degree of the expansion valve 613, and the lower the ambient temperature, the greater the amount of power consumption. Therefore, the evaluation unit 104 calculates the amount of power consumption using each parameter based on a table (not shown) of the amount of power consumption for these parameters or a calculation formula.

[0161] (Communication unit 105) The communication unit 105 transmits the display screen information generated by the output processing unit 106 to the terminal 3 .

[0162] (Output processing unit 106) The output processing unit 106 generates display screen information to be output to the terminal 3 and the output unit 101. Furthermore, the output processing unit 106 calculates the cost and the like to be displayed on the display screen 300C (see FIGS. 17 and 18).

[0163] <Processing> Next, the process (notification method) performed by the notification device 1 will be described with reference to FIGS. Fig. 15 is a flowchart showing the procedure of the process performed by the notification device 1 according to the fourth embodiment. Fig. 16 shows the results of the power consumption amount, which is the evaluation value, being separated by factor. Fig. 14 is referred to as appropriate. The operation information used in the fourth embodiment includes the rotation speed 231 of the compressor 611, the operation time 232 of the compressor 611, the opening of the expansion valve 613 (expansion valve opening 233), the boiling flow rate 234, the heat exchanger outlet water temperature 235, and the heat exchanger inlet water temperature 236. The operation information also includes the hot water supply flow rate 237, the hot water supply outlet water temperature 238, and the tap water temperature 239. Each piece of operation information is acquired at a constant cycle, such as one second to one hour (S101C in FIG. 11), and stored in the storage unit 102. The acquisition cycle of the operation information may be changed at the timing when the operation information changes.

[0164] The expansion valve opening 233 is information obtained from the expansion valve 613. The boiling flow rate 234 is a detection value of the boiling flow rate sensor 632a. The heat exchanger outlet water temperature 235 is a detection value of the water / refrigerant heat exchanger outlet water temperature sensor 631b, and the heat exchanger inlet water temperature 236 is a detection value of the water / refrigerant heat exchanger inlet water temperature sensor 631a. The hot water flow rate 237 is a detection value of the hot water flow rate sensor 632b. The hot water supply port water temperature 238 is a detection value of the hot water supply port water temperature sensor 631d, and the tap water temperature 239 is a detection value of the tap water temperature sensor 631c.

[0165] Incidentally, the rotation speed 231 (information of the driving source) of the compressor 611, the operation time 232 (information of the driving source) of the compressor 611, and the expansion valve opening 233 correspond to the control information 201 of the first embodiment. In this manner, the rotation speed 231 of the compressor 611, the operation time 232 of the compressor 611, and the expansion valve opening 233 corresponding to the control information 201 of the first embodiment include the control information of the driving source. Also, the boiling flow rate 234, the heat exchanger outlet water temperature 235, the heat exchanger inlet water temperature 236, the hot water flow rate 237, the hot water inlet water temperature 238, and the tap water temperature 239 correspond to the first information 202 to the fourth information 205 of the first embodiment.

[0166] The evaluation unit 104 calculates the power consumption amount, which is an evaluation value, by using the rotation speed 231 of the compressor 611, the operation time 232 of the compressor 611, and the expansion valve opening 233 from among the operation information (S111C in FIG. 15). In this way, the evaluation value in the fourth embodiment is the power consumption amount calculated based on the rotation speed 231 of the compressor 611, the operation time 232 of the compressor 611, and the expansion valve opening 233. In other words, the power consumption amount, which is an evaluation value, is calculated based on the control information of the drive source (the rotation speed 231 of the compressor 611, and the operation time 232 of the compressor 611).

[0167] Then, the evaluation unit 104 stores the calculated power consumption amount in the storage unit 102 .

[0168] The separation unit 103 executes a process of separating the power consumption amounts using the information stored in the storage unit 102 (S102C: separation step). Specifically, the separation unit 103 calculates factor-specific coefficients for each explanatory variable by multiple regression analysis using the past operation information stored in the storage unit 102. The multiple regression analysis is performed using the following formula (31).

