JP2021089549A - Repair assisting system and program - Google Patents

Abstract

To provide a repair assisting system, etc., capable of highly precisely estimating a malfunction portion.SOLUTION: In a repair assisting system, a server 20 includes: a storing unit 22 that stores first information on a product name of gas equipment and a symptom thereof, second information on weather information, and a malfunction portion of the gas equipment in association with each other; a receiving unit 211 that receives information on a malfunction repair request; and an estimating unit 214 that estimates the malfunction portion of the gas equipment on the basis of the first information and the second information relating to the gas equipment subjected to the malfunction repair request received by the receiving unit 211, and repair information stored in the storing unit 22.SELECTED DRAWING: Figure 3

Description

The present invention relates to a repair support system and a program.

In recent years, techniques for supporting the repair of equipment have been proposed.
For example, the technique described in Patent Document 1 is configured as follows. That is, the customer is confirmed by using the customer information obtained from the customer information database and the repair information obtained from the repair information database, etc., and the parts information obtained from the parts information database and the master database are used to confirm the customer request. It provides a failure diagnosis repair request site that performs failure diagnosis processing of the target device using the obtained information on failure diagnosis and accepts repairs from customers using the charge information obtained from the charge information database.

Japanese Unexamined Patent Publication No. 2002-358383

In general, when a repairer goes to a place where the equipment to be repaired is located for repair, it is desirable to estimate the faulty part. This is because the repair can be completed in one visit by bringing the necessary tools for repair and parts for replacing the defective part. Therefore, it is desirable to be able to estimate the faulty part with high accuracy.
An object of the present invention is to provide a repair support system or the like capable of estimating a faulty part with high accuracy.

The present invention completed for this purpose is a memory for storing repair information in which the first information regarding the product name and symptom of the gas appliance, the second information regarding the weather information, and the failure part of the gas appliance are linked. A unit, a reception unit that receives information on a failure request, the first information and the second information regarding the gas appliance that has received the failure request received by the reception unit, and the repair information stored in the storage unit. The repair support system is characterized by including an estimation unit for estimating a failure portion of the gas appliance based on the above.
Here, it may have a prediction unit that predicts a failure part from the failure portion estimated by the estimation unit.
Further, it may have a derivation unit for deriving the cost required for repairing the failed part predicted by the prediction unit.
Further, even if it has a secondary prediction unit that predicts a secondary failure component that fails next to the failure component, and the lead-out unit derives the total cost required for repairing the failure component and the secondary failure component. good.
Further, it may have a display control unit that displays the total cost derived by the out-licensing unit and the cost when the gas appliance is replaced on the display unit.
In addition, it may have a prediction estimation unit that predicts and estimates the day when the secondary failure component is expected to fail in consideration of the family structure.
Further, the present invention completed for this purpose is the first information regarding the product name and symptom of the gas device, and the second information of at least one of the manufacturer name, region, weather information, and information on the failure diagnosis sheet. And the storage unit that stores the learned model obtained by machine learning using the first information and the failure part of the gas device stored in association with the second information, and the failure request information. The inference unit that infers the failure part of the gas device by the reception unit that receives the above, the first information and the second information about the gas device that has received the failure request, and the learned model. It is a repair support system characterized by being equipped with.
Here, it may have a prediction unit that predicts a failure part from the failure portion inferred by the reasoning unit.
Further, it may have a derivation unit for deriving the cost required for repairing the failed part predicted by the prediction unit.
Further, even if it has a secondary prediction unit that predicts a secondary failure component that fails next to the failure component, and the lead-out unit derives the total cost required for repairing the failure component and the secondary failure component. good.
Further, it may have a display control unit that displays the total cost derived by the out-licensing unit and the cost when the gas appliance is replaced on the display unit.
In addition, it may have a prediction unit that predicts the date on which the secondary failure component will fail in consideration of the family structure.
Further, the present invention completed for this purpose is a repair in which the first information regarding the product name and symptom of the gas appliance, the second information regarding the weather information, and the faulty part of the gas appliance are linked to the computer. The gas is based on a function of storing information, a function of receiving information on a failure request, the first information and the second information regarding the gas appliance for which the failure request was received, and the repair information. It is a program that realizes the function of estimating the faulty part of a device.
In addition, the present invention completed for this purpose has at least one of the first information on the product name and symptom of the gas appliance, the manufacturer name, the area, the weather information, and the information on the failure diagnosis sheet on the computer. A function to store a learned model obtained by machine learning using the second information, the first information, and the failure part of the gas appliance stored in association with the second information, and a failure request. The function of receiving the information of the above, the first information and the second information about the gas appliance for which the failure request was received, and the function of inferring the failure part of the gas appliance by the learned model are realized. It is a program to let you.

According to the present invention, it is possible to provide a repair support system or the like capable of estimating a faulty part with high accuracy.

