JP6735473B2 - Diagnostic method, diagnostic device, and display device - Google Patents

Description

The present disclosure relates to a diagnostic method and a diagnostic device for diagnosing an operating state of a target electrical device that is a diagnostic target among a plurality of electrical devices, 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 products of a plurality of types. In this system, data such as a sensor value is derived from a product of each group such as a product type, and a statistical value (average value) of the data is calculated. This is the statistic that represents the characteristics of the group. Then, when predicting the quality of the products of a group, after using the statistical values derived from the products of the same group, after normalizing the data collected from the products and excluding the characteristics peculiar to the group, , Predict the quality by applying the given logic (model). As a result, it is possible to predict the quality with a single logic (model) even for a plurality of groups of products having different characteristics.

U.S. Pat. No. 9110452

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

The present disclosure has been made in order to solve the above problems, and the number of samples of log information for calculating statistical information is small, and even when the accuracy of statistical information is low, the operating state of electrical appliances can be highly accurate. It is an object of the present invention to provide a diagnostic method, a diagnostic device, and a display device that can make a diagnosis.

In order to achieve the above object, a diagnostic method according to an aspect of the present disclosure is a diagnostic method for a diagnostic device that diagnoses an operating state of a target electrical appliance to be diagnosed among a plurality of electrical appliances, and the target electrical An acquisition step of acquiring identification information for identifying a device, and first feature information indicating a first feature item of the target electrical appliance and a second feature information different from the first feature item based on the identification information. A specifying step of specifying second characteristic information indicating a characteristic item, and a first characteristic item having the same characteristic item as the first characteristic item indicated by the first characteristic information of the target electric appliance of the plurality of electric appliances Based on the first log information related to the operating state of the same electrical appliance of the same, first identical characteristic statistical information in which the first log information is aggregated, and the first of the plurality of electrical appliances of the target electrical equipment of the first Any one of the second same characteristic statistical information in which the second log information regarding the operating state of the second same characteristic electric appliance having the same characteristic item as the second characteristic item indicated by the second characteristic information is aggregated Deciding whether to use information, and using the determined statistical information, a diagnosis step of diagnosing the operating state of the target electric appliance, and grouping the plurality of electric appliances for each of the first characteristic items The number of groups is larger than the number of groups when the plurality of electrical appliances are grouped by the second characteristic item.

According to the above aspect, even if the number of samples of log information 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.

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 equipment shown in FIG. It is a figure which shows an example of the data structure of the log 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 device shown in FIG. It is a figure which shows an example of the data structure of the log 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. FIG. 3 is a block diagram showing an example of a configuration of the display device shown in FIG. 1. 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 device 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 device shown in FIG. It is a figure which shows an example of the whole sequence of the diagnostic system shown in FIG. 20 is a flowchart showing an example of the log transmission/reception process shown in FIG. 19. 20 is a flowchart showing an example of the device statistical information derivation process shown in FIG. 19. 20 is a flowchart showing an example of the electric appliance diagnosis process shown in FIG. 19. 23 is a flowchart showing an example of the statistical information determination process shown in FIG. 22. 23 is a flowchart showing an example of the diagnosis process shown in FIG. 22. 23 is a flowchart showing an example of the diagnosis result display processing shown in FIG. 22. 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. FIG. 6 is a diagram showing an example of model relation information stored in a model information storage unit shown in FIG. 5. FIG. 33 is a diagram showing an example of a data structure of the first tree-structure device information in the device connection information storage unit shown in FIG. 5 when the model relation information shown in FIG. 32 is used. FIG. 33 is a diagram showing an example of a data structure of device information for the second tree structure in the device connection information storage unit shown in FIG. 5 when the model relation information shown in FIG. 32 is used. FIG. 6 is a diagram showing a first modification of the data structure of the first device statistical information in the device statistical information storage unit shown in FIG. 5. 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. FIG. 3 is a diagram showing a service type (in-house data center type) according to the embodiment of the present disclosure. FIG. 3 is a diagram showing a service type (IaaS usage type) according to an embodiment of the present disclosure. FIG. 3 is a diagram showing a service type (PaaS use type) according to an embodiment of the present disclosure. FIG. 3 is a diagram showing a service type (SaaS use type) in the embodiment of the present disclosure.

(Findings that form the basis of this disclosure)
It is generally known that the accuracy of the statistical value is low when the number of samples of the original data used when calculating the statistical value represented by the average value 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 of predicting quality with a single diagnostic logic on the assumption that a highly accurate statistical value is given. No consideration was given to the accuracy of the values.

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

As mentioned above, a technique for performing diagnosis with a single diagnostic logic is being considered for multiple groups of electric appliances having different characteristics, but the statistics necessary for predicting quality with a single diagnostic logic are being investigated. There is a need to make a diagnosis with high accuracy even when the accuracy of the value is low, and no consideration has been given to a technical solution for satisfying these needs.

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

A diagnostic method according to an aspect of the present disclosure is a diagnostic method for a diagnostic device that diagnoses an operating state of a target electrical device that is a diagnostic target among a plurality of electrical devices, and is identification information for identifying the target electrical device. And a second characteristic indicating a second characteristic item different from the first characteristic information indicating the first characteristic item of the target electrical device, based on the identification information. A specifying step of specifying information, and an operating state of a first same-characteristic electric appliance having the same characteristic item as the first characteristic item indicated by the first characteristic information of the target electric appliance among the plurality of electric appliances. Based on the first log information regarding the first log information, the first identical feature statistical information obtained by collecting the first log information, and the second feature information indicated by the second feature information of the target electrical device among the plurality of electrical devices. Which statistical information is to be used among the second identical characteristic statistical information in which the second log information relating to the operating state of the second identical characteristic electric appliance having the same characteristic item as And a diagnostic step of diagnosing the operating state of the target electrical appliance by using the determined statistical information, wherein the number of groups when the plurality of electrical appliances are grouped for each of the first characteristic items is the plurality of groups. The number of electrical appliances is larger than the number of groups when grouped by the second characteristic item.

With such a configuration, the identification information for identifying the target electrical appliance to be diagnosed is acquired, and based on the acquired identification information, the first characteristic information indicating the first characteristic item of the target electrical appliance, and And a second characteristic information indicating a second characteristic item different from the first characteristic item, and the same characteristic item as the first characteristic item indicated by the first characteristic information of the target electric appliance of the plurality of electric appliances. Based on the first log information related to the operating state of the first same-characteristic electrical appliance having the above, the first same-characteristic statistical information obtained by aggregating the first log information and the second characteristic information of the target electric appliance are indicated. Which statistical information is to be used among the second same characteristic statistical information in which the second log information relating to the operating state of the second same characteristic electric appliance having the same characteristic item as the second characteristic item is aggregated? The determined statistical information is used to diagnose the operating state of the target electrical device.

Here, since the number of groups when a plurality of electric appliances are grouped by the first characteristic item is larger than the number of groups when a plurality of electric appliances are grouped by the second characteristic item, the second same characteristic The number of electric appliances is larger than the number of first same-characteristic electric appliances, and the number of samples of the second log information regarding the operating state of the second same-characteristic electric appliances is the operating state of the first same-characteristic electric appliances. It is larger 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, the second log information having a large number of samples is used as the 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 if the number of log information samples for calculating the 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.

The plurality of electric appliances are connected to a predetermined network, and in the diagnosis step, the connected number indicating the number of the first same-characteristic electric appliances connected to the network, which is identified from the first log information. Depending on whether or not is a predetermined number or more, it is determined which one of the first identical feature statistical information and the second identical feature statistical information is to be used, and the decided statistical information is used. Then, the operating state of the target electrical equipment may be diagnosed.

With such a configuration, it is possible to determine whether the number of connected first electric appliances having the same characteristic connected to the network, which is specified from the first log information, is greater than or equal to a predetermined number, Which of the same characteristic statistical information and the second same characteristic statistical information is to be used is determined, 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 the first same-characteristic electric appliances is equal to or larger than a predetermined number, the number of samples of the first log information increases, and highly accurate first same-characteristic statistical information can be obtained. Therefore, the operating state of the target electrical appliance can be diagnosed with high accuracy by using the highly accurate first identical feature statistical information. On the other hand, when the number of connected devices indicating the number of first identical-feature electric appliances is less 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 sample of the first log information. Since it is more than the number, it is possible to obtain the second identical feature statistical information with sufficient accuracy, so that the operating state of the target electrical equipment is 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 larger than the predetermined number, the operating state of the target electrical appliance may be diagnosed using the first same feature statistical information.

