Vehicle data analysis method and apparatus

Abstract

Vehicle data is acquired (S1) and associated with each respective vehicle component (S2), and a first abnormality degree is obtained with regards to each of the vehicle components before a collision (S4). When a collision is detected (S4), vehicle data is acquired (S5) during self-propelled travel after the collision, past accident data, etc., is referenced if self-propelled travel is not possible, and a post-collision second abnormality degree is calculated (S6). The first abnormality degree and the second abnormality degree are compared (S7), and if the second abnormality degree is greater than the first abnormality degree by at least a prescribed value (S8), the vehicle component is deemed to be degraded or otherwise affected even if located outside an impact area (S13), and necessary information is displayed on a display unit (60). If there has been an intentional change in driving characteristics after the collision, the vehicle component is not recognized as having been degraded or similarly affected (S12, S13).

Description

[Technical Field]

[0001] The present invention relates to a vehicle data analysis technique for appropriately assessing damage to vehicle parts caused by a vehicle collision, particularly damage to vehicle parts that at first glance appear to be unrelated to the collision. [Background technology]

[0002] When a vehicle collides with another vehicle or structure while traveling, parts that are deformed or damaged by the energy of the collision at or near the collision site are identified at a repair shop, and these parts are replaced or repaired. In many cases, the cost is covered by insurance under a contract with an insurance company.

[0003] However, even vehicle parts that at first glance appear unrelated to the collision may be adversely affected in the form of accelerated deterioration or a shortened future lifespan. In such cases, the vehicle owner himself is often unaware, and disputes can easily arise between the parties as to whether the accelerated deterioration or shortened lifespan of these vehicle parts is due to the collision, i.e., the accident.

[0004] Patent Document 1 discloses a technology for analyzing accidents when a vehicle crashes using telematics data including data from gyroscopes, accelerometers, GPS data, video recordings, vehicle diagnostic data, audio recordings, etc. The telematics data is used to identify whether parts within the impact area have been damaged, and repair costs are estimated.

[0005] Such techniques cannot analyze the effects on components outside the impact area.

[0006] Patent Document 2 discloses a technology that generates a digital twin of a vehicle and performs simulations based on this digital twin to grasp the vehicle's condition in real time, for example, to monitor the vehicle's useful life. When an accident is detected, accident event data is recorded and the current vehicle value is updated.

[0007] In Patent Document 2, only accident event data is acquired, and it is not possible to analyze whether or not a vehicle part that at first glance appears unrelated to the collision has been affected by deterioration or the like due to the collision. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] Patent Publication No. 2021-503637 [Patent Document 2] Japanese Patent Publication No. 2020-13557 Summary of the Invention

[0009] The vehicle data analysis method according to the present invention includes: acquiring data on the vehicle component while the vehicle is traveling before the collision, and calculating an abnormality degree of the vehicle component based on the data as a first abnormality degree; Detects vehicle collisions, determining a second abnormality degree of the vehicle component after the collision; The first abnormality degree is compared with the second abnormality degree.

[0010] By comparing the first abnormality level and the second abnormality level before and after the collision in this way, it is possible to evaluate the adverse effects of the collision, such as deterioration of vehicle parts, regardless of the positional relationship between the collision site (impact area) and the vehicle parts, or whether or not the vehicle parts are deformed.

[0011] In one preferred aspect of the present invention, when the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, information about the vehicle part is output as a vehicle part affected by a collision.

[0012] In one preferred embodiment of the present invention, data on the vehicle parts after the collision is acquired while the vehicle is moving after the collision, and the second abnormality degree is calculated based on this data.

[0013] That is, if the vehicle is still capable of moving under its own power after the collision, data can be acquired as the vehicle moves under its own power, and the second abnormality degree can be calculated.

[0014] In another aspect of the present invention, the second abnormality degree is estimated by learning data on vehicle parts before a collision or data at the time of a collision with another vehicle.

[0015] Therefore, even if the vehicle becomes unable to move due to a collision, the effect on the vehicle parts can be estimated by comparing the first abnormality degree with the second abnormality degree.

[0016] For example, information about a collision is acquired, and based on this information, a data group of similar collisions with other vehicles is selected. By using a data group with similar collision conditions and characteristics of the accident vehicle (model, production period, production factory, etc.), the accuracy of the anomaly level can be improved. In one preferred embodiment of the present invention, a comparison is made between the first abnormality degree and the second abnormality degree for each of a plurality of vehicle parts.

[0017] In a preferred embodiment of the present invention, the first and second abnormality degrees are corrected based on vehicle position data, weather data, and driver driving characteristic data. In other words, it is desirable to suppress changes in the first and second abnormality degrees due to external factors.

[0018] In addition, in one preferred embodiment of the present invention, driving characteristic data of the driver immediately before the collision and driving characteristic data of the driver after the collision are obtained, and if it is determined that there has been an intentional change in driving characteristics, the first abnormality level is not compared with the second abnormality level, and vehicle part information is not output.

[0019] For example, if a driver has an insurance contract that compensates for accidents, it is possible that the driver may intentionally drive in a way that accelerates deterioration (drifting, sudden acceleration and braking, etc.). If such intentional driving is observed, the abnormality level will not be compared and vehicle part information will not be output.

[0020] In one aspect of the present invention, data on the fastening torque of the fastening members that secure the vehicle components is acquired, and the fastening torque after the collision is compared with a reference value, thereby determining whether any fastening members have loosened due to the collision.

[0021] For example, if the engagement torque after a collision is equal to or less than a reference value, a request for inspection may be sent to a vehicle repair center, thereby enabling prompt action to be taken.

[0022] In one example, when the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, the vehicle part is included in the insurance claim candidates, If the tightening torque after the collision is equal to or less than the reference value, the vehicle part is excluded from the candidates for insurance claims.

[0023] In other words, even if the degree of abnormality increases after a collision, there is a possibility that the abnormality can be resolved by retightening the fastening members, so the vehicle is excluded from candidates for insurance claims.

[0024] Preferably, if the fastening torque after the collision is equal to or less than the reference value, the user of the vehicle is notified that he or she should refrain from driving and have the vehicle inspected, thereby preventing the vehicle from being driven with the fastening member loosened.

[0025] In one aspect of the present invention, information regarding the intrusion of foreign matter into vehicle parts due to a collision is acquired.

[0026] Preferably, when a foreign object is detected in a vehicle part, an inspection request is sent to a vehicle repair center.

[0027] In one example, when the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, the vehicle part is included in the insurance claim candidates, When a foreign object is detected in a vehicle part, the vehicle part is excluded from candidates for insurance claims.

[0028] In other words, even if the degree of abnormality increases after a collision, there is a possibility that the abnormality can be resolved by removing the foreign object, so the vehicle is excluded from candidates for insurance claims.

