JP2018085589A - Diagnostic method, diagnostic device, and display device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a diagnostic method, a diagnostic device, and a display device capable of highly accurately diagnosing an operation state of an electric appliance even in a situation where the number of samples of log information used to calculate statistical information is small and accuracy of the statistical information is low.SOLUTION: A diagnostic device 3 includes a log reception unit 301 that obtains identification information used to identify a target electric appliance, a device statistical information calculation unit 304 that specifies first and second feature items of the target electric appliance on teh basis of the identification information, a statistical value determination unit 308 that determines statistical information to be used from among first identical feature statistical information obtained by totaling first log information on the basis of the first log information on the operation state of a first identical feature electrified appliance having the same feature item as the first feature item and second identical feature statistical information obtained by totalizing second log information on the operation state of a second identical feature electrified appliance having the same feature item as the second feature item, and a diagnostic unit 307 that diagnoses the operation state of the target electric appliance using the determined statistical information.SELECTED DRAWING: Figure 5

Description

  The present disclosure relates to a diagnostic method and a diagnostic device for diagnosing an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances, and a display device for displaying a diagnostic result of the diagnostic device.

  Conventionally, Patent Document 1 discloses a system for predicting the quality of a plurality of product types. In this system, data such as a sensor value is derived from a product for each group such as a product type, and a statistical value (average value) of the data is calculated. This is a statistical value representing the characteristics of the group. And when predicting the quality of a group of products, use the statistical values derived from the products of the same group to normalize the data collected from the products and eliminate the group-specific features , Predict the quality by applying the given logic (model). As a result, quality can be predicted with a single logic (model) even for a plurality of groups of products having different characteristics.

U.S. Pat.

  However, in the conventional system, no consideration has been given to the accuracy of the statistical information, and further improvement is necessary.

  This disclosure has been made to solve the above-described problem. Even when the number of log information samples for calculating statistical information is small and the accuracy of statistical information is low, the operating state of electrical appliances can be made highly accurate. An object of the present invention is to provide a diagnostic method, a diagnostic device, and a display device that can be diagnosed.

  In order to achieve the above object, a diagnostic method according to an aspect of the present disclosure is a diagnostic method of a diagnostic apparatus that diagnoses an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances, and the target electrification An acquisition step for acquiring identification information for identifying a device, a first feature information indicating a first feature item of the target electrical appliance, and a second feature different from the first feature item based on the identification information A specifying step of specifying second feature information indicating a feature item, and a first feature item having the same feature item as the first feature item indicated by the first feature information of the target appliance among the plurality of appliances Based on the first log information relating to the operating state of the same characteristic electrical appliance, the first identical statistical information obtained by tabulating the first log information, and the first of the target electrical appliances among the plurality of electrical appliances 2 feature information indicates Decide which statistical information to use from the second identical feature statistical information obtained by aggregating the second log information related to the operating state of the second identical feature electrical appliance having the same feature item as the second feature item And a diagnosis step of diagnosing the operating state of the target electrical appliance using the determined statistical information, and the number of groups when the plurality of electrical appliances are grouped for each of the first feature items is the plurality of groups The number of electrical appliances is larger than the number of groups when grouped for each second feature item.

  According to the above aspect, even when the number of log information samples for calculating statistical information is small and the accuracy of the statistical information is low, the operating state of the electrical appliance can be diagnosed with sufficient accuracy.

It is a block diagram showing an example of composition of a diagnostic system in one embodiment of this indication. It is a block diagram which shows an example of a structure of the electrical appliance shown in FIG. It is a figure which shows an example of the data structure of the log memory | storage part shown in FIG. It is a figure which shows an example of the data structure of the identification information storage part shown in FIG. It is a block diagram which shows an example of a structure of the diagnostic apparatus shown in FIG. It is a figure which shows an example of the data structure of the log memory | storage part shown in FIG. It is a figure which shows an example of the data structure of the model information storage part shown in FIG. It is a figure which shows an example of the data structure of the 1st apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows an example of the data structure of the 2nd apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows an example of the data structure of the 3rd apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows an example of the data structure of the 1st apparatus connection information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 2nd apparatus connection information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 3rd apparatus connection information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the logic memory | storage part shown in FIG. It is a block diagram which shows an example of a structure of the display apparatus shown in FIG. It is a figure which shows an example of the input screen displayed on the input / output part of the display apparatus shown in FIG. It is a figure which shows an example of the result display screen displayed on the input / output part of the display apparatus shown in FIG. It is a figure which shows another example of the result display screen displayed on the input / output part of the display apparatus shown in FIG. It is a figure which shows an example of the whole sequence of the diagnostic system shown in FIG. It is a flowchart which shows an example of the log transmission / reception process shown in FIG. It is a flowchart which shows an example of the apparatus statistical information derivation | leading-out process shown in FIG. It is a flowchart which shows an example of the electrical appliance diagnostic process shown in FIG. It is a flowchart which shows an example of the statistical information determination process shown in FIG. It is a flowchart which shows an example of the diagnostic process shown in FIG. It is a flowchart which shows an example of the diagnostic result display process shown in FIG. It is a figure which shows an example of the data structure of the 1st apparatus operation information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 2nd apparatus operation information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 3rd apparatus operation information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 1st apparatus data amount information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 2nd apparatus data amount information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the data structure of the 3rd apparatus data amount information of the apparatus connection information storage part shown in FIG. It is a figure which shows an example of the model relationship information stored in the model information storage part shown in FIG. It is a figure which shows an example of the data structure of the 1st tree structure apparatus information of the apparatus connection information storage part shown in FIG. 5 when the model relation information shown in FIG. 32 is used. It is a figure which shows an example of the data structure of the 2nd tree structure apparatus information of the apparatus connection information storage part shown in FIG. 5 when the model relation information shown in FIG. 32 is used. It is a figure which shows the 1st modification of the data structure of the 1st apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows the 1st modification of the data structure of the 2nd apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows the 1st modification of the data structure of the 3rd apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows the 2nd modification of the data structure of the 1st apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows the 2nd modification of the data structure of the 2nd apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows the 2nd modification of the data structure of the 3rd apparatus statistical information of the apparatus statistical information storage part shown in FIG. It is a figure which shows the whole image of the service provided in embodiment of this indication. It is a figure which shows the type of service (in-house data center type) in embodiment of this indication. It is a figure which shows the type of service (IaaS use type) in embodiment of this indication. It is a figure which shows the type of service (PaaS use type) in embodiment of this indication. It is a figure which shows the type of service (SaaS use type) in embodiment of this indication.

(Knowledge that became the basis of this disclosure)
In general, it is known that the accuracy of a statistical value is low when the number of samples of original data used when calculating a statistical value represented by an average value or the like is small. Therefore, depending on the number of samples, it may be inappropriate as a statistical value representing the characteristics of the group.

  However, the conventional system disclosed in Patent Document 1 only discloses a technique for predicting quality with a single diagnostic logic on the assumption that highly accurate statistical values are given. The accuracy of the value was not taken into account.

  For example, when statistical values are calculated in parallel while collecting data in a certain group, the accuracy of statistical values is low at the beginning of collection, and the judgment accuracy when predicting quality with a single diagnostic logic is low There were challenges.

  As described above, technologies for performing diagnosis with a single diagnosis logic for a plurality of groups of electrical appliances having different characteristics have been studied, but statistics necessary for quality prediction with a single diagnosis logic are being studied. There is a need to make a diagnosis with high accuracy even when the accuracy of the value is low, and no investigation has been made on technical solutions to satisfy these needs.

  In order to solve such a problem, in the present disclosure, for example, a plurality of types of statistical values of the same type (such as an average value) are calculated for a single group, and the accuracy of each statistical value is determined at the time of diagnosis. In consideration of the above, by selecting the optimum statistical value and performing the diagnosis, a system capable of performing the diagnosis with sufficient accuracy is realized.

  A diagnostic method according to an aspect of the present disclosure is a diagnostic method for a diagnostic apparatus that diagnoses an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances, and includes identification information for identifying the target electrical appliance And a second feature indicating a first feature information indicating a first feature item of the target electrical appliance and a second feature item different from the first feature item based on the identification information A specifying step for specifying information, and an operating state of a first identical feature electrical appliance having the same feature item as the first feature item indicated by the first feature information of the target electrical appliance among the plurality of electrical appliances First identical feature statistical information obtained by tabulating the first log information on the basis of the first log information relating to the second, and the second feature information of the target electrical appliance among the plurality of electrical appliances. The same features as It is determined which statistical information is to be used among the second identical feature statistical information obtained by aggregating the second log information related to the operating state of the second identical feature electric appliance having the item, and using the decided statistical information A diagnosis step of diagnosing an operating state of the target electrical appliance, and the number of groups when the plurality of electrical appliances are grouped for each of the first characteristic items is the second electrical appliance as the second electrical appliance. More than the number of groups when grouping by feature item.

  With such a configuration, identification information for identifying a target electrical appliance to be diagnosed is acquired, and based on the acquired identification information, first feature information indicating a first feature item of the target electrical appliance, The second feature information indicating the second feature item different from the one feature item is specified, and the same feature item as the first feature item indicated by the first feature information of the target appliance among the plurality of appliances Based on the first log information related to the operating state of the first identical feature electric appliance having the first log information, the first identical feature statistical information obtained by tabulating the first log information and the second feature information of the target electric appliance indicate Which statistical information is to be used among the second identical feature statistical information obtained by aggregating the second log information related to the operating state of the second identical feature electrical appliance having the same feature item as the second feature item Determine and use the determined statistical information It is to diagnose the health of of equipment.

  Here, since the number of groups when a plurality of electrical appliances are grouped for each first feature item is larger than the number of groups when a plurality of electrical appliances are grouped for each second feature item, the second same feature The number of electrical appliances is greater than the number of first identical feature electrical appliances, and the number of samples of the second log information relating to the operational status of the second identical feature electrical appliance is the operational status of the first identical feature electrical appliance. More than the number of samples of the first log information regarding.

  Therefore, when the number of samples of the first log information is small and the accuracy of the first identical feature statistical information is low, by using the second log information with a large number of samples as log information for calculating the statistical information The second identical feature statistical information with sufficient accuracy can be obtained, and the operating state of the target electrical appliance can be diagnosed with sufficient accuracy using the second identical feature statistical information. As a result, even when the number of log information samples for calculating statistical information is small and the accuracy of statistical information is low, the operating state of the electrical appliance can be diagnosed with sufficient accuracy.

  The plurality of electrical appliances are connected to a predetermined network, and the diagnosis step identifies the number of connected electrical appliances identified from the first log information and indicating the number of the first identical electrical appliances connected to the network. Depending on whether or not is equal to or greater than a predetermined number, it is determined which one of the first same feature statistical information and the second same feature statistical information is to be used, and the determined statistical information is used. Then, the operating state of the target electrical appliance may be diagnosed.

  According to such a configuration, the first number is determined depending on whether the number of connected devices indicating the number of first identical electrical appliances connected to the network identified from the first log information is a predetermined number or more. Which statistical information to use is determined between the same feature statistical information and the second same feature statistical information, and the operating state of the target electrical appliance is diagnosed using the determined statistical information.

  Therefore, when the number of connected devices indicating the number of first identical feature electric appliances is equal to or greater than a predetermined number, the number of samples of the first log information is increased, and the first identical feature statistical information with high accuracy can be obtained. Therefore, the operating state of the target electrical appliance can be diagnosed with high accuracy using the first identical feature statistical information with high accuracy. On the other hand, when the number of connected devices indicating the number of first identical characteristic electrical appliances is smaller than the predetermined number, the number of samples of the first log information is small, but the number of samples of the second log information is the number of samples of the first log information. Since the second identical feature statistical information with sufficient accuracy can be obtained, the operation status of the target electrical appliance can be diagnosed with sufficient accuracy using the second identical feature statistical information with sufficient accuracy. can do.

  In the diagnosis step, when the number of connected devices is equal to or greater than the predetermined number, the operating state of the target electrical appliance may be diagnosed using the first same feature statistical information.