With this configuration, when the number of connected first same-characteristic electric appliances is equal to or larger than a predetermined number, the operating status of the target electric appliance is diagnosed by using the first same-characteristic statistical information. The number of log information samples is increased, and highly accurate first identical feature statistical information can be obtained. By using the highly accurate first identical feature statistical information, 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 operating state of the target electrical device may be diagnosed by using the second identical feature statistical information.

With such a configuration, when the number of connected first electrical appliances with the same characteristics is less than the predetermined number, the operating state of the target electrical appliances is diagnosed using the second statistical information with the same characteristics, so the number of samples is reduced. It is possible to obtain the second identical feature statistical information with sufficient accuracy by using the second log information that is larger than the first log information, and use the second identical feature statistical information with sufficient accuracy to perform the target electrification. It is possible to diagnose the operating state of the device with sufficient accuracy.

The plurality of electric appliances are connected to a predetermined network, and in the diagnosing step, when the data amount of the first log information transmitted from the first same-characteristic electric appliance via the network is a predetermined amount or more. Depending on whether or not there is statistical information to be used among the first identical characteristic statistical information and the second identical characteristic statistical information, the target electrification is performed using the decided statistical information. The operating state of the device may be diagnosed.

With such a configuration, the first same-characteristic statistical information and the first same-characteristic statistical information depending on whether or not the data amount of the first log information transmitted from the first same-characteristic electric appliance via the network is equal to or larger than a predetermined amount. Which statistical information of the second same characteristic statistical information is to be used is determined, 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 increases, and the highly accurate first identical feature statistical information can be obtained. It is possible to highly accurately diagnose the operating state of the target electric appliance by using the first identical feature statistical information. On the other hand, when the data amount of the first log information is smaller 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 operating state of the target electric 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 same-characteristic electrification device is equal to or larger than the predetermined amount, the target same-electricity statistical information is used to perform the target electrification. The operating state of the device may be diagnosed.

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

In the diagnosing step, if the data amount of the first log information transmitted from the first same-characteristic electric appliance is smaller than the predetermined amount, the target same electric appliance is used by using the second same-characteristic 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 electric appliance is diagnosed using the second identical feature statistical information, and thus the number of samples is the first. The second log information, which is larger than the log information of 1, can obtain the second identical feature statistical information with sufficient accuracy, and the second identical feature statistical information with sufficient accuracy can be used for the target electrical appliance. The operating condition can be diagnosed with sufficient accuracy.

In the identifying step, model information indicating a model of the target electrical appliance identified by the identification information is identified as the first feature information, and is identified by the identification information as the second feature information. The attribute information indicating the attribute item relating to at least one of the product model and the capability of the target electric appliance is specified, and the diagnosis step is the same model as the model indicated by the model information of the target electric appliance among the plurality of electric appliances. Based on the same model log information indicating the operating status of the same model electrical appliance, the same model statistical information that aggregates the same model log information and the same attribute item as the attribute item indicated by the attribute information of the target electrical device Deciding which statistical information is to be used among the same-attribute statistical information in which the same-attribute electrical appliances having the same attribute log information are aggregated, and the operating status of the target electrical appliance is diagnosed using the determined statistical information. However, the number of groups when the plurality of electrical appliances are grouped by the model may be larger than the number of groups when the plurality of electrical appliances are grouped by the attribute item.

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 the capability of the target electrical appliance identified by the identification information. Identifies information and aggregates the same model log information based on the same model log information indicating the operating status of the same model that is the same model as the model indicated by the model information of the target electrical device among multiple electrical devices Which statistical information is used, model statistical information or the same attribute statistical information that aggregates the same attribute log information from the same attribute electrical equipment that has the same attribute items as the attribute items indicated by the attribute information of the target electrical equipment? Is determined and the operating state of the target electrical equipment is diagnosed using the determined statistical information.

Here, the number of groups when multiple electric appliances are grouped by model is greater than the number of groups when multiple electric appliances are grouped by attribute item, so the number of appliances with the same attribute is the same model. The number of samples of the same attribute log information regarding the operating status of the same attribute electric appliances is greater than the number of samples of the same model log information regarding the operating status of the same model electrical appliances.

Therefore, when the number of samples of the same model log information of the same model electric appliances is small and the accuracy of the same model statistical information is low, the same attribute log of the same attribute electric appliances with many samples is used as the log information for calculating the statistical information. By using the information, it is possible to obtain the same attribute statistical information with sufficient accuracy, and it is possible to diagnose the operating state of the target electric appliance with sufficient accuracy using the same attribute statistical information with sufficient accuracy.

The attribute information may include year information indicating a year of manufacture of the target electrical device.

With such a configuration, when the number of samples of the same model log information of the same model electric appliances is small and the accuracy of the same model statistical information is low, the log information for calculating statistical information has a large number of samples and the manufacturing year is the same. 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. It can be diagnosed with various accuracy.

The attribute information may include capability information indicating the capability of the target electrical device represented by any of a plurality of levels.

With such a configuration, when the number of samples of the same model log information of the same model electric appliance is small and the accuracy of the same model statistical information is low, the log information for calculating the statistical information has the same ability with a large number of samples. By using the same attribute log information of the same attribute electric appliance, it is possible to obtain the same attribute statistical information with sufficient accuracy. Therefore, by using the same attribute statistical information with sufficient accuracy, the operating status of the target electric appliance is sufficient. Can be diagnosed 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 may be calculated and the statistical value may be applied to a predetermined diagnostic logic to determine whether or not the target electrical device is abnormal.

With such a configuration, the statistical value of the item related to the diagnostic content included in the first log information or the second log information is calculated, and the calculated statistical value is applied to a predetermined diagnostic logic to operate the target electrical appliance. Since it is judged whether there is an abnormality, the number of samples of log information for calculating statistical information is small, and even if the accuracy of statistical information is low, a single diagnostic logic can be used to The operating status of electrical appliances can be diagnosed with sufficient accuracy.

The plurality of electrical appliances include a plurality of air conditioners, and the first log information and the second log information are rotation speed information indicating a rotation speed of a compressor of the plurality of air conditioners and a pipe temperature. You may make it include at least one of the piping temperature information which shows.

With such a configuration, the statistical value of the rotational speed of the compressor and the pipe temperature are calculated from the rotational speed information indicating the rotational speed of the compressor of the air conditioner and the pipe temperature information indicating the pipe temperature, and the calculated statistical value is predetermined. Since it is applied to the diagnostic logic of 1. to determine whether or not the air conditioner is abnormal, statistics of the number of revolutions of the compressor and the pipe temperature of the air conditioner having the same characteristic items as the first characteristic item. The same characteristic item as the second characteristic item even when the number of samples of the rotation speed information and the piping temperature information for calculating the value is small and the accuracy of the statistical values of the rotation speed of the compressor and the piping temperature is low. Using the statistical values of the number of revolutions of the compressor and the pipe temperature of the air conditioner having the above, the operating state of the air conditioner to be diagnosed can be diagnosed with sufficient accuracy.

A determination step of determining the accuracy of the diagnosis according to the statistical information used for the diagnosis in the diagnosis step, and a changing step of changing the 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 accuracy of the determined diagnosis. It is possible to present to the user whether or not the operating state of the target electrical device has been diagnosed with a degree of accuracy.

The identifying step further identifies, based on the identification information, third characteristic information indicating a third characteristic item different from the first and second characteristic items, and sets the plurality of electric appliances to the second characteristic information. The number of groups when grouped for each characteristic item is larger than the number of groups when the plurality of electrical appliances are grouped for each of the third characteristic items, and the first to third characteristic information are used in the diagnosis step. The diagnostic step has a priority used as statistical information, and the diagnostic step is based on the first log information, the second log information, and the priority. The same feature statistical information of 2 and the operation state of the third same feature electrical appliance having the same feature item as the third feature item indicated by the third feature information of the target electrical appliance of the plurality of electrical appliances It is also possible to determine which statistical information is to be used from among the third same feature statistical information obtained by aggregating the third log information regarding.

