Vehicle data analysis methods and devices

Abstract

Vehicle data is acquired (S1), and associated with each vehicle component (S2). A first degree of anomaly is calculated for each vehicle component before the collision (S4). If a collision is detected (S4), vehicle data is acquired while the vehicle is driving autonomously after the collision (S5). If the vehicle cannot drive autonomously, a second degree of anomaly after the collision is calculated by referring to past accident data, etc. (S6). The first degree of anomaly and the second degree of anomaly are compared (S7). If the second degree of anomaly is greater than the first degree of anomaly by a predetermined value (S8), the vehicle component is identified as having deterioration or other effects even outside the collision area (S13), and the necessary information is displayed on the display unit (60). In the case of intentional changes in driving characteristics after the collision, the vehicle component is not identified as having deterioration or other effects (S12, S13).

Description

Technical Field

[0001] The present invention relates to a vehicle data analysis technique for appropriately evaluating damage to vehicle components in the event of a collision, particularly damage to vehicle components that at first glance appear unrelated to the collision. Background Technology

[0002] In the event of a vehicle colliding with other vehicles or structures while in motion, repair shops replace or repair parts that have been deformed or damaged by the impact energy at or near the point of impact. Moreover, this cost is mostly covered by insurance based on a contract with an insurance company.

[0003] However, adverse effects can also occur in vehicle components that at first glance seem unrelated to a collision, manifesting as progressive deterioration or a shortened future lifespan. In such cases, the vehicle owner often fails to notice, and whether the progression of deterioration or the shortening of lifespan of these vehicle components was caused by a collision or accident can easily lead to disputes between the parties involved.

[0004] Patent document 1 discloses a technology that, during a vehicle collision, uses remote data including gyroscope, accelerometer, GPS data, video recordings, vehicle diagnostic data, and sound recordings to analyze the accident. Based on the remote data, repair costs are estimated based on whether components within a specific collision area are damaged.

[0005] Such technology cannot analyze the impact on components outside the collision zone.

[0006] Patent document 2 discloses the following technology: In order to monitor the vehicle's status in real time, a digital twin of the vehicle is generated, and simulations are performed based on this digital twin, such as monitoring the vehicle's service life. If an accident is detected, the accident event data is recorded, and the current value of the vehicle is updated.

[0007] In this patent document 2, it only obtains accident event data, and cannot analyze whether vehicle parts that at first glance are unrelated to the collision have been deteriorated due to the collision.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent Document 1: Japanese Patent Application Publication No. 2021-503637

[0011] Patent Document 2: Japanese Patent Application Publication No. 2020-13557 Summary of the Invention

[0012] This invention provides a vehicle data analysis method, wherein,

[0013] Data about vehicle components is acquired during the vehicle's operation prior to the collision. Based on this data, the anomaly level of the vehicle component is calculated and used as the first anomaly level.

[0014] Detecting vehicle collisions

[0015] The anomaly degree of the vehicle components after the collision is calculated and used as the second anomaly degree.

[0016] Compare the first and second anomalies.

[0017] In this way, by comparing the first and second anomalies before and after the collision, regardless of the positional relationship between the collision site (collision area) and vehicle components or whether the vehicle components are deformed, the adverse effects of the collision, such as the deterioration of vehicle components, can be evaluated.

[0018] In a preferred embodiment of the present invention, when the difference between the first anomaly and the second anomaly is above a predetermined threshold, information about the vehicle component that is affected by the collision is output.

[0019] In a preferred embodiment of the invention, data about the vehicle components after the collision is acquired during the vehicle's self-driving process after the collision, and a second anomaly is calculated based on this data.

[0020] That is, if the vehicle is still able to drive itself after a collision, data can be acquired as the vehicle continues to drive itself, and the second anomaly can be calculated.

[0021] In another aspect of the invention, a second degree of anomaly is estimated by learning data about vehicle components before the collision or data about the collision of other vehicles.

[0022] Therefore, even in cases where a vehicle is unable to move on its own due to a collision, the impact on vehicle components can be estimated by comparing the first and second degrees of anomaly.

[0023] For example, information related to a collision can be acquired, and based on that information, a data set of similar collisions involving other vehicles can be selected. By using data sets with similar collision states and characteristics of the accident vehicles (model, production date, production plant, etc.), the accuracy of the anomaly assessment is increased.