[0029] Preferably, when a foreign object is detected in a vehicle part, a user of the vehicle is notified that the vehicle should be inspected and refrain from driving, thereby preventing the vehicle from being driven with the foreign object still present.

[0030] In one preferred embodiment of the present invention, when a collision is detected, driving data at the time of the collision, information regarding the state of the target vehicle parts before and after the collision, and data used as the basis for calculating the second abnormality level are transmitted to at least one of an insurance official, a police official, or a legal representative.

[0031] In one preferred embodiment of the present invention, the difference between the first abnormality degree and the second abnormality degree is calculated for a plurality of vehicle parts, and the vehicle parts are displayed on the display unit in descending order of the difference.

[0032] This makes it easier to identify vehicle parts that are relatively more affected by a collision.

[0033] In a preferred embodiment of the present invention, extracting vehicle parts from the plurality of vehicle parts, the vehicle parts having a difference between the first abnormality degree and the second abnormality degree equal to or greater than a predetermined threshold; Obtaining information about the collision, and based on this information, classifying vehicle parts into those to which collision energy may have been applied directly and those to which collision energy may have been applied indirectly; The two are displayed separately on the display unit.

[0034] This makes it easier for the driver and others to understand the impact of the collision.

[0035] Furthermore, in one preferred embodiment of the present invention, determining an appropriate maintenance policy for a vehicle part for which the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold; We calculate the estimated repair cost in accordance with this maintenance policy, These are displayed on the display unit.

[0036] In addition, in one preferred aspect of the present invention, data on the driver's driving characteristics immediately before the collision and data on the driver's driving characteristics after the collision are obtained, and when it is determined that there has been an intentional change in driving characteristics, the relevant driving section is displayed on the display unit.

[0037] This makes it easy for insurance companies and the like to know if, for example, a driver intentionally drives recklessly after an accident.

[0038] The vehicle data analysis device of the present invention comprises: a data acquisition unit that acquires data that is the basis for calculating the abnormality degree of a vehicle part; a collision detection unit that detects a vehicle collision; an abnormality degree calculation unit that calculates a first abnormality degree for a target vehicle part based on data from the vehicle while the vehicle was running before the collision, and calculates a second abnormality degree after the collision; a comparison unit that compares the first abnormality degree with the second abnormality degree; Equipped with. [Brief explanation of the drawings]

[0039] [Figure 1] FIG. 1 is a functional block diagram of a data analysis apparatus according to a first embodiment. [Figure 2] 4 is a flowchart showing the flow of processing in the first embodiment. [Figure 3] FIG. 4 is an explanatory diagram showing a display example on a display unit. [Figure 4] FIG. 10 is an explanatory diagram showing another display example on the display unit. [Figure 5] FIG. 10 is an explanatory diagram showing yet another display example on the display unit. [Figure 6] FIG. 10 is a functional block diagram of a data analysis apparatus according to a second embodiment. [Figure 7] 10 is a flowchart showing the flow of processing in a second embodiment. [Figure 8] FIG. 10 is an explanatory diagram showing a display example of the second embodiment. [Figure 9] FIG. 10 is a functional block diagram of a data analysis apparatus according to a third embodiment. [Figure 10] 10 is a flowchart showing the flow of processing according to a third embodiment. [Figure 11] FIG. 11 is an explanatory diagram showing a display example of the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0040] An embodiment of the present invention will be described in detail below. In the following embodiment, the present invention is applied to an accident processing support system, including insurance claim processing in the event of an accident (collision), which is provided as a type of service by insurance companies, automobile dealerships (so-called dealers), etc. to users (automobile insurance policyholders, vehicle purchasers, etc.). The entire data analysis device of the embodiment is configured as a cloud system primarily consisting of a cloud server managed by, for example, an insurance company, automobile dealership, etc., and may include a mobile device or personal computer such as a smartphone owned by the user, multiple data acquisition devices pre-installed in the vehicle to acquire various data from the vehicle, an on-board computer system and display, a dealership terminal, an insurance company terminal, etc.

[0041] 1 is a functional block diagram of a data analysis device according to a first embodiment. This data analysis device includes a data acquisition unit 10, an abnormality degree calculation unit 20, a collision detection unit 30, a comparison unit 40, a transmission unit 50, and a display unit 60.

[0042] The data acquisition unit 10 acquires a large amount of vehicle data from vehicle data acquisition devices and the like while the vehicle is traveling. Vehicle data includes, for example, time-series data related to engine control, such as engine speed and engine temperature, and data indicating the degree of suspension and tire wear. Various other data may also be acquired depending on the target vehicle or functional component. The vehicle data may be time-series data acquired continuously from the past, or instantaneous values such as current values. Furthermore, vehicle data may be acquired not only from on-board devices or vehicle signal measurement devices, but also from devices other than on-board devices, such as measurements at a dealership or past maintenance records. Figure 1 collectively illustrates these as a vehicle information database 90, which also includes information such as engine speed continuously provided by the vehicle. For example, data detected by on-board devices is output to a cloud server managed by, for example, an insurance company, via a connected car system or appropriate communication means. The type of information to be acquired as vehicle data and the means used are optional.

[0043] Furthermore, a comprehensive vehicle information database 90 stores a large amount of past accident data and accident simulation data, as will be described later.

[0044] The anomaly degree calculation unit 20 calculates the anomaly degree for each specific vehicle part based on the vehicle data acquired by the data acquisition unit 10. The anomaly degree, in a broad sense, is the degree of deviation from a normal state. Here, it is an index that represents, for example, the degree of deterioration or damage of a part as the vehicle travels longer or the vehicle's usage time increases. For example, the degree of deviation between a signal detected by some device and a group of normal signals can be used as the anomaly degree. Alternatively, the degree of deviation between each signal and a threshold value can be regarded as the anomaly degree. The anomaly degree may also be expressed by the distance or time that can be traveled from the current vehicle state before a failure occurs. The anomaly degree changes as the vehicle travels longer or the vehicle's usage time increases.

[0045] Here, the abnormality degree is calculated separately for both before and after the collision. That is, the abnormality degree that progresses with the vehicle's travel distance and vehicle usage time before the collision is calculated as a first abnormality degree, and after the collision, a second abnormality degree is calculated separately from the first abnormality degree.