[0169] Power consumption = 31st factor coefficient × (1 / cycle efficiency) + 32nd factor coefficient × (heat supply amount – heat used amount) + 33rd factor coefficient × heat used amount … (31)

[0170] Here, the cycle efficiency is the coefficient of performance of the heat pump cycle, and is a value expressed as the ratio of the amount of heat supplied to the amount of power consumed. The amount of heat supplied and the amount of heat utilized are values ​​calculated using the values ​​of the water temperature sensor 631 and the flow rate sensor 632. The amount of heat supplied is the amount of heat to be applied to the water heated by the water / refrigerant heat exchanger 612, and the amount of heat utilized is the amount of heat of the hot water supplied from the hot water supply outlet 623. Specifically, the amount of heat supplied and the amount of heat utilized are calculated using formulas (32) and (33).

[0171] Supply of heat = Heating flow rate 234 × Specific heat × (Heat exchanger outlet water temperature 235 – Heat exchanger inlet water temperature 236)…(32)

[0172] Heat used = hot water flow rate 237 x specific heat x (hot water temperature 238 - tap water temperature 239) ... (33)

[0173] The specific heat in equations (32) and (33) is the specific heat of water, and is a preset value.

[0174] At this time, (1 / cycle efficiency), the amount of heat supplied-the amount of heat used, and the amount of heat used are set as explanatory variables (that is, equivalent to the first to third factors in the first embodiment (the fourth factor is not used)). In addition, the amount of power consumption is set as the objective variable. Specifically, in step S102C, the separation unit 103 calculates the factor-specific coefficients applied to each explanatory variable based on the formula (31) by multiple regression analysis using the past operation information and the like stored in the storage unit 102. As the method of multiple regression analysis, an optimization calculation such as a gradient method, machine learning using a neural network, and the like can be selected.

[0175] Thus, in the fourth embodiment, the explanatory variables include the inverse of the cycle efficiency, the amount of heat supplied-the amount of heat used, and the amount of heat used.

[0176] As a result, the separating unit 103 uses the calculated factor-specific coefficients and explanatory variables to calculate three factor degrees (each factor degree), namely, the heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 shown in Fig. 16 (S103C). The factor degrees, the heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33, will be described later. The heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 correspond to the first factor degree F1 to the third factor degree F3 in the first actual embodiment. In the fourth embodiment, the equivalent of the fourth factor degree F4 in the first embodiment is not calculated.

[0177] Furthermore, the output processing unit 106 calculates the cost using the values ​​of the heat loss factor degree F32 and the heat utilization factor degree F33 (S104C in FIG. 11). Thereafter, the output processing unit 106 outputs the heat loss factor degree F32, the heat utilization factor degree F33 (each factor degree) and the calculated cost (S105C in FIG. 11: output processing step).

[0178] Next, the heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 will be described with reference to FIG. In FIG. 16, the horizontal axis is the date. In the example shown in FIG. 16, the range of the past six months (January to June) from the evaluation date is displayed. In addition, in FIG. 16, the vertical axis is the power consumption. And, the stacked line graph in FIG. 16 shows the power consumption separated into the heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33, which are stacked and displayed. That is, the total value of the heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 is the power consumption calculated by the evaluation unit 104. In the example shown in FIG. 16, it is displayed that the heat loss factor degree F32 has been gradually increasing since June.

[0179] Similar to the other embodiments, the relationship between the power consumption, the heat source unit factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 is described by the relationship of equation (31) using the factor-specific coefficients and the operation information stored in the storage unit 102. The separation unit 103 determines the factor-specific coefficients by performing multiple regression analysis using the operation information and the power consumption.

[0180] The heat source machine factor degree F31, heat loss factor degree F32, and heat utilization factor degree F33 that make up the power consumption in Fig. 16 correspond to the respective terms in formula (31). Specifically, the heat source machine factor degree F31 corresponds to the 31st factor coefficient × (1 / cycle efficiency) (based on the inverse of cycle efficiency). Also, the heat loss factor degree F32 corresponds to the 32nd factor coefficient × (amount of heat supplied - amount of heat used) (based on the amount of heat supplied - amount of heat used). And the heat utilization factor degree F33 corresponds to the 33rd factor coefficient × amount of heat used (based on the amount of heat used).