  With such a configuration, when the number of connected first identical feature appliances is a predetermined number or more, the first identical feature statistical information is used to diagnose the operating state of the target appliance. The number of samples of log information increases, and the first identical feature statistical information with high accuracy can be obtained. Using the first identical feature statistical information with high accuracy, the operating state of the target electrical appliance can be made highly accurate. Can be diagnosed.

  In the diagnosis step, when the number of connected devices is smaller than the predetermined number, the operation state of the target electrical appliance may be diagnosed using the second same feature statistical information.

  With such a configuration, when the number of connected first identical electrical appliances is less than a predetermined number, the operating state of the target electrical appliance is diagnosed using the second identical feature statistical information, so the number of samples is The second identical feature statistical information with sufficient accuracy can be obtained by using the second log information more than the first log information, and the target electrification can be obtained by using the second identical feature statistical information with sufficient accuracy. The operating state of the device can be diagnosed with sufficient accuracy.

  The plurality of electrical appliances are connected to a predetermined network, and in the diagnosis step, the data amount of the first log information transmitted from the first identical electrical appliance via the network is a predetermined amount or more. According to whether or not there is, it is determined which one of the first same feature statistical information and the second same feature statistical information to use, and using the determined statistical information, the target electrification You may make it diagnose the operating state of an apparatus.

  With such a configuration, depending on whether or not the data amount of the first log information transmitted from the first identical feature electrical appliance via the network is greater than or equal to a predetermined amount, the first identical feature statistical information and Which statistical information to use is determined among the second identical feature statistical information, and the operating state of the target electrical appliance is diagnosed using the determined statistical information.

  Therefore, when the data amount of the first log information is equal to or larger than the predetermined amount, the number of samples of the first log information is increased, and the highly accurate first identical feature statistical information can be obtained. Using the first identical feature statistical information, the operating state of the target electrical appliance can be diagnosed with high accuracy. On the other hand, when the data amount of the first log information is less than the predetermined amount, the number of samples of the first log information is small, but the number of samples of the second log information is larger than the number of samples of the first log information. Since the second identical feature statistical information with sufficient accuracy can be obtained, the operation state of the target electrical appliance can be diagnosed with sufficient accuracy using the second identical feature statistical information with sufficient accuracy. .

  In the diagnosis step, when the data amount of the first log information transmitted from the first identical feature electrical appliance is equal to or larger than the predetermined amount, the target electrification is performed using the first identical feature statistical information. You may make it diagnose the operating state of an apparatus.

  With such a configuration, when the data amount of the first log information is greater than or equal to a predetermined amount, the operating status of the target electrical appliance is diagnosed using the first identical feature statistical information, so the first log The number of information samples increases, and the first identical feature statistical information with high accuracy can be obtained. Using the first identical feature statistical information with high accuracy, the operating state of the target electrical appliance is diagnosed with high accuracy. be able to.

  In the diagnosis step, when the data amount of the first log information transmitted from the first identical feature electrical appliance is smaller than the predetermined amount, the target electrical appliance is used by using the second identical feature statistical information. You may make it diagnose the operating state of.

  With such a configuration, when the data amount of the first log information is smaller than the predetermined amount, the operating state of the target electrical appliance is diagnosed using the second identical feature statistical information, so the number of samples is the first. The second same feature statistical information with sufficient accuracy can be obtained by using the second log information that is larger than the log information of the target appliance, and by using the second same feature statistical information with sufficient accuracy, The operating state can be diagnosed with sufficient accuracy.

  The specifying step specifies model information indicating a model of the target electrical appliance identified by the identification information as the first feature information, and is identified by the identification information as the second feature information. Attribute information indicating an attribute item related to at least one of a product model and capability of the target electrical appliance is specified, and the diagnosis step includes the same model as the model indicated by the model information of the target electrical appliance among the plurality of electrical appliances Based on the same model log information indicating the operating state of the same model electrical appliance, the same model statistical information obtained by aggregating the same model log information, and the same attribute item as the attribute item indicated by the attribute information of the target electrical appliance It is determined which statistical information is to be used from the same attribute statistical information obtained by counting the same attribute log information from the same attribute electric appliances The operating status of the target electrical appliance is diagnosed using the statistical information, and the number of groups when the plurality of electrical appliances are grouped for each model is the number of groups when the plurality of electrical appliances are grouped for each attribute item. You may make it increase more than the number of groups.

  With such a configuration, the model information indicating the model of the target electrical appliance identified by the identification information is specified, and the attribute indicating the attribute item related to at least one of the product model and capability of the target electrical appliance identified by the identification information Identifies the same model by collecting information and identifying the same model log information based on the same model log information that shows the operating status of the same model appliance that is the same model as the model information of the target appliance among multiple appliances Which statistical information to use: model statistical information or the same attribute statistical information obtained by aggregating the same attribute log information from the same attribute electrical equipment that has the same attribute item as the attribute information indicated by the attribute information of the target electrical appliance And the operating status of the target electrical appliance is diagnosed using the determined statistical information.

  Here, the number of groups when multiple electrical appliances are grouped by model is greater than the number of groups when multiple electrical appliances are grouped by attribute item, so the number of identical electrical appliances is the same The number of samples of the same attribute log information related to the operating state of the same attribute electric appliance is larger than the number of samples of the same model log information related to the operating state of the same model electric appliance.

  Therefore, if the number of samples of the same model log information of the same model electrical equipment is small and the accuracy of the same model statistical information is low, the same attribute log of the same attribute electrical equipment with a large number of samples is used as log information for calculating the statistical information. By using the information, the same attribute statistical information with sufficient accuracy can be obtained, and the operating state of the target electrical appliance can be diagnosed with sufficient accuracy using the same attribute statistical information with sufficient accuracy.

  The attribute information may include year information indicating a manufacturing year of the target electrical appliance.

  With this configuration, if the number of samples of the same model log information of the same model of electrical equipment is small and the accuracy of the same model statistical information is low, the production year with the large number of samples is the same as the log information for calculating the statistical information. By using the same attribute log information of the same attribute electrical appliances, it is possible to obtain the same attribute statistical information with sufficient accuracy, so that the operating status of the target electrical appliance is sufficient using the same attribute statistical information with sufficient accuracy Can be diagnosed with high accuracy.

  The attribute information may include capability information indicating the capability of the target electrical appliance expressed by any one of a plurality of levels.

  With such a configuration, if the number of samples of the same model log information of the same model of electrical equipment is small and the accuracy of the same model statistical information is low, the ability to have a large number of samples is the same as the log information for calculating statistical information. By using the same attribute log information of the same attribute electrical appliance, it is possible to obtain the same attribute statistical information with sufficient accuracy, so that the operating status of the target electrical appliance is sufficiently increased using the same attribute statistical information with sufficient accuracy. Diagnose with accuracy.

  In the diagnosis step, as the first identical feature statistical information or the second identical feature statistical information, a statistical value of an item related to a diagnostic content included in the first log information or the second log information is obtained. It is possible to calculate and apply the statistical value to a predetermined diagnosis logic to determine whether or not the target electrical appliance is abnormal.

  With such a configuration, the target electrical appliance can calculate a statistical value of an item related to the diagnostic content included in the first log information or the second log information, and apply the calculated statistical value to a predetermined diagnostic logic. Since it is determined whether or not there is an abnormality, even if the number of log information samples for calculating statistical information is small and the statistical information accuracy is low, a single diagnostic logic is used to It is possible to diagnose the operating state of the electrical appliance with sufficient accuracy.

  The plurality of electrical appliances includes a plurality of air conditioners, and the first log information and the second log information include rotation speed information indicating the number of rotations of a compressor of the plurality of air conditioners and a pipe temperature. May include at least one of the pipe temperature information.

  With such a configuration, the statistical values of the compressor rotation speed and the pipe temperature are calculated from the rotation speed information indicating the rotation speed of the compressor of the air conditioner and the pipe temperature information indicating the pipe temperature. Since it is determined whether or not the air conditioner is abnormal by applying to the diagnosis logic of the above, the compressor rotation speed and the pipe temperature statistics of the air conditioner having the same feature item as the first feature item The same feature item as the second feature item even in a situation where the number of samples of the rotational speed information and pipe temperature information for calculating the value is small and the accuracy of the statistical values of the rotational speed of the compressor and the pipe temperature is low The operating condition of the air conditioner to be diagnosed can be diagnosed with sufficient accuracy using the statistical values of the rotation speed of the compressor and the pipe temperature of the air conditioner having the above.

  A determination step of determining the accuracy of the diagnosis according to the statistical information used in the diagnosis of the diagnosis step, and a changing step of changing a display content of the diagnosis result information indicating the result of the diagnosis according to the accuracy of the diagnosis May be further included.

  With such a configuration, the accuracy of the diagnosis is determined according to the statistical information used for the diagnosis, and the display content of the diagnosis result information indicating the result of the diagnosis is changed according to the determined accuracy of the diagnosis. It is possible to present to the user whether or not the operation status of the target electrical appliance has been diagnosed with a certain degree of accuracy.

  The specifying step further specifies third feature information indicating a third feature item different from the first and second feature items based on the identification information, and the plurality of electrical appliances are set in the second feature item. The number of groups when grouping for each feature item is greater than the number of groups when the plurality of electrical appliances are grouped for each third feature item, and the first to third feature information is determined in the diagnosis step. A priority level used as statistical information, and the diagnosis step includes the first identical feature statistical information and the first log information based on the first log information, the second log information, and the priority. 2 and the operating state of the third identical feature electrical appliance having the same feature item as the third feature item indicated by the third feature information of the target electrical appliance among the plurality of electrical appliances Related The third may be determined whether to use a statistical information from the same feature statistics compiled third log information that.

  With such a configuration, the third feature information indicating the third feature item different from the first and second feature items is further specified based on the identification information for identifying the target electrical appliance to be diagnosed. , First log information, second log information, and first to third feature information based on the priority used as the statistical information, the first identical feature statistical information and the second identical feature statistical information And third log information related to the operating state of the third identical feature electrical appliance having the same feature item as the third feature item indicated by the third feature information of the target electrical appliance among the plurality of electrical appliances. Which statistical information is used from the third identical feature statistical information is determined, and the operating state of the target electrical appliance is diagnosed using the determined statistical information.

  Here, since the number of groups when a plurality of electrical appliances are grouped for each second feature item is larger than the number of groups when a plurality of electrical appliances are grouped for each third feature item, the third identical feature The number of electrical appliances is greater than the number of second identical feature electrical appliances, and the number of samples of the third log information relating to the operational status of the third identical feature electrical appliance is the operational status of the second identical feature electrical appliance. More than the number of samples of the second log information regarding.

  Therefore, when the number of samples of the first log information is small and the accuracy of the first identical feature statistical information is low, the statistical information is changed according to the priority with which the first to third feature information is used as the statistical information. By using the second log information with a large number of samples as the log information for calculation, the second identical feature statistical information with sufficient accuracy can be obtained, and the second identical feature statistical information with sufficient accuracy can be obtained. Can be used to diagnose the operating state of the target electrical appliance with sufficient accuracy.

  Further, when the number of samples of the second log information is small and the accuracy of the second identical feature statistical information is low, the statistical information is changed according to the priority with which the first to third feature information is used as the statistical information. By using the third log information with a large number of samples as the log information for calculation, the third identical feature statistical information with sufficient accuracy can be obtained, and the third identical feature statistical information with sufficient accuracy can be obtained. Can be used to diagnose the operating state of the target electrical appliance with sufficient accuracy.

  The specifying step further specifies third feature information indicating a third feature item different from the first and second feature items based on the identification information, and the plurality of electrical appliances are set in the second feature item. The number of groups when grouping for each feature item is greater than the number of groups when the plurality of electrical appliances are grouped for each third feature item, and the diagnosis step includes the first log information, the second log Log information and the first identical feature statistical information based on model relation information in which the relations of the first to third feature items included in the first to third feature information are defined in a tree structure. And third identical feature electrification having the same feature item as the third feature item indicated by the third feature information of the target electrical appliance among the plurality of electrical appliances and the second identical feature statistical information. Equipment It may be determined whether to use the statistics from the third same feature statistics compiled third log information about the work state.

