Failure correction assistance device, failure correction assistance method, and computer program

Abstract

A failure correction assistance device according to the present invention comprises: a vehicle data acquisition unit that acquires vehicle data related to the traveling of at least one vehicle; and a deviation determination unit that determines a degree of deviation of the vehicle data from reference data.

Description

Fault correction support device, fault correction support method, and computer program

[0001] This application claims priority from Japanese Patent Application No. 2023-145745, filed on September 8, 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0002] Patent document 1 discloses a vehicle information management device that is communicatively connected via a network to a vehicle and a plurality of mobile terminal devices associated with the vehicle, and that, when re-notifying notification information that requires a reservation to be notified in order to use the vehicle, does not re-notify the vehicle in which the user who made the reservation for the notification information is riding or the mobile terminal device of the user who made the reservation for the notification information.

[0003] Japanese Patent Application Laid-Open No. 2021-179836

[0004] A malfunction correction assistance device according to one aspect of the present disclosure includes a vehicle data acquisition unit that acquires vehicle data related to the running of at least one vehicle, and a deviation determination unit that determines the degree of deviation of the vehicle data from reference data.

[0005] The present disclosure can be realized not only as a defect correction assistance device having such a characteristic processing unit, but also as a defect correction assistance method having such characteristic processing steps, or as a computer program for causing a computer to execute such steps, or as a semiconductor integrated circuit that realizes part or all of the defect correction assistance device, or as a system including the defect correction assistance device.

[0006] FIG. 1 is a diagram illustrating an example of the overall configuration of a vehicle monitoring system according to a first embodiment of the present disclosure. FIG. 2 is a block diagram illustrating an example of the configuration of a support server according to the first embodiment of the present disclosure. FIG. 3 is a diagram for explaining a determination process by a deviation determination unit according to the first embodiment of the present disclosure. FIG. 4 is a diagram illustrating an example of malfunction factor candidate information according to the first embodiment of the present disclosure. FIG. 5 is a sequence diagram illustrating an example of a process executed by the vehicle monitoring system according to the first embodiment of the present disclosure. FIG. 6 is a flowchart illustrating an example of details of the deviation determination process (step S4 of FIG. 5). FIG. 7 is a flowchart illustrating an example of details of the malfunction factor identification process (step S5 of FIG. 5). FIG. 8 is a flowchart illustrating an example of details of the malfunction correction preparation process (step S7 of FIG. 5). FIG. 9 is a block diagram illustrating an example of the configuration of a support server according to a second embodiment of the present disclosure. FIG. 10 is a diagram for explaining a determination process by a deviation determination unit according to the second embodiment of the present disclosure. FIG. 11 is a diagram illustrating an example of malfunction factor candidate information according to the second embodiment of the present disclosure. FIG. 12 is a flowchart illustrating an example of details of the deviation determination process (step S4 of FIG. 5).

[0007] When a defect is discovered during the design or manufacturing process of an automobile, the vehicle manufacturer or other party takes corrective measures for the vehicle. Such corrective measures include recalls, improvement measures, and service campaigns (see Patent Document 1). Of these, service campaigns are intended to improve marketability or quality for defects that do not qualify for recalls or improvement measures. For this reason, the importance of correcting the defect is not as high as that of recalls or improvement measures, but it is not desirable to leave the situation unattended.

[0008] Defects that qualify for service campaigns are, by their nature, particularly difficult to detect. For example, if poor fuel economy persists, possible causes include sensor failure, spark plug failure, or brake failure. On the other hand, the driver's driving habits or excessive luggage may be the cause of persistent poor fuel economy. If poor fuel economy persists due to the former cause, it will qualify for a service campaign, but if it is due to the latter cause, it will not qualify for a service campaign.

[0009] However, for example, an event such as persistently poor fuel economy occurs regardless of whether or not corrective measures such as a service campaign are necessary, which poses a problem in that it is difficult to determine whether or not corrective measures are necessary based on the event alone.

[0010] The present disclosure has been made in consideration of these circumstances, and aims to provide a malfunction correction assistance device, a malfunction correction assistance method, and a computer program that can determine whether or not corrective measures are necessary based on an event that occurs in a vehicle.

[0011] Effect of the Present Disclosure According to the present disclosure, it is possible to determine whether or not corrective measures are necessary based on an event that has occurred in a vehicle.

[0012] [Outline of Embodiments of the Present Disclosure] Outlines of embodiments of the present disclosure will be listed and described below. (1) A malfunction correction assistance device according to one embodiment of the present disclosure includes a vehicle data acquisition unit that acquires vehicle data related to the running of at least one vehicle, and a deviation determination unit that determines the degree of deviation of the vehicle data from reference data.

[0013] According to this configuration, when the vehicle data deviates from the reference data, the entire vehicle data indicates values ​​that are different from normal. Therefore, in such cases, it can be determined that corrective measures are necessary for the vehicle. On the other hand, when the vehicle data does not deviate from the reference data, although there may be a malfunction due to a breakdown or the like in an individual vehicle, the entire vehicle data does not indicate values ​​that are different from normal. Therefore, in such cases, it can be determined that corrective measures are not necessary for the vehicle.