[0046] The degree of abnormality, e.g., the first degree of abnormality, may be calculated by prediction. Prediction of future transitions in the degree of abnormality can be performed using a known appropriate prediction method. For example, a method of regarding the deviation of each signal from a threshold value according to conditions and rules as the degree of abnormality and predicting future transitions in the degree of abnormality based on time-series data including engine speed and engine temperature, an approach of estimating future predicted values (future behavior) of vehicle data acquired by the data acquisition unit 10 using a time-series data prediction method such as LSTM (Long Short-Term Memory) and then performing invariant analysis, or a method of regarding the probability of an abnormal state when the vehicle state is classified as a normal state or an abnormal state using machine learning as the degree of abnormality and calculating the probability of future occurrence of an abnormality from the future predicted values of the vehicle data acquired by the data acquisition unit 10. The present invention is not limited to these methods, and any appropriate method for predicting future transitions in the degree of abnormality can be used. In one embodiment, the transitions in the degree of abnormality can be calculated using either the vehicle travel distance or the vehicle travel time as a parameter. The second degree of abnormality may also be calculated by transition prediction when the degree of abnormality is calculated based on the vehicle's self-propelled state after a collision.

[0047] The abnormality degree calculation unit 20 includes a part linking unit 21 that links individual vehicle parts with vehicle data. For example, if an abnormality in one piece of data is related to the operation of multiple vehicle parts, the abnormality degree for each vehicle part is calculated based on the relationship between the two provided by the part linking unit 21. For example, the linking can be performed using a relationship calculated in advance by a simulation or the like, factory test data, theoretical formulas and theoretical values related to part control and operation, information on past repair history, etc.

[0048] If the vehicle is still capable of self-propulsion after the collision, the second abnormality level is calculated based on vehicle data acquired while the vehicle is in motion, similar to the first abnormality level. On the other hand, if the vehicle is no longer capable of self-propulsion due to the collision, vehicle data cannot be acquired while the vehicle is in motion. Therefore, the second abnormality level is calculated based on past accident data and accident simulation data stored in the comprehensive vehicle information database 90 shown in the figure. For example, past accident data and accident simulation data include information on the type of collision (e.g., impact area, magnitude of impact, etc.) and corresponding changes in various vehicle data (or changes in the abnormality level of vehicle components). By referencing this database, post-collision vehicle data can be obtained from a data group of collisions similar to the current collision, and the second abnormality level can be calculated. Preferably, past accident data and accident simulation data include vehicle model information (e.g., vehicle model, production period, production plant, etc.), and the second abnormality level can be calculated from a similar data group including the vehicle model information. Furthermore, when calculating the second abnormality level using this simulation data, the progression of the first abnormality level up to the collision may be taken into account.

[0049] In order to improve the accuracy of the abnormality degree, the first abnormality degree and the second abnormality degree may be corrected based on vehicle position data, weather data, and driving characteristic data of the driver.

[0050] The collision detection unit 30 detects a vehicle collision based on the impact of the collision. Collision detection can be performed using various known means, such as an acceleration sensor or pressure sensor attached to the vehicle body to detect impact, or an on-board camera, sonar, radar, or other means capable of detecting an object or measuring the distance to the object. When a collision is detected in the vehicle, information is transmitted to a cloud server managed by an insurance company or the like via an appropriate communication means. Preferably, the collision detection unit 30 can also detect the location of the collision on the vehicle body (i.e., the impact area). Note that the collision detection unit 30 may also include collision detection by means other than the vehicle itself, such as information provided by a traffic infrastructure camera or information transmitted from a drive recorder of a vehicle before and after the accident. Furthermore, for future data analysis, the collision situation data (such as the location of the accident, the object, the impact area, the magnitude of the impact, and the determination of whether the accident was a self-inflicted or personal injury accident based on camera images) may be simultaneously acquired.

[0051] When a collision is detected by the collision detection unit 30, the comparison unit 40 compares the first abnormality degree with the second abnormality degree at an appropriate timing. For example, if the vehicle is capable of self-driving after the collision, the appropriate timing may be when sufficient vehicle data is acquired through self-driving for a certain period of time to determine a reliable second abnormality degree, when the vehicle is brought into a repair shop after the accident, or at another appropriate timing. If the vehicle is not capable of self-driving, the comparison may be performed immediately after the collision. Here, the comparison is performed between the first abnormality degree and the second abnormality degree for each of a large number of vehicle components. It is particularly desirable to compare the abnormality degrees for vehicle components outside the impact area. For example, the difference between the first abnormality degree and the second abnormality degree is calculated. If this difference is equal to or greater than a predetermined value, it is determined that deterioration or internal damage has occurred due to the impact of the collision, even in vehicle components located away from the impact area or seemingly unrelated to the impact area.

[0052] If there are no vehicle parts outside the impact area where the difference in abnormality level is equal to or greater than a predetermined value, it is determined that only vehicle parts within the impact area are damaged. This corresponds to parts that have been deformed or damaged externally by the impact of the collision, for example. Damaged parts can be identified using publicly known technology such as that described in Patent Document 1 above.

[0053] In addition, if the vehicle is able to move under its own power after the collision, the first abnormality degree that is one of the comparison targets in the comparison unit 40 may be the first abnormality degree that was calculated up to the time of the collision, or it may be the first abnormality degree estimated as a value after driving for a similar period or distance by predicting the progression of the first abnormality degree based on the driving time or distance after the collision.

[0054] The comparison unit 40 includes a correction unit 41 that corrects or amends the comparison results. For example, the correction unit 41 acquires driving characteristic data of the driver immediately before the collision and driving characteristic data of the driver after the collision. If it determines that there is an intentional change in driving characteristics, it does not compare the first abnormality level with the second abnormality level or output vehicle part information. Examples of driving characteristics include aggressive driving and the frequency of dangerous driving, such as sudden acceleration and braking. This assumes that the driver may intentionally accelerate the deterioration and damage of vehicle parts (increase the second abnormality level) when traveling after the accident (e.g., from the accident site to a repair shop) in order to claim more insurance money. Therefore, to avoid inappropriate insurance claims, if there is a significant change in driving characteristics, the vehicle part is not recognized as having been affected by the collision even if the difference between the first abnormality level and the second abnormality level is large. Alternatively, the second abnormality level or the difference in abnormality level may be corrected in accordance with changes in driving characteristics to prevent the difference in abnormality level from exceeding a predetermined value due to differences in driving characteristics. When such a correction is made, the user may be notified that the vehicle part will no longer be covered by insurance.

[0055] The correction of the abnormality degree based on the vehicle position data, weather data, and driver's driving characteristic data may be performed in this correction unit 41.

[0056] To facilitate understanding, a simple example will be given to explain the first abnormality degree and the second abnormality degree, and the comparison between the two in the comparison unit 40. For example, it is known that the deterioration of a suspension can be obtained using the following formula (Japanese Patent No. 4915096, Japanese Patent Laid-Open No. 10-132585, etc.), and this is taken as the abnormality degree of the suspension.