  With this configuration, the third feature information indicating the third feature item different from the first and second feature items is identified based on the identification information for identifying the target electrical appliance to be diagnosed, The first log information, the second log information, and the first to third feature items included in the first to third feature information, based on the model relationship information defined in a tree structure. Same feature statistical information, second same feature statistical information, and third same feature electrification having the same feature item as the third feature item indicated by the third feature information of the target electrical appliance among the plurality of electrical appliances Determine which statistical information is to be used from the third identical feature statistical information obtained by counting the third log information on the operational status of the device, and use the determined statistical information to determine the operational status of the target electrical appliance Is diagnosed.

  Here, since the number of groups when a plurality of electrical appliances are grouped for each second feature item is larger than the number of groups when a plurality of electrical appliances are grouped for each third feature item, the third identical feature The number of electrical appliances is greater than the number of second identical feature electrical appliances, and the number of samples of the third log information relating to the operational status of the third identical feature electrical appliance is the operational status of the second identical feature electrical appliance. More than the number of samples of the second log information regarding.

  Therefore, when the number of samples of the first log information is small and the accuracy of the first identical feature statistical information is low, the statistics of each of the first to third feature items are determined according to the model relationship information defined in a tree structure. By using the second log information with a large number of samples as the log information for calculating the information, the second identical feature statistical information with sufficient accuracy can be obtained, and the second identical feature with sufficient accuracy can be obtained. Using the statistical information, the operating state of the target electrical appliance can be diagnosed with sufficient accuracy.

  Further, when the number of samples of the first and second log information is small and the accuracy of the first and second identical feature statistical information is low, the relationship between the first to third feature items is defined in a tree structure. By using the third log information with a large number of samples as the log information for calculating the statistical information in accordance with the model relation information, it is possible to obtain the third identical feature statistical information with sufficient accuracy, and with sufficient accuracy. The operating status of the target electrical appliance can be diagnosed with sufficient accuracy using the third identical feature statistical information.

  In addition, the present disclosure can be realized not only as a diagnostic method for executing the characteristic processing as described above, but also as a diagnostic device having a characteristic configuration corresponding to the characteristic processing executed by the diagnostic method. It can also be realized. Therefore, also in the following other aspects, the same effect as the above air conditioning control method can be obtained.

  A diagnostic device according to another aspect of the present disclosure is a diagnostic device for diagnosing an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances, and acquires identification information for identifying the target electrical appliance And the first feature information indicating the first feature item of the target electrical appliance and the second feature information indicating the second feature item different from the first feature item based on the identification information. A specifying unit to be specified and a first operation related to the operating state of the first identical feature electrical appliance having the same feature item as the first feature item indicated by the first feature information of the target electrical appliance among the plurality of electrical appliances 1st same feature statistical information which totaled the 1st log information based on 1 log information, and the 2nd feature information which the 2nd feature information of the object electric appliance among the plurality of electric appliances shows A second item having the same characteristic item as the item It is determined which statistical information is to be used from the second identical statistical information obtained by counting the second log information relating to the operating state of one characteristic electrical appliance, and the target electrical appliance is determined using the determined statistical information A diagnosis unit for diagnosing the operating state of the plurality of electrical appliances grouped for each of the first feature items, the number of groups grouped the electrical appliances for each of the second feature items More than the number of groups.

  A display device according to another aspect of the present disclosure is a display device that displays a diagnosis result of a diagnostic device that diagnoses an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances. A transmission unit that transmits identification information for identifying a device to the diagnostic device, and statistical information used by the diagnostic device for diagnosing the target electrical appliance is a first of the target electrical appliances among the plurality of electrical appliances. First identical feature statistical information obtained by aggregating first log information related to an operating state of the first identical feature electrical appliance having the same feature item as the first feature item indicated by the feature information, and the plurality of electrical appliances Log information about the operating state of the second identical feature electrical appliance having the same feature item as the second feature item indicated by the second feature information different from the first feature item of the target appliance 2nd total The receiving unit that receives from the diagnostic device statistical information specifying information indicating which statistical information is the same feature statistical information, and determining the accuracy of the diagnosis based on the statistical information specifying information, the determined A display unit for determining a display content for notifying the diagnosis result information according to the accuracy of diagnosis and displaying a notification screen indicating the determined display content.

  With such a configuration, identification information for identifying the target electrical appliance is transmitted to the diagnostic device, and the statistical information used by the diagnostic device for diagnosis of the target electrical appliance is the first of the target electrical appliances among the plurality of electrical appliances. First identical feature statistical information obtained by aggregating the first log information related to the operating state of the first identical feature electrical appliance having the same feature item as the first feature item indicated by the feature information, and a plurality of electrical appliances The second log information related to the operating state of the second identical feature electrical appliance having the same feature item as the second feature item indicated by the second feature information different from the first feature item of the target electrical appliance is tabulated. The statistical information specifying information indicating which statistical information is the second same feature statistical information is received from the diagnostic device, the accuracy of diagnosis is determined based on the received statistical information specifying information, and the determined diagnosis information Depending on the accuracy Since the display content for notifying the result information is determined and the notification screen showing the determined display content is displayed, the accuracy of the diagnosis is determined according to the statistical information used for the diagnosis, and the diagnosis The display contents of the diagnosis result information indicating the result of diagnosis according to the accuracy can be changed and displayed, and the accuracy of diagnosis of the operating status of the target electrical appliance can be presented to the user, and the statistics Even in a situation where the number of samples of log information for calculating information is small and the accuracy of statistical information is low, the operating state of the electrical appliance can be diagnosed with sufficient accuracy.

  It can also be realized as a computer program that causes a computer to execute characteristic processing included in the above-described diagnosis method. Needless to say, such a computer program can be distributed via a computer-readable non-transitory recording medium such as a CD-ROM or a communication network such as the Internet.

  Note that each of the embodiments described below is for showing a specific example of the present disclosure. Numerical values, shapes, components, steps, order of steps, and the like shown in the following embodiments are merely examples, and are not intended to limit the present disclosure. In addition, among the constituent elements in the following embodiments, constituent elements that are not described in the independent claims indicating the highest concept are described as optional constituent elements. In all the embodiments, the contents can be combined. Moreover, you may comprise as a system which disperse | distributed some components and the other component of the diagnostic apparatus which concerns on one embodiment of the following to several computers.

(Embodiment)
Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings.

(1.1) Diagnosis system First, an overview of the diagnosis system in the present embodiment will be described. FIG. 1 is a block diagram illustrating an example of a configuration of a diagnostic system according to an embodiment of the present disclosure. As shown in FIG. 1, the diagnostic system includes a plurality of electrical appliances 1, a diagnostic device 3, and a display device 4, and each device is communicably connected via the Internet 2. In addition, the number of each apparatus is not specifically limited to this example, For example, you may use the some display apparatus 4. FIG.

  Each electric appliance 1 is installed in a home, for example, and is connected to the Internet 2 directly or indirectly via a communication device (not shown) such as a gateway. The electrical appliance 1 is, for example, a household electrical appliance (home electrical appliance) such as a television, a recorder, an air conditioner (air conditioner), a refrigerator, a washing machine, a microwave oven, or a rice cooker. Each electric appliance 1 is equipped with communication functions such as a wired LAN (Local Area Network), a wireless LAN, and a LTE (Long Term Evolution) standard for Internet connection.

  The Internet 2 is a public communication network. In the present embodiment, each device is communicably connected via the Internet 2, but is not particularly limited to this example, and other various networks can be used.

  The diagnosis device 3 is a server device that diagnoses the operating state of the electrical appliance 1 and is, for example, a cloud server configured on the cloud. The display device 4 is a device that displays the diagnosis result of the diagnosis device 3, and is, for example, a PC (Personal Computer).

  Next, the flow of information in the above diagnostic system will be described. First, each electrical appliance 1 transmits log data generated by the electrical appliance itself to the diagnostic device 3 periodically or irregularly. Here, the log data is, for example, information indicating the operation status, operation date and time, sensor information, etc. of the electrical appliance 1 (log information regarding the operating state of the electrical appliance 1). In the case of a washing machine, the number of revolutions of the drum, in the case of a refrigerator, the number of revolutions of the compressor, in the case of an air conditioner, including the number of revolutions of the compressor and the piping temperature, etc. Various kinds of information that can be acquired from the information may be included.

  Next, the diagnostic device 3 derives device statistical information necessary for diagnosis based on the log data accumulated from each electric appliance 1. And if the identification information for identifying the electric appliance 1 (object electric appliance) used as a diagnostic object is input from the exterior, the display apparatus 4 will transmit it to the diagnostic apparatus 3 as a diagnostic request | requirement. The diagnosis device 3 uses the log data collected from the electrical appliance 1 and the aggregated device statistical information to diagnose the operating state of the electrical appliance 1 that has received the diagnosis request, and returns the diagnosis result to the display device 4. . The display device 4 displays the diagnosis result.

  In the present embodiment, the case where the electrical appliance 1 is a home air conditioner (hereinafter referred to as “air conditioner”) will be described as an example. However, the electrical appliance to which this embodiment is applied is not limited to an air conditioner, and can be applied to any electrical appliance. Below, each apparatus which comprises the diagnostic system shown in FIG. 1 is demonstrated in detail.

(1.2) Electric appliance 1
FIG. 2 is a block diagram showing an example of the configuration of the electrical appliance 1 shown in FIG. As illustrated in FIG. 2, the electrical appliance 1 includes a control unit 101, a log acquisition unit 102, a log storage unit 103, an identification information storage unit 104, a log transmission unit 105, a compressor 106, and a sensor unit 107.

(1) Control unit 101
The control unit 101 controls the main function of the electrical appliance 1. This main function varies depending on the type of the electrical appliance 1. In the present embodiment, since the electrical appliance 1 is an air conditioner, a cooling function for discharging cold air, a heating function for discharging warm air, a dehumidifying function for reducing indoor humidity, and the like are main functions.

  Specifically, the control unit 101 controls components such as a compressor (compressor) 106 and a fan (not shown) provided in the air conditioner body (electric appliance 1) in accordance with an operation from the user of the electric appliance 1. I do. The compressor 106 is controlled by the control unit 101 and notifies the control unit 101 of its own rotation speed. The sensor unit 107 detects the temperature of the pipe through which the refrigerant passes and notifies the control unit 101 of the pipe temperature. The control unit 101 notifies the log acquisition unit 102 of the rotation speed of the compressor 106 and the piping temperature of the sensor unit 107.

  The main function of the electric appliance 1 is not particularly limited to the above example. When the electric appliance 1 is a refrigerator, it is a cooling function that cools the interior using a compressor, and when it is a washing machine, tap water The main function is a washing function for washing laundry with a detergent and a drying function for drying the laundry.

(2) Log acquisition unit 102
The log acquisition unit 102 generates log data in the course of the control operation of the control unit 101. As described above, the log data is information indicating, for example, the operation status, the operation date and time, sensor information, and the like. However, since the electrical appliance 1 is an air conditioner, the rotation speed of the compressor 106 used to compress the refrigerant ( The log data includes the unit (times / second) and the temperature (unit: degrees) of the pipe through which the refrigerant measured by the sensor unit 107 passes. However, the present invention is not limited to such information, and may include various information that can be acquired from various devices.

  The log acquisition unit 102 acquires the rotation speed of the compressor 106 and the piping temperature of the sensor unit 107 from the control unit 101, generates log data, and stores the generated log data in the log storage unit 103 together with a time stamp. The time stamp is date information when the log data is acquired.

(3) Log storage unit 103
The log storage unit 103 includes, for example, a RAM (Random Access Memory) and an EEPROM (Electrically Erasable Programmable Read-Only Memory). FIG. 3 is a diagram illustrating an example of a data structure of the log storage unit 103 illustrated in FIG.