[0014] (2) In the above (1), the reference data may be ideal vehicle data for the vehicle.

[0015] By determining deviations from ideal vehicle data, it is possible to accurately determine whether corrective action is necessary.

[0016] (3) In (1) above, the system may further include a class classification unit, wherein the at least one vehicle includes a first vehicle and a second vehicle, the vehicle data includes first vehicle data and second vehicle data, the class classification unit classifies the first vehicle data into a first class to which the first vehicle belongs and classifies the second vehicle data into a second class to which the second vehicle belongs, and the deviation determination unit may determine the degree of deviation of the first vehicle data and the second vehicle data from the reference data.

[0017] This configuration makes it possible to determine whether corrective measures are necessary for each vehicle class.

[0018] (4) In the above (3), the first class may include a first model of the first vehicle, and the second class may include a second model of the second vehicle.

[0019] According to this configuration, it is possible to determine whether or not corrective measures are necessary for each vehicle model.

[0020] (5) In the above (3) or (4), the reference data may be the first vehicle data, and the deviation determination unit may determine a degree of deviation of the second vehicle data from the first vehicle data.

[0021] According to this configuration, even if ideal vehicle data cannot be obtained, it is possible to determine whether corrective measures are necessary.

[0022] (6) In (3) above, the class classification unit may classify the first vehicle data into the first class based on the acquisition date of the first vehicle data, and classify the second vehicle data into the second class based on the acquisition date of the second vehicle data.

[0023] This configuration makes it possible to determine whether or not there is a deviation from the reference data for each period. This makes it possible to know temporal trends, such as whether deviations are more likely to occur in winter than in summer. This can be useful in identifying the cause of any malfunctions that may occur.

[0024] (7) In (3) above, the class classification unit may classify the first vehicle data into the first class based on location information of the first vehicle included in the first vehicle data, and classify the second vehicle data into the second class based on location information of the second vehicle included in the second vehicle data.

[0025] This configuration allows the presence or absence of deviation from the reference data to be determined for each region. This makes it possible to identify geographical trends, such as the tendency for deviation to occur more easily in cold regions than in other regions. This can be useful in identifying the cause of any malfunctions that may occur.

[0026] (8) In any of (1) to (7) above, the vehicle data may include multiple types of data, and the defect correction support device may further include a defect cause identification unit that identifies the cause of the defect in the vehicle based on the judgment result of the deviation judgment unit and candidate information on the cause of the defect provided for each type of vehicle data.

[0027] With this configuration, the cause of the malfunction can be identified according to the type of vehicle data.

[0028] (9) A fault correction assistance method according to another embodiment of the present disclosure includes the steps of acquiring vehicle data related to the running of at least one vehicle, and determining the degree of deviation of the vehicle data from reference data.

[0029] This configuration includes the characteristic processing steps of the above-described fault correction support device, and therefore can achieve similar actions and effects to those of the above-described fault correction support device.

[0030] (10) A computer program according to another embodiment of the present disclosure causes a computer to function as a vehicle data acquisition unit that acquires vehicle data related to the running of at least one vehicle, and a deviation determination unit that determines the degree of deviation of the vehicle data from reference data.

[0031] This configuration allows the computer to function as the above-described defect correction assistance device, thereby achieving similar actions and effects to those of the above-described defect correction assistance device.

[0032] [Details of the Embodiments of the Present Disclosure] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Note that each of the embodiments described below represents a specific example of the present disclosure. The numerical values, shapes, materials, components, component placement and connection configurations, steps, and step order shown in the following embodiments are examples and do not limit the present disclosure. Furthermore, among the components in the following embodiments, components not recited in independent claims are components that can be added arbitrarily. Furthermore, each figure is a schematic diagram and is not necessarily a precise illustration.

[0033] The same components are denoted by the same reference numerals, and their functions and names are also the same, so their explanations will be omitted where appropriate.

[0034] <First Embodiment> [Overall Configuration of Vehicle Monitoring System] Fig. 1 is a diagram showing an example of the overall configuration of a vehicle monitoring system according to a first embodiment of the present disclosure. The vehicle monitoring system 1 includes a support server 2, an administrator server 3, and an in-vehicle system 5 mounted on a vehicle 4, each of which is connected to a network 6. The vehicle 4 is, for example, an automobile. The vehicle 4 is not limited to an automobile, and may be a two-wheeled vehicle such as a motorcycle.

[0035] The in-vehicle system 5 collects vehicle data related to the traveling of the vehicle 4 in which the in-vehicle system 5 itself is mounted. For example, the in-vehicle system 5 includes an ECU (Electronic Control Unit) that calculates vehicle data, and collects vehicle data indicating the fuel efficiency of the vehicle 4 from the ECU. For example, the ECU collects the travel distance and fuel consumption amount of the vehicle 4 from a sensor, and calculates the fuel efficiency of the vehicle 4 from the collected travel distance and fuel consumption amount.

[0036] The in-vehicle system 5 adds the identification information of the vehicle 4 to the collected vehicle data and periodically transmits the data to the assistance server 2. The in-vehicle system 5 may add the identification information of the in-vehicle system 5 instead of the identification information of the vehicle 4.