[0057] Abnormality level = total vehicle weight x suspension fluctuation amount x correction coefficient (tire pressure, road conditions (rainfall, snowfall, outside temperature, degree of unevenness of the road surface, etc.)) Here, at least the amount of suspension fluctuation detected by the sensor corresponds to the vehicle data. Parameters related to the correction coefficient may also be vehicle data. If, before the collision, the amount of fluctuation (fluctuation amount sensor value) was 0.1 cm, the vehicle weight was 1,350 kg, and the correction coefficient was 1, the first abnormality degree would be 135. If, after the collision, the amount of fluctuation (fluctuation amount sensor value) was 1.0 cm, the second abnormality degree would be 1,350. As a result, the difference between the first abnormality degree and the second abnormality degree would be 1,215. Therefore, by comparing this difference with an appropriate threshold value, it is determined whether deterioration or damage has occurred due to the collision. For example, if the threshold value is set to 1,000, it is determined that the suspension has deteriorated or been damaged due to the collision. In this case, for example, slight deformation or damage to various parts of the suspension, a malfunction of the fluctuation amount sensor, etc. may be included.

[0058] The abnormality degree may be compared with the average value for a certain driving section (driving time or driving distance). For example, to obtain the average value for each hour, if the average of the fluctuation amount sensor value during driving for one hour before the collision is 0.5 cm and the average of the fluctuation amount sensor value during driving for one hour after the collision is 0.7 cm, the first abnormality degree for the one-hour driving section will be 725 and the second abnormality degree will be 995, and the difference in the abnormality degrees will be 270. If the threshold value is 1000, in this example, the difference in the abnormality degrees will be less than the threshold value, and it will be determined that there is no deterioration or damage to the suspension due to the collision.

[0059] It is desirable that the driving section for one hour before the collision and the driving section for one hour after the collision are sections with road conditions and driving conditions that are as similar as possible. For example, it is desirable to extract the first abnormality degree and the second abnormality degree under similar conditions from the first abnormality degree and the second abnormality degree obtained every hour during driving before and after the collision and compare them with each other.

[0060] The magnitude of the difference between the first abnormality degree and the second abnormality degree may be evaluated based on the absolute value of the difference, as in the above example, or may be evaluated based on the ratio or proportion of the first abnormality degree to the second abnormality degree, etc.

[0061] Furthermore, the correction unit 41, for example, quantifies and acquires the driving characteristics when the first abnormality degree was calculated and the driving characteristics when the second abnormality degree was calculated, and compares the difference between the two with a threshold value to determine whether the driving characteristics have been intentionally changed. As an example, the number of sudden starts and sudden braking per certain driving distance section (10 km) is taken as the risky driving rate, i.e., the driving characteristics. If the average number of sudden starts and sudden braking in the driving distance section (10 km) before the collision was 1, and the average number of sudden starts and sudden braking after the collision was 20, the difference in driving characteristics is calculated as "20-1=19." If the threshold value is assumed to be 10 (times), in this example, it is determined that the driver intentionally made sudden starts and sudden braking after the collision.

[0062] In addition to such changes in driving characteristics, it is possible to determine whether or not a difference in abnormality is due to a collision using any criteria or factors, in order to eliminate the influence of factors that are thought to be unrelated to the accident and are the reason for the large difference in abnormality (for example, driving environment such as weather, road conditions, and temperature, or differences in drivers).

[0063] In the above example, the change in driving characteristics is evaluated separately from evaluating the difference between the first abnormality degree and the second abnormality degree, but for example, the change in driving characteristics (e.g., the difference) may be calculated and used as a correction coefficient for the first abnormality degree or the second abnormality degree, or the difference between the two, used in the comparison unit 40. In other words, the difference between the first abnormality degree and the second abnormality degree may be evaluated taking into account the change in driving characteristics.

[0064] In the event of a vehicle collision, the transmitter 50 transmits the results of the above-described processing to one or more display units 60 along with necessary information or data. For example, it transmits a list of vehicle parts requiring repair or replacement, including those within the impact area, vehicle parts for which the difference between the first and second abnormality levels is equal to or exceeds a threshold (i.e., vehicle parts outside the impact area that have been affected by deterioration or other factors), and data providing the basis for this determination (such as the behavior, photos, and sound data of the parts before and after the collision). Other data and information in any format can be included, such as video data from a driving recorder, GPS information including location information indicating the driving section, vehicle signals such as CAN data, image data of the vehicle's exterior and the condition of the parts after the collision, and exterior photos and explanatory diagrams of the installation locations of the relevant parts obtained from a parts list database.

[0065] In addition, as data that serves as evidence for accident analysis and the procedures and processing that accompany it, for example, at the time a collision is detected, the following may also be transmitted: driving data at the time of the collision, driving characteristic data before the collision, driving characteristic data after the collision, data on changes in driving characteristics before and after the collision, behavior, photographs, and sound data of the relevant vehicle parts before and after the collision, post-collision vehicle data used to calculate the second abnormality level, and accident situation data obtained by an on-board camera, etc. (location of the accident, object, impact area and magnitude of the impact, determination of whether it is a self-inflicted accident or a personal injury accident based on camera images, etc.).

[0066] The display unit 60 generates an image to be displayed based on the information transmitted from the transmission unit 50 and displays it on a display. The display unit 60 is configured from, for example, a terminal of an insurance company or automobile dealership that manages the cloud system, a smartphone or personal computer owned by a user, or a display provided in a vehicle.

[0067] In one embodiment, the display unit 60 to which the transmission unit 50 transmits may include, in addition to the above, smartphones, personal computers, etc. of other related parties. The related parties may include, for example, those involved in accident analysis or accident handling (police, etc.), those involved in legal processing (lawyers or agents), personnel from rental car or vehicle dispatch services who arrange for a replacement vehicle, personnel from dealerships, repair shops, towing services such as tow trucks, etc.

[0068] The method for displaying vehicle parts affected by the collision is arbitrary, but for example, to make it easier to understand the extent of damage caused by the collision, the parts can be displayed in descending order of the difference between the first abnormality level and the second abnormality level. Furthermore, to clearly indicate the existence of vehicle parts that are outside the impact area but require repair, vehicle parts may be classified based on information about the collision into vehicle parts that may have been directly subjected to collision energy (in other words, vehicle parts within the impact area) and vehicle parts that may have been indirectly subjected to collision energy (in other words, vehicle parts outside the impact area), and the two types may be displayed separately.

[0069] FIG. 3 is an explanatory diagram showing an example of a display on the display unit 60 of a user or insurance company. In this example, a text message is displayed in area 101 at the top of the screen stating, "Accident damage has been detected in areas other than the collision area. Please inspect before repair." Area 102 on the left side of the screen displays the details of the damage, including the following sections: "Type of Abnormality," "Faulty Part," "Maintenance Details," and "Repair Cost." In this example, the text indicates that the bumper needs to be replaced, there is an engine misfire, and the injectors and engine assembly need to be replaced, costing 150,000 yen. Area 103 at the bottom left of the screen displays photos of the injectors and engine assembly, which are damaged parts that are not visible from the outside, along with a photo of the engine bay. These photos are obtained from the parts list database described above.