It is a figure which shows an example of the schematic structure of the repair support system which concerns on 1st Embodiment. It is a figure which shows an example of the schematic structure of an information processing apparatus. It is a figure which shows an example of the schematic structure of the server apparatus which concerns on 1st Embodiment. It is a figure which shows an example of the repair information which a storage part stores. It is a figure which shows an example of the schematic structure of the server device which concerns on 2nd Embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.
<First Embodiment>
FIG. 1 is a diagram showing an example of a schematic configuration of the repair support system 1 according to the first embodiment.
The repair support system 1 is an information processing device 10 owned by a maintenance company M that receives a repair request from a customer, and a server that estimates a faulty part of the device to be repaired requested in response to a request from the information processing device 10. The device 20 and the like are provided. There are a plurality of maintenance companies M, and each maintenance company M is assigned an area R in charge, and a customer residing in the area R in charge receives a request for repair of a device failure of the customer. In the present embodiment, it can be exemplified that the device is a gas device used by burning or the like using gas as fuel.

The information processing device 10 and the server device 20 can communicate with each other via the network 5. The network 5 is not particularly limited as long as it is a communication network used for data communication between devices, and examples thereof include the Internet, WAN (Wide Area Network), and LAN (Local Area Network). The communication line used for data communication may be a combination of these, regardless of whether it is wired or wireless.

(Information processing device 10)
FIG. 2 is a diagram showing an example of a schematic configuration of the information processing device 10.
The information processing device 10 receives a control unit 11 that controls the entire device, a storage unit 12 that is used for storing data and the like, a display unit 13 that is used for displaying an operation reception screen and an image, and a user's input operation. It includes an operation unit 14 and a communication unit 15 used for communication with an external device. The control unit 11, the storage unit 12, the display unit 13, the operation unit 14, and the communication unit 15 are connected by a bus 16.

The control unit 11 is composed of a CPU (Central Processing Unit) (not shown), a ROM (Read Only Memory) (not shown), and a RAM (Random Access Memory) (not shown). The ROM stores a basic program (operating system) executed by the CPU, various settings, and the like. The CPU uses the RAM as a work area and executes an application program read from the ROM or the storage unit 12. Each part of the information processing apparatus 10 is controlled by the CPU executing the program.

It can be exemplified that the storage unit 12 is a storage device such as a semiconductor memory or an HDD (Hard Disk Drive).
The display unit 13 can be exemplified as a liquid crystal display or an organic EL display.
It can be exemplified that the operation unit 14 is a button, a switch, and a touch panel.
The communication unit 15 can be exemplified as a communication interface (communication I / F).
The information processing device 10 configured as described above includes a notebook PC, a desktop PC, a tablet PC, a tablet terminal, a personal digital assistant (PDA), a multifunctional mobile phone (so-called "smartphone"), and a mobile phone (so-called "feature phone"). ") Etc. can be exemplified.

The control unit 11 controls a receiving unit 111 that receives information transmitted from an external device such as the server device 20, a transmitting unit 112 that transmits information to an external device such as the server device 20, and a display unit 13. The display control unit 113 is provided.

The receiving unit 111 receives a repair request from the customer via the network 5. For example, the receiving unit 111 may exemplify receiving a repair request from a customer by e-mail, writing to a web site provided by the maintenance company M, or the like. When receiving the repair request, the receiving unit 111 receives information such as the product name and the symptom to be repaired, and also receives other information written by the customer on the e-mail or the web site. The repair request received by the receiving unit 111 includes a repair request received from a customer by an employee of the maintenance company M over the telephone via the operation unit 14.
In addition, the receiving unit 111 receives information about the failed portion and the predicted failed component from the server device 20.

When the receiving unit 111 receives the repair request, the transmitting unit 112 transmits a request to the server device 20 to estimate the faulty part of the product to be repaired. At that time, the transmission unit 112 transmits the information provided by the customer, such as the product name to be repaired and the symptom information received by the reception unit 111, to the server device 20.
The display control unit 113 causes the display unit 13 to display the information or the like received by the reception unit 111.

Further, the control unit 11 has a derivation unit 114 for deriving information such as a cost charged to the customer and a period until the repair is completed with respect to the repair request received by the reception unit 111. The derivation unit 114 will be described in detail later.

(Configuration of server device 20)
FIG. 3 is a diagram showing an example of a schematic configuration of the server device 20.
The server device 20 includes a control unit 21 that controls the entire device, a storage unit 22 that is used for storing data and the like, a display unit 23 that is used for displaying an operation reception screen and an image, and an operation that accepts a user's input operation. A unit 24 and a communication unit 25 used for communication with an external device are provided. The control unit 21, the storage unit 22, the display unit 23, the operation unit 24, and the communication unit 25 are connected by a bus 26.

The control unit 21 has a CPU (not shown), a ROM (not shown), a RAM (not shown), and the like.
It can be exemplified that the storage unit 22 is a storage device such as a semiconductor memory or an HDD.
The display unit 23 is a display device that displays a still image, a moving image, or the like. It can be exemplified that it is a liquid crystal display or an organic EL display.
The operation unit 24 is an input device that accepts operations from the user. It can be exemplified that it is a button, a switch, and a touch panel.
The communication unit 25 can be exemplified as a communication interface (communication I / F).