[0037] The assistance server 2 assists in correcting a defect that has occurred in the vehicle 4. The assistance server 2 receives vehicle data from the in-vehicle system 5 of the vehicle 4. Based on the received vehicle data, the assistance server 2 determines whether the vehicle 4 is subject to corrective measures for the defect. Corrective measures include, for example, a recall, improvement measures, and a service campaign, in which order the importance of the defect correction increases.

[0038] A recall is a corrective measure to bring a vehicle into compliance with road vehicle safety standards. A remedial measure is a corrective measure to bring a vehicle into compliance with road vehicle safety standards, but is not specified in the road vehicle safety standards. A remedial measure is a corrective measure to bring a vehicle into compliance when a defect occurs that cannot be ignored for the sake of safety and environmental conservation, and the cause of the defect is determined to be in the design or manufacturing process. A service campaign is a corrective measure to bring a vehicle into compliance with marketability or quality standards that do not qualify for a recall or remedial measure.

[0039] If the support server 2 determines that corrective measures for the malfunction are necessary, the support server 2 identifies the cause of the malfunction and transmits information on the identified cause of the malfunction to the administrator server 3.

[0040] The administrator server 3 is, for example, a server used by the manufacturer of the vehicle 4. The administrator server 3 receives malfunction cause information from the support server 2. The administrator server 3 makes preparations to correct the malfunction of the vehicle 4 based on the received malfunction cause information. For example, the administrator server 3 makes preparations for a recall of the vehicle 4. The administrator server 3 notifies the owner of the vehicle 4 of the results of the preparations. If the malfunction can be corrected by updating the firmware of the vehicle 4, the administrator server 3 may transmit an update program or update data for updating the firmware to the in-vehicle system 5.

[0041] In the following description, the support server 2 and the administrator server 3 are assumed to be separate servers. A single server may be provided with the functions of the support server 2 and the administrator server 3. The support server 2 and the administrator server 3 may be on-premise servers or cloud servers.

[0042] 2 is a block diagram showing an example of the configuration of the support server 2 according to the first embodiment of the present disclosure. The support server 2 includes a communication unit 21, a memory 22, and a processor 23, which are connected to each other via a bus 24.

[0043] The communication unit 21 is a communication interface that connects the support server 2 to the network 6 wirelessly or via a wired connection. The communication unit 21 receives and transmits various data from and to external devices.

[0044] The memory 22 is composed of a volatile memory element such as an SRAM (Static RAM) or a DRAM (Dynamic RAM), a non-volatile memory element such as a flash memory or an EEPROM (Electrically Erasable Programmable Read Only Memory), or a magnetic storage device such as a hard disk.

[0045] The processor 23 is configured by a CPU (Central Processing Unit) and reads and executes a computer program 221 stored in the memory 22. When executing the computer program 221, the processor 23 transmits and receives various data to and from external devices via the communication unit 21.

[0046] Next, a description will be given of each piece of information stored in the memory 22. The memory 22 stores a computer program 221, vehicle data 222, ideal data 223, and corrective action target information 224.

[0047] The computer program 221 is a determination program for determining whether or not the vehicle 4 is a target for corrective measures for a malfunction, based on the vehicle data received from the in-vehicle system 5. The processor 23 executes the computer program 221. The vehicle data 222 is vehicle data received by the assistance server 2 from the in-vehicle system 5.

[0048] The ideal data 223 is ideal vehicle data for the vehicle 4 (for example, data when the vehicle 4 is a new vehicle, catalog specifications). The ideal data 223 is prepared for each class (which also includes the meaning of rank, level, and grade; the same applies below) that classifies vehicles, and is stored in the memory 22. For example, the ideal data 223 is prepared for each model of the vehicle 4, and represents ideal vehicle data for the vehicle 4 for each model. More specifically, the ideal data 223 represents a frequency distribution of ideal vehicle data for the vehicle 4 for each model. For example, the ideal data 223 represents a frequency distribution of fuel efficiency for the vehicle 4 for each model.

[0049] The corrective action target information 224 indicates the class (for example, model) of the vehicle determined to be the target of defect corrective action and the type of vehicle data determined to have a deviation.

[0050] The processor 23 includes, as functional processing units realized by executing the computer program 221, a vehicle data acquisition unit 231, a classification unit 232, a deviation determination unit 233, and a malfunction cause identification unit 234.

[0051] The vehicle data acquisition unit 231 acquires vehicle data by receiving vehicle data of each vehicle 4 from the in-vehicle system 5 of the vehicle 4 via the communication unit 21. The vehicle data acquisition unit 231 writes the acquired vehicle data into the memory 22 as vehicle data 222.

[0052] The classification unit 232 classifies the vehicle data 222 stored in the memory 22 into a class to which the vehicle 4 corresponding to the vehicle data 222 belongs. For example, it is assumed that a class is prepared for each vehicle type. The classification unit 232 extracts, from the vehicle data 222, the identification information of the vehicle 4 added to the vehicle data 222. The classification unit 232 identifies the vehicle type of the vehicle 4 based on the extracted identification information, and classifies the vehicle data 222 into the class of the identified vehicle type.

