JP2023154525A - Vehicle information presentation method and vehicle information presentation apparatus - Google Patents

Abstract

To provide a vehicle information presentation method and a vehicle information presentation apparatus that predict an occurrence of an initial symptom of a malfunction of a vehicle while the vehicle is asymptomatic and notify a user of the occurence.SOLUTION: A vehicle information presentation apparatus 10 acquires malfunction information of a vehicle including symptom information indicating that at least one symptom among a sound, a vibration, a temperature, and a smell different from at the time of a normal traveling state of the vehicle occurs, component information of a component having the symptom occurring, and traveling state information of the vehicle when the symptom occurs, specifies a symptom occurrence range where the symptom occurs based on the malfunction information, sets a predicted symptom occurrence range where the symptom is predicted to occur in a target vehicle based on the specified symptom occurrence range when an abnormality in the target vehicle having no symptom occurring is detected, generates symptom occurrence information for notifying of the predicted symptom occurrence range, and transmits the symptom occurrence information to an external apparatus.SELECTED DRAWING: Figure 1

Description

The present invention relates to a vehicle information presentation method and a vehicle information presentation device for a vehicle.

Patent Document 1 discloses a vehicle estimation method that creates a proposal or estimate for vehicle repair based on predicted repair time and repair parts. Patent Document 1 accepts attribute information and symptom information of various vehicles when the vehicle breaks down. Then, by memorizing in advance the timing of malfunctions that are predicted to occur in various vehicles and the degree of malfunction corresponding to that period, the vehicle to be repaired can be repaired based on the attribute information and symptom information about the target vehicle. Predict when and where repairs will be required.

Japanese Patent Application Publication No. 2001-370844

However, in Patent Document 1, for a vehicle to be repaired in which an initial symptom of a malfunction such as abnormal noise, vibration, or odor has occurred, the time required for repair is estimated based on attribute information and symptom information of the target vehicle. For this reason, it is not possible to predict the occurrence of initial symptoms of a vehicle malfunction and notify the user while there are no symptoms.

The present invention has been made in view of the above-mentioned problems, and its purpose is to predict the occurrence of initial symptoms of a malfunction in a vehicle and notify the user of the same while the symptoms are still present.

In order to solve the above-mentioned problems, a vehicle information presentation method according to an aspect of the present invention provides a method for presenting vehicle information that indicates that the vehicle has generated at least one symptom among sound, vibration, temperature, and smell that is different from when the vehicle is in a normal driving state. Obtain vehicle malfunction information including symptom information, component information for the part that caused the symptom, and vehicle driving condition information when the symptom occurred, and determine the symptom occurrence range in which the symptom occurs based on the malfunction information. If an abnormality is detected in the target vehicle where no symptoms have occurred, a predicted symptom occurrence range in which the target vehicle is predicted to develop symptoms is set based on the identified symptom occurrence range, and the predicted symptom occurrence range is set. Generates symptom occurrence information to notify the range, and transmits the symptom occurrence information to an external device.

According to the present invention, it is possible to predict the occurrence of initial symptoms of a malfunction, such as unusual noise, vibration, or odor in a vehicle, and notify the user while the vehicle is symptomless.

FIG. 1 is a block diagram showing the configuration of a vehicle information presentation device according to a first embodiment of the present invention. FIG. 2 is a flowchart showing the process flow of the vehicle information presentation device according to the first embodiment of the present invention. FIG. 3 is a block diagram showing the configuration of a vehicle information presentation device according to a second embodiment of the present invention. FIG. 4 is a flowchart showing the process flow of the vehicle information presentation device according to the second embodiment of the present invention. FIG. 5 is a block diagram showing the configuration of a vehicle information presentation device according to a third embodiment of the present invention. FIG. 6 is a flowchart showing the process flow of the vehicle information presentation device according to the third embodiment of the present invention. FIG. 7 is a block diagram showing the configuration of a vehicle information presentation device according to a fourth embodiment of the present invention. FIG. 8 is a flowchart showing the process flow of the vehicle information presentation device according to the fourth embodiment of the present invention. FIG. 9 is a block diagram showing the configuration of a modified example of the vehicle information presentation device according to the first embodiment of the present invention. FIG. 10 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the first embodiment of the present invention. FIG. 11 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the second embodiment of the present invention. FIG. 12 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the second embodiment of the present invention. FIG. 13 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the third embodiment and the fourth embodiment of the present invention. FIG. 14 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the third embodiment and the fourth embodiment of the present invention. FIG. 15A is a diagram illustrating a display example of symptom occurrence information generated by the vehicle information presentation device according to the embodiment of the present invention. FIG. 15B is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the embodiment of the present invention. FIG. 16 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device according to the embodiment of the present invention.

Hereinafter, a first embodiment to which the present invention is applied will be described with reference to the drawings. In the description of the drawings, the same parts are denoted by the same reference numerals and the description thereof will be omitted.

[First embodiment]
Referring to FIG. 1, the overall configuration of a vehicle information presentation device and its peripheral devices according to a first embodiment of the present invention will be described. As shown in FIG. 1, the vehicle information presentation device 10 according to this embodiment is connected to a defect information database 20, a target vehicle information database 40, and an external device 50.

The vehicle information presentation device 10 acquires defect information regarding a defective vehicle in which a defect has occurred from the defect information database 20. The malfunction information includes, for example, symptom information, parts information, and driving condition information. Symptom information is information regarding initial symptoms (hereinafter referred to as symptoms) that appear as signs before failure occurs in parts of the defective vehicle or components of the defective vehicle, such as abnormal noises, vibrations, strange odors, and high temperature conditions, for example, This is information indicating that the defective vehicle has generated at least one symptom among sound, vibration, temperature, and odor that is different from that when the vehicle is in a normal running state. The component information is information about a component that has caused a symptom, and is information about at least one component or component that is thought to have caused a symptom. In addition to the part or component in which the symptom occurred, the parts information may include information on parts or replacement parts that were maintained or repaired after the symptom occurred. The driving status information includes at least one or more items related to a distance range or time, such as remaining travelable distance, remaining travelable time, cumulative travelable distance, and cumulative travelable time from the time when the defective vehicle develops a symptom to the time of failure. It is information.

Further, the malfunction information includes vehicle information of the malfunctioning vehicle. The vehicle information is at least one piece of information related to the production of the vehicle, such as the model of the vehicle, country of manufacture, factory, and time of manufacture. It is sufficient that there is at least one piece of information regarding production, but more is preferable.

The defect information database 20 includes, for example, data on a report containing symptom information of a defective vehicle when a user brings the defective vehicle to a dealer or repair shop, report contents of inquiries regarding defects received at the customer support center, and defect information. A database that accumulates information about defective vehicles, such as voice data recording conversations about vehicles or written data of conversations, information obtained remotely from defective vehicles, and word-of-mouth posted on the Internet or SNS. , the sources of information vary. Note that the defect information database 20 may be composed of a single database or a plurality of databases.

The vehicle information presentation device 10 identifies a symptom occurrence range in which initial symptoms of a malfunction occur based on the malfunction information acquired from the malfunction information database 20. The symptom occurrence range is a range of travel distance or travel time of a vehicle in which initial symptoms of a malfunction are predicted to occur in the vehicle based on a plurality of pieces of accumulated malfunction information.

The vehicle information presentation device 10 also acquires target vehicle information regarding a target vehicle that is a vehicle different from the defective vehicle from the target vehicle information database 40. The target vehicle information includes, for example, driving state information of the target vehicle, vehicle information of the target vehicle, and vehicle signals of the target vehicle. A vehicle signal is, for example, signal information such as time series data including engine speed and engine temperature transmitted from a part or component of a target vehicle. The vehicle information presentation device 10 detects an abnormality in the target vehicle based on a vehicle signal of the target vehicle.

The target vehicle information database 40 is a database that stores target vehicle information acquired from target vehicles. The target vehicle information database 40 stores, for example, driving state information of the target vehicle, vehicle information of the target vehicle, and vehicle signals of the target vehicle.

When detecting an abnormality in a target vehicle in which no symptoms have occurred, the vehicle information presentation device 10 sets a predicted symptom occurrence range in which the target vehicle is predicted to develop the symptom, based on the specified symptom occurrence range.

The vehicle information presentation device 10 generates symptom occurrence information for notifying the predicted symptom occurrence range, and transmits the generated symptom occurrence information to the external device 50. Note that the external device 50 may be any device such as a smartphone or a personal computer owned by the user of the target vehicle, or may be a plurality of terminals. Whether or not the symptom occurrence information is displayed and the destination to which it is sent are not particularly limited.

With the above configuration, when an abnormality is detected in a target vehicle in which no symptoms have occurred, it is possible to predict the symptom occurrence range in which the target vehicle will develop the symptom based on the specified symptom occurrence range. Thereby, it is possible to predict the occurrence of initial symptoms of a malfunction in the target vehicle, such as abnormal noise, vibration, and bad odor, and notify the user while the target vehicle has no symptoms.

Next, the detailed configuration of the vehicle information presentation device 10 will be described with reference to FIGS. 1 and 2. As shown in FIG. 1, the vehicle information presentation device 10 according to the first embodiment includes a defect information acquisition section 11, an information classification section 12, a symptom occurrence probability distribution generation section 13, a symptom occurrence range identification section 14, It includes a target vehicle information acquisition section 15, an abnormality detection section 16, a predicted symptom occurrence range setting section 17, a symptom occurrence information generation section 18, and a transmission section 19.

The defect information acquisition unit 11 acquires defect information from the defect information database 20. As described above, the malfunction information includes symptom information of the malfunctioning vehicle, parts information, driving state information, and vehicle information.

The information classification unit 12 classifies the acquired defect information based on symptom information, parts information, driving condition information, and vehicle information. The information classification unit 12 generates a dataset group by classifying the acquired defect information based on, for example, symptom information, parts information, driving condition information, and vehicle information. Details of the method for generating the dataset group will be described later.