  As shown in FIG. 3, the log storage unit 103 is a table composed of n sets (n is an integer of 2 or more) of data that includes a time stamp, a compressor rotation speed, and a pipe temperature as log data. Is remembered. For example, the compressor rotation speed “50” and the pipe temperature “13” are stored for the time stamp “2016/6/28 13:51” as the uppermost data in the table. From this data, it can be seen that the rotation speed of the compressor 106 at 13:51 on June 28, 2016 is 50 times / second, and the piping temperature of the sensor unit 107 is 13 degrees.

(4) Identification information storage unit 104
The identification information storage unit 104 includes, for example, a ROM (Read Only Memory), an EEPROM, and the like. FIG. 4 is a diagram illustrating an example of the data structure of the identification information storage unit 104 illustrated in FIG.

  As shown in FIG. 4, the identification information storage unit 104 stores a table composed of model information and a manufacturing number as identification information. The model information is information indicating the model of the electrical appliance 1, and the production number is a unique number that uniquely identifies the electrical appliance 1 and is a number that is individually assigned at the time of manufacture.

  FIG. 4 shows an example in which the model information is “AC2016-H” and the serial number is “12345”. Here, “AC” is an abbreviation for an air conditioner (air conditioner), “2016” is a model made in 2016, and “H” is an abbreviation for a higher model (HIGH). Note that the configuration of the model information is not particularly limited to the above example, and various changes are possible, and a model number including a series number specifying a product series may be used. Also, the identification information is not particularly limited to the above example, and various modifications are possible. Only the model information is used as the identification information, or only the production number is used as the identification information, and the model information is obtained from the production number. You may specify.

(5) Log transmission unit 105
Based on a predetermined trigger, the log transmission unit 105 combines the model information and the serial number acquired from the identification information storage unit 104, the time stamp acquired from the log storage unit 103, the compressor rotation speed, and the pipe temperature. 2 to the diagnostic device 3.

  The log transmission unit 105 acquires and transmits only log data that has not been transmitted to the diagnostic device 3 among the log data in the log storage unit 103. Therefore, the log data transmitted to the diagnostic device 3 may be configured to be sequentially deleted from the log storage unit 103, or may be determined by preparing a flag for determining whether or not it has been transmitted.

  Moreover, various timings can be used as a trigger for transmitting data from the log transmission unit 105 to the diagnostic device 3. For example, the log transmission unit 105 includes a timer counter and periodically transmits the data every 10 minutes. Alternatively, it may be transmitted at a timing when the state has changed, such as when the operation of the electric appliance 1 is started or when the operation is stopped.

(1.3) Diagnostic device 3
FIG. 5 is a block diagram showing an example of the configuration of the diagnostic apparatus 3 shown in FIG. As illustrated in FIG. 5, the diagnostic device 3 includes a log reception unit 301, a log storage unit 302, a model information storage unit 303, a device statistical information calculation unit 304, a device statistical information storage unit 305, a transmission / reception unit 306, a diagnosis unit 307, A statistical value determination unit 308, a device connection information storage unit 309, and a logic storage unit 310 are provided. The diagnostic device 3 is, for example, a server device.

(1) Log receiving unit 301
The log receiving unit 301 receives the model information and the serial number information as identification information of the electrical appliance 1 and the log data of the electrical appliance 1 from the plurality of electrical appliances 1 periodically or irregularly via the Internet 2. Receive a certain time stamp, compressor speed and pipe temperature information. Then, the log receiving unit 301 stores the received identification information and log data in the log storage unit 302.

(2) Log storage unit 302
The log storage unit 302 includes, for example, a RAM, an EEPROM, and the like. FIG. 6 is a diagram illustrating an example of a data structure of the log storage unit 302 illustrated in FIG. As shown in FIG. 6, the log storage unit 302 includes m sets (m is an integer of 2 or more) of log data, which includes model information, serial number, time stamp, compressor rotation speed, and piping temperature as one set. A table composed of data is stored. For example, the model information “AC2016-H” and the serial number “12345” of the electrical appliance 1 are stored in the top row of the table, and the time stamp “2016/6/28 13:51” of the electrical appliance 1 is stored. The rotational speed “50” of the compressor and the pipe temperature “13” are stored. From these data, the rotational speed of the compressor 106 at 13:51 on June 28, 2016 of the electrical appliance 1 with the model information “AC2016-H” and the production number “12345” is 50 times / second, It turns out that the piping temperature of the part 107 is 13 degree | times.

(3) Model information storage unit 303
The model information storage unit 303 includes, for example, a ROM, an EEPROM, or the like. FIG. 7 is a diagram illustrating an example of a data structure of the model information storage unit 303 illustrated in FIG.

  As shown in FIG. 7, the model information storage unit 303 is a table composed of k sets (k is an integer of 2 or more) of data including model information, year information, and capability information as model information. I remember it. The model information is information indicating the model of the electrical appliance 1 and is the same as the model information stored in the identification information storage unit 104 of the electrical appliance 1.

  The year information is information that identifies in which year the electrical appliance 1 of the corresponding model information was manufactured (manufacturing year). For example, year information “2016” indicates a model made in 2016. Moreover, capability information is information which specifies the capability of the electric appliance 1 of corresponding model information. For example, capability information “HIGH” indicates a high-level model (high capability), “MIDDLE” indicates a medium-level model (capability is medium), and “LOW” indicates a low-level model (low capability).

  In addition, the number of groups when a plurality of electrical appliances 1 are grouped for each model is larger than the number of groups when a plurality of electrical appliances 1 are grouped for each production year, and a plurality of electrical appliances 1 are grouped for each production year. The number of groups at this time is larger than the number of groups when a plurality of electrical appliances 1 are grouped for each capability.

  Here, in the present embodiment, the model information corresponds to an example of first feature information indicating the first feature item of the electrical appliance 1 to be diagnosed, and the year information is the electrical appliance 1 to be diagnosed. This corresponds to an example of second feature information indicating a second feature item different from the first feature item, and the capability information is a third feature item different from the first and second feature items of the electrical appliance 1 to be diagnosed. This corresponds to an example of third feature information indicating the feature item. The feature information is not particularly limited to the above example, and two or more types of feature information may be used, or information indicating other features may be used.

  Moreover, year information and capability information are examples of attribute information indicating attribute items related to at least one of the product model and capability of the electrical appliance to be diagnosed. The attribute information is not particularly limited to the above example, and three or more types of attribute information may be used, or other information regarding at least one of the product model and the capability may be used.

(4) Device statistical information calculation unit 304
The device statistical information calculation unit 304 acquires log data including identification information from the log storage unit 302 and model information from the model information storage unit 303 based on a predetermined trigger. The device statistical information calculation unit 304 calculates device statistical information and device connection information from these pieces of information, and stores the device statistical information in the device statistical information storage unit 305 and the device connection information in the device connection information storage unit 309. . Various triggers can be used as triggers for the device statistical information calculation unit 304 to calculate the device statistical information and the device connection information. For example, the device statistical information calculation unit 304 includes a timer counter and is periodically updated every hour. Or may be calculated at a timing when a diagnosis request from the display device 4 is received.

  Here, as an example, an example will be described in which device statistical information and device connection information are calculated based on the above-described three types of first to third feature information.

  First, a method for calculating device statistical information will be described. The device statistical information calculation unit 304 calculates three types of statistical values as device statistical information, that is, statistical values of model information, statistical values of year information, and statistical values of capability information. Each statistical value includes, for example, log data such as compressor rotation speed and pipe temperature collected from a plurality of electrical appliances 1 for each model indicated by the model information, for each year indicated by the year information, and for each capability indicated by the capability information. This is the average value obtained by aggregation.

  That is, the device statistical information calculation unit 304 calculates the first statistical value for each type (model) of model information, calculates the second statistical value for each type (year) of year information, The eye statistics are calculated for each type of ability information (ability).

  Specifically, the device statistical information calculation unit 304 stores the corresponding model information and the log associated with the model information from the log storage unit 302 as the first statistical value (for each type of model information). Data (compressor speed and pipe temperature) is extracted, and the average value of compressor speed and the average value of pipe temperature are derived for each model.

  Further, the device statistical information calculation unit 304 extracts model information associated with the corresponding year information from the model information storage unit 303 as the second statistical value (for each type of year information), and then The log data associated with the extracted model information is extracted from the log storage unit 302, and the average value of the compressor speed and the average value of the pipe temperature are derived every year.

  Further, the device statistical information calculation unit 304 extracts model information associated with the corresponding capability information from the model information storage unit 303 as a third statistical value (for each type of capability information), and then Log data associated with the extracted model information is extracted from the log storage unit 302, and an average value of the compressor speed and an average value of the piping temperature are derived for each capacity.

  The characteristics of these three statistics will be described. When diagnosing the electrical appliance 1, the first statistical value (for each type of model information) for each model that matches the model of the model information of the electrical appliance 1 to be diagnosed It is desirable to use statistical values. Because the models match, the first statistical value is considered to represent the most characteristic of the electrical appliance 1 to be diagnosed, and is, for example, cooler than other individuals of the same model. This is because accurate diagnosis can be made accurately with high accuracy, such as when it is difficult or when the rotation speed of the compressor is low.

  On the other hand, the second statistical value (for each type of year information) and the third statistical value (for each type of capability information) have characteristics of the electric appliance 1 that do not match the model of the electric appliance 1 to be diagnosed. Therefore, from the first statistical value (for each type of model information), it is unlikely that the feature of the electrical appliance 1 that is the diagnosis target is most representative.

  However, the electrical equipment 1 that matches the fiscal year is not as high as the electrical equipment 1 that matches the model, but represents the characteristics of the electrical equipment 1 that is the diagnosis target. This enables accurate diagnosis. Moreover, although the electric appliance 1 with which capability matches is not as high as the electric appliance 1 with which a fiscal year matches, it represents the characteristic of the electric appliance 1 used as a diagnostic object, and when the 2nd statistics value cannot be used, it is low precision. This enables accurate diagnosis.

  Next, a method for calculating device connection information will be described. Similar to the device statistical information, the device statistical information calculation unit 304 uses the information on the number of electrical appliances 1 used to calculate the statistical values described above, and calculates three types of connected devices. The first device connection information is the number of connections for each type of model information, the second statistical value is the number of connections for each type of year information, and the third is the number of types of capability information for each type. The number of connections.

  Specifically, the device statistical information calculation unit 304 extracts log data that matches the model information from the log storage unit 302 and counts the number of unique serial numbers to determine the first connected number (model information Derived for each type).

  In addition, the device statistical information calculation unit 304 first extracts model information that matches year information from the model information storage unit 303, and then extracts log data that matches the extracted model information from the log storage unit 302. By counting the number of unique serial numbers, the second connected number (for each type of year information) is derived.

  Further, the device statistical information calculation unit 304 first extracts the model information that matches the capability information from the model information storage unit 303, and then extracts the log data that matches the extracted model information from the log storage unit 302. By counting the number of serial numbers, the third connected number (for each type of capability information) is derived. These three pieces of device connection information are used for estimating the accuracy of the corresponding device statistical information. That is, as the number of connected devices increases, it is determined that the accuracy of the device statistical information is high. Conversely, when the device connection information is small, it is determined that the accuracy of the device statistical information is low.

(5) Device statistical information storage unit 305
For example, the device statistical information storage unit 305 includes a RAM, an EEPROM, and the like. 8 is a diagram showing an example of the data structure of the first device statistical information in the device statistical information storage unit 305 shown in FIG. 5, and FIG. 9 shows two examples of the device statistical information storage unit 305 shown in FIG. FIG. 10 is a diagram illustrating an example of the data structure of the device statistical information of the eye, and FIG. 10 is a diagram illustrating an example of the data structure of the third device statistical information in the device statistical information storage unit 305 illustrated in FIG. 8 to 10 exemplify three types of tables, the invention is not particularly limited to this example, and various changes can be made.