[0053] The deviation determination unit 233 determines, for each class, whether the vehicle data 222 of multiple vehicles 4 belonging to that class deviates from predetermined reference data. The reference data represents data equivalent to the vehicle data 222 of a vehicle 4 that is running normally. In the first embodiment, the reference data is the ideal data 223 that belongs to the class. The deviation determination unit 233 determines, for each class, whether the vehicle data 222 of multiple vehicles 4 that belong to that class deviates from the ideal data 223 that belongs to that class. The number of vehicles included in the vehicle data belonging to a predetermined class may be one.

[0054] 3 is a diagram illustrating the determination process by the deviation determination unit 233 according to the first embodiment of the present disclosure. In Fig. 3, the horizontal axis represents fuel efficiency (km / L) and the vertical axis represents the number of vehicles 4. A solid curve 241 represents the frequency distribution of vehicle data 222 of fuel efficiency included in a certain class, and a curve 242 represents the frequency distribution of ideal fuel efficiency data 223 included in the same class as the curve 242.

[0055] The deviation determination unit 233 calculates a representative value of the fuel efficiency from each of the curves 241 and 242. The representative value is, for example, an average value. The representative value is not limited to the average value, and may be a mode value, a median value, or the like.

[0056] The deviation determination unit 233 calculates the distance between the representative value of fuel efficiency calculated from the curve 241 and the representative value of fuel efficiency calculated from the curve 242 as the deviation. The deviation indicates the degree of deviation of the distribution of actual fuel efficiency from the distribution of ideal fuel efficiency.

[0057] If the calculated deviation is greater than a predetermined deviation threshold, the deviation determination unit 233 determines that the vehicle fuel economy data 222 deviates from the ideal fuel economy data 223. If the calculated deviation is less than the deviation threshold, the deviation determination unit 233 determines that the vehicle fuel economy data 222 does not deviate from the ideal fuel economy data 223.

[0058] The deviation determination unit 233 writes the class in which it is determined that a deviation has occurred in the vehicle data and the type of vehicle data in which it is determined that a deviation has occurred as corrective action target information 224 in the memory 22 .

[0059] Referring again to Figure 2, the malfunction cause identification unit 234 identifies, for each class, the cause of the malfunction of the vehicle 4 belonging to that class based on the judgment result of the deviation judgment unit 233 and the candidate information of the cause of the malfunction (hereinafter referred to as "candidate information of the malfunction cause") provided for each type of vehicle data 222.

[0060] 4 is a diagram illustrating an example of defect cause candidate information according to the first embodiment of the present disclosure. The defect cause candidate information includes information made up of a set of a judgment target, a defect cause candidate, and a judgment criterion.

[0061] The "determination target" indicates the type of vehicle data that is the target of deviation determination by the deviation determination unit 233. The determination target includes, for example, fuel efficiency.

[0062] The "candidate malfunction factors" indicate candidate malfunction factors when it is determined that the vehicle data 222 of the "target vehicle" deviates from the ideal data 223. For example, when it is determined that the vehicle data 222 of fuel economy deviates from the ideal data 223, candidate malfunction factors include tire pressure, spark plugs, and brakes.

[0063] The "criteria" indicates the criteria for identifying the cause of a malfunction from among the "candidate causes of a malfunction."

[0064] For example, when it is determined that a deviation has occurred in the judgment target "fuel efficiency" in a certain class, the malfunction factor identification unit 234 determines whether the malfunction factor candidate "tire pressure," "spark plug," and "brake" satisfy the judgment criteria for each of them. The malfunction factor identification unit 234 identifies the malfunction factor candidate that satisfies the judgment criteria as the malfunction factor for the judgment target "fuel efficiency." As an example, when the malfunction factor identification unit 234 determines that the value of the tire pressure sensor (tire pressure) is lower than the reference value, it identifies the malfunction factor for the judgment target "fuel efficiency" as "tire pressure."

[0065] In addition, the tire air pressure, accelerator opening value, brake opening value, etc. used by the malfunction cause identification unit 234 to determine whether the judgment criteria are met may be included in the vehicle data acquired by the vehicle data acquisition unit 231 from the in-vehicle system 5, or the malfunction cause identification unit 234 may inquire of the in-vehicle system 5 via the communication unit 21 and acquire them from the in-vehicle system 5.

[0066] The vehicle data acquisition unit 231 transmits malfunction cause information indicating the judgment target and class for which the malfunction cause has been identified to the administrator server 3 via the communication unit 21 .

[0067] 5 is a sequence diagram showing an example of processing executed by the vehicle monitoring system 1 according to the first embodiment of the present disclosure. The in-vehicle system 5 collects vehicle data related to the traveling of the vehicle 4 in which the in-vehicle system 5 itself is mounted (step S1).

[0068] The in-vehicle system 5 adds identification information of the vehicle 4 to the collected vehicle data and periodically transmits the data to the assistance server 2. The assistance server 2 receives the vehicle data transmitted from the in-vehicle system 5 (step S2).