With such a configuration, further, the third characteristic information indicating the third characteristic item different from the first and second characteristic items is specified based on the identification information for identifying the target electrical device to be diagnosed. , The first log information, the second log information, and the first identical feature statistical information and the second identical feature statistical information based on the priority of using the first to third feature information as statistical information. And the third log information relating to the operating state of the third same-characteristic electric appliance having the same characteristic item as the third characteristic item indicated by the third characteristic information of the target electric appliance among the plurality of electric appliances. Which statistical information is to be used is determined from the third same characteristic statistical information, 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 electric appliances are grouped by the second characteristic item is larger than the number of groups when a plurality of electric appliances are grouped by the third characteristic item, the third same characteristic The number of electric appliances is larger than the number of second same-characteristic electric appliances, and the number of samples of the third log information regarding the operating status of the third same-characteristic electric appliances is the operating status of the second same-characteristic electric appliances. 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 characteristic statistical information is low, the statistical information is stored in accordance with the priority in which the first to third characteristic information are used as the statistical information. By using the second log information having 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 equipment with sufficient accuracy.

In addition, 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 collected according to the priority of the first to third characteristic information used as the statistical information. By using the third log information having 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 equipment with sufficient accuracy.

The identifying step further identifies, based on the identification information, third characteristic information indicating a third characteristic item different from the first and second characteristic items, and sets the plurality of electric appliances to the second characteristic information. The number of groups when grouping by characteristic item is larger than the number of groups when grouping the plurality of electrical appliances by the third characteristic item, and the diagnosis step is performed by the first log information and the second log information. Log information and model relation information in which each relation of the first to third feature items included in the first to third feature information is defined by a tree structure, the first identical feature statistical information. A third same characteristic electrification having the same characteristic item as the third characteristic item indicated by the third characteristic information of the target electric appliance among the plurality of electric appliances and the second same characteristic statistical information; You may decide which statistical information to use from the 3rd same characteristic statistical information which totaled the 3rd log information regarding the operating state of an apparatus.

With such a configuration, the third characteristic information indicating the third characteristic item different from the first and second characteristic items is specified based on the identification information for identifying the target electric appliance to be diagnosed, and 1st log information, 2nd log information, and 1st thru|or 3rd characteristic information The 1st thru|or 3rd characteristic item WHEREIN: Based on the model relationship information which defined each relationship by the tree structure, Third same-feature electrification having the same feature statistical information, second same-feature statistical information, and 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 to use from the third identical feature statistical information that aggregates the third log information related to the operating state of the device, and use the determined statistical information to determine the operating state of the target electrical device Is being diagnosed.

Here, since the number of groups when a plurality of electric appliances are grouped by the second characteristic item is larger than the number of groups when a plurality of electric appliances are grouped by the third characteristic item, the third same characteristic The number of electric appliances is larger than the number of second same-characteristic electric appliances, and the number of samples of the third log information regarding the operating status of the third same-characteristic electric appliances is the operating status of the second same-characteristic electric appliances. 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 are calculated according to the model relation information in which the respective relations of the first to third characteristic items are defined by the tree structure. By using the second log information having 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. The statistical information can be used to diagnose the operating state of the target electrical appliance with sufficient accuracy.

When the number of samples of the first and second log information is small and the accuracy of the first and second same characteristic statistical information is low, the respective relationships of the first to third characteristic items are defined by a tree structure. By using the third log information having a large number of samples as the log information for calculating the statistical information according to the model relation information, it is possible to obtain the third same-characteristic statistical information with sufficient accuracy and sufficient accuracy. It is possible to diagnose the operating state of the target electrical appliance with sufficient accuracy by using the third same-characteristic statistical information.

Further, the present disclosure can be realized not only as a diagnostic method that executes the above-described characteristic processing, but also as a diagnostic device that has a characteristic configuration corresponding to the characteristic processing that the diagnostic method executes. It can also be realized. Therefore, also in the following other modes, it is possible to achieve the same effect as the above-described air conditioning control method.

A diagnostic device according to another aspect of the present disclosure is a diagnostic device that diagnoses an operating state of a target electrical device that is a diagnostic target among a plurality of electrical devices, and acquires identification information for identifying the target electrical device. And a second characteristic information indicating a second characteristic item different from the first characteristic item of the target electrical appliance based on the acquisition unit and the identification information. A specifying unit for specifying and a first operation item of the same characteristic electric appliance having the same characteristic item as the first characteristic item indicated by the first characteristic information of the target electric appliance of the plurality of electric appliances; Based on one piece of log information, first identical characteristic statistical information in which the first log information is aggregated, and second characteristic indicated by the second characteristic information of the target electric appliance among the plurality of electric appliances. It decides and decides which statistical information is to be used among the second same characteristic statistical information in which the second log information concerning the operating state of the second same characteristic electric appliance having the same characteristic item as the item is aggregated. The number of groups when the plurality of electric appliances are grouped for each of the first characteristic items is the plurality of electric appliances. Is larger than the number of groups when grouped by the second characteristic item.

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 device that is a diagnostic target among a plurality of electrical devices. A transmission unit for transmitting identification information for identifying a device to the diagnostic device, and statistical information used by the diagnostic device for diagnosing the target electrical device are the first of the target electrical devices among the plurality of electrical devices. First identical feature statistical information in which the first log information relating to the operating state of the first identical feature electric appliance having the same characteristic item as the first characteristic item indicated by the above-mentioned characteristic information is aggregated, and the plurality of electric appliances. Second log information relating to the operating state of the second same-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 And a receiving unit that receives statistical information specifying information indicating which statistical information is included in the second identical feature statistical information from the diagnostic device, and the accuracy of the diagnosis based on the statistical information specifying information. And a display unit that determines a display content for notifying the diagnosis result information according to the determined accuracy of the diagnosis and displays a notification screen indicating the determined display content.

With such a configuration, the identification information for identifying the target electrical appliance is transmitted to the diagnostic device, and the statistical information used by the diagnostic device for diagnosing the target electrical appliance is the first information of the target electrical appliance among the plurality of electrical appliances. Of the first identical feature statistical information in which the first log information relating to the operating state of the first identical feature electric appliance having the same characteristic item as the first feature item indicated by the characteristic information of Among them, the second log information relating to the operating state of the second same-characteristic electric appliance having the same characteristic item as the second characteristic item indicated by the second characteristic information different from the first characteristic item of the target electric appliance is aggregated. The statistical information specifying information indicating which of the second identical feature statistical information is the statistical information is received from the diagnostic device, the accuracy of the diagnosis is determined based on the received statistical information specifying information, and the determined diagnostic Depending on the accuracy, the display content for notifying the diagnostic result information is determined, and since 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. The display content of the diagnosis result information indicating the diagnosis result is changed and displayed according to the accuracy of the determined diagnosis, and the user is presented with the accuracy with which the operating state of the target electrical equipment is diagnosed. In addition, the number of log information samples for calculating the statistical information is small, and even in a situation where the accuracy of the 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 the characteristic processing included in the above-described diagnosis method. It goes without saying that 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.

It should be noted that each of the embodiments described below is for showing one specific example of the present disclosure. Numerical values, shapes, constituent elements, steps, order of steps, and the like shown in the following embodiments are examples and are not intended to limit the present disclosure. Further, among the constituent elements in the following embodiments, constituent elements not described in the independent claim showing the highest concept are described as arbitrary constituent elements. Further, the contents of each of the embodiments can be combined. Further, it may be configured as a system in which a part of the components of the diagnostic device according to the following embodiment and the other components are distributed to a plurality of computers.

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

(1.1) Diagnostic System First, an overview of the diagnostic system according to the present embodiment will be described. FIG. 1 is a block diagram showing 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 electric appliances 1, a diagnostic device 3, and a display device 4, and each device is communicably connected via the Internet 2. The number of each device is not particularly limited to this example, and for example, a plurality of display devices 4 may be used.

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

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

The diagnostic 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 electric appliance 1 regularly or irregularly transmits log data generated by the electric appliance itself to the diagnostic device 3. Here, the log data is, for example, information indicating the operating status, operating date and time, sensor information, etc. of the electrical appliance 1 (log information regarding the operating status of the electrical appliance 1). In the case of a washing machine, the number of rotations of the drum, in the case of a refrigerator, the number of rotations of the compressor, and in the case of an air conditioner, the number of rotations of the compressor and the pipe temperature, etc. It may include various information that can be obtained from the.

Next, the diagnostic device 3 derives device statistical information required for diagnosis based on the log data accumulated from each electric device 1. When the display device 4 receives the identification information for identifying the electrical device 1 (target electrical device) to be diagnosed from the outside, the display device 4 transmits it to the diagnostic device 3 as a diagnostic request. The diagnostic device 3 uses the log data collected from the electrical appliances 1 and the aggregated device statistical information to diagnose the operating state of the electrical appliance 1 that has received the diagnostic request, and returns the diagnostic result to the display device 4. .. The display device 4 displays the diagnosis result.