  The device statistical information storage unit 305 stores tables as shown in FIGS. The first table shown in FIG. 8 is a table of device statistical information for each type of model information, and one set includes model information, an average value of the rotation speed of the compressor, and an average value of the pipe temperature. (L is an integer of 2 or more). The second table shown in FIG. 9 is a table of device statistical information for each type of year information, and t sets in which the year information, the average value of the rotation speed of the compressor, and the average value of the pipe temperature are set as one set. (T is an integer of 2 or more). The third table shown in FIG. 10 is a table of device statistical information for each type of capability information, and s sets in which the capability information, the average value of the rotation speed of the compressor, and the average value of the pipe temperature are set as one set. (S is an integer of 2 or more).

(6) Transmission / reception unit 306
The transmission / reception unit 306 transmits / receives various information to / from the display device 4 via the Internet 2. The transmission / reception unit 306 has two major functions. As a first function, the transmission / reception unit 306 receives diagnosis request information from the display device 4 and outputs the diagnosis request information to the diagnosis unit 307. The diagnosis request information includes model information and a serial number of the electrical appliance 1 that is a diagnosis target. As a second function, the transmission / reception unit 306 transmits the diagnosis result output from the diagnosis unit 307 to the display device 4.

(7) Diagnosis unit 307
The diagnosis unit 307 receives diagnosis request information from the display device 4 via the transmission / reception unit 306. Then, the diagnosis unit 307 first outputs the model information included in the diagnosis request information to the statistical value determination unit 308, and from the statistical value determination unit 308, the statistical value to be used and the statistical value derivation source information as accompanying information Receive. Here, the derivation source information is statistical information specifying information indicating a unit in which a statistical value is calculated. For example, the derivation source information is information indicating “for each model information”, “for each year”, or “for each capability”.

  Thereafter, the diagnosis unit 307 acquires the latest log data stored in association with the manufacturing number of the electrical appliance 1 to be diagnosed from the log storage unit 302 and also diagnoses items and diagnoses from the logic storage unit 310. Get logic and. The diagnosis unit 307 uses the statistical value received from the statistical value determination unit 308 and the log data of the electrical appliance 1 to be diagnosed received from the log storage unit 302 to check whether the diagnostic logic is applicable. The diagnosis unit 307 determines “applicable” for the diagnosis item of the diagnosis logic when the diagnosis logic is applicable, and determines “not applicable” when the diagnosis logic is not applicable. Then, the diagnosis unit 307 uses the transmission / reception unit 306 as a diagnosis result by combining the information indicating whether each diagnosis item is applicable or not and the derivation source information received from the statistical value determination unit 308 as accompanying information. And responds to the display device 4.

(8) Statistical value determination unit 308
The statistical value determination unit 308 receives model information from the diagnosis unit 307 and outputs a statistical value and accompanying information to the diagnosis unit 307. First, the statistical value determination unit 308 acquires device connection information corresponding to the received model information from the highest priority table 1 in the device connection information storage unit 309. Next, when the acquired device connection information is equal to or greater than a given threshold value, the statistical value determination unit 308 calculates the average value of the compressor rotation speed as the statistical value corresponding to the same model information from the device statistical information storage unit 305. And the average value of the pipe temperature, together with the average value of the compressor rotation speed and the average value of the pipe temperature, the information indicating that these statistical values correspond to the model information is diagnosed as accompanying information. It responds to the unit 307.

  On the other hand, when the acquired device connection information is smaller than the given threshold value, the statistical value determination unit 308 acquires year information corresponding to the same model information from the model information storage unit 303. Then, the statistical value determination unit 308 acquires the device connection information corresponding to the acquired year information from the priority 2 table having the next highest priority in the device connection information storage unit 309. Next, when the acquired device connection information is equal to or greater than a given threshold value, the statistical value determination unit 308 calculates an average value of the compressor rotation speed as a statistical value corresponding to the same year information from the device statistical information storage unit 305. And the average value of the pipe temperature, together with the average value of the compressor rotation speed and the average value of the pipe temperature, information indicating that these statistics correspond to the year information is diagnosed as accompanying information It responds to the unit 307.

  On the other hand, when the device connection information corresponding to the year information is smaller than the given threshold value, the statistical value determination unit 308 finally acquires capability information corresponding to the same model information from the model information storage unit 303. Then, the statistical value determination unit 308 acquires device connection information corresponding to the acquired capability information from the priority 3 table having the next highest priority in the device connection information storage unit 309. Next, when the acquired device connection information is equal to or greater than a given threshold value, the statistical value determination unit 308 calculates an average value of the compressor rotation speed as a statistical value corresponding to the same capability information from the device statistical information storage unit 305. And the average value of the pipe temperature, together with the average value of the compressor rotation speed and the average value of the pipe temperature, information indicating that these statistics correspond to the capacity information is diagnosed as accompanying information It responds to the unit 307.

  On the other hand, when the device connection information is smaller than the given threshold value, the statistical value determination unit 308 responds to the diagnosis unit 307 with a notification that the diagnosis is impossible. Each of the above threshold values is given as, for example, “200”, but is not particularly limited to this example, and various changes can be made, and values different for each model information, year information, and capability information. May be used.

(9) Device connection information storage unit 309
For example, the device connection information storage unit 309 includes a RAM, an EEPROM, and the like. 11 is a diagram showing an example of the data structure of the first device connection information in the device connection information storage unit 309 shown in FIG. 5, and FIG. 12 shows two examples of the device connection information storage unit 309 shown in FIG. FIG. 13 is a diagram illustrating an example of the data structure of the device connection information of the eye, and FIG. 13 is a diagram illustrating an example of the data structure of the third device connection information in the device connection information storage unit 309 illustrated in FIG. In FIG. 11 to FIG. 13, three types of tables are given as examples. However, the present invention is not particularly limited to this example, and various changes can be made.

  The device connection information storage unit 309 stores the tables shown in FIGS. 11 to 13. The first table shown in FIG. 11 is a table of device connection information for each type of model information, and is composed of data of l sets (l is an integer of 2 or more) with model information and the number of connected devices as one set. “1” is stored as the priority of the model information. The second table shown in FIG. 12 is a table of device connection information for each type of year information, and is composed of t sets (t is an integer of 2 or more) of data that includes year information and the number of connected units. “2” is stored as the priority of the year information. The third table shown in FIG. 13 is a table of device connection information for each type of capability information, and is composed of data of s sets (s is an integer of 2 or more) with the capability information and the number of connected devices as one set. “3” is stored as the priority of capability information.

(10) Logic storage unit 310
As an example, the logic storage unit 310 includes a ROM, an EEPROM, or the like. FIG. 14 is a diagram illustrating an example of a data structure of the logic storage unit 310 illustrated in FIG.

  The logic storage unit 310 stores a table shown in FIG. The table shown in FIG. 14 assumes an air conditioner, and is composed of diagnostic items and diagnostic logic. For example, the first diagnostic item is “insufficient piping cooling”, and the diagnostic logic for determination is “5 degrees higher than the average piping temperature”. The diagnostic items and the diagnostic logic are not particularly limited to this example, and various changes can be made.

(1.4) Display device 4
FIG. 15 is a block diagram showing an example of the configuration of the display device 4 shown in FIG. As shown in FIG. 15, the display device 4 includes an input / output unit 401, a diagnosis target specifying unit 402, a diagnosis request transmission processing unit 403, a transmission / reception unit 404, a diagnosis result reception processing unit 405, and a diagnosis result display processing unit 406. .

(1) Input / output unit 401
The input / output unit 401 receives various inputs from an operator (user) of the display device 4 and presents various information to the operator. The input / output unit 401 includes, for example, a keyboard, a mouse, a display, and the like. The operator is a subject who diagnoses the electrical appliance 1, and is, for example, a call center operator who receives a telephone response from a user of the electrical appliance 1, a developer who confirms the quality of the electrical appliance 1, or the like.

(2) Diagnosis target specifying unit 402
The diagnosis target specifying unit 402 acquires information on the electrical appliance 1 to be diagnosed via the input / output unit 401 and outputs the information to the diagnosis request transmission processing unit 403. FIG. 16 is a diagram showing an example of an input screen displayed on the input / output unit 401 of the display device 4 shown in FIG. For example, the input / output unit 401 presents an input screen as shown in FIG. 16 to the operator and prompts for input of model information and a manufacturing number, and the diagnosis target specifying unit 402 inputs the model information and manufacturing input by the operator. Get the number and.

(3) Diagnosis request transmission processing unit 403
The diagnosis request transmission processing unit 403 acquires information (model information and serial number) of the electrical appliance 1 to be diagnosed from the diagnosis target specifying unit 402, and uses the information as diagnosis request information via the transmission / reception unit 404. 3 to send. The diagnosis request information here includes model information and a serial number.

(4) Transmission / reception unit 404
The transmission / reception unit 404 transmits / receives various data to / from the diagnostic device 3 via the Internet 2. The transmission / reception unit 404 has two major functions. As a first function, the transmission / reception unit 404 receives diagnosis request information from the diagnosis request transmission processing unit 403 and transmits it to the diagnosis device 3. As a second function, the transmission / reception unit 404 receives a diagnosis result from the diagnosis device 3 and outputs it to the diagnosis result reception processing unit 405.

(5) Diagnosis result reception processing unit 405
The diagnostic result reception processing unit 405 receives the diagnostic result from the diagnostic device 3 via the transmission / reception unit 404. Then, the diagnosis result reception processing unit 405 outputs the received diagnosis result to the diagnosis result display processing unit 406.

(6) Diagnosis result display processing unit 406
The diagnosis result display processing unit 406 receives the diagnosis result from the diagnosis result reception processing unit 405 and presents the diagnosis result to the operator via the input / output unit 401. FIG. 17 is a diagram illustrating an example of a result display screen displayed on the input / output unit 401 of the display device 4 illustrated in FIG. 15, and FIG. 18 is displayed on the input / output unit 401 of the display device 4 illustrated in FIG. 15. It is a figure which shows another example of a result display screen.

  FIG. 17 is an example of a result display screen that displays a diagnosis result when the determination accuracy is high, and FIG. 18 is an example of a result display screen that displays a diagnosis result when the determination accuracy is low. As shown in FIGS. 17 and 18, for each diagnosis item, “probability of abnormality” is displayed if the diagnosis logic is applicable, and “normal” is displayed if the diagnosis logic is not applicable. As the accompanying information, when the derivation source of the statistical value used in the diagnosis logic is “model information”, the determination accuracy is displayed as “high” as shown in FIG. In the case of “information”, the determination accuracy is displayed as “medium”, and when the statistical value derivation source is “ability information”, the determination accuracy is displayed as “low” as shown in FIG. The operator interprets the determination result with reference to the determination accuracy, and uses it for the customer response at the time of telephone reception and the quality check of the electrical appliance 1.

(1.5) Operation Hereinafter, after describing the operation of the above-described diagnostic system, detailed operations of the electrical appliance 1, the diagnostic device 3, and the display device 4 will be described.

(1) Operation of Diagnostic System The operation of the diagnostic system shown in FIG. 1 will be described with reference to FIG. FIG. 19 is a diagram showing an example of the entire sequence of the diagnostic system shown in FIG.

  The electrical appliance 1 acquires log data relating to the operating state of the electrical appliance 1 and transmits it to the diagnostic device 3, and the diagnostic device 3 stores the received log data (step S100). Then, the diagnostic device 3 derives and stores device statistical information using the stored log data and model information (step S200). Finally, the display device 4 transmits diagnosis request information to the diagnosis device 3 in response to a request from the operator, and the diagnosis device 3 performs a diagnosis based on the received diagnosis request information and displays the diagnosis result on the display device. In response to 4, the display device 4 presents the diagnosis result to the operator (step S300). In addition, these processes show the outline | summary of the process of a diagnostic system, and various changes are possible for the order of each process, the frequency | count of each process, etc. FIG.

(2) Detailed Operation of Step S100 FIG. 20 is a flowchart illustrating an example of the log transmission / reception process S100 illustrated in FIG. 19, and the log transmission / reception process S100 is a process in which the electrical appliance 1 transmits log data to the diagnostic device 3. .