[0069] The assistance server 2 classifies the plurality of vehicle data 222 acquired from the in-vehicle system 5 into classes of the vehicle 4 based on the identification information of the vehicle 4 added to the vehicle data 222 (step S3).

[0070] The assistance server 2 determines, for each class of vehicle 4, whether the vehicle data 222 of the plurality of vehicles 4 belonging to that class deviates from the ideal data 223 belonging to that class (step S4).

[0071] 6 is a flowchart showing an example of the details of the deviation determination process (step S4 in FIG. 5). The support server 2 executes the process of loop A2 (described later) for each class (loop A1).

[0072] The assistance server 2 repeatedly executes the following steps S11 to S15 for each determination target (type of vehicle data) (loop A2).

[0073] The support server 2 acquires the vehicle data 222 of the determination target classified into the class to be processed from the class classification unit 232 (step S11).

[0074] The support server 2 acquires the ideal data 223 to be judged for the class to be processed from the memory 22 (step S12).

[0075] The assistance server 2 determines whether the acquired vehicle data 222 deviates from the acquired ideal data 223 (step S13).

[0076] If the support server 2 determines that the vehicle data 222 deviates from the ideal data 223 (YES in step S14), it registers information indicating the class to be processed and the object to be judged as correction measure target information 224 in the memory 22 (step S15).

[0077] Referring again to FIG. 5, the support server 2 identifies the cause of the defect that caused the deviation in the vehicle data 222 for the class and the judgment target indicated in the corrective action target information 224 (step S5).

[0078] 7 is a flowchart showing an example of the details of the defect cause identification process (step S5 in FIG. 5). The support server 2 repeatedly executes the processes of step S21 and loop B2 described below for each class and each judgment target indicated in the corrective action target information 224 (loop B1).

[0079] The support server 2 extracts malfunction factor candidates linked to the target of interest by referring to the malfunction factor candidate information, an example of which is shown in Fig. 4 (step S21). For example, if the target of interest is fuel economy, tire air pressure, spark plugs, brakes, etc. are extracted as malfunction factor candidates.

[0080] The support server 2 determines whether each extracted malfunction factor candidate satisfies the judgment criteria indicated in the malfunction factor candidate information. If the judgment criteria are satisfied, the support server 2 identifies the malfunction factor candidate as a malfunction factor (step S22).

[0081] Referring to FIG. 5, the support server 2 transmits malfunction cause information indicating the identified malfunction cause to the administrator server 3, and the administrator server 3 receives the malfunction cause information (step S6).

[0082] The administrator server 3 executes a preparation process for correcting the defect based on the received defect cause information (step S7).

[0083] 8 is a flowchart showing an example of the details of the defect correction preparation process (step S7 in FIG. 5). The administrator server 3 executes the following processes from step S31 to step S36 for each received defect cause information.

[0084] That is, the administrator server 3 determines whether or not the cause of the defect indicated in the defect cause information is subject to recall (step S31).

[0085] If it is determined that the vehicle 4 is subject to a recall (YES in step S31), the administrator server 3 prepares for the recall (step S32). For example, if the firmware of the vehicle 4 needs to be updated, an update program is prepared.

[0086] If it is determined that the product is not subject to recall (NO in step S31), the administrator server 3 determines whether the defect cause indicated in the defect cause information is subject to improvement measures (step S33).

[0087] If it is determined that the vehicle 4 is subject to the remedial action (YES in step S33), the administrator server 3 prepares the remedial action (step S34). For example, if the firmware of the vehicle 4 needs to be updated, the administrator server 3 prepares an update program.

[0088] If it is determined that the problem is not the target of the improvement measures (NO in step S33), the administrator server 3 determines whether the problem cause indicated in the problem cause information is the target of a service campaign (step S33).

[0089] If it is determined that the vehicle 4 is eligible for the service campaign (YES in step S35), the administrator server 3 prepares for the service campaign (step S36). For example, if the firmware of the vehicle 4 needs to be updated, an update program is prepared.

[0090] 5 , the administrator server 3 transmits correction information for correcting the malfunction of the vehicle 4 to the in-vehicle system 5, and the in-vehicle system 5 receives the correction information (step S8). The correction information is, for example, an update program or update data for updating the firmware of the vehicle 4. The correction information may also be, for example, notification information for notifying the owner of the vehicle 4 that an in-vehicle part needs to be replaced. The administrator server 3 may transmit the notification information to the owner of the vehicle 4 by email.

[0091] The in-vehicle system 5 executes a correction process based on the received correction information (step S9). For example, if the correction information is the update program or update data described above, the in-vehicle system 5 updates the firmware of the vehicle 4 based on the update program or update data. If the correction information is the notification information described above, the notification information is displayed on a display panel in the vehicle 4 to notify the owner of the vehicle 4.

[0092] As explained above, when the frequency distribution of the vehicle data 222 of multiple vehicles 4 belonging to a class deviates from the frequency distribution of the ideal data 223, all of the vehicle data 222 included in the class will exhibit values ​​that are different from normal. In such cases, it can be determined that corrective measures are necessary for the vehicles 4. When the frequency distribution of the vehicle data 222 of multiple vehicles 4 belonging to a class does not deviate from the frequency distribution of the ideal data 223, there is a possibility that a malfunction has occurred due to a breakdown or the like of an individual vehicle 4. Not all of the vehicle data 222 included in the class exhibits values ​​that are different from normal. In such cases, it can be determined that no corrective measures are necessary for the vehicles 4.