The symptom occurrence probability distribution generating section 13 generates a symptom occurrence probability distribution representing the probability that a symptom will occur (hereinafter referred to as "symptom occurrence probability") with respect to the driving condition, for each defect information classified by the information classification section 12. The symptom occurrence probability distribution generation unit 13 calculates, for example, the cumulative number of symptom occurrences for each section of the driving state for each data set group generated by the information classification unit 12, and calculates the cumulative number by the data number of the data set group. By dividing, a symptom occurrence probability distribution for each data set group is generated. In addition, the symptom occurrence probability distribution generation unit 13 calculates the cumulative number of symptom occurrences for each section of the driving state for each dataset group, and calculates the cumulative number of symptoms based on the sales volume of vehicles that match the vehicle information of the dataset group or By dividing by the number of production units, a symptom occurrence probability distribution for each data set group may be generated. Note that the symptom occurrence probability distribution generation unit 13 may create a cumulative number distribution of the number of symptom occurrences for each section of the driving state for each data set group. Details of the symptom occurrence probability distribution will be described later.

The symptom occurrence range identification unit 14 identifies a range in which the symptom occurrence probability is equal to or greater than a preset threshold in the symptom occurrence probability distribution for each classified defect information as the symptom occurrence range in which the symptom occurs. For example, the symptom occurrence range identification unit 14 identifies, as the symptom occurrence range, a range in which the symptom occurrence probability is equal to or greater than a preset threshold in the symptom occurrence probability distribution for each generated data set group. Note that a different threshold value may be set for each data set group, or a uniform threshold value may be set. In addition, the threshold value may be set for each arbitrary information of the defective vehicle, such as for each model of the defective vehicle classified into the dataset group, for each manufacturing factory where the defective vehicle was manufactured, or for each part information where the symptom occurred. Good too. Details of the method for identifying the symptom occurrence range will be described later.

The target vehicle information acquisition unit 15 acquires target vehicle information from the target vehicle information database 40. As described above, the target vehicle information includes, for example, the driving state information of the target vehicle, the vehicle information of the target vehicle, and the vehicle signal of the target vehicle. A vehicle signal is, for example, signal information such as time series data including engine speed and engine temperature transmitted from a part or component of a target vehicle.

The abnormality detection unit 16 detects an abnormality in the target vehicle based on the acquired vehicle signal of the target vehicle. Anomaly detection methods for the target vehicle include, for example, methods that detect outliers from the threshold values of each signal based on predetermined conditions for time-series data including engine speed and engine temperature, and invariant analysis. There are various methods such as (a method that looks at the correlation between multiple signals and detects when some of the correlations break down) and a method that uses machine learning to determine the state of the vehicle (normal or abnormal). The method for detecting an abnormality in the target vehicle is a known technique, so a detailed explanation will be omitted. The method for detecting abnormalities in the target vehicle is not limited to the methods mentioned here, and any method may be used as long as it detects abnormalities in parts and components of the vehicle.

The abnormality detection unit 16 estimates driving state information of the target vehicle when detecting an abnormality in the target vehicle. The driving status information of the subject vehicle includes the remaining drivable distance, remaining drivable time, and the remaining drivable time from the current moment (when the abnormality is detected) to the occurrence of the failure, if the subject vehicle continues to drive without performing repairs. At least one distance or time range, such as cumulative travel distance or cumulative travel time. Furthermore, the abnormality detection unit 16 estimates the part or component (hereinafter referred to as a failed part) of the target vehicle in which the abnormality has been detected.

The predictive symptom occurrence range setting unit 17 determines whether the target vehicle is exhibiting symptoms based on the symptom occurrence range specified for each classified malfunction information when the abnormality detection unit 16 detects an abnormality in the target vehicle in which no symptoms have occurred. Set the predicted range of symptoms that are expected to occur. The predicted symptom occurrence range setting unit 17 sets a predicted symptom occurrence range based on the defect information of the defective vehicle whose vehicle information matches the vehicle information of the target vehicle. For example, the predictive symptom occurrence range setting unit 17 selects a malfunctioning vehicle that has the highest degree of matching with the target vehicle based on the driving state information, failed parts, and vehicle information of the target vehicle, from among the symptom occurrence ranges specified for each data set group. A data set group is identified, and the symptom occurrence range of the identified data set group is set as the predicted symptom occurrence range of the target vehicle. A method of setting the predicted symptom occurrence range will be described later.

The symptom occurrence information generating section 18 generates symptom occurrence information for notifying the predicted symptom occurrence range set by the predicted symptom occurrence range setting section 17. The symptom occurrence information may include information about the part of the target vehicle in which the abnormality has been detected. The symptom occurrence information may include information on a failed part of the target vehicle detected by the abnormality detection unit 16, predicted symptom information of the target vehicle, driving state information of the target vehicle, vehicle information of the target vehicle, etc. The information included in the symptom occurrence information and the display format are not particularly limited. Note that display examples of symptom occurrence information will be described later with reference to FIGS. 10 to 16.

The transmitter 19 transmits the symptom occurrence information generated by the symptom occurrence information generator 18 to the external device 50 . The transmitter 19 transmits symptom occurrence information to an external device from the time when an abnormality in the target vehicle is detected until the target vehicle reaches the predicted symptom occurrence range. Note that the external device 50 is, for example, a terminal owned by a user, a vehicle owner, or a vehicle manager, or a display device installed in a target vehicle, and the external device 50 to which the symptom occurrence information is transmitted may be a single or multiple external device. good.

With the above configuration, when the vehicle information presentation device 10 according to the first embodiment detects an abnormality in a target vehicle in which the initial symptoms of the problem have not appeared, the vehicle information presentation device 10 according to the first embodiment can display , it is possible to set a predicted symptom occurrence range in which the symptom is predicted to occur in the target vehicle. By transmitting symptom occurrence information to the external device 50 to notify the predicted symptom occurrence range, the user can detect the occurrence of initial symptoms of a malfunction in the target vehicle, such as unusual noises, vibrations, and bad odors, while the target vehicle is symptomless. may be notified.

An example of the process flow of the vehicle information presentation device 10 according to the first embodiment will be described with reference to the flowchart shown in FIG. 3.

First, in step S101, the defect information acquisition unit 11 acquires defect information from the defect information database 20. The malfunction information includes symptom information of the malfunctioning vehicle, parts information, driving state information, and vehicle information. Here, the remaining travelable distance at the time when the problem occurs in the defective vehicle is acquired as the driving state information. The remaining mileage at the time the symptom occurs is the cumulative mileage of the defective vehicle at the time the defective vehicle developed the symptom, and the time when a failure or abnormality may occur if the vehicle continues to be driven. This is the difference from the cumulative mileage of the defective vehicle. For example, assume that the cumulative mileage at the time the symptoms occur is 1,200 km, and the cumulative mileage when a failure or abnormality is likely to occur if the vehicle continues to be driven is 1,250 km. In this case, the remaining travel distance at the time the symptoms occur is 1250 (km) - 1200 (km) = 50 (km).

In the manner described above, the remaining travelable distance at the time the symptom occurs can be acquired as driving state information. Note that the method of acquiring driving state information described here is just an example, and the method is not limited to this method. The method for acquiring the driving state information may be any method that calculates the difference in distance or time from the time when the defective vehicle develops a symptom to the time when a failure or abnormality is likely to occur.

The process proceeds to step S102, where the information classification unit 12 classifies the acquired defect information based on symptom information, parts information, driving condition information, and vehicle information. Specifically, the information classification unit 12 links symptom information of the defective vehicle, parts information, driving state information, and vehicle information. Then, defect information whose symptom information, parts information, and vehicle information match is classified. The process proceeds to step S103, where the information classification unit 12 generates a data set group for each classified defect information. Note that the classification conditions for defect information may be based on symptom information, parts information, driving condition information, and single element or multiple element conditions of vehicle information of the defective vehicle, but the condition for which the symptom occurs exactly In order to specify the range of occurrence, it is desirable to have as many conditions as possible.

(How to classify defect information)
Here, an example of a method for classifying defect information in the first embodiment will be described. In the following description, it is assumed, for example, that the defect information acquisition unit 11 has acquired first to fifth defect information. The content of the first defect information is that the symptom information is "abnormal noise, rattling noise", the parts information is "engine", the vehicle information is "car model A", the manufacturing plant is "factory A", and the manufacturing plant is "factory A". Assume that the time is “2019” and the remaining travelable distance (driving status information) is “10 km”. The content of the second defect information is that the symptom information is "vibration", the parts information is "engine", the vehicle information is "car model B", the manufacturing plant is "factory A", and the manufacturing date is "2018". It is assumed that the year is 2013, and the remaining travel distance is 14 km. The content of the third defect information is that the symptom information is "high temperature state", the parts information is "battery", the vehicle information is "car model A", the manufacturing plant is "factory B", and the manufacturing date is " 2016,” and the remaining drivable distance is “17 km.” The contents of the fourth defect information are: symptom information is "abnormal noise, rattling noise", parts information is "engine", vehicle information is "car model C", manufacturing plant is "factory C", manufacturing Assume that the year is "2017" and the remaining travel distance is "5 km". The content of the fifth defect information is that the symptom information is "abnormal noise, rattling noise", the parts information is "engine", the vehicle information is "car model A", the manufacturing plant is "factory A", and the manufacturing Assume that the time is "2019" and the remaining travel distance is "14 km".

If the above first to fifth malfunction information is classified so that the symptom information, parts information, and vehicle information match, the first to fifth malfunction information will be classified into the first to fourth data as follows. Classified into set groups. The first defect information and the fifth defect information are classified into the first data set group. The second defect information is classified into the second data set group. The third defect information is classified into the third data set group. The fourth defect information is classified into the fourth data set group.

In the manner described above, the information classification unit 12 classifies defect information. Note that the method of classifying defect information and the content of defect information described here are merely examples, and the present invention is not limited to these methods and contents.