[0181] The heat source unit factor degree F31 is a factor degree proportional to the reciprocal of the coefficient of performance of the heat pump unit 610. Therefore, the heat source unit factor degree F31 increases when the heat pump unit 610 deteriorates and the amount of power consumption increases. For example, when the amount of heat supplied decreases due to heat transfer deterioration of the water / refrigerant heat exchanger 612 even if the same amount of power consumption is added, the heat source unit factor degree F31 increases.

[0182] The heat loss factor degree F32 is a factor degree proportional to the difference between the amount of heat supplied by the operation of the heat pump unit 610 and the amount of heat used at the hot water supply port 623. Therefore, the heat loss factor degree F32 increases when the amount of heat used becomes less than the amount of heat supplied due to a decrease in the insulation performance of the hot water storage container 621, a hot water leak from a valve or a pipe joint, or the like.

[0183] The heat utilization factor F33 is a factor proportional to the amount of heat used by the user, and increases or decreases depending on the amount of heat of hot water used in daily life.

[0184] In the example shown in Fig. 16, the heat source unit factor degree F31 increases from January to March when the temperature is low. During this period, the coefficient of performance of the heat pump unit 610 decreases due to the low outdoor temperature, and the amount of power consumed increases. After June, the heat stored in the hot water storage container 621 is lost for some reason, and as a result, the heat loss factor degree F32 increases.

[0185] Of the three factor degrees separated from the power consumption, the heat source machine factor degree F31 and the heat loss factor degree F32 are not phenomena that inevitably occur starting from the user's actions. In other words, both the heat source machine factor degree F31 and the heat loss factor degree F32 are factor degrees caused by the heat pump water heater 600. In other words, the heat source machine factor degree F31 and the heat loss factor degree F32 correspond to the main body factor degree F11 in the second embodiment and the main body factor degree F21 in the third embodiment. The heat utilization factor degree F33 represents the result of the user's utilization of hot water, and is therefore a factor degree of direct utilization (direct factor degree). Therefore, the indirect factor degree is not calculated in the fourth embodiment.

[0186] <Display screen 300C> 17 and 18 are diagrams showing an example of a display screen 300C that displays the results performed in the fourth embodiment. FIG. 14 and FIG. 16 are referenced as appropriate. Each piece of information displayed on the display screen 300C is generated by the output processing unit 106 and output to the terminal 3. 17 and 18 show a state in which the diagnosis result is displayed on the display screen 300C of the terminal 3 owned by the user. The display screen 300C of FIG. 17 displays an evaluation value information field 310C1, a supply limit information field 310C2, an action comparison information field 320C, and an action proposal information field 330C. The display screen 300C of FIG. 18 displays an action proposal information field 330 that is more specific than the action proposal information field 330C of FIG. 17. The screen of FIG. 18 appears when the screen of FIG. 17 is scrolled. The evaluation value information field 310C1, the action comparison information field 320C, and the action proposal information field 330C correspond to the evaluation value information field 310, the action comparison information field 320, and the action proposal information field 330 of the first embodiment, respectively.

[0187] In the example of the evaluation value information column 310C1 in FIG. 17, the changes in the heat source machine factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 shown in FIG. 16 over the last 8 weeks are displayed as bar graphs. These bar graphs correspond to the time change in the factor degree and the degree of abnormality of the heat pump water heater 600. That is, the output processing unit 106 outputs information on the degree of abnormality of the heat pump water heater 600 based on the time change in the factor degree. In the example shown in FIG. 17, the horizontal axis is weekly and the vertical axis is power consumption. Also, in the example shown in FIG. 17, the heat source machine factor degree F31, the heat loss factor degree F32, and the heat utilization factor degree F33 are displayed as the names "heat source machine performance", "heat radiation loss", and "hot water utilization", respectively.

[0188] Incidentally, the power consumption amount corresponds to the first evaluation value and the second evaluation value in the first embodiment.