[0024] In a preferred embodiment of the present invention, a first degree of anomaly and a second degree of anomaly are compared for multiple vehicle components respectively.

[0025] In a preferred embodiment of the present invention, the first anomaly and the second anomaly are corrected based on vehicle location data, weather data, and driver driving characteristic data. That is, it is desirable to suppress changes in the first or second anomaly caused by external factors.

[0026] In addition, in a preferred embodiment of the present invention, the driver's driving characteristic data before the collision and the driver's driving characteristic data after the collision are obtained. When it is determined that there is an intentional change in driving characteristics, the comparison of the first degree of abnormality and the second degree of abnormality or the output of vehicle component information is not performed.

[0027] For example, in the case of an insurance contract that covers accidents, it is also considered that the driver intentionally accelerated the deterioration (drifting, driving with sudden starts and stops, sudden braking, etc.). If such intentional driving occurs, no anomaly comparison or vehicle component information output will be performed.

[0028] In another aspect of the invention, data on the tightening torque of the fastening member securing the vehicle component is acquired, and the tightening torque after the collision is compared with a reference value. This determines the presence of a fastening member that has loosened during the collision.

[0029] For example, if the tightening torque after a collision is below the baseline value, an inspection request can be sent to the vehicle's repair shop. This allows for a rapid response.

[0030] Additionally, in one example, if the difference between the first and second anomalies exceeds a predetermined threshold, the vehicle component is included in the insurance claim candidate.

[0031] If the tightening torque after a collision is below the baseline value, the vehicle part is excluded from the insurance claim pool.

[0032] That is, even if the degree of abnormality increases after a collision, it is possible to eliminate the abnormality by re-tightening the fastening components, and therefore, it is excluded from the insurance claim candidate.

[0033] Preferably, when the tightening torque after a collision is below a reference value, the vehicle user is notified to control driving and conduct an inspection. This helps to prevent driving while the fastening components are loose.

[0034] In another aspect of the invention, information related to the incorporation of foreign objects into vehicle components due to a collision is obtained.

[0035] Preferably, when foreign matter is detected to have entered a vehicle component, an inspection request is sent to the vehicle's repair shop.

[0036] Additionally, in one example, if the difference between the first and second anomalies exceeds a predetermined threshold, the vehicle component is included in the insurance claim candidate.

[0037] If a foreign object is detected mixed into a vehicle component, that vehicle component will be excluded from the insurance claim pool.

[0038] That is, even if the degree of abnormality increases after a collision, it is possible to eliminate the abnormality by removing the foreign object, and therefore, it is excluded from the insurance claim pool.

[0039] Preferably, when foreign matter is detected entering a vehicle component, the vehicle user is notified to control driving and conduct an inspection. This helps to prevent driving while foreign matter is present.

[0040] In a preferred embodiment of the present invention, upon detection of a collision, driving data at the time of the collision, information relating to the pre-collision and post-collision states of the vehicle components of the object, and data serving as the basis for calculating the second anomaly are sent to at least one of the following: insurance personnel, police personnel, and legal representatives.

[0041] In a preferred embodiment of the present invention, the difference between a first degree of anomaly and a second degree of anomaly is calculated for a plurality of vehicle components, and the vehicle components are displayed on the display unit in descending order of the difference.

[0042] Therefore, it is easier to identify vehicle components that are relatively more affected by a collision.

[0043] Furthermore, in a preferred embodiment of the present invention,

[0044] Among multiple vehicle components, extract those whose difference between the first and second anomalies is above a specified threshold.

[0045] Obtain collision-related information, and based on this information, classify vehicle components that were directly subjected to collision energy into those that were indirectly subjected to collision energy.

[0046] The two are distinguished and displayed on the display section.

[0047] Therefore, drivers and others can easily understand the impact of a collision.

[0048] Furthermore, in a preferred embodiment of the present invention,

[0049] For vehicle components where the difference between the first and second anomalies exceeds a specified threshold, an appropriate maintenance policy is determined.

[0050] Calculate the estimated repair price according to this maintenance policy.

[0051] Display them on the display panel.

[0052] In another preferred embodiment of the present invention, driving characteristic data of the driver before the collision and driving characteristic data of the driver after the collision are obtained, and when it is determined that there is an intentional change in driving characteristics, the corresponding driving range is displayed on the display unit.