The ROM of the control unit 21 stores a basic program (operation system) executed by the CPU, various settings, and the like. The CPU uses the RAM as a work area and executes an application program read from the ROM or the storage unit 22. When the CPU executes the program, the functions of the control unit 21 described below are realized.

The control unit 21 has a receiving unit 211 that receives information transmitted from an external device such as the information processing device 10, and a transmitting unit 212 that transmits information to an external device such as the information processing device 10. .. Further, the control unit 21 has an estimation unit 214 that estimates a failure part using weather information, a prediction unit 215 that predicts a failure part that may have failed from the failure part estimated by the estimation unit 214, and a prediction unit 215. It has a secondary prediction unit 216 that predicts a component that fails next to the failed component predicted by (hereinafter, the component that fails next may be referred to as a "secondary failure component").

The receiving unit 211 receives the information transmitted from the information processing device 10. For example, the receiving unit 211 receives the information of the repair request received by the maintenance company M, the information of the content that the maintenance company M has dealt with for the repair, and the like. Then, the receiving unit 211 stores the repair information regarding the received repair in the storage unit 22.
Further, the receiving unit 211 receives a request from the maintenance company M for estimating the faulty part, which is transmitted from the information processing device 10.

FIG. 4 is a diagram showing an example of repair information stored in the storage unit 22.
The storage unit 22 stores information about the repair of the gas appliance performed by the maintenance company M, which is sent from the information processing device 10.
As repair information, as shown in FIG. 4, the reception number, the reception date, the repair completion date, the product name, the symptom, the manufacturer name, the model name, the customer number, the number of family members, the maintenance company M number, and the number of years elapsed , Information on the failure diagnosis sheet, failure part, failure part, countermeasures, cause of failure, repair cost, etc. are stored in association with each other.

Here, the customer number is a number assigned to each customer, and the address, telephone number, family structure, etc. of the customer may be stored in association with the number.
The number of the maintenance company M is a number assigned to each maintenance company M, and the area R in charge of the maintenance company M is stored in association with the number.
The number of years elapsed is the number of years that have passed since the product was first used. The number of years that have passed since the date of manufacture of the product may also be stored.
It can be exemplified that the information on the failure diagnosis sheet is the information of the number displayed on the remote controller. Alternatively, it may be the content of the failure described in the failure diagnosis sheet, for example, a combustion fan abnormality, a malfunction, or the like. It can be exemplified that these are records of information transmitted from a customer, for example, by telephone.

The faulty part is a part that actually fails, and for example, it can be exemplified that it is a heat generating part, a remote controller, and an ignition part inside the water heater main body.
The failed component means a failed component, and examples thereof include a fan motor, an electrical board, and a heat exchanger.
The content of the countermeasure is the content of the processing performed for the failure, for example, the replacement of the defective part, the replacement of the defective product with a new product, and the position of the component without replacing the component. It can be exemplified that the repair or the like is performed by reattaching the parts in the correct positions and repairing the parts without replacing the parts.

As the cause of failure, the cause that can be considered as the cause of the failure is stored. For example, it is stored as lightning if it breaks down due to lightning, typhoon if it breaks down due to a typhoon, aging deterioration if it simply deteriorates, and improper installation if it is installed incorrectly. It can be exemplified that these are the causes grasped by the person who performed the repair when the repair was actually performed.

In addition, in the storage unit 22, for each product, the manufacturer name, model name, unit price, the name of the part of the product with the model name, the manufacturer name, model name, unit price of the part, the presence or absence of inventory, and the case where there is no inventory. The number of days required from ordering to delivery is stored.

Here, the storage unit 22 stores the repair information including the weather information, and the estimation unit 214 estimates in consideration of the weather information for the following reasons. For example, when a peculiar natural phenomenon such as a storm, heavy rain, heavy snowfall, flood, earthquake, tsunami, eruption, or lightning occurs, the probability of gas equipment failure increases. In particular, outdoor water heaters are susceptible to peculiar natural phenomena. For example, when a typhoon causes a storm and heavy rain, rainwater may infiltrate into the inside through the water heater exhaust port. In addition, if lightning strikes, the electrical board may break down.
In view of such matters, the server device 20 estimates the faulty part in consideration of the weather information. The estimation unit 214 estimates using the weather information included in the failure request received by the reception unit 211. Since the information provided when the customer requests repair may not include the weather information, the server device 20 may acquire the weather information by itself. For example, the receiving unit 211 periodically acquires the weather information via the network 5, and stores the acquired weather information in the storage unit 22. The storage unit 22 stores, for example, weather information for the past one month. Then, the estimation unit 214 acquires the weather information of the area where the product to be repaired is located before the reception date of the repair request from the weather information stored in the storage unit 22, and obtains the acquired weather information. It is good to use it to estimate the faulty part.