[0189] In the supply limit information field 310C2, two factor indices, the heat loss factor degree F32 and the heat utilization factor degree F33 in FIG. 16, are displayed as a bar graph with the horizontal axis representing the week and the vertical axis representing the amount of heat consumed. In the example shown in FIG. 17, which is information related to an abnormality in the heat pump water heater 600, the part of the bar graph for the day of diagnosis is clearly indicated as "today", and the history of the past four weeks from the time of diagnosis and the predicted value for the future three weeks from the time of diagnosis are shown. The predicted value is estimated by using machine learning or the like. In addition, the supply limit information field 310C2 displays the level of "supply limit" (line L11) as the limit of heat supply by the heat pump unit 610. The level of the supply limit is a predetermined level. That is, according to the supply limit information field 310C2, the total value of the heat loss factor degree F32 and the heat utilization factor degree F33 will exceed the upper limit of the amount of heat supplied by the heat pump unit 610 three weeks after the time of diagnosis. In this way, the output processing unit 106 predicts the time when a specific function of the heat pump water heater 600 will reach a predetermined level according to information related to an abnormality in the heat pump water heater 600, and outputs the prediction result to the supply limit information field 310C2. This makes it clear that there may be a case where hot water cannot be supplied from the hot water outlet 623 in about three weeks.

[0190] The behavior comparison information field 320C displays three comments on the past state when the diagnosis was performed. The first comment indicates that the heat loss factor degree F32 ("heat radiation loss") at the time of diagnosis, as information on the operation of the heat pump water heater 600 by the user, has deteriorated (increased) compared to the past normal state at the time of diagnosis. In this way, the output processing unit 106 outputs information on the operation of the heat pump water heater 600 by the user to the behavior comparison information field 320C based on the time change of the factor degree (heat loss factor degree F32 in the example of the behavior comparison information field 320C). Note that the behavior comparison information field 320C displays the heat loss factor degree F32 as "heat retention state of the hot water storage tank". It also displays that the degree of deterioration is "U" Wh in terms of the amount of power. The amount of power conversion is the value of the heat loss factor degree F32 in formula (31). These pieces of information are generated based on the information displayed in the evaluation value information field 310C1.

[0191] The second notifies the user that there is a possibility of a hot water shortage in the future (three weeks from now in the example shown in FIG. 17) if the current trend continues. This information is generated based on the information displayed in the supply limit information column 310C2. The third displays the effect of improving the heat loss cause degree F32 (power consumption, which is the evaluation value) converted into an electricity bill. That is, the output processing unit 106 calculates the evaluation value (converted into an electricity bill in the example shown in FIG. 17) improved by the improvement action proposal, and outputs it to the action comparison information column 320C. The information displayed in the action comparison information column 320C is generated by utilizing a generation AI or the like based on the information displayed in the evaluation value information column 310C1.

[0192] The action suggestion information field 330C shown in Fig. 18 displays three measures (proposed improvement actions that the user can take) for achieving the improvement amount shown in the action comparison information field 320C in Fig. 17. The first option is an option that encourages the user to check the area around the hot water storage unit 620 (tank) and make sure there is no water leakage from joints, etc. If there is a loose joint, the symptoms may be improved by tightening the screws, etc.

[0193] The second option is to request a contractor to inspect the appliance (through a service) since it is difficult for the user to make improvements on their own. The number of days required until the inspection is performed and an estimate of the inspection cost are displayed in the reservation information field 331C. The user can reserve the inspection by pressing a selection key 332C.

[0194] The third type is a display that encourages replacement rather than repair. In the example shown in FIG. 18, the options of replacing with a new product and replacing with a refurbished product are displayed. For each option, the expected electricity bill reduction amount in the case of replacement (reference numerals 333C and 336C), the purchase price (cost required to execute the improvement action plan), the number of days to wait until delivery (reference numerals 334C and 337C), and selection keys 335C and 338C for displaying detailed information are displayed. In this way, the output processing unit 106 calculates the cost required to execute the improvement action plan and outputs the cost to the action proposal information column 330C.

[0195] These three options are listed in order of the least expensive cost of carrying out the proposed action.

[0196] The information displayed in the action suggestion information column 330C is generated by utilizing a generation AI or the like using the information displayed in the action comparison information column 320C. As described above, the information displayed in the action comparison information column 320C is generated and output based on the factor degrees, so the output processing unit 106 generates improvement action proposals that the user can take based on the factor degrees and outputs them to the action suggestion information column 330C.