The process proceeds to step S104 in FIG. 2, where the symptom occurrence probability distribution generation unit 13 generates a symptom occurrence probability distribution for each generated data set group. For example, the symptom occurrence probability distribution generation unit 13 generates a symptom occurrence probability distribution based on the driving state information at the time when the symptom occurs in the defective vehicle, the cumulative number of occurrences of symptoms, and the number of data items of defect information. The symptom occurrence probability distribution generation unit 13 aggregates the number of occurrences of symptoms of the defect information classified into data set groups for each at least one section (class) of the driving condition information, thereby generating a section of the driving condition information. Find the cumulative number of occurrences of each symptom. Then, by dividing the cumulative number of occurrences for each section of driving condition information by the number of data items of defect information classified into data set groups, the probability of symptom occurrence for each section of driving condition information is calculated, and the probability of symptom occurrence is distributed. generate.

(Method for generating probability distribution of symptom occurrence and method for identifying range of symptom occurrence)
Here, an example of a method for generating a symptom occurrence probability distribution and a method for specifying a symptom occurrence range will be described. In the following description, it is assumed, for example, that the information classification unit 12 has classified six pieces of defect information into a certain data set group. At this time, it is assumed that the remaining travelable distances for each of the six pieces of defect information are 15.0 km, 15.6 km, 14.0 km, 15.5 km, 16.0 km, and 18.0 km.

In the data set group of the above example, when the cumulative number of symptoms that occur for each 1 km section of the remaining drivable distance is tallied, the cumulative number of symptoms that occur for each section of the remaining drivable distance is tallied as follows. The cumulative number of symptoms occurring in the section where 17.0 km<remaining travel distance≦18.0 km is 1. The cumulative number of symptoms occurring in the section where 16.0 km<remaining travel distance≦17.0 km is 0. The cumulative number of symptoms occurring in the section where 15.0 km<remaining travel distance≦16.0 km is 3. The cumulative number of symptoms occurring in the section where 14.0km<remaining travelable distance≦15.0km is 1. The cumulative number of symptoms occurring in the section where 13.0 km<remaining travel distance≦14.0 km is 1.

By dividing the cumulative number of occurrences determined for each section of the remaining drivable distance by the number of data items in the dataset group, the probability of symptom occurrence for each section of the remaining drivable distance is calculated. In the data set group of the above example, the number of data items is six, so the probability of symptom occurrence for each section of remaining travelable distance is calculated as follows. The probability of symptom occurrence in the section where 17.0 km<remaining travelable distance≦18.0 km is 1 case/6 cases=15%. The probability of symptom occurrence in the section where 16.0 km<remaining travelable distance≦17.0 km is 0 cases/6 cases=0%. The probability of symptom occurrence in the section where 15.0 km<remaining drivable distance≦16.0 km is 3 cases/6 cases=50%. The probability of symptom occurrence in the section where 14.0 km<remaining travelable distance≦15.0 km is 1/6 = 15%. The probability of symptom occurrence in the section where 13.0 km<remaining travel distance≦14.0 km is 1/6 = 15%.

The process proceeds to step S105, where the symptom occurrence range identifying unit 14 identifies, as the symptom occurrence range, an interval in which the symptom occurrence probability is equal to or greater than a preset threshold in the symptom occurrence probability distribution for each generated data set group. . Furthermore, it is expected that the symptoms of the problem will worsen as the distance and time traveled increase. Therefore, the symptom occurrence range identifying unit 14 identifies the interval after the interval in which the probability of symptom occurrence first becomes equal to or higher than the threshold in the generated symptom occurrence probability distribution for each dataset group, as the symptom occurrence range for each dataset group. You may.

For example, if the threshold for the probability of symptom occurrence is 50%, in the data set group of the example above, the probability of symptom occurrence first becomes equal to or greater than the threshold in the section where 15.0 km < remaining drivable distance ≤ 16.0 km. . In this case, the symptom occurrence range identification unit 14 identifies a section where the remaining travelable distance is 16 km or more (a section where the remaining travel distance is from 16 km to 0 km) as the symptom occurrence range.

As described above, the symptom occurrence probability distribution generation unit 13 generates a symptom occurrence probability distribution for each classified defect information. Further, the symptom occurrence range identification unit 14 identifies the symptom occurrence range for each classified defect information. Note that the method of generating the probability distribution of symptom occurrence and the method of specifying the range of symptom occurrence described here are merely examples, and the present invention is not limited to these methods. In addition, the driving status information includes not only the remaining drivable distance, but also information regarding distance sections or times such as the remaining drivable time, cumulative drivable distance, and cumulative drivable time of the defective vehicle at the time the symptom occurred. Good too. Further, instead of the symptom occurrence probability distribution, a symptom occurrence number distribution may be generated.

The process proceeds to step S106, where the target vehicle information acquisition unit 15 acquires target vehicle information from the target vehicle information database 40. The target vehicle information includes driving state information of the target vehicle, vehicle information of the target vehicle, and vehicle signals of the target vehicle.

The process proceeds to step S107, and the abnormality detection unit 16 detects an abnormality in the target vehicle based on the acquired vehicle signal of the target vehicle. If the abnormality detection unit 16 detects an abnormality in the target vehicle in which no symptoms have occurred, the process proceeds to step S108, and the abnormality detection unit 16 estimates the driving state information of the target vehicle in which the abnormality has been detected. . The process proceeds to step S109, and the abnormality detection unit 16 estimates the failed part of the target vehicle in which the abnormality has been detected, based on the acquired vehicle signal.

The process proceeds to step S110, where the predicted symptom occurrence range setting unit 17 sets a predicted symptom occurrence range in which the target vehicle is predicted to develop the symptom, based on the identified symptom occurrence range. For example, the predictive symptom occurrence range setting unit 17 selects a faulty vehicle whose parts information and vehicle information most match the faulty parts and vehicle information of the target vehicle, from among the symptom occurrence ranges specified for each data set group. A data set group is identified, and the symptom occurrence range of the identified data set group is set as the predicted symptom occurrence range of the target vehicle.

The process proceeds to step S111, and the symptom occurrence information generation unit 18 generates symptom occurrence information for notifying the set predicted symptom occurrence range. The process proceeds to step S112, where the transmitter 19 transmits the symptom occurrence information to the external device 50, and ends the process of FIG. The external device 50 receives the symptom occurrence information transmitted from the transmitter 19, and notifies and displays the symptom occurrence information to the user. Note that information included in the symptom occurrence information and display examples of the symptom occurrence information will be described later with reference to FIGS. 10 to 16.

(Operations and effects of the first embodiment)
As explained above, in the vehicle information presentation method and its device according to the first embodiment of the present invention, when the vehicle generates at least one symptom of sound, vibration, temperature, and odor that is different from when the vehicle is in a normal driving state, Vehicle malfunction information is acquired, including symptom information indicating the problem, component information about the component that caused the symptom, and information about the driving state of the vehicle at the time the symptom occurred. Identify the symptom occurrence range in which the symptom occurs based on the defect information. When an abnormality is detected in a target vehicle in which no symptoms have occurred, a predicted symptom occurrence range in which the target vehicle is predicted to develop the symptom is set based on the specified symptom occurrence range. Generates symptom occurrence information to notify the predicted symptom occurrence range, and transmits the symptom occurrence information to an external device.

When an abnormality is detected in a target vehicle in which no symptoms have occurred, the user can set a predicted symptom occurrence range in which the target vehicle is predicted to develop symptoms based on the symptom occurrence range identified based on the defect information. may be notified. Thereby, it is possible to predict the occurrence of initial symptoms of a malfunction in the target vehicle, such as abnormal noise, vibration, and bad odor, and notify the user while the target vehicle has no symptoms.

Furthermore, in the vehicle information presentation method and device according to the present embodiment, malfunction information is classified based on symptom information, parts information, and driving state information of the malfunctioning vehicle. For each classified defect information, a symptom occurrence probability distribution representing the probability that a symptom will occur with respect to the driving condition is generated. In the symptom occurrence probability distribution, a range where the symptom occurrence probability is equal to or greater than a threshold is set as the symptom occurrence range. This makes it possible to notify the user of the probability of occurrence of initial symptoms of a malfunction in the target vehicle, such as abnormal noise, vibration, and bad odor, while the target vehicle has no symptoms. Information regarding initial symptoms can be conveyed to the user in an easy-to-understand manner while the target vehicle is asymptomatic.

Furthermore, in the vehicle information presentation method and device according to the present embodiment, the symptom occurrence information includes information on the part of the target vehicle in which the abnormality has been detected. Thereby, the user can be notified of information about parts of the target vehicle where the symptoms will occur in the future.

Furthermore, in the vehicle information presentation method and device according to the present embodiment, the symptom occurrence information is transmitted from the time when an abnormality in the target vehicle is detected until the target vehicle reaches the symptom occurrence range. As a result, the user can be notified of the occurrence of initial symptoms of a malfunction in the target vehicle, such as abnormal noise, vibration, and bad odor, even though the target vehicle has no symptoms. Furthermore, if the notification information overlaps with other notification information, it is possible to notify the occurrence of the initial symptom of a problem in the target vehicle at a different time.

In addition, the vehicle information presentation method and device according to the present embodiment further includes vehicle information indicating at least one of the vehicle type, manufacturing country, manufacturing factory, and manufacturing time of the defective vehicle, and the vehicle information includes the vehicle information of the target vehicle. A predicted symptom occurrence range is set based on the defect information of the defective vehicle that matches the information. The predicted symptom occurrence range can be set based on the defect information of defective vehicles that have the same model, country of manufacture, factory, and date of manufacture as the target vehicle. Thereby, the accuracy of predicting the occurrence of initial symptoms of a problem in the target vehicle can be improved.

In addition, in the vehicle information presentation method and its device according to the present embodiment, the malfunction information includes report data stored in the database containing symptom information of the malfunctioning vehicle, and recorded conversation content regarding the malfunctioning vehicle. The information is acquired from at least one of audio data or text data of conversation content, data obtained remotely from the defective vehicle, and information about the defective vehicle posted on the Internet. Thereby, defect information can be obtained from various information sources, and data can be collected efficiently. Furthermore, the accuracy of the symptom occurrence probability distribution can be improved.