[0093] <Second Embodiment> In the first embodiment, the support server 2 determines whether the vehicle 4 is a target for a defect correction measure based on the vehicle data 222 and the ideal data 223. In the second embodiment, the support server 2 determines whether the vehicle 4 is a target for a defect correction measure based on the vehicle data 222 without using the ideal data 223.

[0094] The following description will focus on the differences from the first embodiment, and the same points as the first embodiment will not be described again.

[0095] 1 , the in-vehicle system 5 collects vehicle data indicating the number of sudden accelerations of the vehicle 4 from an ECU that calculates vehicle data. The ECU collects acceleration of the vehicle 4 from an acceleration sensor mounted on the vehicle 4, and calculates the number of times that the acceleration has exceeded a predetermined acceleration threshold during a predetermined period (e.g., the past 24 hours) as the number of sudden accelerations.

[0096] 9 is a block diagram showing an example of the configuration of the support server 2 according to the second embodiment of the present disclosure. The hardware configuration of the support server 2 according to the second embodiment is the same as the hardware configuration of the support server 2 according to the first embodiment shown in FIG. 2. However, it differs in that the ideal data 223 is not stored in the memory 22.

[0097] The classification unit 232 classifies the vehicle data 222 stored in the memory 22 into classes based on the vehicle model to which the vehicle 4 corresponding to the vehicle data 222 belongs and the acquisition date of the vehicle data 222. For example, the classification unit 232 classifies vehicle data 222 for the same vehicle model acquired in the same month into the same class.

[0098] The deviation determination unit 233 determines, for each class, whether the vehicle data 222 of the multiple vehicles 4 belonging to that class deviates from predetermined reference data. In the second embodiment, the reference data is vehicle data 222 classified into a class other than the class in question. The deviation determination unit 233 determines, for each class, whether the vehicle data 222 of the multiple vehicles 4 classified into that class deviates from the vehicle data 222 classified into a class other than the class in question.

[0099] FIG. 10 is a diagram illustrating the determination process by the deviation determination unit 233 according to the second embodiment of the present disclosure. FIG. 10 is a diagram illustrating the percentage (%) of vehicles 4 that experienced sudden acceleration at least once (hereinafter referred to as the "sudden acceleration occurrence rate") for each class. FIG. 10 illustrates the sudden acceleration occurrence rates for 16 classes as an example. For example, the sudden acceleration occurrence rate for vehicles 4 that belong to the class of vehicle model (type) "AAA-111G" and the acquisition month (January 2023) of the vehicle data 222 is 1%.

[0100] The deviation determination unit 233 calculates the sudden acceleration occurrence rate based on the vehicle data 222 of the number of sudden accelerations of the vehicles 4 belonging to each class.

[0101] The deviation determination unit 233 compares the sudden acceleration occurrence rates between different vehicle models for each acquisition month to determine whether the vehicle data 222 classified into a class of interest deviates from the vehicle data 222 classified into other classes. That is, for each class for each acquisition month, the deviation determination unit 233 compares the sudden acceleration occurrence rate of the class with the sudden acceleration occurrence rates of all other classes. If the result shows a deviation of more than a predetermined deviation threshold, the deviation determination unit 233 determines that the vehicle data 222 classified into the class deviates from the vehicle data 222 classified into the other classes.

[0102] For example, the threshold for determining whether or not there is a deviation is set to 2. In this case, none of the sudden acceleration occurrence rates of each class for the acquisition month (January 2023) deviates from the sudden acceleration occurrence rates of other classes by 2 or more. Therefore, the deviation determination unit 233 determines that the vehicle data 222 classified into each class for the acquisition month (January 2023) does not deviate from the vehicle data 222 classified into other classes.

[0103] On the other hand, the sudden acceleration occurrence rate (4.5) of the vehicle model "BBB-ZZZG" class in the acquisition month (February 2023) deviates by 2 or more compared to the sudden acceleration occurrence rates of all other vehicle models (sudden acceleration occurrence rate (0.8) of vehicle model "AAA-111G", sudden acceleration occurrence rate (1.3) of vehicle model "AAA-111W", and sudden acceleration occurrence rate (1.1) of vehicle model "BBB-YYYW"). Therefore, the deviation determination unit 233 determines that the vehicle data 222 classified into the vehicle model "BBB-ZZZG" class in the acquisition month (February 2023) deviates from the vehicle data 222 classified into other classes.

[0104] Similarly, the deviation determination unit 233 determines that the vehicle data 222 classified into the vehicle model "BBB-ZZZG" class for each acquisition month (March 2023) and acquisition month (April 2023) deviates from the vehicle data 222 classified into other classes.