[0053] Therefore, insurance companies and other entities can easily detect, for example, instances where violent driving was intentionally committed after an accident.

[0054] This invention provides a vehicle data analysis device, which comprises:

[0055] The data acquisition department acquires the data that forms the basis for calculating the anomalies of vehicle components;

[0056] The collision detection unit detects vehicle collisions.

[0057] The anomaly calculation unit calculates a first anomaly for the vehicle component of the object based on the data during its driving before the collision, and calculates a second anomaly after the collision.

[0058] The comparison section compares the first anomaly score with the second anomaly score. Attached Figure Description

[0059] Figure 1 This is a functional block diagram of the data analysis device in the first embodiment.

[0060] Figure 2 This is a flowchart illustrating the processing flow of the first embodiment.

[0061] Figure 3 This is an explanatory diagram showing an example of a display in the display section.

[0062] Figure 4 This is an explanatory diagram showing other display examples in the display section.

[0063] Figure 5 This is an explanatory diagram showing other display examples in the display section.

[0064] Figure 6 This is a functional block diagram of the data analysis device in the second embodiment.

[0065] Figure 7 This is a flowchart illustrating the processing flow of the second embodiment.

[0066] Figure 8 This is an explanatory diagram showing a display example of the second embodiment.

[0067] Figure 9 This is a functional block diagram of the data analysis device in the third embodiment.

[0068] Figure 10 This is a flowchart illustrating the processing flow of the third embodiment.

[0069] Figure 11 This is an explanatory diagram showing a display example of the third embodiment. Detailed Implementation

[0070] The following describes a specific embodiment of the present invention. As an example, this embodiment applies the present invention to an accident handling assistance system, which includes insurance claims made by users (car insurance policyholders or vehicle buyers, etc.) during an accident (collision) as a service provided by insurance companies or car dealerships (so-called dealers). The overall data analysis device of one embodiment is configured as, for example, a cloud system primarily consisting of a cloud server managed by an insurance company or car dealership, and may include a user-owned smartphone or other portable device or personal computer, multiple data acquisition devices pre-installed in the vehicle to acquire various data from the vehicle, an in-vehicle computer system and display, a dealership terminal, an insurance company terminal, etc.

[0071] Figure 1 This is a functional block diagram of the data analysis device according to the first embodiment. The data analysis device is configured to include a data acquisition unit 10, an anomaly calculation unit 20, a collision detection unit 30, a comparison unit 40, a transmission unit 50, and a display unit 60.

[0072] The data acquisition unit 10 acquires various vehicle data during vehicle operation from vehicle data acquisition equipment, etc. Vehicle data includes, for example, time-series data related to engine control such as engine speed or engine temperature, data indicating the wear level of the suspension and tires, and various data depending on the vehicle component or functional component being acquired. Furthermore, vehicle data can be time-series data acquired continuously from the past, or instantaneous values ​​such as the current value. Moreover, vehicle data is not limited to values ​​acquired from onboard equipment or vehicle signal measuring devices; it can also be acquired from devices other than onboard equipment, such as measurements from dealerships or past maintenance records. Figure 1 In this context, these are collectively represented as a vehicle information database 90, but it also includes information such as engine speed provided sequentially from the vehicle side. For example, data detected by equipment on the vehicle side is output to a cloud server managed by, for example, an insurance company, via a connection system or appropriate communication unit. The type of information obtained as vehicle data and the means used are arbitrary.

[0073] Furthermore, as will be described later, the vehicle information database 90, which is shown in the accompanying diagram, stores data on multiple past accidents, accident simulation data, and so on.

[0074] The anomaly calculation unit 20 calculates the anomaly degree for specific vehicle components based on the vehicle data acquired by the data acquisition unit 10. Anomaly degree, broadly speaking, refers to the degree of deviation from the normal state. Here, it can be an indicator representing the degree of deterioration or damage of a component as the vehicle's travel distance or usage time progresses. For example, the deviation of a signal detected by certain devices from a set of normal signals can be used as the anomaly degree. Alternatively, the deviation of each signal from a threshold can be considered as the anomaly degree. Furthermore, the anomaly degree can also be expressed as the distance or time that the vehicle can travel from its current state until a fault occurs. The anomaly degree increases as the vehicle's travel distance or usage time progresses.