  First, the log acquisition unit 102 of the electrical appliance 1 acquires the rotation speed of the compressor 106, the piping temperature of the sensor unit 107, and the like from the control unit 101, and generates log data (step S101). Next, the log acquisition unit 102 stores the log data in the log storage unit 103 (step S102). Thereafter, the log transmission unit 105 combines the model information and serial number acquired from the identification information storage unit 104, the time stamp acquired from the log storage unit 103, the compressor rotation speed, and the pipe temperature based on a predetermined trigger. Then, it is transmitted to the diagnostic apparatus 3 via the Internet 2 (step S103). At this time, the log receiving unit 301 of the diagnostic device 3 receives log data or the like from the electrical appliance 1 via the Internet 2 and stores the received log data or the like in the log storage unit 302 (step S104).

(3) Detailed Operation of Step S200 FIG. 21 is a flowchart showing an example of the device statistical information derivation process S200 shown in FIG. 19, and the device statistical information derivation process S200 is a process in which the diagnostic apparatus 3 derives the device statistical information. is there.

  First, the device statistical information calculation unit 304 of the diagnostic device 3 counts up a timer counter (step S201). Next, the device statistical information calculation unit 304 determines whether or not the count value is equal to or greater than a predetermined threshold (for example, a value corresponding to one hour) (step S202). NO at S202), the process of step S201 is repeated.

  On the other hand, when the count value is equal to or greater than the predetermined threshold (YES in step S202), the device statistical information calculation unit 304 uses three types of device statistical information as statistical values for each type of model information and for each type of year information. A statistical value and a statistical value for each type of capability information are calculated and recorded in the device statistical information storage unit 305 (step S203).

  Next, the device statistical information calculation unit 304 calculates the number of connections for each type of model information, the number of connections for each type of year information, and the number of connections for each type of capability information as three types of device connection information. And recorded in the device connection information storage unit 309 (step S204). Finally, the device statistical information calculation unit 304 resets the timer counter, returns to step S201, and continues the subsequent processing (step S205).

(4) Detailed Operation of Step S300 FIG. 22 is a flowchart illustrating an example of the electrical appliance diagnosis process S300 illustrated in FIG. 19. The electrical appliance diagnosis process S300 is performed by the electrical appliance 1 to be diagnosed with respect to the display device 4. The diagnosis request is transmitted to the diagnosis device 3, the diagnosis device 3 diagnoses the operating state using the log data collected from the electrical appliance 1 and the device statistical information, and returns the diagnosis result to the display device 4. This is a process of displaying the diagnosis result on the display device 4.

  First, the input / output unit 401 of the display device receives an input from an operator, and the diagnosis target specifying unit 402 acquires information on the electrical appliance 1 to be diagnosed and outputs the information to the diagnosis request transmission processing unit 403 (step). S301). Next, the diagnosis request transmission processing unit 403 acquires information of the electrical appliance 1 that is a diagnosis target from the diagnosis target specifying unit 402, and transmits the information as diagnosis request information to the diagnosis device 3 via the transmission / reception unit 404. (Step S302).

  Next, the transmission / reception unit 306 of the diagnostic device 3 receives the diagnostic request information from the display device 4 via the Internet 2 and outputs the diagnostic request information to the diagnostic unit 307. The diagnostic unit 307 The diagnosis request information is received from the display device 4 (step S303). Next, the diagnosis unit 307 outputs the model information included in the diagnosis request information to the statistical value determination unit 308. The statistical value determination unit 308 receives the model information from the diagnosis unit 307, determines the statistical information, and the diagnosis unit A statistical value and accompanying information are output to 307 (step S304).

  Next, the diagnosis unit 307 receives the statistical value to be used and the derivation source information of the statistical value as accompanying information, and then acquires the diagnostic item and the diagnostic logic from the logic storage unit 310, and from the statistical value determination unit 308. A diagnosis is performed by confirming whether the diagnostic logic is applicable using the received statistical value (step S305). Next, the diagnosis unit 307 transmits the diagnosis result to the display device 4 via the transmission / reception unit 306 (step S306).

  Next, the transmission / reception unit 404 of the display device 4 receives the diagnosis result from the diagnosis device 3 and outputs it to the diagnosis result reception processing unit 405, and the diagnosis result reception processing unit 405 passes through the transmission / reception unit 404. The diagnosis result from is received (step S307). Finally, the diagnostic result display processing unit 406 outputs the received diagnostic result to the diagnostic result display processing unit 406, and the diagnostic result display processing unit 406 receives the diagnostic result from the diagnostic result reception processing unit 405, and the input / output unit 401. Is used to present the diagnosis result to the operator (step S308).

(5) Detailed Operation of Step S304 FIG. 23 is a flowchart showing an example of the statistical information determination process S304 shown in FIG.

  First, the statistical value determination unit 308 of the diagnostic apparatus 3 acquires device connection information corresponding to the received model information from the device connection information storage unit 309 (step S311). Next, the statistical value determination unit 308 acquires device connection information corresponding to the received model information from the priority level 1 table in the device connection information storage unit 309, and the device connection information is predetermined. It is determined whether or not the threshold value is greater than or equal to (step S312).

  If the device connection information is equal to or greater than the threshold (YES in step S312), the statistical value determination unit 308 obtains an average value of the rotation speed of the compressor as a statistical value corresponding to the same model information from the device statistical information storage unit 305. Obtain the average value of the pipe temperature, together with the average value of the rotation speed of the compressor and the average value of the pipe temperature, and information indicating that these statistical values correspond to the model information as the accompanying information, the diagnostic unit It responds to 307 (step S313).

  On the other hand, if the device connection information is smaller than the threshold (NO in step S312), the statistical value determination unit 308 determines whether or not the device connection information having the next priority is in the device connection information storage unit 309 ( Step S314). When there is device connection information having the next priority (YES in step S314), the statistical value determination unit 308 acquires wider information (for example, year information) corresponding to the same model information from the model information storage unit 303. The device connection information corresponding to the acquired wide information is acquired from the next priority table in the device connection information storage unit 309 (step S315). Next, the statistical value determination unit 308 shifts the process to step S312, determines whether the acquired device connection information is equal to or greater than the threshold value, and repeats the subsequent processes.

  On the other hand, when there is no device connection information having the next priority (NO in step S314), the statistical value determination unit 308 responds to the diagnosis unit 307 with a notification that the diagnosis is impossible (step S316). In this way, the device statistical information is determined in the order of model information, year information, and capability information according to the priority.

(6) Detailed Operation of Step S305 FIG. 24 is a flowchart showing an example of the diagnostic process S305 shown in FIG. First, the diagnosis unit 307 of the diagnosis device 3 receives the diagnosis request information from the display device 4 via the transmission / reception unit 306, outputs the model information included in the diagnosis request information to the statistical value determination unit 308, and then the statistics A statistical value to be used and statistical value derivation source information are received as accompanying information from the value determining unit 308 (step S321).

  Next, the diagnosis unit 307 acquires the diagnosis item and the diagnosis logic from the logic storage unit 310, and confirms whether the diagnosis logic is applicable using the statistical value received from the statistical value determination unit 308 (step S322). ). In addition, the diagnosis unit 307 sets the corresponding diagnosis item as “applicable” when the diagnosis logic is applied, and sets the corresponding diagnosis item as “not applicable” when the diagnosis logic is not applied, and “applicable” or “not applicable” for each diagnosis item. ”And the statistical value derivation source information received from the statistical value determination unit 308 as the accompanying information are returned as a diagnosis result to the display device 4 via the transmission / reception unit 306 (step S322).

(7) Detailed Operation of Step S308 FIG. 25 is a flowchart showing an example of the diagnostic result display process S308 shown in FIG. First, the diagnostic result display processing unit 406 of the display device 4 receives the diagnostic result from the diagnostic result reception processing unit 405 (step S331). Next, the diagnosis result display processing unit 406 confirms the derivation source of the statistical value used in the diagnosis logic as accompanying information (step S332).

  At this time, when the statistical value derivation source is “model information”, the diagnosis result display processing unit 406 generates a first message that the determination accuracy is “high” (step S333), and the statistical value derivation source is “year”. In the case of “information”, a second message with a determination accuracy of “medium” is generated (step S334), and when the statistical value derivation source is “ability information”, a third message with a determination accuracy of “low” is generated ( Step S335).

  Finally, the diagnosis result display processing unit 406 displays each created message and the determination result on the input / output unit 401 (step S336).

(1.6) Summary and Effect As described above, in the present embodiment, when the number of connected electrical appliances of the same model as the electrical appliance 1 to be diagnosed is less than a predetermined threshold, electrification of the same model Since the log data of the equipment is small, the accuracy of the statistical value of the log data of the same type of electrical equipment is low, but since the number of connected electrical equipment in the same manufacturing year as the electrical equipment 1 to be diagnosed is large, the statistical value is By using log data of electric appliances in the same manufacturing year as log data for calculation, statistical values with sufficient accuracy can be obtained. Therefore, when the number of connected electrical appliances of the same model is smaller than a predetermined threshold, the operating state of the electrical appliance 1 to be diagnosed can be diagnosed with sufficient accuracy using the statistical values of the electrical appliances in the same manufacturing year. it can.

  In addition, when the number of connected electrical appliances in the same manufacturing year is less than a predetermined threshold, the log data of electrical appliances in the same manufacturing year is small, so the accuracy of the statistical values of the log data of electrical appliances in the same manufacturing year is low. Since there are many connected electrical appliances with the same performance as the electrical appliance 1 to be diagnosed, statistical data with sufficient accuracy can be obtained by using the log data of electrical appliances with the same performance as the log data for calculating statistical values. A value can be obtained. Therefore, when the number of connected electrical appliances in the same manufacturing year is less than a predetermined threshold, the operating state of the electrical appliance 1 to be diagnosed can be diagnosed with sufficient accuracy using the statistical values of electrical appliances having the same performance. it can.

  As described above, in the present embodiment, when there are a plurality of groups of electrical appliances 1 having different characteristics, statistical values necessary for quality prediction with a single diagnostic logic are calculated in parallel. Even in such a case, a system capable of predicting quality with sufficient accuracy by calculating a plurality of types of statistical values and selecting an optimal statistical value from among the plurality of types of statistical values is realized.

(Modification)
(1) In the above embodiment, an air conditioner has been described as an example. However, the electrical appliance 1 is not limited to an air conditioner, and may be any home appliance that is directly or indirectly connected to the Internet. For example, a refrigerator, a washing machine, a microwave oven, a rice cooker, an air purifier, a television, a recorder, or the like may be used.

  (2) The device connection information storage unit 309 holds the device connection information (number of connected devices) illustrated in FIGS. 11 to 13, but is not limited to this as long as the accuracy of the statistical value can be predicted. For example, a period from when the electric appliance 1 (for example, an air conditioner) is turned on to when it is turned off may be defined as one operation, and device operation information indicating the number of operations may be used. 26 is a diagram showing an example of the data structure of the first device operation information in the device connection information storage unit 309 shown in FIG. 5, and FIG. 27 shows two examples of the device connection information storage unit 309 shown in FIG. FIG. 28 is a diagram illustrating an example of a data structure of eye device operation information, and FIG. 28 is a diagram illustrating an example of a data structure of third device operation information in the device connection information storage unit 309 illustrated in FIG. 5.

  As shown in FIGS. 26 to 28, the device connection information storage unit 309 stores three types of tables including the number of operations. The first table shown in FIG. 26 is a table of device operation information for each type of model information, and is composed of data of l sets (l is an integer of 2 or more) with model information and the number of operations as one set. “1” is stored as the priority of the model information. The second table shown in FIG. 27 is a table of device operation information for each type of year information, and is composed of data of t sets (t is an integer of 2 or more) with the year information and the number of operations as one set. “2” is stored as the priority of the year information. The third table shown in FIG. 28 is a table of device operation information for each type of capability information, and is composed of s groups (s is an integer of 2 or more) with the capability information and the number of operations as one set. “3” is stored as the priority of capability information.