[0070] In one embodiment, the data analysis device preferably has an additional function of linking with a maintenance and repair database (not shown) for each vehicle part, and by referring to this maintenance and repair database, determines an appropriate maintenance policy for a vehicle part identified as having a difference between the first abnormality level and the second abnormality level equal to or greater than a threshold, and calculates an approximate repair price in accordance with this maintenance policy. The "Maintenance Contents" and "Response Costs" in Figure 3 are displayed in accordance with this maintenance policy and approximate repair price.

[0071] Additionally, in area 104 at the top right of the screen, a bar graph is displayed comparing the degree of abnormality of the injector before the collision with the degree of abnormality of the injector after the collision, as evidence that the injector is faulty.

[0072] In area 105 at the bottom right of the screen, the list of parts to be replaced is listed in order of "damage level," and is particularly divided into "damage due to collision" and "parts other than collision." The former includes the "front bumper," and the latter includes the "injector" and "engine assembly," with the "injector" listed above the "engine assembly" in order of damage level.

[0073] FIG. 4 shows another example of the display on the display unit 60 for a user or a repair shop. In this example, a text message appears in area 201 at the top of the screen: "The injector injection volume may have decreased due to an accident. Please take and send post-accident data of the relevant part. If the vehicle does not operate, please contact your dealer." Area 202 in the upper left corner of the screen displays a series of photographs showing the fuel injection status of the injector as evidence of an injector malfunction. Area 202a displays the post-accident photograph, while area 202b displays the pre-accident photograph. The photographs in area 202a will be displayed when the user submits the photograph data in accordance with the text message; in this example, they are still blank. The pre-accident photograph 202b includes information such as the date and time of the photograph, such as "Date and time of pre-accident data acquisition: 12 / 24 12:01" and "Data source: Data taken during vehicle inspection."

[0074] In area 203 at the bottom left of the screen, under the title "Positional relationship between collision point and affected part," a photograph of the injector, a damaged part that is not apparent from the outside, is displayed together with an explanatory diagram of a top view of the vehicle, and the position of the injector and the collision point are shown on the explanatory diagram of the top view. In addition, area 204 on the right side of the screen displays a bar graph comparing the degree of abnormality of the injector before and after the collision, as evidence that the injector is faulty.

[0075] Figure 5 shows an example of a display on the display unit 60 when the correction unit 41 determines that the driver intentionally changed their driving characteristics after a collision to include sudden acceleration and braking. In this example, a text message appears in the upper area 301 of the screen stating, "Intentional driving that promotes deterioration has been detected. Therefore, insurance will only cover the damaged parts that were subjected to impact." The left area 302 of the screen displays a map of the driving sections where sudden braking and sudden acceleration occurred, along with a heading titled "Locations of sudden braking and sudden acceleration." The locations of sudden braking and sudden acceleration are displayed on the map. The upper right area 303 of the screen displays a text message stating, "[Confirmation indicator] Number of sudden braking and sudden acceleration events," and "[Before the accident] 1 → [After the accident] 6." The lower right area 304 of the screen displays a list of parts to be replaced, sorted by damage level, similar to Figure 3. The area below that, 305, displays a photograph of the front bumper, the replacement part, along with a top-view diagram of the collision site, similar to Figure 4.

[0076] In this example, because the car was subjected to intentional sudden braking and acceleration, the injectors, which are outside the impact area as explained in the example of Figure 3, are excluded from the list of replacement parts, and only the front bumper, which was directly subjected to the impact energy, is listed.

[0077] In this way, based on a comparison between the first abnormality level before the collision and the second abnormality level after the collision, vehicle parts that are outside the impact area but have been affected by some kind of deterioration or internal damage can be identified and clearly communicated to users, insurance companies, etc., making it easier to resolve insurance claims and other matters between the parties involved.

[0078] FIG. 2 is a flowchart showing the processing flow in the vehicle data analysis device of the first embodiment. First, various vehicle data are acquired (step 1), and this vehicle data is linked (associated) with each vehicle component (step 2). Next, a first abnormality degree is calculated for each vehicle component based on the vehicle data acquired in step 1 (step 3). This first abnormality degree is repeatedly calculated while the vehicle is traveling. As mentioned above, its progression may be predicted.

[0079] Next, in step 4, a collision is detected. Calculation of the first abnormality degree continues until a collision is detected. Once a collision is detected, the process proceeds to step 5, where vehicle data is acquired while the vehicle is moving after the collision. If the vehicle is unable to move after the collision, in step 5, instead of acquiring vehicle data while moving, post-collision vehicle data for similar collisions is acquired from past accident data and accident simulation data accumulated in the vehicle information database 90 as described above. Then, in step 6, a second abnormality degree, which is the post-collision abnormality degree for each vehicle part, is calculated in the same way as the first abnormality degree.

[0080] Next, in step 7, the first abnormality degree and the second abnormality degree of each vehicle part are compared, and in step 8, it is determined whether there are any vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more, that is, whether the difference between the two is greater than a predetermined threshold. If the determination in step 8 is NO, the process proceeds to step 9, where the faulty part (damaged part) is identified based only on the collision location (impact area). Then, the process proceeds to step 10, where the necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to the display unit 60 and displayed.

[0081] On the other hand, if the determination in step 8 is YES, the process proceeds from step 8 to step 11, where the driving characteristics at the time of calculating the first abnormality degree and the driving characteristics at the time of calculating the second abnormality degree are calculated and compared. Then, in step 12, it is determined whether the difference between these two driving characteristics is small. If the difference in driving characteristics is small, it is assumed that there has been no intentional deterioration of the second abnormality degree, and the process proceeds to step 13. In step 13, vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more are identified as vehicle parts affected by deterioration, internal damage, etc., even if they are outside the impact area. Then, the process proceeds to step 10, where necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to and displayed on the display unit 60.

[0082] If it is determined in step 12 that the difference between the two driving characteristics is large, it is assumed that there has been an intentional worsening of the second abnormality level, and the process proceeds to step 9. In this case, as described above, the faulty part (damaged part) is identified and displayed based only on the collision location (impact area).

[0083] Next, a second embodiment of the present invention will be described. In the second embodiment, even if the second abnormality level of a vehicle part increases following a collision, if the increase is simply due to loosening of a fastening member, such as a fastening nut, the part is excluded from the scope of insurance claims. In other words, the increase in the abnormality level due to loosening of a fastening nut is not due to the intrinsic deterioration or failure of the vehicle part itself, so it is handled separately.

[0084] 6 is a functional block diagram of a data analysis device according to Example 2. Similar to Example 1, this data analysis device includes a data acquisition unit 10, an abnormality degree calculation unit 20, a collision detection unit 30, a comparison unit 40, a transmission unit 50, and a display unit 60, and further includes a fastening confirmation unit 70.