[0197] According to the fourth embodiment, in the calculation of the power consumption amount, which is the evaluation value, the operation information of the compressor 611 that supplies the energy to generate hot water is used. Therefore, the calculation of the power consumption amount can be easily performed. Furthermore, since the power consumption amount can be converted into the electricity bill, the user can recognize the degree of loss as an economic value (electricity bill). This allows the user to understand the seriousness of the loss by comparing it with other actions that involve electricity bills. Furthermore, in the fourth embodiment, the change in the power consumption amount is separated into the factor degree of use resulting from the use of hot water (heat utilization factor degree F33) and the factor degree of the heat pump water heater 600 resulting from the heat source machine and heat radiation (heat source machine factor degree F31 and heat loss factor degree F32). In this way, the user can easily grasp the factor degree of the increase in the power consumption amount and its influence degree.

[0198] In the fourth embodiment, the heat loss factor degree F32 and the heat utilization factor degree F33 are displayed in a combined manner in the supply limit information field 310C2 in Fig. 17. This allows the user to recognize that the amount of heat consumed is increasing day by day, and that the amount of hot water consumed that has been stored in the hot water storage unit 620 is increasing day by day. This allows the user to easily understand that the abnormality is progressing. In addition, the supply limit information field 310C2 displays the supply limit of the amount of heat that the heat pump unit 610 can supply. This allows the user to specifically understand the seriousness of the increase in the heat loss factor degree F32.

[0199] As shown in FIG. 18, the action proposal information field 330C presents action proposals in the order of lowest consumption cost. This allows the user to understand the cause of the abnormality along with the difference in the cost of the countermeasures. The user can try countermeasures in order starting from the least burdensome one. If the user cannot improve the abnormality by himself, he can ask a contractor to inspect it or consider replacing it with a refurbished product or a new product. At this time, when replacing it with a refurbished product or a new product, the initial values ​​of the heat source device factor degree F31 and the heat loss factor degree F32 may change. Therefore, as shown in the action proposal information field 330C in FIG. 18, the expected reduction in electricity bills (reference numerals 333C and 336C) are displayed. This allows the user to compare the replacement cost with the expected reduction in electricity bills and consider whether replacement is appropriate. Furthermore, in the case of inspection and replacement, the selection keys 332C, 335C, and 338C can be used to make a reservation for an inspection contractor, confirm a detailed estimate, and place an order. This enables users to smoothly implement countermeasures and understand in detail the actions they need to take, such as arranging maintenance.

[0200] The contents displayed in the supply limit information column 310C2 and the action suggestion information column 330C shown in Figures 17 and 18 are generated by the evaluation unit 104 using a generation AI or the like based on the information displayed in the evaluation value information column 310C1 and the supply limit information column 310C2.

[0201] As described above, according to the fourth embodiment, a number of proposals for improving power consumption can be quantitatively and clearly presented along with the consumption costs to a user of the heat pump water heater 600, thereby encouraging the user to take improvement action.

[0202] Although the second to fourth embodiments have been described with respect to three products, namely, refrigerator 400, washer / dryer 500, and heat pump water heater 600, notification device 1 of the present embodiment can be applied to products other than these. For example, the method of detecting dirt on filter 508 and duct 505 of washer / dryer 500 can also be applied to a vacuum cleaner (not shown). When applied to a vacuum cleaner, specifically, the suction pressure and remaining battery time are set as objective variables, and the pressure of each part is set as an explanatory variable, and then multiple regression analysis is performed. This allows the cause of the change in the objective variable to be separated, and it is possible to urge the user to clean the filter or replace the dust bag that holds the sucked-in dust, etc.

[0203] The method of separating the power consumption of the heat pump water heater 600 can also be applied to an air conditioner (not shown). Specifically, the power consumption calculated from operation data such as data of a compressor installed in an outdoor unit (not shown), the opening degree of an expansion valve, and outdoor temperature is set as a response variable of a regression equation in a multiple regression analysis. Then, the multiple regression analysis is performed with the temperature of an outdoor heat exchanger installed in the outdoor unit, the temperature of an indoor heat exchanger installed in the indoor unit, and the air temperature at the inlet and outlet of the indoor unit as explanatory variables of the regression equation. This makes it possible to separate whether an increase in the power consumption of the air conditioner is caused by the outdoor unit or the indoor unit. Furthermore, the dirtiness of the filter installed in the indoor unit can be extracted as a factor of use, and users can be encouraged to clean the filter, etc.