[0105] When the sudden acceleration occurrence rate deviates compared to a predetermined number or more of other classes, the deviation determination unit 233 may determine that the vehicle data 222 classified into the class of interest deviates from the vehicle data 222 classified into the other classes. When the rate of classes in which the sudden acceleration occurrence rate deviates is equal to or greater than a predetermined threshold, the deviation determination unit 233 may determine that the vehicle data 222 classified into the class of interest deviates from the vehicle data 222 classified into the other classes.

[0106] The deviation determination unit 233 may also perform determination processing for other determination targets (e.g., fuel economy). For example, the deviation determination unit 233 calculates average fuel economy for each class based on the vehicle data 222 of the fuel economy classified into that class. The deviation determination unit 233 compares the average fuel economy between different vehicle models for each acquisition month to determine whether the vehicle data 222 classified into a class of interest deviates from the vehicle data 222 classified into other classes. The determination processing is the same except that the sudden acceleration occurrence rate has been changed to average fuel economy. Therefore, a detailed description of the determination processing will not be repeated.

[0107] As in the first embodiment, the malfunction factor identification unit 234 identifies the cause of the malfunction of the vehicle 4 belonging to the class based on the determination result of the deviation determination unit 233 and the malfunction factor candidate information. However, the malfunction factor candidate information is different from that in the first embodiment.

[0108] 11 is a diagram illustrating an example of defect cause candidate information according to the second embodiment of the present disclosure. The defect cause candidate information includes information made up of a set of a judgment target, a defect cause candidate, and a judgment criterion.

[0109] The "determination target" indicates the type of vehicle data that is the target of deviation determination by the deviation determination unit 233. The determination target includes, for example, the number of sudden accelerations.

[0110] The "candidate malfunction factor" indicates a candidate malfunction factor when it is determined that the vehicle data 222 of the "target vehicle" deviates from the ideal data 223. For example, when it is determined that the vehicle data 222 of the number of sudden accelerations deviates from the ideal data 223, the candidate malfunction factor includes the engine speed during idling.

[0111] The "criteria" indicates the criteria for identifying the cause of a malfunction from among the "candidate causes of a malfunction."

[0112] For example, when it is determined that a deviation has occurred in the judgment target "number of sudden accelerations" in a certain class, the malfunction factor identification unit 234 determines whether the judgment criterion for the malfunction factor candidate "engine speed while idling" is met. When the judgment criterion is met, the malfunction factor identification unit 234 identifies the malfunction factor candidate "engine speed while idling" as the malfunction factor for the judgment target "number of sudden accelerations". As an example, when it is determined that the engine speed while idling is lower than the reference value, the malfunction factor identification unit 234 identifies the malfunction factor for the judgment target "number of sudden accelerations" as "engine speed while idling".

[0113] The engine speed during idling and the like used by the malfunction factor identification unit 234 to determine whether the determination criteria are satisfied may be included in the vehicle data acquired by the vehicle data acquisition unit 231 from the in-vehicle system 5, or the malfunction factor identification unit 234 may inquire of the in-vehicle system 5 via the communication unit 21 and acquire the information from the in-vehicle system 5. When the malfunction factor identification unit 234 identifies a malfunction factor for the determination target "fuel efficiency," it identifies the malfunction factor based on malfunction factor candidate information, an example of which is shown in Fig. 4, as described in the first embodiment.

[0114] The process executed by the vehicle monitoring system 1 is the same as that shown in Fig. 5. However, the deviation determination process (step S4) is partially different.

[0115] 12 is a flowchart showing an example of the details of the deviation determination process (step S4 in FIG. 5). The support server 2 executes the process of loop D2 (described later) for each class (loop D1).

[0116] That is, the assistance server 2 repeatedly executes the following steps S41 to S45 for each determination target (type of vehicle data) (loop D2).

[0117] The support server 2 acquires the vehicle data 222 of the determination target classified into the class to be processed from the class classification unit 232 (step S41).

[0118] The support server 2 acquires the vehicle data 222 of the determination target that has been classified into classes other than the class to be processed from the class classification unit 232 for each class (step S42).

[0119] The support server 2 determines whether the vehicle data 222 to be judged of the class to be processed deviates from the vehicle data 222 to be judged of the other classes for each of the other classes (step S43).

[0120] If the support server 2 determines that the vehicle data 222 of the class to be processed deviates from the vehicle data 222 of all other classes (YES in step S44), it registers information indicating the class to be processed and the object to be judged as correction measure target information 224 in the memory 22 (step S45).

[0121] As described above, according to the second embodiment, even if the ideal data 223 cannot be obtained, it is possible to determine whether or not corrective measures for the vehicle 4 are necessary.

[0122] It is possible to determine the deviation of the vehicle data 222 for each month in which the vehicle data 222 is acquired. For example, it is possible to know a temporal trend, such as whether deviation is more likely to occur in winter than in summer. This can be useful in identifying the cause of a malfunction when one occurs.

[0123] <Modification of Second Embodiment> In the second embodiment, classes are set for each vehicle model of the vehicle 4 and each month in which the vehicle data 222 is acquired, and the vehicle data 222 is classified into each class. The method of setting classes is not limited to this. For example, classes may be set for each vehicle model of the vehicle 4 and each area in which the vehicle data 222 is collected. The area may indicate, for example, a prefecture or a state.