[0075] Here, the anomaly degree is calculated separately before and after the collision. That is, the anomaly degree that progresses with the vehicle's travel distance or usage time before the collision is calculated as the first anomaly degree, and the anomaly degree calculated after the collision is separated from the first anomaly degree as the second anomaly degree.

[0076] Anomaly levels, such as the first anomaly level, can also be predicted. Future anomaly level shifts can be predicted using well-known and appropriate prediction methods. For example, methods such as: considering deviations from various signal thresholds as anomalies according to conditions and rules, and predicting future anomaly level shifts based on time-series data including engine speed or engine temperature; after estimating future predicted values ​​(future behavior) of vehicle data acquired by the data acquisition unit 10 using prediction methods for time-series data such as LSTM (Long Short-Term Memory), using approximation methods with invariant analysis or machine learning to classify the vehicle's state as normal or abnormal, considering the probability of an abnormal state as an anomaly level, and calculating the probability of future anomalies based on future predicted values ​​of vehicle data acquired by the data acquisition unit 10. The present invention is not limited to this; appropriate methods for predicting future anomaly level shifts can be used. In one embodiment, the anomaly level shift can be determined using either vehicle travel distance or vehicle travel time as a parameter. Regarding the second anomaly, it can be obtained through shift prediction, in cases where the anomaly is determined by observing the vehicle's self-driving behavior after a collision.

[0077] The anomaly calculation unit 20 includes a component association unit 21 that associates various vehicle components with vehicle data. For example, if an anomaly in a certain data is associated with the operation of multiple vehicle components, the anomaly degree of each vehicle component is calculated based on the association provided by the component association unit 21. For example, the association can be performed using relationships derived in advance through simulation, factory test data, theoretical formulas or values ​​related to component control or operation, and information from past repair history.

[0078] If the vehicle is able to move on its own after a collision, the second anomaly is calculated based on vehicle data acquired during vehicle movement, just like the first anomaly. On the other hand, if the vehicle is unable to move on its own due to a collision, vehicle movement data cannot be acquired. Therefore, the second anomaly is calculated based on past accident data or accident simulation data stored in the vehicle information database 90 shown in the accompanying illustration. For example, past accident data or accident simulation data may contain information about the type of collision (collision area or magnitude of impact, etc.) and corresponding changes in various vehicle data (or changes in the anomaly of vehicle components). Referring to this database, vehicle data after a collision can be obtained from a data set of collisions similar to the current collision, and the second anomaly can be calculated. Preferably, the past accident data or accident simulation data includes vehicle model information (vehicle model, production period, production plant, etc.), and the second anomaly is calculated from a similar data set including the vehicle model information. Furthermore, when obtaining the second anomaly using this simulation data, the shift of the first anomaly up to the point of collision can also be taken into account.

[0079] In addition, to improve the accuracy of anomalies, the first and second anomalies can be corrected using vehicle location data, weather data, and driver driving characteristic data.

[0080] The collision detection unit 30 detects vehicle collisions based on the impact during a collision. Collision detection can be performed using various known units, such as impact detection units (e.g., acceleration sensors or pressure sensors mounted on the vehicle body), vehicle-mounted cameras, sonar, radar, etc., which can detect objects or determine the distance to objects. When a collision is detected in the vehicle, information is sent to a cloud server managed by an insurance company or similar entity via a suitable communication unit. Preferably, the collision detection unit 30 can simultaneously detect which part of the vehicle body was impacted (i.e., the collision area). Furthermore, collision detection by units other than the vehicle itself can also be included in the collision detection unit 30, for example, information from cameras on traffic infrastructure, information from dashcams of vehicles in front and behind at the time of the accident. Additionally, for future data analysis, accident situation data (location of the accident, object, collision area and impact magnitude, and the determination of whether it is a self-inflicted injury accident or a personal injury accident based on camera images, etc.) can also be acquired simultaneously.