[Second embodiment]
A second embodiment to which the present invention is applied will be described below with reference to the drawings. In the following description, an information presentation device and its peripheral devices according to a second embodiment of the present invention will be explained, focusing on the differences from the first embodiment.

With reference to FIG. 3, the configuration of a vehicle information presentation device and its peripheral devices according to a second embodiment of the present invention will be described. As shown in FIG. 3, the vehicle information presentation device 10 according to this embodiment is connected to a defect information database 20, a target vehicle information database 40, an external device 50, and a vehicle signal database 60.

The vehicle information presentation device 10 according to the second embodiment acquires defect information regarding a defective vehicle in which a defect has occurred from the defect information database 20. The defect information includes symptom information, parts information, driving state information, and vehicle information of the defective vehicle, as well as driving history information of at least one defective vehicle. Driving history information includes the driving frequency of the vehicle, the country or region in which it is driving, the driving area such as highlands, coastal areas, and residential areas, and the driving ratio of ordinary roads and expressways (expressway usage ratio). This is information regarding the past driving environment and driving characteristics of the defective vehicle. Driving history information may be obtained, for example, from the driving history recorded in the defective vehicle or the car navigation system of the defective vehicle, or vehicle owner information registered in a service provided by a dealership or vehicle sales company. (home or work address) may be obtained from an external terminal such as a smartphone held by the vehicle owner.

Further, the vehicle information presentation device 10 according to the second embodiment acquires, from the vehicle signal database 60, the vehicle signal of the malfunctioning vehicle that was measured at the time when the symptom occurred in the malfunctioning vehicle or before the symptom occurred. A vehicle signal is, for example, signal information transmitted from a defective vehicle part or component, such as time series data including engine speed and engine temperature. The vehicle signal of the malfunctioning vehicle may be acquired, for example, when the dealer connects the malfunctioning vehicle to a malfunction diagnostic device, or the vehicle signal transmitted remotely by the malfunctioning vehicle at the time of malfunction reporting or malfunction consultation may be acquired.

The vehicle signal database 60 is a database that stores vehicle signals of the defective vehicle acquired from the defective vehicle. The vehicle signal database 60 stores vehicle signals of defective vehicles measured at or before the occurrence of the symptoms.

Furthermore, the vehicle information presentation device 10 according to the second embodiment acquires target vehicle information regarding the target vehicle from the target vehicle information database 40. The target vehicle information includes driving state information of the target vehicle, vehicle information of the target vehicle, vehicle signals of the target vehicle, and driving history information of at least one target vehicle.

The target vehicle information database 40 is a database that stores target vehicle information acquired from target vehicles. The target vehicle information database 40 stores driving state information of the target vehicle, vehicle information of the target vehicle, vehicle signals of the target vehicle, and driving history information of the target vehicle.

As shown in FIG. 3, the vehicle information presentation device 10 according to the second embodiment includes a defect information acquisition section 11, an information classification section 12, a target vehicle information acquisition section 15, an abnormality detection section 16, and a similar vehicle identification section. section 71 , estimated vehicle signal acquisition section 72 , signal similarity calculation section 73 , predicted symptom occurrence range setting section 17 , symptom occurrence information generation section 18 , and transmission section 19 . The functions and configurations of the abnormality detecting section 16, the symptom occurrence information generating section 18, and the transmitting section 19 are the same as those of the vehicle information presentation device 10 according to the first embodiment, so a description thereof will be omitted.

The defect information acquisition unit 11 acquires defect information from the defect information database 20. As described above, the defect information includes symptom information, parts information, driving condition information, and vehicle information of the defective vehicle, as well as at least one piece of driving history information.

Furthermore, the malfunction information acquisition unit 11 acquires, from the vehicle signal database 60, the vehicle signal of the malfunctioning vehicle that was measured at the time of occurrence of the symptom of the malfunction vehicle or before the occurrence of the symptom. The malfunction information acquisition unit 11 acquires a vehicle signal in an arbitrary time range from the vehicle signals of the malfunctioning vehicle measured at the time of occurrence of the symptom or before the time of occurrence of the symptom. For example, the malfunction information acquisition unit 11 may acquire vehicle signals of the malfunctioning vehicle from two hours before the symptoms occur to the time the symptoms occur, and if the symptoms do not occur from two hours before the symptoms occur. The vehicle signal of the defective vehicle up to 15 minutes before the occurrence of the defect may be acquired.

Note that, if the behavior of the vehicle signal and the symptoms at the time of occurrence of the symptom are clear, it is desirable to obtain the vehicle signal of the defective vehicle that includes the time period in which the behavior of the vehicle signal and the symptom occurred. Furthermore, when considering the time span for acquiring the vehicle signal of the defective vehicle, the usage time (operating time) of the defective vehicle may be used as a standard, or the vehicle signal may be calculated by including the time period when the defective vehicle is not running. You may obtain it. Further, the time width of the vehicle signal to be acquired may be set uniformly, or it may be set flexibly, such as using different conditions depending on each defective vehicle.

The information classification unit 12 classifies the acquired defect information based on symptom information, parts information, driving condition information, vehicle information, and driving history information. The information classification unit 12 stores, for example, symptom information of a defective vehicle, parts information, driving state information, vehicle information, driving history information, and vehicle signals in association with each other.

The target vehicle information acquisition unit 15 acquires target vehicle information from the target vehicle information database 40. The target vehicle information includes driving state information, vehicle information, vehicle signals, and driving history information of the target vehicle.

When the abnormality detection unit 16 detects an abnormality in a target vehicle in which no symptoms have occurred, the similar vehicle identifying unit 71 identifies a defective vehicle that is most similar to the target vehicle (hereinafter referred to as a similar vehicle). The similar vehicle identifying unit 71 identifies, as a similar vehicle, a defective vehicle whose vehicle information and travel history information are most similar to the target vehicle from among defective vehicles whose parts information is the same as the failed part of the target vehicle in which an abnormality has been detected. . The similar vehicle identifying unit 71 identifies at least one similar vehicle from among the defective vehicles. Note that details of the method for identifying similar vehicles will be described later.

When the abnormality detection unit 16 detects an abnormality in the target vehicle in which no symptoms have occurred, the estimated vehicle signal acquisition unit 72 calculates a predicted value ( (hereinafter referred to as estimated vehicle signal). Note that the estimated vehicle signal to be acquired is a vehicle signal of the same type as the vehicle signal of the similar vehicle, which is acquired from the same failed part as the component information of the similar vehicle. The estimated vehicle signal to be acquired may be a single signal or a plurality of signals as long as it is the same type of vehicle signal as the vehicle signal of a similar vehicle. Note that the method for calculating the estimated vehicle signal is a known technique, so a detailed explanation will be omitted.

The signal similarity calculation unit 73 calculates the similarity between the vehicle signal of the defective vehicle and the estimated vehicle signal of the target vehicle. More specifically, the signal similarity calculation unit 73 calculates the similarity between the vehicle signal of the similar vehicle identified by the similar vehicle identification unit 71 and the estimated vehicle signal of the target vehicle acquired by the estimated vehicle signal acquisition unit 72. Calculate. The degree of similarity is calculated using a method such as correlation analysis or template matching, for example. Details of the method for calculating the similarity will be described later.

The predicted symptom occurrence range setting unit 17 sets a predicted symptom occurrence range based on the calculated similarity. The predicted symptom occurrence range setting unit 17 sets the range in which the calculated degree of similarity is equal to or greater than the threshold value as the predicted symptom occurrence range in which the target vehicle is predicted to develop the symptom. Note that a constant threshold value may be set for all malfunction symptoms, or a threshold value may be set for each vehicle model, for each manufacturing plant (area where the factory is located), or for each failed part, for example. Details of how to set the predicted symptom occurrence range will be described later.

With the above configuration, the vehicle information presentation device 10 according to the second embodiment is able to detect symptoms even when the target vehicle has insufficient accumulation of defect information, such as a new model or a vehicle that has just undergone a model change. A predictive symptom occurrence range that is predicted to occur can be set and notified to the user.

An example of the process flow of the vehicle information presentation device 10 according to the second embodiment will be described with reference to the flowchart shown in FIG. 4. The processes from step S205 to step S207 and step S212 to step S213 in FIG. 4 are the same as the processes from step S107 to step S109 and step S111 to step S112 in FIG. 2, so the description thereof will be omitted.

First, in step S201, the defect information acquisition unit 11 acquires defect information from the defect information database 20. The defect information includes symptom information, parts information, driving condition information, vehicle information, and driving history information of the defective vehicle. Here, the remaining travelable distance at the time when the problem occurs in the defective vehicle is acquired as the driving state information.

The process proceeds to step S202, where the malfunction information acquisition unit 11 acquires, from the vehicle signal database 60, the vehicle signal of the malfunctioning vehicle that was measured at or before the symptom occurrence of the malfunction vehicle.

The process proceeds to step S203, where the information classification unit 12 classifies the acquired defect information based on symptom information, parts information, driving condition information, vehicle information, and driving history information. Specifically, the information classification unit 12 associates and stores symptom information, parts information, driving state information, vehicle information, driving history information, and vehicle signals of the defective vehicle.

The process proceeds to step S204, where the target vehicle information acquisition unit 15 acquires target vehicle information from the target vehicle information database 40. The target vehicle information includes vehicle signals, driving state information, vehicle information, and driving history information of the target vehicle.

In steps S205 to S207, if the abnormality detection unit 16 detects an abnormality in the target vehicle in which no symptoms have occurred, the process proceeds to step S208, and the estimated vehicle signal acquisition unit 72 detects an abnormality in the target vehicle. Obtain the estimated vehicle signal of the target vehicle after the point in time.

The process proceeds to step S209, and the similar vehicle identification unit 71 selects a defective vehicle whose vehicle information and driving history information are most similar to the target vehicle from among the defective vehicles that have the same failed part as the failed part of the target vehicle in which the abnormality was detected. is identified as a similar vehicle.