[0124] The classification unit 232 classifies the vehicle data 222 stored in the memory 22 into classes based on the vehicle model of the vehicle 4 corresponding to the vehicle data 222 and the location where the vehicle 4 collected the vehicle data 222. For example, the classification unit 232 classifies vehicle data 222 of the same vehicle model collected in the same area into the same class. The location information where the vehicle 4 collected the vehicle data 222 is included in the vehicle data 222 as the location information of the vehicle 4.

[0125] The deviation determination unit 233 compares the indexes to be determined, such as the rate of sudden acceleration occurrence or average fuel efficiency, between classes, as in the second embodiment. As a result, the deviation determination unit 233 determines, for each class, whether the vehicle data 222 of the plurality of vehicles 4 classified into that class deviates from the vehicle data 222 classified into classes other than that class.

[0126] According to this modification, it is possible to determine deviations in the vehicle data 222 for each collection area of ​​the vehicle data 222. For example, it is possible to know geographical trends, such as that deviations are more likely to occur in cold regions than in other regions. This can be useful in identifying the cause of any malfunctions that may occur.

[0127] In the above-described embodiment, the assistance server 2 classifies the vehicle data received from the vehicle 4. However, if the vehicle data received by the assistance server 2 is limited to a certain class, the assistance server 2 does not need to perform the class classification process for the vehicle data.

[0128] Each process (each function) in the above-described embodiments is realized by a processing circuit (circuitry) including one or more processors. The processing circuit may be configured as an integrated circuit or the like that combines one or more memories, various analog circuits, and various digital circuits in addition to one or more processors. The one or more memories store programs (instructions) that cause the one or more processors to execute each process. The one or more processors may execute each process according to a program read from one or more memories, or may execute each process according to a logic circuit designed in advance to execute each process. The processor may be any of various processors suitable for computer control, such as a CPU, a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), or an ASIC (Application Specific Integrated Circuit). Note that multiple physically separated processors may cooperate with each other to execute each process. For example, processors installed in multiple physically separated computers may cooperate with each other via a network such as a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet to execute each process. The program may be installed into memory from an external server device or the like via a network, or may be distributed in a state stored on a recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a semiconductor memory, and then installed into memory from the recording medium. Furthermore, at least some of the above-described embodiments and modifications may be combined in any desired manner.

[0129] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present disclosure is defined by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims.

[0130] REFERENCE SIGNS LIST 1 Vehicle monitoring system 2 Support server (fault correction support device) 3 Administrator server 4 Vehicle 5 In-vehicle system 6 Network 21 Communication unit 22 Memory 23 Processor 24 Bus 221 Computer program 222 Vehicle data 223 Ideal data 224 Corrective action target information 231 Vehicle data acquisition unit 232 Classification unit 233 Deviation determination unit 234 Fault cause identification unit 241 Curve 242 Curve

Claims

1. A fault correction assistance device comprising: a vehicle data acquisition unit that acquires vehicle data related to the running of at least one vehicle; and a deviation determination unit that determines the degree of deviation of the vehicle data from reference data.

2. The fault correction assistance device according to claim 1, wherein the reference data is ideal vehicle data for the vehicle.

3. A fault correction assistance device as described in claim 1, further comprising a class classification unit, wherein the at least one vehicle includes a first vehicle and a second vehicle, and the vehicle data includes first vehicle data and second vehicle data, the class classification unit classifies the first vehicle data into a first class to which the first vehicle belongs, and classifies the second vehicle data into a second class to which the second vehicle belongs, and the deviation determination unit determines the degree of deviation of the first vehicle data and the second vehicle data from the reference data.

4. A fault correction assistance device as described in claim 3, wherein the first class includes a first model of the first vehicle, and the second class includes a second model of the second vehicle.

5. A fault correction assistance device as described in claim 3 or claim 4, wherein the reference data is the first vehicle data, and the deviation determination unit determines the degree of deviation of the second vehicle data from the first vehicle data.

6. The fault correction assistance device of claim 3, wherein the class classification unit classifies the first vehicle data into the first class based on the acquisition date of the first vehicle data, and classifies the second vehicle data into the second class based on the acquisition date of the second vehicle data.

7. The fault correction assistance device described in claim 3, wherein the class classification unit classifies the first vehicle data into the first class based on location information of the first vehicle contained in the first vehicle data, and classifies the second vehicle data into the second class based on location information of the second vehicle contained in the second vehicle data.

8. A defect correction support device as described in any one of claims 1 to 7, wherein the vehicle data includes multiple types of data, and the defect correction support device further comprises a defect cause identification unit that identifies a cause of the defect of the vehicle based on the judgment result of the deviation judgment unit and candidate information of the cause of the defect provided for each type of vehicle data.

9. A method for assisting in correcting a malfunction, comprising the steps of: acquiring vehicle data relating to the running of at least one vehicle; and determining the degree of deviation of the vehicle data from reference data.

10. A computer program for causing a computer to function as: a vehicle data acquisition unit that acquires vehicle data related to the running of at least one vehicle; and a deviation determination unit that determines the degree of deviation of the vehicle data from reference data.

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