In the present embodiment, a 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 electric appliances to which the present embodiment is applied are not limited to air conditioners, and are applicable to all electric appliances. Below, each device which comprises the diagnostic system shown in FIG. 1 is demonstrated in detail.

(1.2) Electrical appliances 1
FIG. 2 is a block diagram showing an example of the configuration of the electric appliance 1 shown in FIG. As shown in FIG. 2, the electric 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 electric appliance 1. This main function differs depending on the type of electric appliance 1. In the present embodiment, since the electric appliance 1 is an air conditioner, its main functions are a cooling function for discharging cold air, a heating function for discharging warm air, and a dehumidifying function for reducing indoor humidity.

Specifically, the control unit 101 controls components such as a compressor (compressor) 106 and a fan (not shown) included in the air conditioner main body (electrical equipment 1) in response to an operation by a user of the electrical equipment 1. I do. The compressor 106 is controlled by the control unit 101, and notifies the control unit 101 of the number of rotations of itself. 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 pipe temperature of the sensor unit 107.

Note that the main function of the electric appliance 1 is not particularly limited to the above example, and when the electric appliance 1 is a refrigerator, it has a cooling function of cooling the inside of the refrigerator with a compressor, and when it is a washing machine, tap water is used. The main functions are the washing function of washing laundry with a detergent and a detergent, and the drying function of drying laundry.

(2) Log acquisition unit 102
The log acquisition unit 102 generates log data during the control operation of the control unit 101. As described above, the log data is, for example, information indicating operating conditions, operation dates and times, sensor information, and the like, but since the electric appliance 1 is an air conditioner, the rotation speed of the compressor 106 used for compressing the refrigerant ( The unit is times/second), the temperature of the pipe through which the refrigerant passes measured by the sensor unit 107 (unit is degree), and the like are log data. However, the information is not limited to these, 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 pipe temperature of the sensor unit 107 from the control unit 101 to generate log data, and stores the generated log data in the log storage unit 103 together with a time stamp. The time stamp is date and time information when the log data was acquired.

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

As illustrated in FIG. 3, the log storage unit 103 is a table configured as log data, which includes n sets (n is an integer of 2 or more) of data each including a time stamp, a compressor rotation speed, and a pipe temperature. I remember. For example, as the data at the top of the table, the compressor rotation speed “50” and the pipe temperature “13” are stored for the time stamp “2016/6/28 13:51”. From this data, it is found that the rotation speed of the compressor 106 at 13:51 on June 28, 2016 is 50 times/second, and the pipe 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) and an EEPROM. FIG. 4 is a diagram showing an example of a data structure of the identification information storage unit 104 shown in FIG.

As illustrated in FIG. 4, the identification information storage unit 104 stores, as identification information, a table including model information and a manufacturing number. The model information is information indicating the model of the electrical device 1, and the manufacturing number is a unique number that uniquely identifies the electrical device 1, and is a number that is individually assigned during manufacturing.

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

(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, and the Internet. 2 to the diagnostic device 3.

Note that the log transmission unit 105 acquires and transmits only the log data that has not yet 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 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.

Further, various timings can be used as a trigger for transmitting data from the log transmitting unit 105 to the diagnostic device 3. For example, the log transmitting unit 105 includes a timer counter, and the log transmitting unit 105 transmits the data periodically every 10 minutes. Alternatively, the transmission may be performed at a timing when the state changes, such as when the electrical appliance 1 starts operating or when it stops operating.

(1.3) Diagnostic device 3
FIG. 5 is a block diagram showing an example of the configuration of the diagnostic device 3 shown in FIG. As shown 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 diagnostic 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 periodically or non-periodically from the plurality of electrical appliances 1 via the Internet 2 includes model information and serial number information that are identification information of the electrical appliances 1, and log data of the electrical appliances 1. It receives a certain time stamp, compressor rotation 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 and an EEPROM. FIG. 6 is a diagram showing an example of the data structure of the log storage unit 302 shown in FIG. As shown in FIG. 6, the log storage unit 302 stores, as log data, m sets (m is an integer of 2 or more) of which the model information, the manufacturing number, the time stamp, the compressor rotation speed, and the pipe temperature are one set. It stores a table composed of data. For example, the model information “AC2016-H” and the serial number “12345” of the electric device 1 are stored in the uppermost row of the table, and the time stamp “2016/6/28 13:51” of the electric device 1 is stored. The rotation speed "50" of the compressor and the pipe temperature "13" are stored. From these data, the rotation speed of the compressor 106 at 13:51 on June 28, 2016 of the electrical equipment 1 having the model information “AC2016-H” and the manufacturing number “12345” is 50 times/second, and the sensor It can be seen that the pipe temperature of the portion 107 is 13 degrees.

(3) Model information storage unit 303
The model information storage unit 303 is composed of, for example, a ROM and an EEPROM. FIG. 7 is a diagram showing an example of the data structure of the model information storage unit 303 shown in FIG.

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

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

Further, the number of groups when grouping a plurality of electric appliances 1 by model is larger than the number of groups when a plurality of electric appliances 1 are grouped by manufacturing year, and the plurality of electric appliances 1 are grouped by capacity manufacturing year. The number of groups when this is done is larger than the number of groups when a plurality of electrical appliances 1 are grouped by capability.

Here, in the present embodiment, the model information corresponds to an example of the first characteristic information indicating the first characteristic item of the electrical device 1 to be diagnosed, and the year information is the electrical device 1 to be diagnosed. Corresponds to an example of second characteristic information indicating a second characteristic item different from the first characteristic item of No. 3, and the capability information is different from the first and second characteristic items of the electrical appliance 1 to be diagnosed. Corresponds to an example of third characteristic information indicating the characteristic item. The feature information is not particularly limited to the above example, two or four or more types of feature information may be used, or information indicating another feature may be used.

Further, the year information and the capability information are examples of the attribute information indicating the attribute items relating to at least one of the product model and the capability of the electrical equipment 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. Then, the device statistical information calculation unit 304 calculates the device statistical information and the 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 timings can be used as a trigger 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 periodically. May be calculated, or may be calculated at the timing when the diagnostic request from the display device 4 is received.

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

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

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

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

In addition, the device statistical information calculation unit 304 extracts the model information associated with the relevant 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 rotation speed and the average value of the pipe temperature are derived for each year.

Further, the device statistical information calculation unit 304 extracts the model information associated with the relevant capability information from the model information storage unit 303 as the third statistical value (for each type of capability 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 rotation speed and the average value of the pipe temperature are derived for each capability.

The characteristics of these three statistical values will be described. When diagnosing the electrical appliance 1, the first statistical value (for each type of model information) of each model that matches the model of the model information of the electrical device 1 to be diagnosed It is desirable to use statistical values. Because the models match, the first statistic is considered to most represent the characteristics of the electrical appliance 1 to be diagnosed, and compared with other individuals of the same model, for example, coldness. This is because it is possible to perform accurate and accurate diagnosis 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) also have features of the electrical appliance 1 that do not match the model of the electrical appliance 1 to be diagnosed. Therefore, it is unlikely that the first statistical value (for each type of model information) represents the characteristics of the electrical appliance 1 to be diagnosed most.

However, although the electrical appliances 1 with which the year matches are not the same as the electrical appliances 1 with which the model matches, they represent the characteristics of the electrical appliances 1 to be diagnosed, and if the first statistical value is not available, the intermediate precision It enables accurate diagnosis. Moreover, although the electrical appliances 1 with matching capabilities are not as good as the electrical appliances 1 with matching year, they represent the characteristics of the electrical appliances 1 to be diagnosed, and if the second statistical value cannot be used, the accuracy is low. It enables accurate diagnosis.

Next, a method of 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 above-described statistical values, and calculates three types of connected devices. The first device connection information is the number of connected devices for each type of model information, the second statistical value is the number of connected devices for each type of year information, and the third is the number of connected devices for each type of capability information. The number of connected units.

Specifically, the device statistical information calculation unit 304 extracts log data with matching model information from the log storage unit 302 and counts the number of unique serial numbers to determine the first number of connected devices (model information). Derive).

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

Further, the device statistical information calculation unit 304 first extracts from the model information storage unit 303 model information that matches the capability information, and then extracts log data that matches the extracted model information from the log storage unit 302 to make it unique. The third connected number (for each type of capability information) is derived by counting the number of different production numbers. These three pieces of device connection information are used to estimate the accuracy of the corresponding device statistical information. That is, the greater the number of connected devices, the higher the accuracy of the device statistical information is determined, and conversely, the less the device connection information, the lower the accuracy of the device statistical information.