(Method for identifying similar vehicles)
Here, an example of a method for identifying similar vehicles will be described. In the following explanation, for example, the malfunction information acquisition unit 11 acquires first to third malfunction information as malfunction information of three malfunctioning vehicles that have the same part information as the malfunctioning part of the target vehicle in which the malfunction has been detected. Suppose we did. Part of the content of the first defect information is that the vehicle type in the vehicle information is "Vehicle Model A," the manufacturing factory is "Factory A," the manufacturing date is "2019," and the driving frequency in the driving history information is "5 times a week." Assume that the driving area is "Area A", the driving area is "Highland", and the expressway usage rate is "30% to 40%". Part of the content of the second defect information is that the vehicle type in the vehicle information is "Model B," the manufacturing factory is "Factory A," and the manufacturing date is "2018," and the driving frequency in the driving history information is "7 days a week." Assume that the driving area is "Area B", the driving area is "Coastal", and the expressway usage rate is "60% to 70%". Part of the content of the third defect information is that the vehicle type in the vehicle information is "Model A," the production factory is "Factory A," the production date is "2019," and the driving frequency in the driving history is "2 per week." Assume that the driving area is "Region C", the driving area is "residential area/city center", and the expressway usage rate is "20% to 30%".

In the above case, the vehicle type in the vehicle information of the target vehicle in which the abnormality was detected is "Model A", the production factory is "Factory A", the production time is "2019", and the driving frequency in the driving history information is Assume that the vehicle travels "two days a week," the driving area is "Region C," the driving area is "residential area/city center," and the expressway usage rate is "30% to 40%."

In this case, the similar vehicle identification unit 71 selects the first defect information and the third defect information whose vehicle information and driving history information are similar to the target vehicle in which the abnormality was detected from among the first to third defect information. Extract. Then, the degree of coincidence between the travel history information items of the first defect information and the third defect information is calculated. Since the driving history information of the first defect information matches the driving history information of the target vehicle in one item (expressway usage rate) out of four items, the degree of matching of the driving history information of the first defect information is 1/4. On the other hand, for the driving history information of the vehicle subject to the third defect information, 3 out of 4 items (driving frequency, driving area, driving area) match the driving history information of the target vehicle. The matching degree of defect information of No. 3 is 3/4. The similar vehicle identification unit 71 selects, from among the first to third defect information, a defective vehicle whose vehicle information matches and whose driving history information has the highest matching degree, as a similar vehicle. Specify as.

As described above, the similar vehicle identifying unit 71 selects a similar vehicle whose vehicle information and driving history information are most similar to the target vehicle from among defective vehicles whose parts information is the same as the failed part of the target vehicle in which an abnormality has been detected. Identify. Note that the method for identifying similar vehicles, the content of defect information, and the content of target vehicle information described here are merely examples, and the present invention is not limited to these methods and content.

The process proceeds to step S210, and the signal similarity calculation unit 73 calculates the similarity between the vehicle signal of the similar vehicle and the estimated vehicle signal of the target vehicle.

(Method of calculating similarity and setting method of predicted symptom occurrence range)
Here, an example of a method of calculating the degree of similarity between a vehicle signal of a similar vehicle and an estimated vehicle signal of a target vehicle and a method of setting a predicted symptom occurrence range will be described. In the following explanation, it is assumed that the vehicle signal of a similar vehicle is a signal (sampling rate: 1 second) from 5 seconds before the symptom occurrence until the symptom occurrence. The value of the vehicle signal of a similar vehicle for 5 seconds before the occurrence of the symptom is "1", "2", "3", "4", or "5", and the value of the vehicle signal of the similar vehicle at the time of occurrence of the symptom Suppose that is "6".

Furthermore, in the above case, the values of the estimated vehicle signal of the target vehicle for 11 seconds are "2", "1", "1", "1", "1", "1", "2", "3". ”, “4”, “5”, and “6” (sampling rate: 1 second). The signal similarity calculation unit 73 shifts the interval (time window) of the vehicle signal of the similar vehicle every second, and calculates the correlation coefficient between the vehicle signal of the similar vehicle and the estimated vehicle signal of the target vehicle for each interval. The similarity between the vehicle signal of the similar vehicle and the estimated vehicle signal of the target vehicle is calculated. In the above example, the signal similarity calculation unit 73 sets the similarity of the estimated vehicle signal of the target vehicle to -0.65 for the 1 to 6 second interval, 0.65 for the 2 to 7 second interval, and 0.65 for the 2 to 7 second interval. The similarity for the 3 to 8 second interval is 0.83, the similarity for the 4 to 9 second interval is 0.93, the similarity for the 5 to 10 second interval is 0.98, and the similarity for the 6 to 11 second interval. Calculate the degree as 1.00.

Note that if the value of the estimated vehicle signal of the target vehicle does not change (for example, it uniformly takes a value of 2), the degree of similarity may be set to 0. Furthermore, if the correlation coefficient takes a negative value, the degree of similarity may be set to 0. Furthermore, although the time windows overlap in the above example, the degree of similarity may be calculated while shifting the time windows without overlapping.

The process proceeds to step S211 in FIG. 4, where the predicted symptom occurrence range setting unit 17 sets a predicted symptom occurrence range based on the calculated similarity. The predicted symptom occurrence range setting unit 17 sets the range in which the calculated degree of similarity is equal to or greater than the threshold value as the predicted symptom occurrence range in which the target vehicle is predicted to develop the symptom.

For example, if the similarity threshold is 0.9, in the above example, the similarity is is greater than or equal to the threshold. In this case, the predicted symptom occurrence range setting unit 17 sets the interval from 4 to 11 seconds as the predicted symptom occurrence range. Furthermore, it is expected that the symptoms of the problem will worsen as the distance and time traveled increase. Therefore, the predicted symptom occurrence range setting unit 17 may set the interval after the interval in which the degree of similarity first becomes equal to or greater than the threshold value as the predicted symptom occurrence range.

As described above, the signal similarity calculation unit 73 calculates the similarity between the vehicle signal of a similar vehicle and the estimated vehicle signal of the target vehicle. Further, the predicted symptom occurrence range setting unit 17 sets a predicted symptom occurrence range based on the degree of similarity. Note that the method and vehicle signal values described here are just examples, and the method and values are not limited to these.

(Operations and effects of the second embodiment)
As explained above, in the vehicle information presentation method and its device according to the second embodiment of the present invention, the vehicle signal of the defective vehicle measured at or before the occurrence of the symptom is acquired, and the vehicle information of the target vehicle is The predicted value of the signal is acquired, the degree of similarity between the vehicle signal of the defective vehicle and the predicted value of the vehicle signal of the target vehicle is calculated, and a range where the degree of similarity is equal to or greater than a threshold is set as a predicted symptom occurrence range. Thereby, even if there is little accumulation of defect information, it is possible to predict and notify the user of the initial symptoms of a defect in the target vehicle, such as abnormal noises, vibrations, and bad odors, even when the target vehicle has no symptoms.

Furthermore, in the vehicle information presentation method and device according to the present embodiment, the malfunction information of the malfunctioning vehicle further includes driving history information of the malfunctioning vehicle, and the driving history information of the target vehicle is further acquired, and from among the malfunctioning vehicles, The defective vehicle whose driving history information is most similar to the target vehicle is identified as a similar vehicle, the degree of similarity between the vehicle signal of the similar vehicle and the predicted value of the vehicle signal of the target vehicle is calculated, and the range where the degree of similarity is greater than or equal to a threshold value is calculated. is set as the predicted symptom occurrence range. This makes it possible to identify the range in which the symptoms occur in the target vehicle even if there is only a small amount of defect information accumulated. It is possible to improve the accuracy of predicting the occurrence of early symptoms of a problem in a target vehicle, such as abnormal noise, vibration, and bad odor, even when the target vehicle is asymptomatic.

[Third embodiment]
A third embodiment to which the present invention is applied will be described below with reference to the drawings. In the following description, a vehicle information presentation device and its peripheral devices according to a third embodiment of the present invention will be explained, focusing on the differences from the second embodiment.

With reference to FIG. 5, the configuration of a vehicle information presentation device and its peripheral equipment according to a third embodiment of the present invention will be described.

The vehicle information presentation device 10 according to the third embodiment acquires defect information regarding a defective vehicle in which a defect has occurred from the defect information database 20. The defect information includes symptom information, parts information, driving condition information, vehicle information, and driving history information of at least one defective vehicle, as well as information about the defective vehicle when the defective vehicle is in a normal state. Includes a reference vehicle signal indicating the vehicle signal. The reference vehicle signal is a vehicle signal of a vehicle (hereinafter referred to as a normal vehicle) when the vehicle whose vehicle information matches that of the defective vehicle is in a normal state. The reference vehicle signal is, for example, a signal model recorded in advance in the defect information database 20 for each vehicle information. Note that the reference vehicle signal may be acquired by transmitting a request from a device such as a server or an external terminal for a vehicle signal of a normal vehicle whose vehicle information matches that of the defective vehicle.

As shown in FIG. 5, the vehicle information presentation device 10 according to the third embodiment includes a defect information acquisition section 11, a first deviation degree calculation section 74, an information classification section 12, a target vehicle information acquisition section 15, Abnormality detection unit 16, similar vehicle identification unit 71, estimated vehicle signal acquisition unit 72, second deviation degree calculation unit 75, deviation degree determination unit 76, predicted symptom occurrence range setting unit 17, symptom occurrence information generation section 18 and a transmitting section 19. The functions and configurations of the target vehicle information acquisition section 15, the abnormality detection section 16, the similar vehicle identification section 71, the estimated vehicle signal acquisition section 72, the symptom occurrence information generation section 18, and the transmission section 19 are the same as those in the second embodiment. Since it is the same as the vehicle information presentation device 10 according to the above, the explanation will be omitted.

The defect information acquisition unit 11 acquires defect information from the defect information database 20. As described above, the malfunction information includes symptom information of the malfunctioning vehicle, parts information, driving state information, vehicle information, and at least one or more driving history information, as well as a reference vehicle signal.

The first degree of deviation calculation unit 74 calculates the first degree of deviation between the vehicle signal of the defective vehicle and the reference vehicle signal. The first degree of deviation calculation unit 74 derives the first degree of deviation, for example, from the difference between the value of the vehicle signal of the defective vehicle and the average value of the value of the reference vehicle signal. Note that details of the method for calculating the first degree of deviation will be described later.