  Also in this case, similarly to the above-described embodiment, the statistical value determination unit 308 refers to each table in order of priority, and determines the predetermined threshold value and the electric appliance of the same model as the electric appliance 1 to be diagnosed. The type of statistical value to be used by sequentially comparing the number of operations, the number of operations of the electric appliance in the same manufacturing year as the electric appliance 1 to be diagnosed, and the number of electric appliances with the same performance as the electric appliance 1 to be diagnosed The same effects as those of the above embodiment can be obtained. Note that the device operation information is not particularly limited to the above example, and other operation states of the electric appliance 1 may be used.

  In addition, the amount of log data transmitted from the electrical appliance 1 (for example, an air conditioner) (for example, the number of records when one log data is stored in one record in the storage area of the device connection information storage unit 309). The device data amount information shown may be used. FIG. 29 is a diagram illustrating an example of the data structure of the first device data amount information in the device connection information storage unit 309 illustrated in FIG. 5. FIG. 30 illustrates a second example of the device connection information storage unit 309 illustrated in FIG. FIG. 31 is a diagram illustrating an example of a data structure of first device data amount information, and FIG. 31 is a diagram illustrating an example of a data structure of third device data amount information in the device connection information storage unit 309 illustrated in FIG. is there.

  As illustrated in FIGS. 29 to 31, the device connection information storage unit 309 stores three types of tables including the amount of log data. The first table shown in FIG. 29 is a table of device data amount information for each type of model information, and is composed of l sets (l is an integer equal to or greater than 2) with 1 set of model information and data amount. “1” is stored as the priority of the model information. The second table shown in FIG. 30 is a table of device data amount information for each type of year information, and is composed of t sets (t is an integer of 2 or more) of data having one set of year information and data amount. And “2” is stored as the priority of the year information. The third table shown in FIG. 31 is a table of device data amount information for each type of capability information, and is composed of data of s sets (s is an integer of 2 or more) with the capability information and the data amount as one set. And “3” is stored as the priority of capability information.

  Also in this case, similarly to the above-described embodiment, the statistical value determination unit 308 refers to each table in order of priority, and determines the predetermined threshold value and the electric appliance of the same model as the electric appliance 1 to be diagnosed. The type of statistical value to be used by sequentially comparing the amount of data, the amount of data of the electrical appliance in the same manufacturing year as the electrical appliance 1 to be diagnosed, and the amount of electrical equipment with the same performance as the electrical appliance 1 to be diagnosed The same effects as those of the above embodiment can be obtained. The device data amount information is not particularly limited to the above example, and other information indicating the data amount of log data may be used.

  (3) The device connection information shown in FIGS. 11 to 13 has priority, but is not limited thereto. For example, the model relationship table of the electrical appliance 1 may be defined with a tree structure, and the priority may be omitted using model relationship information representing this tree structure. FIG. 32 is a diagram illustrating an example of model relation information stored in the model information storage unit 303 illustrated in FIG.

  For example, when the electrical appliance 1 is an air conditioner, even in the same 2016 product, an old model compressor may be used as a popular machine, and a new model compressor may be used as a high-end machine. Here, when the new model compressor is represented by “Compressor A”, the old model compressor is represented by “Compressor B”, and all models are represented by “Overall”, the model relationship table of the electrical appliance 1 is 32, model relation information representing the tree structure is stored in the model information storage unit 303.

  In the example shown in FIG. 32, the root node indicating “whole” as the feature item is the highest node, and the nodes indicating “compressor A” and “compressor B” as the feature items are intermediate nodes. A leaf node indicating each model (for example, “AC2016-H” or the like) is the lowest-order node, and these relationships are stored as model relationship information. Therefore, it is possible to express the relationship between various feature items that cannot be expressed with simple priority.

  In this case, the number of groups when the electric appliances 1 are grouped by model is 4, which is larger than the number of groups 2 when the electric appliances 1 are grouped by the feature items “compressor A” and “compressor B”. Further, the number of groups when the electric appliances 1 are grouped by the feature items “compressor A” and “compressor B” is two, which is larger than the number of groups 1 when the electric appliances 1 are grouped by the feature item “whole”.

  The tree structure is not particularly limited to this example, and various changes can be made. For example, a tree structure having four or more layers may be used. Also, the feature items are not particularly limited to this example, and other feature items such as a product series may be used.

  33 is a diagram showing an example of the data structure of the first tree structure device information in the device connection information storage unit 309 shown in FIG. 5 when the model relation information shown in FIG. 32 is used. 32 is a diagram illustrating an example of a data structure of second tree structure device information in the device connection information storage unit 309 illustrated in FIG. 5 when the model relation information illustrated in FIG. 32 is used.

  When the model relation information shown in FIG. 32 is used, for example, the device connection information storage unit 309 includes, as device operation information, a table including model information and the number of operations shown in FIG. 33, and feature items and operations shown in FIG. And a table including the number of times. The first table shown in FIG. 33 is a table of device operation information for each type of model information, and is composed of four sets of data with model information and the number of operations as one set. The second table shown in FIG. 34 is a table of device operation information for each type of feature item, and is composed of three sets of data with one set of feature items and the number of operations.

  In this case, the statistical value determination unit 308 refers to the model relation information stored in the model information storage unit 303 and starts searching for the model of the electrical appliance 1 to be diagnosed from the leaf node (lowest mode). If the operation frequency of the leaf node is not equal to or greater than the predetermined threshold value, by sequentially moving to the node above it, the predetermined threshold value, the operation frequency of the electric appliance of the same model as the electric appliance 1 to be diagnosed, and the diagnosis target It is possible to determine the type of statistical value to be used by sequentially comparing the number of operations of the electric appliance using the same compressor as the electric appliance 1 and the number of operations of all the electric devices 1. Therefore, even when various feature items that cannot be expressed with a simple priority order are used, the same effects as those of the above embodiment can be obtained.

  (4) The device statistical information storage unit 305 uses the average value shown in FIGS. 8 to 10 as the statistical value, but is not limited thereto. For example, dispersion may be added. 35 is a diagram showing a first modification of the data structure of the first device statistical information in the device statistical information storage unit 305 shown in FIG. 5, and FIG. 36 is a device statistical information storage unit shown in FIG. FIG. 37 is a diagram showing a first modification of the data structure of the second device statistical information 305, and FIG. 37 shows the data structure of the third device statistical information in the device statistical information storage unit 305 shown in FIG. It is a figure which shows a 1st modification.

  The first table shown in FIG. 35 is a table of device statistical information for each type of model information. The model information, the average value of the compressor rotation speed, and the variance of the compressor rotation speed are set as one set. It consists of l sets (l is an integer of 2 or more). The second table shown in FIG. 36 is a table of device statistical information for each type of year information. The year information, the average value of the compressor rotation speed, and the variance of the compressor rotation speed are set as one set. It is composed of t sets (t is an integer of 2 or more). The third table shown in FIG. 37 is a table of device statistical information for each type of capability information, and sets the capability information, the average value of the compressor speed, and the variance of the compressor speed as one set. It is composed of s sets (s is an integer of 2 or more). In this case, the original data can be normalized from -1 to 1. In addition to the variance, a median value, likelihood, or the like may be used.

  (5) The device statistical information storage unit 305 uses the average value of the single sensor values shown in FIGS. 8 to 10 as the statistical value, but is not limited thereto. For example, the diagnostic device 3 may obtain an average value of values obtained by calculating a plurality of sensor values using a predetermined calculation formula. 38 is a diagram illustrating a second modification of the data structure of the first device statistical information in the device statistical information storage unit 305 illustrated in FIG. 5, and FIG. 39 illustrates the device statistical information storage unit illustrated in FIG. FIG. 40 is a diagram illustrating a second modification of the data structure of the second device statistical information 305, and FIG. 40 is a diagram illustrating the data structure of the third device statistical information in the device statistical information storage unit illustrated in FIG. It is a figure which shows the modification of 2.

  The first table shown in FIG. 38 is a table of device statistical information for each type of model information. The model information and the average value of values obtained by dividing the piping temperature by the number of rotations of the compressor are set as one set. It consists of l sets (l is an integer of 2 or more). The second table shown in FIG. 39 is a table of device statistical information for each type of year information, and sets the year information and an average value of values obtained by dividing the pipe temperature by the number of rotations of the compressor. It is composed of t sets (t is an integer of 2 or more). The third table shown in FIG. 40 is a table of device statistical information for each type of capability information, and sets the capability information and an average value of values obtained by dividing the piping temperature by the rotation speed of the compressor. It is composed of s sets (s is an integer of 2 or more). Also in this case, the same effect as the above-described embodiment can be obtained. In addition, a sensor value and a calculation formula are not specifically limited to said example, You may use another sensor value and another calculation formula.

  (6) You may combine said each structure arbitrarily. In this case, the same effect as described above can be obtained.

(Overview of services provided)
FIG. 41A shows an overall image of the service according to the present embodiment. For example, some or all of the blocks of the above-described cloud server (diagnostic device 3) belong to either the cloud server 111 of the data center operating company 110 or the server 121 of the service provider 120 shown in FIG.

  The group 100 is, for example, a company, an organization, a home, etc., and its size is not limited. In the group 100, there are a plurality of devices (electric appliances) 1, device A, device B, and home gateway 102G. The plurality of devices 1 include devices that can be connected to the Internet (for example, smartphones, PCs, TVs, etc.) and devices that cannot be connected to the Internet by themselves (for example, lighting, washing machines, refrigerators, etc.). To do. Even if the device itself cannot be connected to the Internet, there may be a device that can be connected to the Internet via the home gateway 102G. The group 100 includes a user 10Y who uses a plurality of devices 1.

  The data center operating company 110 has a cloud server 111. The cloud server 111 is a virtualization server that cooperates with various devices via the Internet. The cloud server 111 mainly manages huge data (big data) that is difficult to handle with a normal database management tool or the like. The data center operating company 110 performs data management, management of the cloud server 111, operation of the data center that performs them, and the like. Details of services performed by the data center operating company 110 will be described later. Here, the data center operating company 110 is not limited to a company that performs only data management, operation of the cloud server 111, or the like. For example, when a device manufacturer that develops and manufactures one of a plurality of devices 1 also performs data management, management of the cloud server 111, and the like, the device manufacturer is in charge of the data center operating company 110. Applicable (FIG. 41B). The data center operating company 110 is not limited to one company. For example, when the device manufacturer and another management company jointly or share the data management and operation of the cloud server 111, both or one of them corresponds to the data center operating company 110 (FIG. 41). (C)).

  The service provider 120 has a server 121. The server 121 referred to here includes, for example, a memory in a personal PC regardless of the scale. In some cases, the service provider does not have the server 121.

  In the above service, the home gateway 102G is not essential. For example, when the cloud server 111 performs all data management, the home gateway 102G is unnecessary. In addition, there may be no device that cannot be connected to the Internet by itself, as in the case where every device in the home is connected to the Internet.

  Next, the flow of device log information (operation history information, operation history information, etc.) in the service will be described.

  First, the device A or device B of the group 100 transmits each log information to the cloud server 111 of the data center operating company 110. The cloud server 111 accumulates log information of the device A or the device B (FIG. 41 (a)). Here, the log information is information indicating, for example, driving conditions and operation dates / times of the plurality of devices 1. For example, TV viewing history, recorder recording reservation information, washing machine operation date / time, amount of laundry, refrigerator opening / closing date / time, number of times of opening / closing, etc. This information. The log information may be provided directly to the cloud server 111 from a plurality of devices 1 themselves via the Internet. Further, log information may be temporarily accumulated from the plurality of devices 1 in the home gateway 102G and provided to the cloud server 111 from the home gateway 102G.