(5) Device statistical information storage unit 305
The device statistical information storage unit 305 includes, for example, a RAM and an EEPROM. FIG. 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 is two of the device statistical information storage unit 305 shown in FIG. FIG. 10 is a diagram showing an example of the data structure of the device statistical information of the eye, and FIG. 10 is a diagram showing an example of the data structure of the third device statistical information of the device statistical information storage unit 305 shown in FIG. 5. It should be noted that although FIGS. 8 to 10 exemplify three types of tables, they are not particularly limited to this example, and various modifications are possible.

The device statistical information storage unit 305 stores a table as shown in FIGS. 8 to 10. The first table shown in FIG. 8 is a table of device statistical information for each type of model information, and is a set of model information, an average value of the number of revolutions 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 number of revolutions of the compressor, and the average value of the pipe temperature are 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 number of revolutions of the compressor, and the average value of the pipe temperature are one set. (S is an integer of 2 or more).

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

(7) Diagnostic unit 307
The diagnosis unit 307 receives the 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 the statistical value determination unit 308 uses the statistical value to be used and the derivation source information of the statistical value as accompanying information. To receive. Here, the derivation source information is statistical information specifying information indicating a unit for calculating a statistical value, and is, for example, information indicating “each model information”, “each year”, or “each capability”.

After that, the diagnosis unit 307 acquires from the log storage unit 302 the latest log data stored in association with the serial number of the electrical device 1 to be diagnosed, and at the same time, from the logic storage unit 310, the diagnosis item and the diagnosis item. Get logic and. The diagnostic 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. When the diagnostic logic is applicable, the diagnostic unit 307 determines that the diagnostic item of the diagnostic logic is “corresponding”, and when it is not applicable, the diagnostic unit 307 determines “not applicable”. Then, the diagnosis unit 307 combines, for each diagnostic item, information as to whether the item is applicable or not and the derivation source information received from the statistical value determination unit 308 as accompanying information, as a diagnostic result, via the transmission/reception unit 306. And responds to the display device 4.

(8) Statistics value determination unit 308
The statistical value determination unit 308 receives the model information from the diagnosis unit 307 and outputs the statistical value and accompanying information to the diagnosis unit 307. First, the statistical value determination unit 308 acquires the device connection information corresponding to the received model information from the table of the highest priority level 1 in the device connection information storage unit 309. Next, when the acquired device connection information is equal to or more than the given threshold value, the statistical value determination unit 308 determines that the average value of the rotation speed of the compressor is 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 are acquired, along with the average value of the number of revolutions of the compressor and the average value of the pipe temperature, information indicating that these statistical values correspond to the model information is used as additional information for diagnosis. Respond to section 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 the 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 table of the second highest priority in the device connection information storage unit 309. Next, when the acquired device connection information is equal to or larger than the given threshold value, the statistical value determination unit 308 determines that the average value of the number of rotations of the compressor is the statistical value corresponding to the same year information from the device statistical information storage unit 305. And the average value of the pipe temperature are acquired, and the average value of the rotational speed of the compressor and the average value of the pipe temperature, together with the information indicating that these statistical values correspond to the year information, are used as the accompanying information for diagnosis. Respond to section 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 the capability 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 capability information from the table of the priority 3 having the next highest priority in the device connection information storage unit 309. Next, when the acquired device connection information is equal to or more than the given threshold value, the statistical value determination unit 308 determines that the average value of the number of rotations of the compressor is the statistical value corresponding to the same capability information from the device statistical information storage unit 305. And the average value of the pipe temperature are acquired, along with the average value of the rotational speed of the compressor and the average value of the pipe temperature, information indicating that these statistical values correspond to the capability information is used as additional information for diagnosis. Respond to section 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 diagnosis is not possible. It should be noted that the above-mentioned thresholds are given as, for example, “200”, but are not particularly limited to this example, and various changes are possible, and different values for each model information, year information, and capability information. May be used.

(9) Device connection information storage unit 309
The device connection information storage unit 309 includes, for example, a RAM and an EEPROM. FIG. 11 is a diagram showing an example of the data structure of the first device connection information of the device connection information storage unit 309 shown in FIG. 5, and FIG. 12 is two of the device connection information storage unit 309 shown in FIG. FIG. 13 is a diagram showing an example of the data structure of the device connection information of the eye, and FIG. 13 is a diagram showing an example of the data structure of the third device connection information of the device connection information storage unit 309 shown in FIG. Note that although FIGS. 11 to 13 exemplify three types of tables, 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 1 set of data (1 is an integer of 2 or more) with the 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 made up of t sets of data (t is an integer of 2 or more) with the year information and the number of connected devices as one set. , "2" is stored as the priority of 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 the capability information.

(10) Logic storage unit 310
The logic storage unit 310 includes, for example, a ROM, an EEPROM and the like. FIG. 14 is a diagram showing an example of the data structure of the logic storage unit 310 shown in FIG.

The logic storage unit 310 stores the 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 pipe cooling”, and the diagnostic logic for determining is “higher than the average pipe temperature by 5 degrees or more”. The diagnosis item and the diagnosis 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 identification 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 the 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 an operator who diagnoses the electrical equipment 1, and is, for example, a call center operator who receives a telephone call from a user of the electrical equipment 1 or a developer who confirms the quality of the electrical equipment 1.

(2) Diagnostic target specifying unit 402
The diagnosis target specifying unit 402 acquires the information of the electrical appliance 1 to be the diagnosis target 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 the operator with an input screen as shown in FIG. 16 and prompts the operator to input the model information and the manufacturing number. Get the number and.

(3) Diagnostic request transmission processing unit 403
The diagnostic request transmission processing unit 403 acquires information (model information and serial number) of the electrical device 1 to be diagnosed from the diagnostic target specifying unit 402, and uses it as diagnostic request information via the transmitting/receiving unit 404 to diagnose the diagnostic device. Send to 3. The diagnosis request information here includes model information and manufacturing 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 main functions. As a first function, the transmission/reception unit 404 receives the diagnosis request information from the diagnosis request transmission processing unit 403 and transmits it to the diagnostic device 3. As a second function, the transmission/reception unit 404 receives the diagnostic result from the diagnostic device 3 and outputs it to the diagnostic 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 diagnostic result reception processing unit 405 outputs the received diagnostic result to the diagnostic result display processing unit 406.

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

FIG. 17 is an example of a result display screen that displays a diagnostic result when the determination accuracy is high, and FIG. 18 is an example of a result display screen that displays a diagnostic result when the determination accuracy is low. As shown in FIGS. 17 and 18, “diagnosis possibility” is displayed for each diagnostic item if the diagnostic logic is applicable, and “normal” is displayed if the diagnostic logic is not applicable. When the derivation source of the statistical value used for the diagnostic logic is “model information” as the incidental information, the determination accuracy is displayed as “high” as shown in FIG. 17, and the derivation source of the statistical value is “fiscal year”. In the case of "information", the determination accuracy is displayed as "medium", and when the deriving source of the statistical value is "capability information", the determination accuracy is displayed as "low" as shown in FIG. The operator interprets the judgment result by looking at this judgment accuracy, and utilizes it for the customer's response at the time of answering a telephone call and for the quality confirmation of the electric appliance 1.

(1.5) Operation Hereinafter, the operation of the diagnostic system will be described, and then 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 overall 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 the device statistical information using the stored log data and model information (step S200). Finally, the display device 4 transmits diagnostic request information to the diagnostic device 3 in response to a request from the operator, the diagnostic device 3 performs a diagnosis based on the received diagnostic request information, and the diagnostic result is displayed on the display device. In response to 4, the display device 4 presents the diagnosis result to the operator (step S300). Note that these processes show an outline of the processes of the diagnostic system, and the order of each process, the number of times of each process, and the like can be variously changed.

(2) Detailed Operation of Step S100 FIG. 20 is a flowchart showing an example of the log transmission/reception process S100 shown 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 electric appliance 1 acquires the rotation speed of the compressor 106, the pipe temperature of the sensor unit 107, and the like from the control unit 101 to generate log data (step S101). Next, the log acquisition unit 102 stores the log data in the log storage unit 103 (step S102). After that, the log transmission unit 105 combines the model information and the manufacturing number acquired from the identification information storage unit 104, the time stamp, the compressor rotation speed, and the pipe temperature acquired from the log storage unit 103 based on a predetermined trigger. , To the diagnostic device 3 via the Internet 2 (step S103). At this time, the log receiving unit 301 of the diagnostic device 3 receives the log data and the like from the electrical appliance 1 via the Internet 2 and stores the received log data and 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. 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 larger than a predetermined threshold value (for example, a value corresponding to one hour) (step S202), and when the count value is not equal to or larger than the predetermined threshold value (step S202). (NO in S202), the process of step S201 is repeated.