The information classification unit 12 classifies the acquired defect information based on symptom information, parts information, driving condition information, vehicle information, and driving history information. The information classification unit 12 links, for example, symptom information of the defective vehicle, parts information, driving condition information, vehicle information, driving history information, vehicle signal, reference vehicle signal, and first deviation degree. memorize it.

The second discrepancy calculation unit 75 stores the estimated vehicle signal of the target vehicle acquired by the estimated vehicle signal acquisition unit 72 and the defect information of a similar vehicle whose vehicle information matches, identified by the similar vehicle identification unit 71. A second degree of deviation from the reference vehicle signal is calculated. The second degree of deviation calculation unit 75 calculates the second degree of deviation, for example, from the difference between the value of the estimated vehicle signal of the target vehicle and the average value of the reference vehicle signal. Further, the second deviation degree calculation unit 75 obtains, for example, a reference vehicle signal of a normal vehicle whose vehicle information matches that of the target vehicle, and calculates the value of the estimated vehicle signal of the target vehicle and the average of the reference vehicle signals. The second degree of deviation may be calculated from the difference in values. Note that details of the method for calculating the second degree of deviation will be described later.

The deviation degree determination unit 76 determines whether there is a range in which the second deviation degree is greater than or equal to the first deviation degree.

When the deviation degree determination unit 76 determines that there is a range in which the second deviation degree is greater than or equal to the first deviation degree, the predictive symptom occurrence range setting unit 17 sets a range in which the second deviation degree is greater than or equal to the first deviation degree. is set as the predicted symptom occurrence range. Note that the details of how to set the predicted symptom occurrence range will be described later.

With the above configuration, the vehicle information presentation device 10 according to the third embodiment of the present invention can detect the target vehicle even for vehicles with insufficient accumulation of defect information, such as a new model or a vehicle immediately after model change. A predicted symptom occurrence range within which symptoms are expected to occur can be set and notified to the user.

An example of the process flow of the vehicle information presentation device 10 according to the third embodiment will be described with reference to the flowchart shown in FIG. 6. The processing in steps S302, S305 to S310, and S313 to S314 in FIG. 6 is the same as the processing in step S202, S204 to S209, and S212 to S213 in FIG. 4, so the explanation will be omitted. .

First, in step S301, the defect information acquisition unit 11 acquires defect information from the defect information database 20. The malfunction information includes symptom information of the malfunctioning vehicle, parts information, driving state information, vehicle information, driving history information, and a reference vehicle signal. Here, the remaining travelable distance at the time when the problem occurs in the defective vehicle is acquired as the driving state information.

In step S303, the first deviation degree calculation unit 74 calculates the first deviation degree between the vehicle signal of the defective vehicle and the reference vehicle signal. The first degree of deviation calculation unit 74 calculates the first degree of deviation, for example, from the difference between the value of the vehicle signal of the defective vehicle and the average value of the value of the reference vehicle signal. One value of the first deviation degree is calculated for each defect information.

(Calculation method of first deviation degree)
Here, an example of a method for calculating the first degree of deviation will be described. In the following description, it is assumed that the average value of the reference vehicle signal values is "1". Further, it is assumed that the vehicle signal of the defective vehicle is a signal (sampling rate: 1 second) from 5 seconds before the occurrence of the symptom until the time the symptom occurs. The value of the vehicle signal of a similar vehicle for 5 seconds before the occurrence of the symptom is "1", "2", "3", "4", or "5", and the value of the vehicle signal of the similar vehicle at the time of occurrence of the symptom Suppose that is "6".

The first degree of deviation calculation unit 74 calculates the difference between the value of the vehicle signal of the similar vehicle at the time of occurrence of the symptom and the average value of the values of the reference vehicle signal as the first degree of deviation. In the above case, the first deviation degree calculation unit 74 calculates the difference "5" between the vehicle signal value "6" of the similar vehicle at the time of occurrence of the symptom and the average value "1" of the reference vehicle signal values as the first deviation degree calculation unit 74. Calculated as the degree of deviation.

In addition, the first deviation degree calculation unit 74 calculates not only the difference between the vehicle signal value of the similar vehicle at the time of occurrence of the symptom and the average value of the reference vehicle signal value, but also the difference between all sections or any arbitrary section of the vehicle signal of the similar vehicle. In the section, the difference between the average value of the vehicle signal value of the similar vehicle and the reference vehicle signal value may be calculated, and the average value of these differences may be used as the first deviation degree. That is, in the above case, the first deviation degree calculation unit 74 calculates the vehicle signals "1", "2", "3", "4", "5" of the similar vehicle for 5 seconds before the occurrence of the symptom. The difference ``0'', ``1'', ``2'', ``3'', ``4'' from the average value ``1'' of the reference vehicle signal, and the value ``6'' of the vehicle signal of a similar vehicle at the time of the symptom occurrence, and the difference between the standard vehicle signal and the average value ``1''. Calculate the difference "5" from the average value "1" of the values, and calculate the average value "2.5" of these differences "0" "1" "2" "3" "4" "5". , may be the first degree of deviation.

As described above, the first degree of deviation calculation unit 74 calculates the first degree of deviation between the vehicle signal of a similar vehicle and the reference vehicle signal. Note that the method and vehicle signal values described here are just examples, and the method and values are not limited to these.

The process proceeds to step S304, and the information classification unit 12 divides the symptom information of the defective vehicle, the parts information, the driving condition information, the vehicle information, the driving history information, the vehicle signal, the reference vehicle signal, and the first deviation I remember it by linking it to the degree.

In step S311, the second deviation degree calculation unit 75 calculates the estimated vehicle signal of the target vehicle acquired by the estimated vehicle signal acquisition unit 72 and defect information of a similar vehicle whose vehicle information matches, identified by the similar vehicle identification unit 71. A second degree of deviation from the reference vehicle signal stored in the reference vehicle signal is calculated. The second degree of deviation calculation unit 75 derives the second degree of deviation, for example, from the difference between the value of the estimated vehicle signal of the target vehicle and the average value of the reference vehicle signal. The second degree of deviation is calculated for each section of the estimated vehicle signal.

(Method of calculating second degree of deviation and method of setting predicted symptom occurrence range)
Here, an example of a method of calculating the second degree of deviation and a method of setting the predicted symptom occurrence range will be described. In the following description, it is assumed that the average value of the reference vehicle signal values is "1". Further, it is assumed that the values of the estimated vehicle signal for 6 seconds are "1", "2", "3", "4", "5", and "6" (sampling rate: 1 second). In this case, the second deviation degree calculation unit 75 calculates the second deviation degree by calculating the difference between the estimated vehicle signal value and the average value of the reference vehicle signal value in all sections of the estimated vehicle signal. do. That is, in the above example, the second deviation degree calculation unit 75 uses the estimated vehicle signal values for 6 seconds as "1", "2", "3", "4", "5", and "6" as the standard. The differences “0”, “1”, “2”, “3”, “4”, and “5” from the average value “1” of the vehicle signal are used as the second degree of deviation of the estimated vehicle signal for 6 seconds. calculate.

The process proceeds to step S312 in FIG. 6, where the deviation degree determination unit 76 determines whether there is a range in which the second deviation degree is greater than or equal to the first deviation degree. If the deviation degree determination unit 76 determines that there is a range in which the second deviation degree is greater than or equal to the first deviation degree, the process proceeds to step S313, and the predictive symptom occurrence range setting unit 17 determines that there is a range in which the second deviation degree is greater than or equal to the first deviation degree. 2. Based on the degree of deviation, a predicted symptom occurrence range is set. The predicted symptom occurrence range setting unit 17 sets the range in which the second deviation degree is equal to or greater than the first deviation degree as the predicted symptom occurrence range in which the target vehicle is predicted to develop the symptom.

For example, in the above example, if the first deviation degree is "2.5", the second deviation degree of the estimated vehicle signal of the target vehicle is "0", "1", "2", "3", "4". , "5" has a first deviation degree of "2.5" or more in the 4-6 second interval ("3", "4", "5"). In this case, the predicted symptom occurrence range setting unit 17 sets an interval of 4 to 6 seconds as the predicted symptom occurrence range. Furthermore, it is expected that the symptoms of the problem will worsen as the distance and time traveled increase. Therefore, the predicted symptom occurrence range setting unit 17 may set the interval after the interval in which the second degree of deviation first becomes equal to or greater than the first degree of deviation as the predicted symptom occurrence range. That is, in the above example, the predicted symptom occurrence range setting unit 17 may set the interval after the 4-second interval in which the second degree of deviation first becomes equal to or greater than the first degree of deviation as the predicted symptom occurrence range.

As described above, the second degree of deviation calculation unit 75 calculates the second degree of deviation between the estimated vehicle signal of the target vehicle and the reference vehicle signal. Further, the predicted symptom occurrence range setting unit 17 sets the predicted symptom occurrence range based on the first degree of deviation and the second degree of deviation. Note that the method and vehicle signal values described here are just examples, and the method and values are not limited to these.

(Operations and effects of the third embodiment)
As explained above, in the vehicle information presentation method and its device according to the third embodiment of the present invention, the vehicle signal of the defective vehicle is acquired, and the vehicle signal of the defective vehicle measured when the symptom occurs and the vehicle signal of the defective vehicle that is normal. A first degree of deviation from the reference vehicle signal indicating the vehicle signal of the vehicle in the state is calculated, a predicted value of the vehicle signal of the target vehicle is obtained, and a difference between the predicted value of the vehicle signal of the target vehicle and the reference vehicle signal is calculated. A second degree of deviation is calculated, and a range in which the second degree of deviation is greater than or equal to the first degree of deviation is specified as a predicted symptom occurrence range.

This makes it possible to identify the range in which the symptom occurs in the target vehicle, even if there is little accumulated malfunction information, it is not recorded in the malfunction information of the vehicle, and the vehicle signal at the time of the symptom occurrence is unknown. It is possible to improve the accuracy of predicting the occurrence of early symptoms of a problem in a target vehicle, such as abnormal noise, vibration, and bad odor, even when the target vehicle is asymptomatic.