For example, the estimation unit 214 includes the product name (for example, a water heater, a gas stove) and a symptom (for example, no hot water) of the gas appliance for which a failure request has been received by the reception unit 211, weather information, and a storage unit 22. Based on the repair information stored in the gas appliance, the faulty part of the gas appliance is estimated. The estimation unit 214 counts, for example, the number of faulty parts stored in the repair information of the storage unit 22 in association with the product name, the symptom, and specific weather information (for example, lightning), and has the largest number of counts. It can be exemplified that the faulty part is estimated as the faulty part of this time.
In addition, the estimation unit 214 counts the number of each failure part stored in the storage unit 22 in association with the product name, the symptom, and the failure part stored in the storage unit 22 in association with the weather information, and also The top plurality (for example, 5) failure sites having a large number of counts may be estimated as the current failure sites.

The prediction unit 215 predicts a faulty part that may have failed from the faulty part estimated by the estimation unit 214 based on the repair information stored in the storage unit 22. The storage unit 22 stores the failed component in association with the failed portion. For example, the prediction unit 215 counts the number of each failure part stored in the storage unit 22 in association with the failure part estimated by the estimation unit 214, and sets the failure part having the largest count number as the failure part this time. Prediction can be illustrated.

The prediction unit 215 may predict the failed part in consideration of the elapsed years. For example, when the customer provides information on the elapsed years of the target product when requesting repair, the prediction unit 215 stores the information in the storage unit 22 in association with the failure part estimated by the estimation unit 214, for example. Among the failed parts that have been provided, the number of failed parts whose elapsed years is equal to or greater than the elapsed years of the target product provided may be counted. As a result, the prediction accuracy can be improved.
The prediction unit 215 counts the number of each failure component stored in the storage unit 22 in association with the failure site estimated by the estimation unit 214, and also counts the top plurality (for example, five) failure parts having a large number of counts. May be predicted as the faulty part this time.

The secondary prediction unit 216 predicts the secondary failure component based on the failure component predicted by the prediction unit 215 and the information stored in the storage unit 22. The storage unit 22 stores the manufacturer name and model name of the product having the faulty part and the repair date for repairing the faulty part in association with the faulty part predicted by the prediction unit 215. Therefore, the secondary prediction unit 216 can exemplify the prediction of the secondary failure component as follows. The secondary prediction unit 216 first extracts a list of repairs having the same manufacturer name and model name of the product having the failed part predicted by the prediction unit 215. After that, the secondary prediction unit 216 counts the number of repair requests for each defective part that is the target of repair when the repair request is received again after repairing the defective part from the extracted list. At the same time, the faulty part with the highest count is predicted as the secondary faulty part. The secondary prediction unit 216 counts the number of each faulty part that was the target of repair when receiving a repair request after repairing the faulty part, and also counts the top plurality (for example, five) with a large number of counts. ) May be predicted as a secondary failure part.

In addition, the secondary prediction unit 216 predicts the date on which the secondary failure component is expected to fail. The storage unit 22 stores the number of days elapsed from the reception date when the failure of the failed component predicted by the prediction unit 215 is received to the reception date when the failure of the secondary failure component is received. Therefore, the secondary prediction unit 216 exemplifies that the day when the average value of the elapsed days is added to the reception day when the failure is accepted is predicted as the day when the secondary failure part is expected to fail. Can be done.

In addition, the secondary prediction unit 216 may consider the family structure of the customer when predicting the date when the secondary failure component is expected to fail. For example, when the storage unit 22 stores the number of family members of the customer used for calculating the average value of the elapsed days, the average value of the number of family members is calculated. Then, the secondary prediction unit 216 divides the coefficient α (= the average value of the number of family members / the number of family members of the current customer) by dividing the number of family members of the current customer by the average value of the number of family members, and sets the average of the above-mentioned elapsed days. The date obtained by multiplying the value by the value obtained by adding the value obtained by multiplying the value to the reception date when the current failure is accepted is predicted as the expected failure date of the secondary failed part. That is, the secondary prediction unit 216 can exemplify the prediction as the failure prediction date by using the following equation (1).
Expected failure date = reception date + average number of elapsed days / α ... (1)

As described above, the prediction unit 215 predicts the failed component, and the secondary predictor 216 predicts the date when the secondary failed component or the secondary failed component is expected to fail. Then, the transmission unit 212 transmits the information predicted by the prediction unit 215 and the secondary prediction unit 216 to the information processing device 10.

(Derivation unit 114 of the control unit 11 of the information processing device 10)
Next, the derivation unit 114 of the control unit 11 of the information processing device 10 will be described.
The out-licensing unit 114 derives the repair cost required for repairing the failed part predicted by the prediction unit 215. The out-licensing unit 114 takes into consideration the unit price of a new part received by the receiving unit 111 and newly installed by removing the failed component predicted by the prediction unit 215, the work cost required to perform the work of installing the new component, and the like. Derive repair costs. It can be exemplified that the out-licensing unit 114 derives the repair cost by substituting the unit price and the working cost of the new part into the predetermined formula. The unit price of the new component may be acquired from the server device 20 via the network 5, or may be stored in the storage unit 12 in advance and acquired from the storage unit 12. In addition, it can be exemplified that the work cost is set for each new part.