On the other hand, when the count value is equal to or larger than the predetermined threshold value (YES in step S202), the device statistical information calculation unit 304 sets, as the three types of device statistical information, the statistical value for each type of model information and the type for each year information. The statistical value and the 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 similarly calculates the number of connected devices for each type of model information, the number of connected devices for each type of year information, and the number of connected devices for each type of capability information as three types of device connection information. It is 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 showing an example of the electric appliance diagnostic processing S300 shown in FIG. 19. The electric appliance diagnostic processing S300 indicates that the electric appliance 1 to be diagnosed is displayed on the display device 4. The diagnostic request is input, the diagnostic request is transmitted to the diagnostic device 3, the diagnostic device 3 diagnoses the operating state using the log data and the device statistical information collected from the electrical appliance 1, and the diagnostic result is returned to the display device 4. , 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 the operator, and the diagnosis target specifying unit 402 acquires the information of the electrical appliance 1 as the diagnosis target and outputs it to the diagnosis request transmission processing unit 403 (step S301). Next, the diagnosis request transmission processing unit 403 acquires the information of the electrical appliance 1 to be diagnosed from the diagnosis target specifying unit 402, and transmits it as the diagnosis request information to the diagnostic device 3 via the transmission/reception unit 404. (Step S302).

Next, the transmitting/receiving 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 diagnosis request information is received from the display device 4 via the display device (step S303). Next, the diagnostic unit 307 outputs the model information included in the diagnostic request information to the statistical value determination unit 308, and the statistical value determination unit 308 receives the model information from the diagnostic unit 307 and determines the statistical information, and the diagnostic unit The statistical value and the 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 the accompanying information, then acquires the diagnostic item and the diagnostic logic from the logic storage unit 310, and from the statistical value determination unit 308. Using the received statistical values, it is confirmed whether the diagnostic logic is applicable or not and the diagnosis is performed (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 diagnostic result from the diagnostic device 3 and outputs it to the diagnostic result reception processing unit 405, and the diagnostic result reception processing unit 405 causes the diagnostic device 3 via the transmission/reception unit 404. The diagnostic 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, 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 processing S304 shown in FIG.

First, the statistical value determination unit 308 of the diagnostic device 3 acquires the 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 the device connection information corresponding to the received model information from the table of the highest priority level 1 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 equal to or more than the threshold value (step S312).

When the device connection information is equal to or more than the threshold value (YES in step S312), the statistical value determination unit 308 determines that the statistical value corresponding to the same model information from the device statistical information storage unit 305 is the average value of the rotation speed of the compressor. The average value of the pipe temperature and the average value of the rotational speed of the compressor and the average value of the pipe temperature, together with the information indicating that these statistical values correspond to the model information, as ancillary information, the diagnostic unit It responds to 307 (step S313).

On the other hand, when the device connection information is smaller than the threshold value (NO in step S312), the statistical value determination unit 308 determines whether the device connection information storage unit 309 has the device connection information having the next priority ((step S312)). 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 broad 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 processing to step S312, determines whether or not the acquired device connection information is equal to or more than a threshold value, and repeats the subsequent processing.

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 diagnosis is not possible (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. From the value determination unit 308, the statistical value to be used and the derivation source information of the statistical value are received as accompanying information (step S321).

Next, the diagnosis unit 307 acquires the diagnosis item and the diagnosis logic from the logic storage unit 310, and uses the statistical value received from the statistical value determination unit 308 to check whether the diagnostic logic is applicable (step S322). ). In addition, the diagnosis unit 307 sets the applicable diagnostic item to “applicable” when the diagnostic logic is applicable, and sets the applicable diagnostic item to “not applicable” when the diagnostic logic does not apply, and “applies” or “not applicable” to each diagnostic item. Information and the derivation source information of the statistical value received from the statistical value determination unit 308 as the accompanying information, and the response is sent to the display device 4 via the transmission/reception unit 306 as a diagnostic result (step S322).

(7) Detailed Operation of Step S308 FIG. 25 is a flowchart showing an example of the diagnostic result display processing 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 the accompanying information (step S332).

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

Finally, the diagnostic 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 value, electrification of the same model is performed. Since the log data of the device is small, the statistical value of the log data of the same type of electrical device is less accurate, but since the number of connected electrical devices in the same manufacturing year as the electrical device 1 to be diagnosed is large, the statistical value is By using the log data of the electric appliances of the same manufacturing year as the log data for calculation, it is possible to obtain a statistical value with sufficient accuracy. Therefore, when the number of connected electrical appliances of the same model is less than a predetermined threshold value, the operating state of the electrical appliances 1 to be diagnosed can be diagnosed with sufficient accuracy by using the statistical value of the electrical appliances of the same manufacturing year. it can.

Also, when the number of connected electrical appliances in the same manufacturing year is less than the predetermined threshold, the log data of electrical appliances in the same manufacturing year is small, so the accuracy of the statistical value of the log data of electrical appliances in the same manufacturing year is low. Since the number of connected electrical appliances having the same performance as the electrical appliance 1 to be diagnosed is large, statistics with sufficient accuracy can be obtained by using the log data of the electrical appliances having the same performance as the log data for calculating the statistical value. You can get the value. Therefore, when the number of connected electrical appliances in the same manufacturing year is less than a predetermined threshold value, the operating state of the electrical appliances 1 to be diagnosed can be diagnosed with sufficient accuracy by using the statistical value of the electrical appliances of 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, a statistical value required for quality prediction is calculated in parallel by a single diagnostic logic. Even in such a case, a system capable of predicting quality with sufficient accuracy is realized by calculating a plurality of types of statistic values and selecting an optimal statistic value from the plurality of types of statistic values.

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

(2) The device connection information storage unit 309 holds the device connection information (the number of connected devices) shown in FIGS. 11 to 13, but the device connection information storage unit 309 is not limited to this as long as the accuracy of statistical values can be predicted. For example, the period from turning on the power of the electric appliance 1 (for example, an air conditioner) to turning it off may be defined as one operation, and the device operation information indicating the number of times of operation may be used. FIG. 26 is a diagram showing an example of the data structure of the first device operation information of the device connection information storage unit 309 shown in FIG. 5, and FIG. 27 is two of the device connection information storage unit 309 shown in FIG. FIG. 29 is a diagram showing an example of the data structure of the device operation information of the eye, and FIG. 28 is a diagram showing an example of the data structure of the third device operation information of the device connection information storage unit 309 shown 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 times of operation. The first table shown in FIG. 26 is a table of device operation information for each type of model information, and is composed of 1 set of data (l is an integer of 2 or more) with one set of model information and the number of times of operation. , "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 t sets of data (t is an integer of 2 or more) with one set of year information and the number of times of operation. , "2" is stored as the priority of 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 data of s sets (s is an integer of 2 or more) with the capability information and the number of times of operation as one set. , "3" is stored as the priority of the capability information.

Also in this case, similarly to the above-mentioned embodiment, the statistical value determination unit 308 refers to each table in the order of priority, and determines the predetermined threshold and the electric appliance of the same model as the electric appliance 1 to be diagnosed. Type of statistical value to be used by sequentially comparing the number of times of operation, the number of times of operation of the electrical equipment 1 to be diagnosed with the same manufacturing year, and the number of times of operation of electrical equipment 1 to be diagnosed with the same performance Can be determined, and the same effect as that of the above-described embodiment can be obtained. 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 data amount of log data transmitted from the electric 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 showing an example of the data structure of the first device data amount information of the device connection information storage unit 309 shown in FIG. 5, and FIG. FIG. 31 is a diagram showing an example of a data structure of device data amount information, and FIG. 31 is a diagram showing an example of a data structure of device data amount information of the third device connection information storage unit 309 shown in FIG. 5. is there.

As shown in FIGS. 29 to 31, the device connection information storage unit 309 stores three types of tables including the data 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 configured from 1 set (l is an integer of 2 or more) of data, where the model information and the data amount are one set. In addition, “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 of data (t is an integer of 2 or more) with one set of year information and data amount. Therefore, “2” is stored as the priority of 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 s sets (s is an integer of 2 or more) of data having one set of capability information and data amount. Therefore, “3” is stored as the priority of the capability information.