[0204] Conventional techniques cannot distinguish between factors caused by the monitored device 2 itself and behavioral factors caused by the user. In particular, it is not possible to distinguish between types of user behavior, such as the number of times the door is opened and closed, or whether ingredients for cooling are inserted. According to this embodiment, it is possible to present to the user factors caused by the monitored device 2 itself and behavioral factors caused by the user after distinguishing between them. In particular, it is possible to distinguish between types of user behavior, such as the number of times the door is opened and closed, or whether ingredients for cooling are inserted.

[0205] Furthermore, conventional technologies have focused only on actions that can be directly sensed, and cannot extract actions and phenomena that cannot be directly sensed (such as putting in hot ingredients). In addition, there is no perspective for showing the extent to which the user himself can improve. According to this embodiment, it is possible to extract actions and phenomena that cannot be directly sensed (such as putting in hot ingredients). In addition, by distinguishing the cause degree into main body cause degrees F11 and F21 and others, it is possible to show the extent to which the user himself can improve.

[0206] The present invention is not limited to the above-described embodiment, and includes various modified examples. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to all of the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, it is possible to add, delete, or replace a part of the configuration of each embodiment with another configuration.

[0207] In this embodiment, as shown in FIG. 1, the detection value of the sensor 251 provided in the monitored device 2 and the information sent from the terminal 3 are used, but the present invention is not limited to this. For example, behavioral sensing information of the user by a behavioral sensor such as a wearable terminal may be used. By using such information, the factor degree may be further classified into finer categories. In this way, the user's behavior can be directly sensed, and the estimation accuracy of the factor degree can be improved.

[0208] In addition, in all of the first to fourth embodiments, multiple regression analysis is used as a method for separating the evaluation value into a plurality of factor degrees, but the method for obtaining the effect of the present invention is not limited to this. For example, before the product is released, it is possible to prepare a model in which the correspondence between various explanatory variables and factor degrees is prepared as teacher data and trained using machine learning such as a neural network. After the product is released, it is also possible to calculate the factor degree by inputting the acquired operation information into the trained model and to notify it. In this case, since the factor degree can be calculated without performing multiple regression analysis, the calculation load for performing regression calculation can be reduced. In addition, a method of setting a discrimination rule based on the magnitude relationship of various explanatory variables and discriminating the magnitude relationship of the factor degree according to the magnitude relationship of various explanatory variables and notifying it is also possible.