Further, the out-licensing unit 114 derives a period until the repair of the failed part predicted by the prediction unit 215 is completed. The out-licensing unit 114 is, for example, a predetermined number of days from the day when the receiving unit 111 receives the information of the failed part predicted by the prediction unit 215 to the day when the maintenance company M who performs the repair acquires a new part. It can be exemplified that the number of days obtained by adding the above is derived as the period. The number of days until the date of acquiring a new component may be acquired from the server device 20 via the network 5, or may be stored in the storage unit 12 in advance and acquired from the storage unit 12. Further, the predetermined number of days is the number of days required from the day when the maintenance company M acquires a new part to the repair, and when the maintenance company M performs the repair on the day following the day when the new part is acquired. Is one day.

Further, the out-licensing unit 114 derives the secondary repair cost required for repairing the secondary failed part predicted by the secondary prediction unit 216. The lead-out unit 114 is required to perform the unit price of the secondary new component received by the receiving unit 111 and the secondary failure component predicted by the secondary prediction unit 216 and newly attached, and the work of attaching the secondary new component. Derivation of secondary repair costs in consideration of secondary work costs, etc. It can be exemplified that the out-licensing unit 114 derives the secondary repair cost by substituting the unit price of the secondary new part and the secondary work cost into a predetermined formula. The unit price of the secondary new component may be acquired from the server device 20 via the network 5, or may be stored in the storage unit 12 in advance and acquired from the storage unit 12. Further, it can be exemplified that the secondary work cost is set for each secondary new part.

In addition, the out-licensing unit 114 adds the current repair cost and the secondary repair cost, so that the total repair cost estimated to be required in the future if the product currently in use is continued to be used. To derive.
In addition, the out-licensing unit 114 derives the purchase cost when the product currently in use is newly replaced. In such a case, it can be exemplified that the out-licensing unit 114 derives the cost when purchasing a product having the exact same model name as the model name currently used. However, when the out-licensing unit 114 purchases a successor product of the product with the model name currently in use or a product having the same performance as the product currently in use. You may derive the cost of.

As described above, the out-licensing unit 114 totals the repair cost required for repairing the failed part predicted by the secondary prediction unit 216, the period until the repair is completed, and the secondary repair cost required for repairing the secondary failed part. The repair cost, the purchase cost when the product currently in use is replaced with a new one, etc. are derived. Then, the display control unit 113 may display the information derived by the derivation unit 114 on the display unit 13. As a result, the employee of the maintenance company M can recognize this information. In addition, the employee of the maintenance company M can make the customer recognize by printing this information and showing it to the customer.

The repair support system 1 configured as described above stores repair information in which the first information regarding the product name and symptom of the gas appliance, the second information regarding the weather information, and the faulty part of the gas appliance are linked. It includes a storage unit 22 and a reception unit 111 as an example of a reception unit that receives information on a failure request. Further, the repair support system 1 is based on the first information and the second information regarding the gas appliance for which the failure request has been received by the receiving unit 111, and the repair information stored in the storage unit 22, of the gas appliance. The estimation unit 214 for estimating the failure site is provided. According to this repair support system 1, the faulty part can be estimated with higher accuracy than when the faulty part is not estimated using the weather information.

Further, the repair support system 1 has a prediction unit 215 that predicts a failure part from a failure portion estimated by the estimation unit 214. As a result, according to the repair support system 1, a failed part can be estimated with high accuracy.
Further, the repair support system 1 has a derivation unit 114 for deriving the cost required for repairing the failed part predicted by the prediction unit 215. As a result, according to the repair support system 1, it becomes possible for the customer to recognize the cost required for repair with high accuracy.

Further, the repair support system 1 further has a secondary prediction unit 216 that predicts a secondary failure part that fails next to the failure part, and the out-licensing unit 114 is the total cost required for repairing the failure part and the secondary failure part. To derive. As a result, according to the repair support system 1, it becomes possible for the customer to recognize the total cost including the cost required for repairing the failure that occurs in the future.
Further, the repair support system 1 further includes a display control unit 113 for displaying the total cost derived by the out-licensing unit 114 and the cost when the gas appliance is replaced on the display unit 13. As a result, according to the repair support system 1, the employee of the maintenance company M can recognize the total cost and the cost in the case of replacement by purchase and make the customer aware of it.

Further, the repair support system 1 has a secondary prediction unit 216 as an example of a prediction unit that predicts the date when the secondary failure component fails in consideration of the family structure. As a result, according to the repair support system 1, the employee of the maintenance company M can recognize the day when the total cost is required and make the customer aware of it. As a result, the customer can easily choose whether to complete the repair requested this time or replace it with a new product.