Also in this case, similarly to the above-mentioned embodiment, the statistical value determination unit 308 refers to each table in the order of priority, and determines the predetermined threshold and the electric appliance of the same model as the electric appliance 1 to be diagnosed. The amount of data, the amount of data of the electrical equipment in the same manufacturing year as the electrical equipment 1 to be diagnosed, and the amount of electrical equipment of the same performance as the electrical equipment 1 to be diagnosed are sequentially compared, and the type of statistical value to be used Can be determined, and the same effect as that of the above-described 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) Although the device connection information shown in FIGS. 11 to 13 has priority, the device connection information is not limited to this. For example, the model relation table of the electrical equipment 1 may be defined in a tree structure, the model relation information representing this tree structure may be used, and the priority may be omitted. FIG. 32 is a diagram showing an example of model relation information stored in the model information storage unit 303 shown in FIG.

For example, when the electric 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-class 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 “whole”, the model relation table of the electric appliance 1 is 32 is defined by the tree structure shown in FIG. 32, and the model relation information representing this 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 characteristic item is the highest node, the nodes indicating “compressor A” and “compressor B” are the intermediate nodes, and the electrical appliance 1 The leaf node indicating each model (for example, “AC2016-H”) is the lowest node, and the relationship between them is stored as model relationship information. Therefore, it is possible to express the relationship between various characteristic items that cannot be expressed by a simple priority order.

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

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

FIG. 33 is a diagram showing an example of the data structure of the first tree-structure device information of the device connection information storage unit 309 shown in FIG. 5 when the model relation information shown in FIG. 32 is used, and FIG. 32 is a diagram showing an example of the data structure of the second tree-structure device information of the device connection information storage unit 309 shown in FIG. 5 when the model relation information shown 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 stores, as device operation information, a table including the model information and the number of times of operation shown in FIG. 33, the characteristic item and the operation 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, one set including model information and the number of times of operation. The second table shown in FIG. 34 is a table of device operation information for each type of characteristic item, and is composed of three sets of data with one set of characteristic items and the number of times of operation.

In this case, the statistical value determination unit 308 refers to the model-related 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 (the lowest mode). If the number of times of operation of the leaf node is not greater than or equal to the predetermined threshold, the node is sequentially moved to a node above the predetermined threshold, and the number of times of operation of the electric appliance of the same model as the electric appliance 1 to be diagnosed It is possible to determine the type of statistical value to be used by sequentially comparing the number of times of operation of an electric appliance that uses the same compressor as the electric appliance 1 and the number of times of operation of all the electric appliances 1. Therefore, even when various characteristic items that cannot be expressed by a simple priority order are used, the same effect as that of the above-described 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 the present invention is not limited to this. For example, dispersion may be added. 35 is a diagram showing a first modification of the data structure of the first device statistical information of 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 of 305, and FIG. 37 is a diagram of the data structure of the third device statistical information of the device statistical information storage unit 305 shown in FIG. 5. It is a figure which shows the 1st modification.

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

(5) The device statistical information storage unit 305 uses the average value of the individual sensor values shown in FIGS. 8 to 10 as the statistical value, but the present invention is not limited to this. For example, the diagnostic device 3 may obtain an average value of a plurality of sensor values calculated using a predetermined calculation formula. 38 is a diagram showing a second modification of the data structure of the first device statistical information of the device statistical information storage unit 305 shown in FIG. 5, and FIG. 39 is a device statistical information storage unit shown in FIG. FIG. 40 is a diagram showing a second modified example of the data structure of the second device statistical information of 305, and FIG. 40 is a diagram showing the second data structure of the device statistical information of the third device statistical information storage unit shown in FIG. It is a figure which shows the modification of No. 2.

The first table shown in FIG. 38 is a table of device statistical information for each type of model information, and the model information and the average value of the values obtained by dividing the pipe temperature by the rotation speed of the compressor are set as one set. It is composed of 1 set (1 is an integer of 2 or more) of data. The second table shown in FIG. 39 is a table of device statistical information for each type of year information, and the year information and the average value of the values obtained by dividing the pipe temperature by the rotation speed of the compressor are set as one set. It is composed of t sets of data (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 the capability information and the average value of the values obtained by dividing the pipe temperature by the rotation speed of the compressor are set as one set. It is composed of s sets (s is an integer of 2 or more) of data. Also in this case, the same effect as that of the above-described embodiment can be obtained. The sensor value and the calculation formula are not particularly limited to the above examples, and other sensor values and other calculation formulas may be used.

(6) The above configurations may be arbitrarily combined. In this case, the same effect as above can be obtained.

(Overview of services provided)
FIG. 41(A) shows an overview of the service according to this embodiment. For example, some or all of the blocks of the cloud server (diagnostic device 3) described above 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 household, etc., and its scale does not matter. In the group 100, there are a plurality of devices (electrical devices) 1, a device A, a device B, and a home gateway 102G. Among the plurality of devices 1, there are devices that can connect to the Internet (for example, smartphones, PCs, TVs, etc.), and there are devices that cannot connect to the Internet by themselves (for example, lighting, washing machines, refrigerators, etc.) To do. There may be a device that cannot connect to the Internet by itself or a device that can connect to the Internet via the home gateway 102G. Further, 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 enormous data (big data) that is difficult to handle with a normal database management tool or the like. The data center operating company 110 manages data, manages the cloud server 111, and operates a data center that performs such management. Details of the 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 only manages data and operates the cloud server 111. For example, when a device manufacturer who develops and manufactures one of the plurality of devices 1 also manages the data and the cloud server 111, the device manufacturer sets the data center operating company 110. This is applicable (FIG. 41(B)). Further, the data center operating company 110 is not limited to one company. For example, when the device maker and another management company jointly or share the data management and the 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 mentioned here includes, for example, a memory in a personal PC, regardless of its scale. In addition, the service provider may not own the server 121.

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

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

First, the device A or the 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 the log information of the device A or the device B (FIG. 41(a)). Here, the log information is information indicating, for example, the driving status, the operation date and time, etc. of the plurality of devices 1. Examples include TV viewing history, recorder recording reservation information, operating date/time of laundry, amount of laundry, opening/closing date/time of refrigerator, etc. Of information. The log information may be directly provided to the cloud server 111 from the plurality of devices 1 themselves via the Internet. Alternatively, log information may be temporarily accumulated in the home gateway 102G from a plurality of devices 1 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 accumulated log information to the service provider 120 in a certain unit. Here, it may be a unit capable of organizing the information accumulated by the data center operating company and providing it to the service provider 120, or a unit requested by the service provider 120. Although the unit is described as a fixed unit, it may not be fixed and the amount of information provided may change depending on the situation. The log information is stored in the server 121 owned by the service provider 120 as needed (FIG. 41(b)). Then, the service provider 120 organizes the log information into information suitable for the service provided to the user and provides it to the user. The user to be provided may be the user 10Y who uses the plurality of devices 1 or an external user 20Y. The service providing method to the user may be provided directly from the service provider to the user (FIGS. 41(f) and 41(e)). In addition, the service providing method to the user may be provided to the user, for example, via the cloud server 111 of the data center operating company 110 again (FIGS. 41C and 41D). Further, 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.

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

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

(Service type 1: In-house data center type)
FIG. 42 shows a 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 the function of a data center operating company. That is, the service provider owns the 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 a data center (cloud server 111) (203). Further, 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 a service type 2 (IaaS use type). Here, IaaS is an abbreviation for infrastructure as a service, and is a cloud service providing model that provides the infrastructure itself for constructing and operating a computer system as a service via the Internet.

In this type, a data center operating company operates and manages a data center (cloud server 111) (203). Further, 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 use type)
FIG. 44 shows a service type 3 (PaaS use type). Here, PaaS is an abbreviation for platform as a service, and is a cloud service provision model that provides a platform as a base for building 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 also 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 a service type 4 (SaaS use type). Here, SaaS is an abbreviation for software as a service. For example, a function that allows a company/individual (user) who does not have a data center (cloud server) to use an application provided by a platform provider who has a data center (cloud server) via a network such as the Internet It is a cloud service provision model that has.

In this type, the data center operating company 110 manages an application (201), an OS (202), and operates and manages a data center (cloud server 111) (203). The service provider 120 also 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 has performed a service providing action. Further, for example, the service provider or the data center operating company may develop the OS, the application, the database of the big data, or the like by itself, or may outsource it to a third party.

A diagnostic system according to one aspect of the present disclosure diagnoses an operating state of an electrical appliance with sufficient accuracy even when 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 diagnostic system for diagnosing home electric appliances and the like.