[0085] The data acquisition unit 10, the abnormality degree calculation unit 20, the collision detection unit 30, the comparison unit 40, the transmission unit 50, and the display unit 60 are basically the same as those in the first embodiment described above.

[0086] The fastening confirmation unit 70 checks whether the fastening members securing the vehicle components (particularly the vehicle components for which the comparison unit 40 determines that the second abnormality level is greater than the first abnormality level by a predetermined value or more) have loosened due to the collision. For example, a technology for monitoring the fastening torque of fastening members such as bolts and nuts and communicating the information to an external device is known (e.g., JP 2006-346784 A), and such technology is used to acquire fastening torque information for each vehicle component. Then, for a vehicle component for which the second abnormality level is greater than the first abnormality level by a predetermined value or more, if the fastening torque is equal to or less than a reference value, the fastening confirmation unit 70 determines that the fastening member is loose, and displays a notice or instruction to, for example, a user or a repair shop, via the transmission unit 50 on one or more display units 60, indicating that the fastening member should be tightened or inspected. Furthermore, because continuing to drive with such loose fastening members may worsen the abnormality of the vehicle components or cause unexpected trouble, the transmission unit 50 and display unit 60 notify the user that they should refrain from driving and inspect the vehicle components. Furthermore, even if the second abnormality level of a vehicle part is greater than the first abnormality level by a predetermined value or more, if the fastening torque is equal to or less than a reference value, the vehicle part is classified and displayed as a vehicle part that is not subject to insurance claims, for example. This vehicle part basically corresponds to a vehicle part that can be used without any problems if fastening members such as bolts and nuts are tightened.

[0087] FIG. 8 shows an example of a display on a display unit 60 (e.g., a user's smartphone or a vehicle display) for a user when the tightening torque of a fastening member is below a reference value. In this example, a text message is displayed in area 401 at the top of the screen stating, "There are areas where the tightening torque is insufficient. Please refrain from driving and have the vehicle inspected immediately. Tightening-required areas are not covered by insurance." Area 402 on the left side of the screen displays the areas where the tightening torque is below the reference value as text, "Tightening-required area: Engine assembly." Area 403 below displays a photograph of the engine assembly, which requires tightening, along with a photograph of the engine bay. These photographs are obtained from the parts list database described above. Area 404 at the top right of the screen displays a bar graph comparing the abnormality level of the engine assembly before the collision (i.e., the first abnormality level) with the abnormality level after the collision (i.e., the second abnormality level). Similar displays may also be displayed on terminals of insurance companies, repair shops, etc.

[0088] By displaying such information, users can be made aware of any loose bolts or nuts that may have been caused by a collision, allowing them to have the item inspected and repaired at a repair shop before any unexpected problems occur. In addition, by clearly indicating that the item is not covered by insurance claims, disputes between the parties involved can be reduced.

[0089] Next, Fig. 7 is a flowchart showing the processing flow in the vehicle data analysis device of the second embodiment. Except for the part that checks the fastening torque of the fastening members, this is the same as the first embodiment. First, various vehicle data is acquired (Step 1), and this vehicle data is linked (associated) with each vehicle component (Step 2). Next, based on the vehicle data acquired in Step 1, a first abnormality degree is calculated for each vehicle component (Step 3).

[0090] Next, in step 4, a collision is detected. If a collision is detected, the process proceeds to step 5, where vehicle data is acquired while the vehicle is moving after the collision. If the vehicle cannot move after the collision, in step 5, instead of acquiring vehicle data while the vehicle is moving, post-collision vehicle data is acquired from past accident data, etc., from the vehicle information database 90. In step 6, similar to the first abnormality degree, a second abnormality degree, which is the abnormality degree after the collision, is calculated for each vehicle part.

[0091] Next, in step 7, the first abnormality degree and the second abnormality degree of each vehicle part are compared, and in step 8, it is determined whether there are any vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more, that is, whether the difference between the two is greater than a predetermined threshold. If the determination in step 8 is NO, the process proceeds to step 9, where the faulty part (damaged part) is identified based only on the collision location (impact area). Then, the process proceeds to step 10, where the necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to the display unit 60 and displayed.

[0092] On the other hand, if the determination in step 8 is YES, the process proceeds from step 8 to step 11, where the driving characteristics at the time of calculating the first abnormality degree and the driving characteristics at the time of calculating the second abnormality degree are calculated and compared with each other. Then, in step 12, it is determined whether the difference between these two driving characteristics is small. In other words, it is determined whether there has been any intentional worsening of the second abnormality degree.

[0093] If it is determined in step 12 that the difference between the two driving characteristics is small, the process proceeds from step 12 to step 13, and vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more are provisionally identified as vehicle parts that have been affected by deterioration, internal damage, etc., even if they are outside the impact area.

[0094] In the second embodiment, the process then proceeds to step 21, where the fastening torque of the fastening members of each vehicle component is confirmed, that is, the fastening torque is compared with a reference value.

[0095] Then, in step 22, it is determined whether there are any vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more and whose fastening torque is equal to or less than a reference value. If the result is NO here, the process proceeds to step 23, and vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more are finally identified as vehicle parts affected by deterioration, internal damage, etc., even if they are outside the impact area. Then, the process proceeds to step 10, and necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to and displayed on the display unit 60. The display in this case will be similar to that shown in FIGS. 3 and 4.

[0096] On the other hand, if it is determined in step 22 that there is a vehicle part whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more and whose fastening torque is equal to or less than the reference value, the process proceeds to step 24, where a repair shop (e.g., a dealer) is notified of an inspection and fastening instruction, and in step 25, the user is notified to refrain from driving the vehicle. Then, in step 26, vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more are finally identified as vehicle parts affected by deterioration, internal damage, etc., even if they are outside the impact area, with vehicle parts with insufficient fastening torque excluded from the scope of insurance claims. Finally, in step 10, necessary information to be displayed (part information, behavior, photographs, sound data, etc. of the part before and after the accident) is sent to and displayed on the display unit 60. In this case, a display such as that shown in FIG. 8 is produced.

[0097] Next, a third embodiment of the present invention will be described. In the third embodiment, vehicle parts whose second abnormality level has increased due to foreign matter (for example, stones scattered by the impact of the accident, broken pieces of parts, moisture, etc.) entering or entering the vehicle following a collision are excluded from the scope of insurance claims. In other words, the increase in the abnormality level due to the intrusion of foreign matter is not due to the intrinsic deterioration or failure of the vehicle part itself, so this is handled separately.

[0098] 9 is a functional block diagram of a data analysis device according to Example 3. Similar to Example 1, this data analysis device includes a data acquisition unit 10, an abnormality degree calculation unit 20, a collision detection unit 30, a comparison unit 40, a transmission unit 50, and a display unit 60, and further includes a foreign object detection unit 80.