Further, in the repair support system 1 described above, the information processing device 10 requests the server device 20 to estimate the faulty part and the like, but the present invention is not particularly limited to this mode. For example, the control unit 11 of the information processing device 10 acquires the repair information stored in the storage unit 22 of the server device 20 and stores it in the storage unit 12, and the control unit 11 repairs the repair information stored in the storage unit 12. The information may be used to estimate the faulty part or the like and predict the faulty part or the like. That is, the control unit 11 of the information processing device 10 may have the functions of the estimation unit 214, the prediction unit 215, and the secondary prediction unit 216 of the control unit 21. In such a case, the information processing device 10 is a storage unit that stores repair information in which the first information regarding the product name and symptom of the gas device, the second information regarding the weather information, and the failure part of the gas device are linked. A 12 and a receiving unit 111 as an example of a receiving unit that receives information on a failure request are provided. Further, the information processing apparatus 10 is based on the first information and the second information regarding the gas appliance for which the failure request has been received by the receiving unit 111 and the repair information stored in the storage unit 12, and the information processing apparatus 10 has the gas appliance. It includes a control unit 11 for estimating a faulty part.

The processing performed by the server device 20 described above can be realized by the cooperation of software and hardware resources. In this case, the CPU of the control unit 21 of the server device 20 executes a program that realizes each function of the server device 20, and realizes each of these functions. For example, a non-temporary computer-readable recording medium on which a program is recorded is provided to the server device 20, and the CPU of the server device 20 reads the program stored in the recording medium. In this case, the program itself read from the recording medium realizes the function of the above-described embodiment, and the program itself and the recording medium on which the program is recorded constitute the present invention.

The program that realizes the function of the server device 20 stores in the computer the first information about the product name and symptom of the gas appliance, the second information about the weather information, and the repair information that links the faulty part of the gas appliance. The failure of the gas appliance based on the function of receiving the information of the failure request, the first information and the second information regarding the gas appliance for which the failure request was received, and the repair information. The function of estimating the part is realized.
Recording media for supplying such programs include, for example, flexible disks, CD-ROMs, DVD-ROMs, hard disks, optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, and ROMs. It can be exemplified.

<Second embodiment>
FIG. 5 is a diagram showing an example of a schematic configuration of the server device 30 according to the second embodiment.
The repair support system 2 according to the second embodiment includes the information processing device 10, a server device 30 that infers a faulty part of the device to be repaired requested in response to a request from the information processing device 10, and the like. It has. The server device 30 is different from the server device 20 according to the first embodiment in that a failure site is estimated by using so-called artificial intelligence (AI). Hereinafter, the server device 30 will be described mainly different from the server device 20. The server device 20 and the server device 30 having the same function are designated by the same reference numerals, and detailed description thereof will be omitted.
The server device 30 includes a control unit 31 that controls the entire device, a storage unit 32, a display unit 23, an operation unit 24, and a communication unit 25. The control unit 31, the storage unit 32, the display unit 23, the operation unit 24, and the communication unit 25 are connected by a bus 36. The storage unit 32 stores the repair information illustrated in FIG.

The control unit 31 has a CPU (not shown), a ROM (not shown), a RAM (not shown), and the like. The control unit 31 has a receiving unit 311 that receives information transmitted from an external device such as the information processing device 10, and a transmitting unit 212 that transmits information to an external device such as the information processing device 10. .. Further, the control unit 31 has a creation unit 313 that creates a trained model, and an inference unit 314 that infers a failure site using the trained model created by the creation unit 313. Further, the control unit 31 predicts a failure part 215 that may have failed from the failure part inferred by the reasoning unit 314, and a part that fails next to the failure part predicted by the prediction unit 215. It has a next prediction unit 216.

The receiving unit 311 receives the information transmitted from the information processing device 10. For example, the receiving unit 311 receives a request from the maintenance company M for inferring the faulty part, which is transmitted from the information processing apparatus 10, in addition to the function of the receiving unit 211.

The creation unit 313 creates a trained model by performing machine learning using so-called artificial intelligence (AI). Machine learning can be exemplified as deep learning. The creation unit 313 creates a trained model using the repair information stored in the storage unit 32. At that time, the creation unit 313 puts the product name of the gas appliance (for example, a water heater, a gas stove), the symptom (for example, no hot water), the manufacturer name, the area, the weather information, the information of the failure diagnosis sheet, etc. on the input layer. , The repair information stored in the storage unit 32 is input. Further, the output layer is provided with teacher data (correct answer) such as a faulty part of the gas appliance, for example, a “heat generating part inside the water heater body”. In addition, the creation unit 313 creates a trained model by calculating the bond strength (weighting) of the neurons in the intermediate layer so that the input and the output match. The creation unit 313 stores the created trained model in the storage unit 32.