  Next, the cloud server 111 of the data center operating company 110 provides the collected log information to the service provider 120 in a certain unit. Here, a unit that can organize and provide the information collected by the data center operating company to the service provider 120 or a unit requested by the service provider 120 may be used. Although described as a fixed unit, it may not be fixed, and the amount of information to be provided may change depending on the situation. The log information is stored in the server 121 owned by the service provider 120 as necessary (FIG. 41 (b)). Then, the service provider 120 organizes the log information into information suitable for the service provided to the user, and provides the information to the user. The providing user may be a user 10Y who uses a plurality of devices 1 or an external user 20Y. The service providing method for the user may be provided directly to the user from the service provider, for example (FIGS. 41 (f) and (e)). The service providing method for the user may be provided to the user via the cloud server 111 of the data center operating company 110 again (FIGS. 41 (c) and (d)). Alternatively, the cloud server 111 of the data center operating company 110 may organize the log information into information suitable for the service provided to the user and provide the information to the service provider 120.

  Note that the user 10Y and the user 20Y may be different or the same.

  The technology described in the above aspect can be realized, for example, in the following types of cloud services. However, the type in which the technique described in the above embodiment is realized is not limited to this.

(Service type 1: In-house data center type)
FIG. 42 shows service type 1 (in-house data center type). This type is a type in which the service provider 120 acquires information from the group 100 and provides a service to the user. In this type, the service provider 120 has a function of a data center operating company. That is, the service provider has a cloud server 111 that manages big data. Therefore, there is no data center operating company.

  In this type, the service provider 120 operates and manages the data center (cloud server 111) (203). The service provider 120 manages the OS (202) and the application (201). The service provider 120 provides a service using the OS (202) and the application (201) managed by the service provider 120 (204).

(Service type 2: IaaS usage type)
FIG. 43 shows service type 2 (IaaS usage type). Here, IaaS is an abbreviation for infrastructure as a service, and is a cloud service provision model that provides a base for constructing and operating a computer system as a service via the Internet.

  In this type, the data center operating company operates and manages the data center (cloud server 111) (203). The service provider 120 manages the OS (202) and the application (201). The service provider 120 provides a service using the OS (202) and the application (201) managed by the service provider 120 (204).

(Service type 3: PaaS usage type)
FIG. 44 shows service type 3 (PaaS usage type). Here, PaaS is an abbreviation for Platform as a Service, and is a cloud service provision model that provides a platform as a foundation for constructing and operating software as a service via the Internet.

  In this type, the data center operating company 110 manages the OS (202) and operates and manages the data center (cloud server 111) (203). The service provider 120 manages the application (201). The service provider 120 provides a service using the OS (202) managed by the data center operating company and the application (201) managed by the service provider 120 (204).

(Service type 4: SaaS usage type)
FIG. 45 shows service type 4 (SaaS usage type). Here, SaaS is an abbreviation for software as a service. For example, a function that allows applications provided by a platform provider who owns a data center (cloud server) to be used via a network such as the Internet by a company / individual (user) who does not have a data center (cloud server). This is a cloud service provision model.

  In this type, the data center operating company 110 manages the application (201), manages the OS (202), and operates and manages the data center (cloud server 111) (203). The service provider 120 provides a service using the OS (202) and the application (201) managed by the data center operating company 110 (204).

  In any of the above types, it is assumed that the service provider 120 performs a service providing action. Further, for example, the service provider or the data center operating company may develop an OS, an application, a big data database, or the like, or may be outsourced to a third party.

  The diagnostic system according to an aspect of the present disclosure diagnoses the operating state of an electrical appliance with sufficient accuracy even in a situation where the number of log information samples for calculating statistical information is small and the accuracy of statistical information is low Therefore, it is useful for a diagnosis system for diagnosing home appliances.

DESCRIPTION OF SYMBOLS 1 Electrical appliance 2 Internet 3 Diagnosis apparatus 4 Display apparatus 101 Control part 102 Log acquisition part 103 Log storage part 104 Identification information storage part 105 Log transmission part 106 Compressor 107 Sensor part 301 Log reception part 302 Log storage part 303 Model information storage part 304 device statistical information calculation unit 305 device statistical information storage unit 306 transmission / reception unit 307 diagnosis unit 308 statistical value determination unit 309 device connection information storage unit 310 logic storage unit 401 input / output unit 402 diagnosis target identification unit 403 diagnosis request transmission processing unit 404 transmission / reception 405 Diagnostic result reception processing unit 406 Diagnostic result display processing unit

Claims (17)

A diagnostic method of a diagnostic device for diagnosing the operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances,
An acquisition step of acquiring identification information for identifying the target electrical appliance;
A specifying step for identifying, based on the identification information, first feature information indicating a first feature item of the target electrical appliance and second feature information indicating a second feature item different from the first feature item; ,
Based on the first log information related to the operating state of the first identical feature electrical appliance having the same feature item as the first feature item indicated by the first feature information of the target electrical appliance among the plurality of electrical appliances. And the same feature item as the second feature item indicated by the second feature information of the target electrical appliance out of the plurality of electrical appliances. Which statistical information is to be used among the second identical feature statistical information obtained by tabulating the second log information related to the operating state of the second identical feature electric appliance having the same, and using the decided statistical information Diagnosing the operating state of the target electrical appliance,
The number of groups when grouping the plurality of electrical appliances for each of the first feature items is greater than the number of groups when grouping the plurality of electrical appliances for each of the second feature items,
Diagnosis method. The plurality of electrical appliances are connected to a predetermined network,
In the diagnosis step, depending on whether or not the number of connected devices indicating the number of the first identical electrical appliances connected to the network identified from the first log information is a predetermined number or more, Deciding which statistical information to use between the first identical feature statistical information and the second identical feature statistical information, and using the determined statistical information, diagnose the operating state of the target electrical appliance,
The diagnostic method according to claim 1. In the diagnosis step, when the number of connected devices is equal to or greater than the predetermined number, the operating state of the target electrical appliance is diagnosed using the first same feature statistical information.
The diagnostic method according to claim 2. In the diagnosis step, when the number of connected devices is smaller than the predetermined number, the operation status of the target electrical appliance is diagnosed using the second same feature statistical information.
The diagnostic method according to claim 2 or 3. The plurality of electrical appliances are connected to a predetermined network,
In the diagnosis step, the first same feature is determined depending on whether or not the data amount of the first log information transmitted from the first same feature electric appliance via the network is a predetermined amount or more. Deciding which statistical information to use between statistical information and the second same feature statistical information, using the determined statistical information, diagnose the operating state of the target electrical appliance,
The diagnostic method according to claim 1. In the diagnosis step, when the data amount of the first log information transmitted from the first identical feature electrical appliance is equal to or larger than the predetermined amount, the target electrification is performed using the first identical feature statistical information. Diagnose the operating status of the equipment,
The diagnostic method according to claim 5. In the diagnosis step, when the data amount of the first log information transmitted from the first identical feature electrical appliance is smaller than the predetermined amount, the target electrical appliance is used by using the second identical feature statistical information. Diagnosing the operating status of
The diagnostic method according to claim 5 or 6. The specifying step specifies model information indicating a model of the target electrical appliance identified by the identification information as the first feature information, and is identified by the identification information as the second feature information. Identify attribute information indicating an attribute item related to at least one of the product model and capability of the target electrical appliance,
The diagnosis step is based on the same model log information based on the same model log information indicating an operating state of the same model electrical appliance that is the same model as the model indicated by the model information of the target electrical appliance among the plurality of electrical appliances. And the same attribute statistical information obtained by counting the same attribute log information from the same attribute electrical equipment having the same attribute item as the attribute item indicated by the attribute information of the target electrical appliance. Decide whether to use statistical information, diagnose the operating status of the target electrical appliance using the determined statistical information,
The number of groups when grouping the plurality of electrical appliances for each model is larger than the number of groups when grouping the plurality of electrical appliances for each attribute item,
The diagnostic method according to claim 1. The attribute information includes year information indicating a manufacturing year of the target electrical appliance,
The diagnostic method according to claim 8.   The diagnostic method according to claim 8 or 9, wherein the attribute information includes capability information indicating the capability of the target electrical appliance expressed by any one of a plurality of levels. In the diagnosis step, as the first identical feature statistical information or the second identical feature statistical information, a statistical value of an item related to a diagnostic content included in the first log information or the second log information is obtained. Calculating and determining whether or not the target electrical appliance is abnormal by applying the statistical value to a predetermined diagnosis logic;
The diagnostic method according to claim 1. The plurality of electrical appliances includes a plurality of air conditioners,
The first log information and the second log information include at least one of rotation speed information indicating a rotation speed of a compressor of the plurality of air conditioners and pipe temperature information indicating a pipe temperature.
The diagnostic method according to claim 1. A determination step of determining the accuracy of the diagnosis according to the statistical information used in the diagnosis of the diagnosis step;
According to the accuracy of the diagnosis, further includes a changing step of changing the display content of the diagnosis result information indicating the result of the diagnosis,
The diagnostic method according to claim 1. The specifying step further specifies third feature information indicating a third feature item different from the first and second feature items based on the identification information;
The number of groups when grouping the plurality of electrical appliances for each second feature item is greater than the number of groups when grouping the plurality of electrical appliances for each third feature item,
The first to third feature information has a priority used as statistical information in the diagnosis step,
The diagnosis step includes the first identical feature statistical information, the second identical feature statistical information, and the plurality of electrifications based on the first log information, the second log information, and the priority. 3rd which totaled the 3rd log information about the operating state of the 3rd same feature electric appliance which has the same feature item as the 3rd feature item which the 3rd feature information of the object electric appliance among the devices shows Decide which statistic to use from the same feature statistic
The diagnostic method according to claim 1. The specifying step further specifies third feature information indicating a third feature item different from the first and second feature items based on the identification information;
The number of groups when grouping the plurality of electrical appliances for each second feature item is greater than the number of groups when grouping the plurality of electrical appliances for each third feature item,
The diagnosis step defines a relationship between the first log information, the second log information, and the first to third feature items included in the first to third feature information in a tree structure. Based on the model relation information, the first identical feature statistical information, the second identical feature statistical information, and the third feature information of the target electrical appliance among the plurality of electrical appliances are indicated by the third feature information. Decide which statistical information is to be used from among the third identical feature statistical information obtained by aggregating the third log information relating to the operating state of the third identical feature electrical appliance having the same feature item as the feature item ,
The diagnostic method according to claim 1. A diagnostic device for diagnosing the operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances,
An acquisition unit for acquiring identification information for identifying the target electrical appliance;
A specifying unit that specifies first feature information indicating a first feature item of the target electrical appliance and second feature information indicating a second feature item different from the first feature item based on the identification information; ,
Based on the first log information related to the operating state of the first identical feature electrical appliance having the same feature item as the first feature item indicated by the first feature information of the target electrical appliance among the plurality of electrical appliances. And the same feature item as the second feature item indicated by the second feature information of the target electrical appliance out of the plurality of electrical appliances. Which statistical information is to be used among the second identical feature statistical information obtained by tabulating the second log information related to the operating state of the second identical feature electric appliance having the same, and using the decided statistical information A diagnostic unit for diagnosing the operating state of the target electrical appliance,
The number of groups when grouping the plurality of electrical appliances for each of the first feature items is greater than the number of groups when grouping the plurality of electrical appliances for each of the second feature items,
Diagnostic device. A display device that displays a diagnosis result of a diagnostic device that diagnoses an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances,
A transmission unit that transmits identification information for identifying the target electrical appliance to the diagnostic device;
The statistical information used by the diagnostic device for diagnosing the target electrical appliance has the same feature item as the first feature item indicated by the first feature information of the target electrical appliance among the plurality of electrical appliances. The first same feature statistical information obtained by collecting the first log information related to the operating state of the same feature electrical appliance, and a second feature different from the first feature item of the target electrical appliance among the plurality of electrical appliances Any of the statistical information of the second identical feature statistical information obtained by aggregating the second log information related to the operating state of the second identical feature electrical appliance having the same feature item as the second feature item indicated by the information. A receiving unit for receiving statistical information specifying information from the diagnostic device;
Determining the accuracy of the diagnosis based on the statistical information specifying information, determining the display content for notifying the diagnosis result information according to the determined accuracy of the diagnosis, and displaying a notification screen indicating the determined display content A display unit for displaying,
Display device.

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