[0099] The data acquisition unit 10, the abnormality degree calculation unit 20, the collision detection unit 30, the comparison unit 40, the transmission unit 50, and the display unit 60 are basically the same as those in the first embodiment described above.

[0100] The foreign object detection unit 80 detects the presence of foreign objects in various vehicle components following a collision, particularly those related to the abnormality level of a vehicle component determined by the comparison unit 40 to have a second abnormality level greater than the first abnormality level by a predetermined value or more. For example, foreign objects may be detected in specific locations on the vehicle using technologies such as object detection using an onboard camera, distance measurement using laser sonar, or foreign object detection based on brightness values. As with collision detection, foreign object detection may be performed using a means other than the vehicle itself, such as information transmitted from vehicle dashcams before and after the accident. If a foreign object is detected, the transmission unit 50 displays a notice or instruction on one or more display units 60 to, for example, the user or a repair shop, to remove the foreign object or perform an inspection. Furthermore, because continuing to drive the vehicle with such foreign objects may worsen the abnormality in the vehicle component or cause unexpected problems, the transmission unit 50 and display unit 60 notify the user to refrain from driving and inspect the component. Furthermore, even if the second abnormality level of a vehicle part is greater than the first abnormality level by a predetermined value or more, if a foreign object is involved, the vehicle part is classified and displayed as a vehicle part that is not subject to insurance claims, for example. This vehicle part basically corresponds to a vehicle part that will not be affected if the foreign object is removed.

[0101] FIG. 11 shows an example of a display on a display unit 60 (e.g., a user's smartphone or vehicle display) for a user when a foreign object is detected. In this example, a text message is displayed in area 501 at the top of the screen, stating, "There is a foreign object. Please refrain from driving and have the vehicle inspected immediately. The foreign object is not covered by insurance." An area 502 on the left side of the screen displays the location where the foreign object was detected as text, "Location of foreign object: Turbocharger." An area 503 at the bottom left of the screen displays instruction text, stating, "If possible, remove the foreign object. Please send a photo of the part and indicate whether or not the removal was performed." Between areas 502 and 503, an area 504 is provided with the title "After the accident." When a user sends image data in accordance with the text instructions in area 503, the image will be displayed. Additionally, area 505 to the right of area 504 is provided with buttons 505a and 505b labeled "Removed Foreign Matter" and labeled "Performed" and "Not Performed," and the user or the like is allowed to select either of them in accordance with the instructions in the text in area 503. Also, area 506 in the upper right corner of the screen displays a bar graph comparing the degree of abnormality of the turbocharger before the collision (i.e., the first degree of abnormality) with the degree of abnormality after the collision (i.e., the second degree of abnormality). A similar display may be provided on a terminal of an insurance company, a repair shop, etc.

[0102] By displaying such information, users can be made aware of the presence of foreign matter in the vehicle and can have it inspected and repaired at a repair shop before any unexpected problems occur. In addition, by clearly indicating that the product is not covered by insurance claims, disputes between the parties involved can be reduced.

[0103] Next, Fig. 10 is a flowchart showing the processing flow in the vehicle data analysis device of the third embodiment. Except for the part that checks for foreign matter contamination, this is the same as the first embodiment. First, various vehicle data are acquired (step 1), and this vehicle data is linked (associated) with each vehicle part (step 2). Next, based on the vehicle data acquired in step 1, a first abnormality degree is calculated for each vehicle part (step 3).

[0104] Next, in step 4, a collision is detected. If a collision is detected, the process proceeds to step 5, where vehicle data is acquired while the vehicle is moving after the collision. If the vehicle cannot move after the collision, in step 5, instead of acquiring vehicle data while the vehicle is moving, post-collision vehicle data is acquired from past accident data, etc., from the vehicle information database 90. In step 6, similar to the first abnormality degree, a second abnormality degree, which is the abnormality degree after the collision, is calculated for each vehicle part.

[0105] Next, in step 7, the first abnormality degree and the second abnormality degree of each vehicle part are compared, and in step 8, it is determined whether there are any vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more, that is, whether the difference between the two is greater than a predetermined threshold. If the determination in step 8 is NO, the process proceeds to step 9, where the faulty part (damaged part) is identified based only on the collision location (impact area). Then, the process proceeds to step 10, where the necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to the display unit 60 and displayed.

[0106] On the other hand, if the determination in step 8 is YES, the process proceeds from step 8 to step 11, where the driving characteristics at the time of calculating the first abnormality degree and the driving characteristics at the time of calculating the second abnormality degree are calculated and compared with each other. Then, in step 12, it is determined whether the difference between these two driving characteristics is small. In other words, it is determined whether there has been any intentional worsening of the second abnormality degree.

[0107] If it is determined in step 12 that the difference between the two driving characteristics is small, the process proceeds from step 12 to step 13, and vehicle parts whose second abnormality degree is greater than the first abnormality degree by a predetermined value or more are provisionally identified as vehicle parts that have been affected by deterioration, internal damage, etc., even if they are outside the impact area.

[0108] In the third embodiment, the process then proceeds to step 31, where each vehicle part is checked for the presence or absence of foreign matter.

[0109] Then, in step 32, it is determined whether there are any vehicle parts whose second abnormality level is greater than the first abnormality level by a predetermined value or more and in which foreign matter has been detected. If the result is NO, the process proceeds to step 33, and vehicle parts whose second abnormality level is greater than the first abnormality level by a predetermined value or more are finally identified as vehicle parts affected by deterioration, internal damage, etc., even if they are outside the impact area. Then, the process proceeds to step 10, and necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to and displayed on the display unit 60. The display in this case will be similar to that shown in FIGS. 3 and 4.

[0110] On the other hand, if it is determined in step 32 that a vehicle part exists in which the second abnormality level is greater than the first abnormality level by a predetermined value or more and in which foreign matter has been detected, the process proceeds to step 34, where a repair shop (e.g., a dealer) is notified of an inspection and foreign matter removal instruction, and in step 35, the user is notified to refrain from driving the vehicle. Then, in step 36, vehicle parts in which foreign matter has been detected are excluded from the insurance claim targets, and vehicle parts in which the second abnormality level is greater than the first abnormality level by a predetermined value or more are finally identified as vehicle parts affected by deterioration, internal damage, etc., even if they are outside the impact area. Finally, in step 10, necessary information to be displayed (part information, behavior, photos, sound data, etc. of the part before and after the accident) is sent to and displayed on the display unit 60. In this case, a display such as that shown in FIG. 11 is displayed.

[0111] Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment and various modifications are possible. For example, the example of the degree of abnormality related to the suspension described above is merely one example given for the purpose of explanation. In the present invention, the degree of abnormality may be grasped in any known manner.