When the maintenance company M receives a request for inferring the faulty part, the reasoning unit 314 infers the faulty part from the information about the fault received by the receiving unit 311 using the trained model. The information about the failure received by the receiving unit 311, in other words, the information provided when the customer requests repair, includes the product name of the gas appliance such as a water heater and symptoms such as no hot water. Is common, the reasoning unit 314 inputs the first information about the product name and the symptom into the trained model. Further, the inference unit 314 also uses the second information, which is the information about the failure received by the receiving unit 311 and is information other than the product name and the symptomatism, as the information to be input to the trained model. The second information exemplifies that it is at least one of the name of the manufacturer that manufactured the failed product, the area in charge of the maintenance company M who requested the inference, the weather information of the area, and the information of the failure diagnosis sheet. can do. When the first information and the second information are input to the trained model, the inference unit 314 inputs the first information and the second information after converting them into data in a predetermined format.

Based on the repair information stored in the storage unit 32, the prediction unit 215 predicts a failed part that may have failed from the failure portion inferred by the inference unit 314. The storage unit 32 stores the failed component in association with the failed portion. For example, the prediction unit 215 counts the number of each failure part stored in the storage unit 32 in association with the failure part inferred by the reasoning unit 314, and sets the failure part having the largest count number as the failure part this time. Prediction can be illustrated.
The prediction unit 215 may predict the failed part in consideration of the elapsed years.
Further, the prediction unit 215 counts the number of each failure component stored in the storage unit 32 in association with the failure site inferred by the reasoning unit 314, and also counts the number of high-order plurality (for example, five) failure parts having a large number of counts. May be predicted as the faulty part this time.

As described above, the prediction unit 215 predicts the failed component, and the secondary predictor 216 predicts the date when the secondary failed component or the secondary failed component is expected to fail. Then, the transmission unit 212 transmits the information predicted by the prediction unit 215 and the secondary prediction unit 216 to the information processing device 10.

The repair support system 2 configured as described above includes the first information regarding the product name and symptom of the gas device, and at least one of the second information among the manufacturer name, region, weather information, and information on the failure diagnosis sheet. The storage unit 32 stores a learned model obtained by machine learning using the first information and the failure portion of the gas device stored in association with the second information. Further, the repair support system 2 has learned the reception unit 111 as an example of the reception unit that receives the failure request information, the first information and the second information regarding the gas appliance for which the failure request has been received by the reception unit 111, and the second information. It is provided with an inference unit 314 that infers a faulty part of the gas appliance based on the model. According to this repair support system 2, the faulty part can be estimated with higher accuracy than when the faulty part is not inferred by the trained model.

Further, the repair support system 2 has a prediction unit 215 that predicts a failure part from the failure part inferred by the reasoning unit 314. As a result, according to the repair support system 2, the defective part can be estimated with high accuracy.

In the repair support system 2 described above, the creation unit 313 may update the trained model at a predetermined timing. It can be exemplified that the predetermined timing is when new failure information is stored in the storage unit 32. In addition, the predetermined timing may be when a plurality of (for example, 10) new failure information is stored in the storage unit 32. Further, it may be a predetermined time such as midnight every day or midnight on the first day of every month.

Further, in the repair support system 2 described above, the inference unit 314 infers the failure part using the learned model, and the prediction unit 215 predicts the failure part from the failure part inferred by the inference unit 314. It is not limited to such an embodiment. For example, the inference unit 314 may infer the faulty part using the trained model. In such a case, the creation unit 313 inputs information such as the product name, symptom, manufacturer name, region, weather information, and failure diagnosis sheet information of the gas appliance to the input layer, and the output layer of the gas appliance. The teacher data (correct answer) of the failed component, for example, "electrical board" and "motor" is given. In addition, the creation unit 313 creates a trained model by calculating the bond strength (weighting) of the neurons in the intermediate layer so that the input and the output match. Then, the creation unit 313 may store the created trained model in the storage unit 32.

Further, in the repair support system 2 described above, the information processing device 10 requests the server device 30 to infer the faulty part or the like, but the present invention is not particularly limited to this mode. For example, the control unit 11 of the information processing device 10 acquires the learned model stored in the storage unit 32 of the server device 30 and stores it in the storage unit 12, and the control unit 11 is stored in the storage unit 12. The trained model may be used to infer the faulty part or the like and predict the faulty part or the like. That is, the control unit 11 of the information processing device 10 may have the functions of the inference unit 314, the prediction unit 215, and the secondary prediction unit 216 of the control unit 31. In such a case, the information processing apparatus 10 includes the first information regarding the product name and the symptom of the gas device, the second information of at least one of the manufacturer name, the area, the weather information, and the information on the failure diagnosis sheet. The storage unit 12 stores a learned model obtained by machine learning using the first information and the failure portion of the gas device stored in association with the second information. Further, the information processing device 10 has already learned the receiving unit 111 as an example of the receiving unit that receives the failure request information, the first information and the second information regarding the gas appliance that has received the failure request received by the receiving unit 111, and the second information. A control unit 11 for inferring a failure portion of the gas appliance based on the model is provided. Further, the control unit 11 of the information processing device 10 may have the function of the creation unit 313 of the control unit 31 of the server device 30 to create a trained model.