1 Electrical Appliance 2 Internet 3 Diagnostic Device 4 Display Device 101 Control Unit 102 Log Acquisition Unit 103 Log Storage Unit 104 Identification Information Storage Unit 105 Log Transmission Unit 106 Compressor 107 Sensor Unit 301 Log Reception Unit 302 Log Storage Unit 303 Model Information Storage Unit 304 device statistical information calculation unit 305 device statistical information storage unit 306 transmission/reception unit 307 diagnostic unit 308 statistical value determination unit 309 device connection information storage unit 310 logic storage unit 401 input/output unit 402 diagnostic target identification unit 403 diagnostic request transmission processing unit 404 transmission/reception Part 405 Diagnostic result reception processing part 406 Diagnostic result display processing part

Claims (17)

A method of diagnosing a diagnostic device for diagnosing an operating state of a target electrical device to be diagnosed among a plurality of electrical devices,
An acquisition step of acquiring identification information for identifying the target electrical appliance,
A specifying step of specifying, based on the identification information, first characteristic information indicating a first characteristic item of the target electrical appliance and second characteristic information indicating a second characteristic item different from the first characteristic item; ,
Based on the first log information regarding the operating state of the first same-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 characteristic item as the second characteristic item indicated by the first identical characteristic statistical information in which the first log information is aggregated and the second characteristic information of the target electrical device among the plurality of electrical devices. It is determined which statistical information is to be used among the second identical feature statistical information in which the second log information related to the operating state of the second identical feature electrical appliances having the same is used, and the determined statistical information is used. A diagnosis step for diagnosing an operating state of the target electrical equipment,
The number of groups when the plurality of electric appliances are grouped by the first characteristic item is larger than the number of groups when the plurality of electric appliances are grouped by the second characteristic item.
Diagnostic method. The plurality of electric appliances are connected to a predetermined network,
In the diagnosis step, the number of connected devices indicating the number of the first same-characteristic electric appliances connected to the network, which is identified from the first log information, is greater than or equal to a predetermined number, and Which of the first same-characteristic statistical information and the second same-characteristic statistical information is to be used is determined, and the operating status of the target electrical appliance is diagnosed using the determined statistical information.
The diagnostic method according to claim 1. In the diagnosis step, when the number of connected devices is equal to or larger than the predetermined number, the operating state of the target electrical appliance is diagnosed by 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 less than the predetermined number, the operating state of the target electrical appliance is diagnosed by using the second identical feature statistical information.
The diagnostic method according to claim 2 or 3. The plurality of electric appliances are connected to a predetermined network,
In the diagnosis step, the first same characteristic is determined according to whether or not a data amount of the first log information transmitted from the first same characteristic electric appliance via the network is equal to or more than a predetermined amount. Which of the statistical information and the second identical feature statistical information is to be used is determined, and the operating state of the target electrical appliance is diagnosed using the determined statistical information.
The diagnostic method according to claim 1. In the diagnosis step, when the data amount of the first log information transmitted from the first same-characteristic electrification device is equal to or larger than the predetermined amount, the target same-electricity statistical information is used to perform the target electrification. Diagnose the operating status of equipment,
The diagnostic method according to claim 5. In the diagnosing step, if the data amount of the first log information transmitted from the first same-characteristic electric appliance is smaller than the predetermined amount, the target same electric appliance is used by using the second same-characteristic statistical information. To diagnose the operating status of
The diagnostic method according to claim 5 or 6. In the identifying step, model information indicating a model of the target electrical appliance identified by the identification information is identified as the first feature information, and is identified by the identification information as the second feature information. Identifying attribute information indicating attribute items relating to at least one of the product model and the capability of the target electrical appliance,
The diagnosis step is based on the same model log information indicating the operating state of the same model electric appliance that is the same model as the model indicated by the model information of the target electric appliance among the plurality of electric appliances, and the same model log information. Of the same model statistical information and the same attribute statistical information obtained by collecting the same attribute log information from the same attribute electrical device having the same attribute item as the attribute item indicated by the attribute information of the target electrical device. Determine whether to use statistical information, using the determined statistical information to diagnose the operating state of the target electrical equipment,
The number of groups when grouping the plurality of electric appliances by the model is larger than the number of groups when grouping the plurality of electric appliances by the attribute item.
The diagnostic method according to claim 1. The attribute information includes year information indicating a manufacturing year of the target electrical equipment,
The diagnostic method according to claim 8. The diagnostic method according to claim 8 or 9, wherein the attribute information includes capability information indicating a capability of the target electrical device represented by any 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. Calculate and determine whether the target electrical device is abnormal by applying the statistical value to a predetermined diagnostic logic,
The diagnostic method according to claim 1. The plurality of electrical appliances include a plurality of air conditioners,
The first log information and the second log information include at least one of rotational speed information indicating a rotational 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 for the diagnosis of the diagnosis step,
Further comprising a changing step of changing the display content of the diagnosis result information indicating the result of the diagnosis according to the accuracy of the diagnosis,
The diagnostic method according to claim 1. The specifying step further specifies third characteristic information indicating a third characteristic item different from the first and second characteristic items based on the identification information,
The number of groups when the plurality of electrical appliances are grouped by the second characteristic item is larger than the number of groups when the plurality of electrical appliances are grouped by the third characteristic item.
The first to third characteristic information have a priority used as statistical information in the diagnosis step,
The diagnosing step is based on the first log information, the second log information, and the priority, the first identical feature statistical information, the second identical feature statistical information, and the plurality of electrifications. Of the devices, the third feature information item having the same feature item as the third feature item indicated by the third feature information item of the target electrical device is the third log information item regarding the operating state of the third identical feature electrical device item. Decide which statistical information to use from the same feature statistical information,
The diagnostic method according to claim 1. The specifying step further specifies third characteristic information indicating a third characteristic item different from the first and second characteristic items based on the identification information,
The number of groups when the plurality of electrical appliances are grouped by the second characteristic item is larger than the number of groups when the plurality of electrical appliances are grouped by the third characteristic item.
In the diagnosing step, a relationship between each of the first log information, the second log information, and the first to third characteristic items included in the first to third characteristic information is defined by a tree structure. On the basis of the model relation information, the first identical feature statistical information, the second identical feature statistical information, and a third characteristic information of the target electrical equipment among the plurality of electrical equipment indicated by the third characteristic information. It is determined which statistical information is to be used from the third same characteristic statistical information in which the third log information relating to the operating state of the third same characteristic electric appliance having the same characteristic item is collected. ,
The diagnostic method according to claim 1. A diagnostic device for diagnosing an operating state of a target electrical device to be diagnosed among a plurality of electrical devices,
An acquisition unit for acquiring identification information for identifying the target electrical appliance,
A specifying unit that specifies, based on the identification information, first characteristic information indicating a first characteristic item of the target electrical appliance and second characteristic information indicating a second characteristic item different from the first characteristic item. ,
Based on the first log information regarding the operating state of the first same-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 characteristic item as the second characteristic item indicated by the first identical characteristic statistical information in which the first log information is aggregated and the second characteristic information of the target electrical device among the plurality of electrical devices. It is determined which statistical information is to be used among the second identical feature statistical information in which the second log information related to the operating state of the second identical feature electrical appliances having the same is used, and the determined statistical information is used. A diagnostic unit for diagnosing the operating state of the target electrical equipment,
The number of groups when the plurality of electric appliances are grouped by the first characteristic item is larger than the number of groups when the plurality of electric appliances are grouped by the second characteristic item.
Diagnostic device. A display device for displaying a diagnostic result of a diagnostic device for diagnosing an operating state of a target electrical device to be diagnosed among a plurality of electrical devices,
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 characteristic item as the first characteristic item indicated by the first characteristic information of the target electrical appliance among the plurality of electrical appliances. Same characteristic statistical information in which the first log information relating to the operating state of the same electric appliance is aggregated, and a second characteristic different from the first characteristic item of the target electric appliance among the plurality of electric appliances. Any of the statistical information of the second same characteristic statistical information in which the second log information regarding the operating state of the second same characteristic electric appliance having the same characteristic item as the second characteristic item indicated by the information is aggregated. A receiving unit that receives from the diagnostic device statistical information specifying information indicating that
The accuracy of the diagnosis is determined based on the statistical information specifying information, the display content for notifying the diagnosis result information is determined according to the accuracy of the determined diagnosis, and a notification screen indicating the determined display content is displayed. And a display unit for displaying,
Display device.

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