Claims

1. data on the vehicle parts is acquired while the vehicle is running, and based on this data, the abnormality degree of the vehicle parts that changes as the vehicle is used is repeatedly calculated; Detects vehicle collisions, The abnormality degree when traveling from the time of the collision to a certain comparison time point is estimated based on the predicted transition of the abnormality degree calculated up to the time of the collision, and this is set as a first abnormality degree. data on the vehicle component after the collision is acquired while the vehicle is moving after the collision, and an abnormality degree of the vehicle component at the comparison time point is calculated based on the data, and this is set as a second abnormality degree; comparing the first abnormality degree with the second abnormality degree; Vehicle data analysis methods.

2. When the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, information about the vehicle part is output as a vehicle part affected by a collision. The vehicle data analysis method according to claim 1 .

6. comparing the first abnormality degree with the second abnormality degree for each of the plurality of vehicle components; The vehicle data analysis method according to claim 1 .

7. correcting the first abnormality degree and the second abnormality degree based on vehicle position data, weather data, and driver's driving characteristic data; The vehicle data analysis method according to claim 1 .

8. acquiring driving characteristic data of the driver immediately before the collision and driving characteristic data of the driver after the collision, and when it is determined that there is an intentional change in the driving characteristic, not comparing the first abnormality degree with the second abnormality degree or not outputting vehicle part information; The vehicle data analysis method according to claim 2 .

9. acquiring data on the fastening torque of the fastening members that fix the vehicle components, and comparing the fastening torque after the collision with a reference value; The vehicle data analysis method according to claim 1 .

10. If the tightening torque after the collision is below the standard value, an inspection request is sent to the vehicle repair center. The vehicle data analysis method according to claim 9.

11. When the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, the vehicle part is included in the insurance claim candidates; If the tightening torque after the collision is below the reference value, the vehicle part is excluded from the candidates for insurance claims. The vehicle data analysis method according to claim 9.

12. If the tightening torque after a collision is below the standard value, the vehicle user is notified to refrain from driving and to have the vehicle inspected. The vehicle data analysis method according to claim 9.

13. Obtaining information about the contamination of vehicle parts with foreign matter due to a collision; The vehicle data analysis method according to claim 1 .

14. If a foreign object is detected in a vehicle part, an inspection request is sent to the vehicle repair center. The vehicle data analysis method according to claim 13.

15. When the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, the vehicle part is included in the insurance claim candidates; When a foreign object is detected in a vehicle part, the vehicle part is excluded from candidates for insurance claims. The vehicle data analysis method according to claim 13.

16. When a foreign object is detected in a vehicle part, the system notifies the vehicle user to refrain from driving and to inspect the vehicle. The vehicle data analysis method according to claim 13.

17. When a collision is detected, transmitting the driving data at the time of the collision, information on the state of the target vehicle part before and after the collision, and data used as the basis for calculating the second abnormality degree to at least one of an insurance official, a police official, or a legal representative. The vehicle data analysis method according to claim 1 .

18. determining a difference between the first abnormality degree and the second abnormality degree for a plurality of vehicle parts; The vehicle parts are displayed on the display in descending order of difference. The vehicle data analysis method according to claim 1 .

19. extracting vehicle parts from the plurality of vehicle parts, the vehicle parts having a difference between the first abnormality degree and the second abnormality degree equal to or greater than a predetermined threshold; Obtaining information about the collision, and based on this information, classifying vehicle parts into those to which collision energy may have been applied directly and those to which collision energy may have been applied indirectly; The two are displayed separately on the display. The vehicle data analysis method according to claim 1 .

20. determining an appropriate maintenance policy for a vehicle part for which the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold; We calculate the estimated repair cost in accordance with this maintenance policy, These are displayed on the display. The vehicle data analysis method according to claim 1 .

21. The driving characteristic data of the driver immediately before the collision and the driving characteristic data of the driver after the collision are acquired, and when it is determined that there has been an intentional change in the driving characteristic, the corresponding driving section is displayed on the display unit. The vehicle data analysis method according to claim 1 .

22. a data acquisition unit that acquires data that is the basis for calculating the degree of abnormality of a vehicle part while the vehicle is running; a collision detection unit that detects a vehicle collision; an abnormality degree calculation unit that estimates the abnormality degree of a target vehicle part when the vehicle is driven from the time of the collision to a certain comparison time point based on a transition prediction of the abnormality degree calculated up to the time of the collision, and sets this as a first abnormality degree, and calculates the abnormality degree of the vehicle part at the comparison time point based on data acquired during driving while the vehicle is driving after the collision, and sets this as a second abnormality degree; a comparison unit that compares the first abnormality degree with the second abnormality degree; A vehicle data analysis device comprising:

23. acquiring data on the vehicle component during driving before the collision, and calculating an abnormality degree of the vehicle component based on the data as a first abnormality degree; Detects vehicle collisions, determining a second abnormality degree of the vehicle component after the collision; comparing the first abnormality degree with the second abnormality degree; When the difference between the first abnormality degree and the second abnormality degree is equal to or greater than a predetermined threshold, the vehicle part is included in the insurance claim candidates; Additionally, information about current vehicle parts is obtained, When the information of at least some of the vehicle parts corresponds to a predetermined abnormality, Exclude the vehicle part from potential insurance claims; Vehicle data analysis methods.

24. A specified abnormality is an abnormality that can be resolved by repair without replacing parts.

24. The method of analyzing vehicle data according to claim 23.

25. acquiring data on the fastening torque of the fastening members that fix the vehicle components, and comparing the fastening torque after the collision with a reference value; 25. The method of analyzing vehicle data according to claim 24.

26. If the tightening torque after the collision is below the standard value, an inspection request is sent to the vehicle repair center.

26. The method of analyzing vehicle data according to claim 25.

27. If the tightening torque after the collision is below the reference value, the vehicle part is excluded from the candidates for insurance claims.

26. The method of analyzing vehicle data according to claim 25.

28. If the tightening torque after a collision is below the standard value, the vehicle user is notified to refrain from driving and to have the vehicle inspected.

26. The method of analyzing vehicle data according to claim 25.

29. Obtaining information about the contamination of vehicle parts with foreign matter due to a collision; 25. The method of analyzing vehicle data according to claim 24.

30. If a foreign object is detected in a vehicle part, an inspection request is sent to the vehicle repair center.

30. The method of analyzing vehicle data according to claim 29.

31. When a foreign object is detected in a vehicle part, the vehicle part is excluded from candidates for insurance claims.

30. The method of analyzing vehicle data according to claim 29.

32. When a foreign object is detected in a vehicle part, the system notifies the vehicle user to refrain from driving and to inspect the vehicle.

30. The method of analyzing vehicle data according to claim 29.

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