The processing performed by the server device 30 described above can be realized by the cooperation of software and hardware resources. In this case, the CPU of the control unit 31 of the server device 30 executes a program that realizes each function of the server device 30, and realizes each of these functions. For example, a non-temporary computer-readable recording medium on which a program is recorded is provided to the server device 30, and the CPU of the server device 30 reads the program stored in the recording medium. In this case, the program itself read from the recording medium realizes the function of the above-described embodiment, and the program itself and the recording medium on which the program is recorded constitute the present invention.

The program that realizes the function of the server device 30 tells the computer the first information about the product name and symptom of the gas device and the second information of at least one of the manufacturer name, the area, the weather information, and the information of the failure diagnosis sheet. A function of storing a learned model by machine learning using the first information and a failure part of the gas device stored in association with the second information, and a function of receiving failure request information. And, the function of inferring the failure part of the gas device by the first information and the second information about the gas device for which the failure request was received and the learned model are realized.

1,2 ... Repair support system, 5 ... Network, 10 ... Information processing device, 11 ... Control unit, 12 ... Storage unit, 20, 30 ... Server device, 21,31 ... Control unit, 22, 32 ... Storage unit, 111 ... Receiver unit, 112 ... Transmitter unit, 113 ... Display control unit, 114 ... Derivation unit, 211, 311 ... Receiver unit, 212 ... Transmitter unit, 214 ... Estimate unit, 215 ... Prediction unit, 216 ... Secondary prediction unit, 313 … Creation department, 314… Reasoning department

Claims (14)

A storage unit that stores repair information that links the first information regarding the product name and symptomatology of the gas appliance, the second information regarding the weather information, and the faulty part of the gas appliance.
The reception department that accepts failure request information and
Based on the first information and the second information regarding the gas appliance for which the failure request was received by the reception unit, and the repair information stored in the storage unit, the failure portion of the gas appliance is determined. Estimating part to estimate and
A repair support system characterized by being equipped with. The repair support system according to claim 1, further comprising a prediction unit that predicts a failed part from the failure portion estimated by the estimation unit. The repair support system according to claim 2, further comprising a derivation unit for deriving the cost required for repairing the failed part predicted by the prediction unit. It also has a secondary prediction unit that predicts the secondary failure component that fails next to the failed component.
The repair support system according to claim 3, wherein the out-licensing unit derives the total cost required for repairing the failed part and the secondary failed part. The repair support system according to claim 4, further comprising a display control unit for displaying the total cost derived by the out-licensing unit and the cost when the gas appliance is replaced on the display unit. The repair support system according to claim 5, further comprising a predictive unit that predicts the date on which the secondary failed component will fail in consideration of the family structure. The first information regarding the product name and symptom of the gas device, the second information of at least one of the manufacturer name, area, weather information, and the information on the failure diagnosis sheet, and the first information and the second information are linked. A storage unit that stores a learned model obtained by machine learning using the faulty part of the gas device that has been stored in the above, and a storage unit that stores the learned model.
The reception department that accepts failure request information and
An inference unit that infers a failure portion of the gas appliance based on the first information and the second information regarding the gas appliance for which the failure request has been received by the reception unit, and the learned model.
A repair support system characterized by being equipped with. The repair support system according to claim 7, further comprising a prediction unit that predicts a failure part from the failure part inferred by the reasoning unit. The repair support system according to claim 8, further comprising a derivation unit for deriving the cost required for repairing the failed part predicted by the prediction unit. It also has a secondary prediction unit that predicts the secondary failure component that fails next to the failed component.
The repair support system according to claim 9, wherein the out-licensing unit derives the total cost required for repairing the failed component and the secondary failed component. The repair support system according to claim 10, further comprising a display control unit for displaying the total cost derived by the out-licensing unit and the cost when the gas appliance is replaced on the display unit. The repair support system according to claim 11, further comprising a predictive unit that predicts the date on which the secondary failed component will fail in consideration of the family structure. On the computer
A function to store the first information about the product name and symptomatology of the gas appliance, the second information about the weather information, and the repair information that links the faulty part of the gas appliance.
A function to accept failure request information and
A program that realizes a function of estimating a failure part of the gas appliance based on the first information and the second information of the gas appliance for which the failure request has been received, and the repair information. On the computer
The first information about the product name and symptom of the gas equipment, the second information of at least one of the manufacturer name, area, weather information, and the information of the failure diagnosis sheet, and the first information and the second information are linked. A function to memorize the learned model obtained by machine learning using the memorized failure part of the gas device, and
A function to accept failure request information and
A function of inferring a failure part of the gas appliance based on the first information and the second information of the gas appliance for which the failure request was received and the learned model.
A program that realizes.

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