[0209] Further, the above-mentioned configurations, functions, the separation unit 103, the evaluation unit 104, the output processing unit 106, the storage unit 102, etc. may be realized in hardware by designing some or all of them as an integrated circuit, for example. As shown in Fig. 3, the above-mentioned configurations, functions, etc. may be realized in software by a processor such as a CPU interpreting and executing a program that realizes each function. Information such as a program, table, file, etc. that realizes each function can be stored in a recording device such as a memory 111 or a solid state drive (SSD), or a recording medium such as an integrated circuit (IC) card, a secure digital (SD) card, or a digital versatile disc (DVD). In addition, in each embodiment, the control lines and information lines are those that are considered necessary for the explanation, and not all control lines and information lines in the product are necessarily shown. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0210] 1 Notification device 2. Monitored Devices 3. Terminal 21 Control device 101 Output section 102 Storage section 103 Separation part 104 Evaluation Department 105 Communications Department 106 Output processing section (cost calculation section) 201 Control Information 202 1st information 203 Second information 204 Third information 205 4th information 211 Rotational speed (drive source control information, operation information) 212 Operation time (drive source control information, operation information) 213 Temperature inside and outside the storage (temperature inside the storage, temperature outside the storage, temperature change inside the storage, operation information, explanatory variables) 214 Number of door openings and closings (operation information, explanatory variables) 215 Installation information (operation information, explanatory variables) 221 Rotational speed (drive source control information, operation information) 222 Operation time (drive source control information, operation information) 223 Post-heating temperature (operation information, used as explanatory variable) 224 Evaporator temperature (operation information, used as explanatory variable) 225 Load (operation information, explanatory variables) 226 Duct inlet pressure (operation information, used as explanatory variable) 227 Pre-filter pressure (operational information, used as explanatory variable) 228 Filtered pressure (operation information, used as explanatory variable) 231 Rotational speed (drive source control information, operation information) 232 Operation time (drive source control information, operation information) 233 Expansion valve opening (operation information) 234 Boiling flow rate (operation information, used as explanatory variable) 235 Heat exchanger outlet water temperature (operation information, used as explanatory variable) 236 Heat exchanger inlet water temperature (operation information, used as explanatory variable) 237 Hot water flow rate (operation information, used as explanatory variable) 238 Hot water temperature (operation information, used as explanatory variable) 239 Tap water temperature (operation information, used as explanatory variable) 300,300A~300C Display screen 310 Evaluation value information column 310A, 310B Evaluation value information column (outputs the factor level excluding the main body factor level, and outputs information on the degree of abnormality of the monitored device) 310C1 Evaluation value information column (information on the degree of abnormality of the monitored device is output) 310C2 Supply limit information column (outputs the time when a specified function in the monitored device reaches a predetermined level) 320, 320A to 320C Behavior comparison information column (information regarding the operation of the monitored device by the user is output) 330 Action proposal information field (proposed improvement actions are output) 330A Action proposal information column (improvement action proposal is output, evaluation value improved by improvement action proposal is output) 330B Action proposal information column (improvement action proposal is output, evaluation value improved by improvement action proposal is output) 330C Action proposal information field (proposed improvement actions are output) 331 Code (Cost) 331B Improvement amount information (evaluation value to be improved by improvement action plan) 332B Consumption Cost Information 332C Selection key 333B Selection key 334C Code (Cost) 335C Selection key 337C Code (Cost) 338C Selection key 400 Refrigerator (monitored device) 410 Compressor (drive source) 500 Washer / dryer (monitored device) 504 Blower (drive source) 600 Heat pump water heater 610 Heat Pump Unit 611 Compressor (drive source) F1 First factor degree (factor degree, main factor degree) F2 Secondary factor degree (factor degree, direct factor degree) F3 Third factor degree (factor degree, indirect factor degree) F4 4th factor level (factor level, indirect factor level) F11 Main body factor (factor) F12 Installation environment factor level (factor level) F13 Door opening and closing factor (factor, direct factor) F14 Internal load factor (factor, indirect factor) F21 Main body factor (factor) F22 Clothing quantity factor degree (factor degree, direct factor degree) F23 Duct dirt factor (factor, indirect factor) F24 Filter soiling factor (factor, indirect factor) F31 Heat source machine factor level (factor level, main body factor level) F32 Heat loss factor (factor, main body factor) F33 Heat utilization factor (factor, direct factor) L1 dashed line L11 line (predetermined level) S102 Separation process (separation step, divide evaluation value into factors using multiple regression analysis) S102A Separation process (separation step, dividing evaluation values ​​into factors using multiple regression analysis) S102B Separation process (separation step, dividing evaluation values ​​into factors using multiple regression analysis) S102C Separation process (separation step, dividing evaluation values ​​into factors using multiple regression analysis) S105 Output (output processing step) S105A Output (output processing step) S105B Output (output processing step) S105C Output (output processing step) S111A Cooling abnormality calculation (calculation of evaluation value) S111B Drying time calculation (calculation of evaluation value) S111C Power consumption calculation (calculation of evaluation value)

Claims

1. a separator that separates an evaluation value indicating a performance of a monitored device based on operation information regarding the monitored device into factor degrees corresponding to a plurality of explanatory variables based on the acquired operation information; an output processing unit that outputs the factor degree; having The factor level is the degree of influence that a user's operation has on the performance of the monitored device. Notification device.

2. The separation unit is The degree of direct causation resulting from sensing the operation of the user on the monitoring target device is output.

2. The notification device according to claim 1.

3. The separation unit is Outputs the degree of indirect cause caused by secondary results of user actions 2. The notification device according to claim 1.

4. The separation unit is extracting a main body factor degree caused by the performance of the monitoring target device itself from the factor degrees; The output processing unit includes: Among the factors, the factors other than the main body factor are output.

2. The notification device according to claim 1.

5. The output processing unit includes: Based on the time-dependent change in the degree of cause, information regarding the degree of abnormality of the monitored device is output.

2. The notification device according to claim 1.

6. The output processing unit includes: According to information on an abnormality in the monitored device, a time when a predetermined function in the monitored device will reach a predetermined level is predicted and output.