[Fourth embodiment]
A fourth embodiment to which the present invention is applied will be described below with reference to the drawings. In the following description, an information presentation device and its peripheral devices according to a fourth embodiment of the present invention will be explained, focusing on the differences from the third embodiment.

In the vehicle information presentation device 10 according to the third embodiment, a vehicle signal of a normal vehicle whose vehicle information matches that of a defective vehicle, which is recorded in advance for each vehicle information, is used as the reference vehicle signal. On the other hand, in the vehicle information presentation device 10 according to the fourth embodiment, a past vehicle signal of the defective vehicle that was measured when the defective vehicle was in a normal state is acquired and used as a reference vehicle signal.

As shown in FIG. 7, the vehicle information presentation device 10 according to the fourth embodiment includes a defect information acquisition section 11, a normal/symptom occurrence section acquisition section 77, a first deviation degree calculation section 74, and an information classification section 12. , the target vehicle information acquisition unit 15, the abnormality detection unit 16, the similar vehicle identification unit 71, the estimated vehicle signal acquisition unit 72, the second deviation degree calculation unit 75, the deviation degree determination unit 76, and the predicted symptom occurrence It includes a range setting section 17, a symptom occurrence information generation section 18, and a transmission section 19. Abnormality detection unit 16, similar vehicle identification unit 71, estimated vehicle signal acquisition unit 72, second deviation degree calculation unit 75, deviation degree determination unit 76, predicted symptom occurrence range setting unit 17, symptom occurrence information generation The functions and configurations of the unit 18 and the transmitting unit 19 are the same as those of the vehicle information presentation device 10 according to the third embodiment, so the description thereof will be omitted.

The defect information acquisition unit 11 acquires defect information from the defect information database 20. The defect information includes symptom information of the defective vehicle, parts information, driving state information, vehicle information, and at least one piece of driving history information. Furthermore, the malfunction information acquisition unit 11 acquires, from the vehicle signal database 60, vehicle signals of the malfunctioning vehicle that were measured at the time of occurrence of the symptom of the malfunction vehicle and before the occurrence of the symptom.

The normal/symptom occurrence section acquisition unit 77 estimates the normal section where the defective vehicle was in a normal state and the symptom occurrence section where the symptom occurred in the defective vehicle in the acquired vehicle signal of the defective vehicle. The method for estimating the normal section may be, for example, the section that has the highest degree of similarity to the normal model of the vehicle signal stored in advance for each vehicle information, and repair/maintain the defective vehicle or repair/maintain it again immediately after the vehicle inspection. It may also be a predetermined period and distance section in which the driver was able to travel without any problems. Further, it may be a predetermined period and distance section immediately after purchase, and various methods are available.

The first degree of deviation calculation unit 74 calculates, as a reference vehicle signal, a first degree of deviation between a vehicle signal in a normal section of the defective vehicle and a vehicle signal in a symptom occurrence section of the defective vehicle. The method for calculating the first deviation degree is the same as that of the vehicle information presentation device 10 according to the third embodiment, except that the reference vehicle signal is a vehicle signal in a normal section of the defective vehicle, so detailed explanation will be omitted. do.

The information classification unit 12 classifies the acquired defect information based on symptom information, parts information, driving condition information, vehicle information, and driving history information. The information classification unit 12 includes, for example, symptom information of the defective vehicle, parts information, driving state information, vehicle information, driving history information, vehicle signals, and reference vehicle signals (vehicle signals in normal sections of the defective vehicle). and the first degree of deviation are stored in association with each other.

With the above configuration, the vehicle information presentation device 10 according to the fourth embodiment of the present invention can set the predicted symptom occurrence range based on the vehicle signal of a single defective vehicle, and Even for vehicles for which insufficient fault information has been accumulated, such as a vehicle that has just undergone a model change, it is possible to set a predicted symptom occurrence range in which the target vehicle is expected to develop symptoms and notify the user.

An example of the process flow of the vehicle information presentation device 10 according to the fourth embodiment will be described with reference to the flowchart shown in FIG. 8. The processing from step S406 to step S415 in FIG. 8 is similar to the processing from step S305 to step S314 in FIG. 6, so the description thereof will be omitted.

First, in step S401, defect information is acquired from the defect information database 20. As described above, the malfunction information includes symptom information of the malfunctioning vehicle, parts information, driving condition information, vehicle information, and at least one or more driving history information.

The process proceeds to step S402, where the malfunction information acquisition unit 11 acquires, from the vehicle signal database 60, the vehicle signal of the malfunctioning vehicle that was measured at the time of occurrence of the symptom of the malfunction vehicle and before the occurrence of the symptom.

The process proceeds to step S403, where the normal/symptom occurrence section acquisition unit 77 estimates the normal section where the defective vehicle was in a normal state and the symptom occurrence section where the symptom occurred in the defective vehicle in the acquired vehicle signal of the defective vehicle. do.

The process proceeds to step S404, where the first degree of deviation calculation unit 74 calculates the first degree of deviation between the vehicle signal of the defective vehicle in the normal section and the vehicle signal of the defective vehicle in the symptom occurrence section.

The process proceeds to step S405, where the information classification unit 12 collects symptom information, parts information, driving condition information, vehicle information, driving history information, vehicle signals, and reference vehicle signals (normal and normal for the defective vehicle) of the defective vehicle. (vehicle signal of the section) and the first degree of deviation are stored in association with each other.

(Operations and effects of the fourth embodiment)
As explained above, in the vehicle information presentation method and device according to the fourth embodiment of the present invention, the reference vehicle signal is the vehicle signal of the defective vehicle that was measured when the defective vehicle was in a normal state. This makes it possible to identify the predicted range of symptom occurrence for the target vehicle, even if there is less fault information accumulated, it is not recorded in the vehicle's fault information, and the vehicle signal at the time of the symptom occurrence is unknown. I can do it.

[Modified example]
Modifications of the first embodiment to which the present invention is applied will be described below with reference to the drawings. The vehicle information presentation device 10 according to the first embodiment acquired defect information from information stored in the defect information database 20. In addition to the information stored in the defect information database 20, the vehicle information presentation device 10 may search for and obtain information regarding defects in the defective vehicle from a server or the Internet. For example, in the case of initial symptoms of a malfunction in a vehicle such as a ride-hailing service or an unmanned taxi where the owner and the occupant are different, it is thought that the vehicle occupant is unlikely to directly report the malfunction to a dealer or the like. Therefore, by searching and acquiring information regarding the symptoms of a malfunctioning vehicle from a server or the Internet, it becomes possible to efficiently collect malfunction information regarding the malfunctioning vehicle.

As shown in FIG. 9, the vehicle information presentation device 10 of the first embodiment includes a defect information acquisition section 11, a defect report search section 78, an information classification section 12, a symptom occurrence probability distribution generation section 13, and a symptom occurrence probability distribution generation section 13. The system may include a range identification section 14, a target vehicle information acquisition section 15, an abnormality detection section 16, a predicted symptom occurrence range setting section 17, a symptom occurrence information generation section 18, and a transmission section 19.

The defect report search unit 78 searches for and obtains information regarding the symptoms of a defective vehicle from, for example, reports, word of mouth, posted articles, etc. regarding the defective vehicle posted on the Internet, SNS, applications, and the like. Possible search words for the search include, for example, the model name of the defective vehicle, the name of the dispatch service, and the name of the event in which the defective vehicle is used. The malfunction report search unit 78 records the symptoms and vehicle information of the malfunctioning vehicle, such as audio data recording the content of conversations between the passengers of the malfunctioning vehicle, and video data and image data from a camera mounted on the malfunctioning vehicle. Information regarding the symptoms of the defective vehicle may be searched and obtained from data in any format. Further, defect information may be searched by replacing vehicle information, symptoms, and parts recognized from video data, image data, or audio data with text data.

The malfunction report search unit 78 acquires at least one of symptom information, parts information, driving state information, and vehicle information from the information regarding the symptoms of the found malfunctioning vehicle.

With the above configuration, information regarding the symptoms of a defect in a defective vehicle is searched and acquired from the server Internet, and therefore defect information can be collected more efficiently. Furthermore, defect information can also be obtained from information sources where vehicle information is not directly recorded, such as the name of a ride-hailing service or the name of an event where a vehicle is exhibited.

[Display example of symptom occurrence information]
Display examples of symptom occurrence information generated by the vehicle information presentation device 10 according to the embodiment of the present invention will be described with reference to FIGS. 10 to 16.

FIG. 10 is a diagram showing a display example of symptom occurrence information generated by the vehicle information presentation device 10 according to the first embodiment of the present invention. As shown in FIG. 10, the vehicle information presentation device 10 according to the first embodiment of the present invention starts from the time when an abnormality in the target vehicle is detected (anomaly prediction time) to the time when a failure is predicted to occur in the target vehicle (failure prediction time). Symptom occurrence information is generated to notify the remaining travelable distance until the occurrence of abnormal noise, the symptom occurrence probability distribution with respect to the remaining travelable distance, the symptom-free section, and the predicted symptom occurrence range (abnormal noise occurrence section). In addition, in the display example of FIG. 10, for example, the remaining drivable distance of the target vehicle from the time when the abnormality is predicted until the occurrence of the failure, and the remaining drivable distance of the target vehicle from the time when the abnormality is predicted to the section where the abnormal noise occurs are shown as " You will be notified by the message ``Remaining driving distance 50km'' and ``20km until abnormal noise section.''

11 and 12 are diagrams showing display examples of symptom occurrence information generated by the vehicle information presentation device 10 according to the second embodiment of the present invention. As shown in FIG. 11, the vehicle information presentation device 10 according to the second embodiment of the present invention displays vehicle information (model, production location, production period) of a defective vehicle that is identified as a similar vehicle that is most similar to the target vehicle. , symptom occurrence information for notifying symptom information (failure symptoms) of similar vehicles, parts information (symptom-occurring parts), driving history information, and vehicle signals of similar vehicles may be generated.

Furthermore, as shown in FIG. 12, the vehicle information presentation device 10 according to the second embodiment of the present invention can operate from the time when an abnormality in the target vehicle is detected to the time when it is predicted that a failure will occur in the target vehicle (failure occurrence). Generates symptom occurrence information to notify the remaining travelable distance, the estimated vehicle signal (predicted signal value) of the target vehicle in the remaining travelable distance, and the predicted symptom occurrence range (abnormal noise occurrence section). Good too. Furthermore, in the display example of FIG. 12, a range A is displayed in which the degree of similarity between the signal value of the vehicle signal of a similar vehicle and the signal value of the estimated vehicle signal of the target vehicle is higher than the threshold value. Furthermore, in the display example of FIG. 12, for example, the remaining travelable distance from the time when the target vehicle's abnormality is detected until the failure occurs, and the remaining travelable distance from the time when the target vehicle's abnormality is predicted to the section where the abnormal noise occurs. However, the driver is notified by messages such as ``50km remaining to travel'' and ``20km to the section where the abnormal noise occurs.''

13 and 14 are diagrams showing display examples of symptom occurrence information generated by the vehicle information presentation device 10 according to the third embodiment and the fourth embodiment of the present invention. As shown in FIG. 13, the vehicle information presentation device 10 according to the third embodiment and the fourth embodiment provides vehicle information (model, place of production, time of production) of a defective vehicle identified as a similar vehicle that is most similar to the target vehicle. ), symptom information (defect symptoms) of similar vehicles, parts information (parts where symptoms occur), driving history information, and a first deviation degree (deviation degree) between the reference vehicle signal and the similar vehicle signal. Symptom occurrence information may also be generated.

In addition, as shown in FIG. 14, the vehicle information presentation device 10 according to the third embodiment and the fourth embodiment of the present invention can be used from the time when an abnormality in the target vehicle is detected to the time when it is predicted that a failure will occur in the target vehicle. the remaining drivable distance, the second degree of deviation between the estimated vehicle signal of the target vehicle and the reference vehicle signal in the remaining drivable distance, the value of the first degree of deviation which is a threshold value of the second degree of deviation, and the predicted symptom occurrence. Symptom occurrence information may be generated to notify the range (abnormal noise occurrence section). In addition, in the display example of FIG. 14, for example, the remaining travelable distance from the time when the target vehicle's abnormality is detected until the failure occurs, and the remaining travelable distance from the time when the target vehicle's abnormality is predicted to the section where the abnormal noise occurs are shown. , messages such as ``Remaining drivable distance 50 km'' and ``20 km until the section where the abnormal noise occurs'' are sent.

Furthermore, as shown in FIGS. 15A, 15B, and 16, the vehicle information presentation device 10 according to the embodiment of the present invention provides symptom notification for visually recognizing the predicted symptom occurrence range and driving state information. Occurrence information may also be generated. For example, as shown in FIGS. 15A and 15B, a case where the subject vehicle is traveling in a symptom-free section with the remaining drivable distance, and a case where the subject vehicle is traveling within the predicted symptom occurrence range with the remaining drivable distance. The remaining travel distance is displayed in different colors. Additionally, a message will be displayed informing you of the remaining distance that can be traveled from the time an abnormality is detected in the target vehicle until the occurrence of the failure, and whether the target vehicle is traveling in a symptom-free zone or within the predicted symptom range. You may also do so. Further, as shown in FIG. 16, the symptom-free section and the predicted symptom occurrence range in the remaining travelable distance may be displayed in different colors so that they can be distinguished. This allows the user to appropriately recognize the predicted symptom occurrence range while preventing erroneous recognition by the user.

Although not shown in the drawings, the vehicle information presentation device 10 according to the embodiment of the present invention can, for example, detect the detection of an abnormality in the target vehicle, information on the parts of the target vehicle where the abnormality has been detected, and the time point at which the abnormality was predicted for the target vehicle. Symptom occurrence information may be generated to notify the remaining travelable distance from the point in time until the occurrence of the failure, the remaining travelable distance from the time when the abnormality of the target vehicle is predicted to the predicted symptom occurrence range, and the like. For example, a message such as "An abnormality has been detected in the vehicle. The failed part is the engine. The remaining driving distance before failure is currently 50 km, but it is predicted that an abnormal noise will occur around 30 km remaining." May be displayed. In addition, "An abnormality has been detected in the vehicle. The malfunctioning part is the engine. The remaining travel distance before failure is currently 50 km, but the probability of abnormal noise occurring is as high as 70% when the remaining travel distance is around 30 km. You may display a message such as "Please be careful when driving from then on."

Although not shown in the drawings, the vehicle information presentation device 10 according to the present invention can, for example, provide information on parts or vehicle elements in which an abnormality has been detected in the target vehicle, the position or location of the part in which an abnormality has been detected in the target vehicle, replacement Symptom occurrence information may be generated to notify the part number or model number of the part, the cost of the part, and the like.

As described above, embodiments of the present invention have been described, but the statements and drawings that form part of this disclosure should not be understood as limiting the present invention. Various alternative embodiments, implementations, and operational techniques will be apparent to those skilled in the art from this disclosure.

11 Malfunction information acquisition unit 14 Symptom occurrence range identification unit 17 Predicted symptom occurrence range setting unit 18 Symptom occurrence information generation unit 19 Transmission unit
10 Vehicle information presentation device

Claims (12)

Symptom information indicating that the vehicle has generated at least one symptom among sounds, vibrations, temperatures, and odors that are different from those in normal driving conditions, component information of the part that generated the symptom, and the time when the symptom occurred. obtaining malfunction information of the vehicle, including driving state information of the vehicle;
Identifying a symptom occurrence range in which the symptom occurs based on the defect information,
When an abnormality in the target vehicle in which the symptom does not occur is detected, a predicted symptom occurrence range in which the target vehicle is predicted to cause the symptom is set based on the specified symptom occurrence range,
generating symptom occurrence information for notifying the predicted symptom occurrence range;
A vehicle information presentation method characterized by transmitting the symptom occurrence information to an external device. classifying the malfunction information based on the symptom information, the parts information, and the driving state information of the vehicle;
Generating a symptom occurrence probability distribution representing the probability that the symptom will occur with respect to the driving condition for each of the classified defect information,
2. The vehicle information presentation method according to claim 1, wherein a range in which the probability is equal to or greater than a threshold value in the symptom occurrence probability distribution is specified as the symptom occurrence range. Obtaining a vehicle signal of the vehicle measured at the time of occurrence of the symptom or before the time of occurrence of the symptom,
obtaining a predicted value of a vehicle signal of the target vehicle;
Calculating the degree of similarity between the vehicle signal of the vehicle and the predicted value of the vehicle signal of the target vehicle;
2. The vehicle information presentation method according to claim 1, wherein a range in which the degree of similarity is equal to or greater than a threshold is set as the predicted symptom occurrence range. The malfunction information of the vehicle further includes driving history information of the vehicle,
further acquiring driving history information of the target vehicle;
Identifying a vehicle whose driving history information is most similar to the target vehicle from among the vehicles as a similar vehicle;
Calculating the degree of similarity between the vehicle signal of the similar vehicle and the predicted value of the vehicle signal of the target vehicle;
4. The vehicle information presentation method according to claim 3, wherein a range in which the degree of similarity is equal to or greater than a threshold value is set as the predicted symptom occurrence range. obtaining a vehicle signal of the vehicle;
Calculating a first degree of deviation between a vehicle signal of the vehicle measured when the symptom occurs and a reference vehicle signal indicating a vehicle signal of the vehicle when the vehicle is in a normal state;
obtaining a predicted value of a vehicle signal of the target vehicle;
Calculating a second degree of deviation between the predicted value of the vehicle signal of the target vehicle and the reference vehicle signal,
2. The vehicle information presentation method according to claim 1, wherein a range in which the second degree of deviation is greater than or equal to the first degree of deviation is specified as the predicted symptom occurrence range. 6. The vehicle information presentation method according to claim 5, wherein the reference vehicle signal is a vehicle signal of the vehicle measured when the vehicle was in a normal state. 7. The vehicle information presentation method according to claim 1, wherein the symptom occurrence information includes information about a part of the target vehicle in which an abnormality has been detected. 7. The symptom occurrence information is transmitted from the time when the abnormality of the target vehicle is detected until the target vehicle reaches the predicted symptom occurrence range. Vehicle information presentation method described in section. The malfunction information further includes vehicle information indicating at least one of the vehicle model, country of manufacture, factory, and time of manufacture of the vehicle,
7. The predicted symptom occurrence range is set based on the malfunction information of the vehicle in which the vehicle information matches the vehicle information of the target vehicle. Vehicle information presentation method. The malfunction information may include report data containing symptom information of the vehicle stored in a database, audio data recording conversation content regarding the vehicle, data transcribing the conversation content, data from the vehicle, etc. 7. The vehicle information presentation method according to claim 1, wherein the vehicle information presentation method is obtained from at least one of remotely acquired data and information about the vehicle posted on the Internet. 7. The vehicle information presentation method according to claim 1, wherein the malfunction information includes information obtained by searching for information regarding malfunctions of the vehicle from a server or the Internet. Symptom information indicating that the vehicle has generated at least one symptom among sounds, vibrations, temperatures, and odors that are different from those in normal driving conditions, component information of the part that generated the symptom, and the time when the symptom occurred. a malfunction information acquisition unit that acquires malfunction information of the vehicle, including driving state information of the vehicle;
a symptom occurrence range identifying unit that identifies a symptom occurrence range in which the symptom occurs based on the defect information;
A predicted symptom occurrence range that sets a predicted symptom occurrence range in which the target vehicle is predicted to cause the symptom based on the specified symptom occurrence range when an abnormality is detected in the target vehicle in which the symptom does not occur. Setting section and
a symptom occurrence information generation unit that generates symptom occurrence information for notifying the predicted symptom occurrence range;
A vehicle information presentation device comprising: a transmitter that transmits the symptom occurrence information to an external device.

Images