6. The notification device according to claim 5.

7. The operation information includes at least control information of a drive source that provides a predetermined function to the monitored device, and the evaluation value is calculated based on the control information of the drive source.

2. The notification device according to claim 1.

8. The output processing unit includes: Based on the time change of the factor degree, information regarding the operation of the monitored device by the user is output.

2. The notification device according to claim 1.

9. The output processing unit includes: Based on the factor level, a proposal for improvement action that the user can take is generated and output.

2. The notification device according to claim 1.

10. The output processing unit includes: Calculate and output the evaluation value improved by the improvement action plan.

10. The notification device according to claim 9.

11. The cost calculation section Calculate the cost required to implement said proposed remedial action; The output processing unit includes: Output the cost 10. The notification device according to claim 9.

12. The separation unit separates the evaluation value into the plurality of factor degrees by multiple regression analysis.

2. The notification device according to claim 1.

13. the monitored device is a refrigerator, The operation information is The information includes a rotation speed of a compressor, an operating time of the compressor, an inside temperature, an outside temperature, a number of times the door is opened and closed, and installation information of the refrigerator. The evaluation value is a cooling abnormality degree obtained by normalizing an input energy amount calculated based on the rotation speed of the compressor and an operation time of the compressor and an internal temperature change amount, and then adding them together; The explanatory variables include the outside temperature, the number of times the door is opened and closed, the amount of change in the inside temperature, and the installation information, The factor degree is The temperature of the outside of the refrigerator is determined based on a main body factor degree, a door opening / closing factor degree based on the number of times the door is opened and closed, an internal load factor degree based on an amount of change in the temperature inside the refrigerator, and an installation environment factor degree based on the installation information.

2. The notification device according to claim 1.

14. the monitored device is a washer / dryer, The operation information is Including the rotation speed of the blower, the operation time of the blower, the temperature after heating, the temperature of the evaporator, the load of the washing tub, the duct inlet pressure, the pressure before the filter, and the pressure after the filter; The evaluation value is A drying time calculated based on the rotation speed of the blower and the operation time of the blower, The explanatory variables are: The heating temperature minus the evaporator temperature, the washing tub load, the duct inlet pressure minus the pre-filter pressure, and the pre-filter pressure minus the post-filter pressure, The factor degree is A body factor degree based on the inverse of (the post-heating temperature - the evaporator temperature), a clothes amount factor degree based on the load of the washing tub, a duct dirt factor degree based on the duct inlet pressure - the pre-filter pressure, and a filter dirt factor degree based on the pre-filter pressure - the post-filter pressure.

2. The notification device according to claim 1.

15. the monitored device is a heat pump water heater, The operation information is The compressor rotation speed, the operation time of the compressor, the opening degree of the expansion valve, the boiling flow rate, the heat exchanger outlet water temperature, the heat exchanger inlet water temperature, the hot water supply flow rate, the hot water supply outlet water temperature, and the tap water temperature, The evaluation value is The power consumption is calculated based on the rotation speed of the compressor, the operation time of the compressor, and the opening degree of the expansion valve, The explanatory variables are: The inverse of the cycle efficiency, the amount of heat supplied minus the amount of heat used, and the amount of heat used; The factor degree is A heat source machine factor based on the inverse of the cycle efficiency, a heat loss factor based on the amount of heat supplied minus the amount of heat used, and a heat utilization factor based on the amount of heat used, The amount of heat supplied is expressed by the following formula (1), the amount of heat used is expressed by the following formula (2), and the cycle efficiency is the ratio of the amount of heat supplied to the amount of power consumed.

2. The notification device according to claim 1. Heat supply amount = boiling flow rate x specific heat x (water temperature at outlet of heat exchanger - water temperature at inlet of heat exchanger)... (1) Heat used = hot water flow rate x specific heat x (hot water temperature at the hot water inlet - tap water temperature) ... (2)

16. The notification device a separation step of dividing an evaluation value indicating a performance of a monitored device into factor degrees corresponding to a plurality of explanatory variables based on the acquired operation information on the monitored device; an output processing step for outputting the factor degree; Do the following: The factor level is the degree of influence that a user's operation has on the performance of the monitored device. Notification method.

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