[0081] When a collision is detected by the collision detection unit 30, the comparison unit 40 compares a first anomaly and a second anomaly at an appropriate time. An appropriate time could be, for example, when the vehicle is able to drive itself after the collision, and sufficient vehicle data is obtained through a period of self-driving to determine a reliable second anomaly, or when the vehicle enters a repair shop after the accident, or other appropriate times. If the vehicle cannot drive itself, it could also be after the collision. Here, the first and second anomalies are compared separately for multiple vehicle components. In particular, it is preferable to compare the anomalies of vehicle components outside the collision area. For example, if the difference between the first and second anomalies is calculated and exceeds a predetermined value, even vehicle components far from the collision area or seemingly unrelated at first glance are judged to have suffered deterioration or internal damage due to the impact of the collision.

[0082] If there are no vehicle parts outside the collision zone with an abnormality difference exceeding a specified value, it is determined that only vehicle parts within the collision zone are damaged. This is equivalent to, for example, parts that are deformed or visually damaged due to the impact of the collision. For example, parts with specific damage can be targeted using techniques known as those described in Patent Document 1 above.

[0083] In addition, if the vehicle is able to drive on its own after a collision, the first anomaly that becomes one of the comparison objects in the comparison unit 40 can be a first anomaly calculated before the moment of collision, or it can be a first anomaly estimated as a value after driving for the same amount of time or distance by predicting the shift of the first anomaly based on the driving time or driving distance after the collision.

[0084] The comparison unit 40 includes a correction unit 41 that corrects or modifies the comparison results. In this correction unit 41, for example, driving characteristic data of the driver before and after the collision is acquired. When it is determined that there is an intentional change in driving characteristics, a comparison of the first and second anomalies or vehicle component information is not performed. Driving characteristics, for example, refer to the frequency of dangerous driving such as aggressive driving, rapid acceleration, and sudden braking. This assumes that the driver, in order to claim more insurance money, might intentionally accelerate the deterioration or damage of vehicle components (increasing the second anomaly) during post-accident movement (e.g., from the accident scene to a repair shop). Therefore, to avoid inappropriate insurance claims, even if the difference between the first and second anomalies is large, the vehicle component is not considered to be affected by the collision if the change in driving characteristics is large. Alternatively, correction of the second anomaly or the difference in anomalies can be performed based on the change in driving characteristics to prevent the difference in anomalies from exceeding a predetermined value due to different driving characteristics. When such correction is performed, the user can be notified that the vehicle component is not eligible for insurance.

[0085] In addition, the correction unit 41 can also perform corrections for anomalies based on the vehicle's location data, weather data, and driver's driving characteristic data.

[0086] To facilitate understanding, a simple example is given to illustrate the comparison between the first degree of abnormality, the second degree of abnormality, and the two in the comparison section 40. For example, the deterioration of the suspension is known to be obtained by the following formula (Japanese Patent No. 4915096, Japanese Unexamined Patent Application Publication No. 10-132585, etc.), and therefore, it is taken as the degree of abnormality of the suspension.

[0087] Anomaly degree = Total weight of vehicle × Suspension variation × Correction factor (tire pressure, road conditions (rain, snow, outside temperature, unevenness of the road surface, etc.))

[0088] Here, at least the suspension variation detected by the sensors corresponds to vehicle data. The parameters related to the correction factor can also be vehicle data. Assuming the variation (variation sensor value) before the collision is 0.1 cm, the vehicle weight is 1350 kg, and the correction factor is 1, then the first anomaly is 135. Furthermore, assuming the variation (variation sensor value) after the collision is 1.0 cm, then the second anomaly is 1350. Thus, the difference between the first and second anomalies is 1215. Therefore, by comparing this difference with an appropriate threshold, it is determined whether the suspension has deteriorated or been damaged due to the collision. For example, if the threshold is set to 1000, it is determined that the suspension has deteriorated or been damaged due to the collision. In this case, for example, minor deformation or damage to various parts of the suspension, or a malfunction of the variation sensor, could be included.

[0089] Anomaly comparisons can also be based on the average value of a certain driving interval (driving time or driving distance). For example, if the average value is calculated every hour, and the average value of the change sensor during the hour before the collision is 0.5 cm, and the average value during the hour after the collision is 0.7 cm, then the first anomaly score for the one-hour driving interval is 725, the second anomaly score is 995, and the difference in anomalies is 270. Assuming a threshold of 1000 is set, in this example, the difference in anomalies is below the threshold, indicating that there is no suspension deterioration or damage caused by a collision.

[0090] Furthermore, it is preferable that the driving range in the one hour before and after the collision be a range where road conditions and driving conditions are as similar as possible. For example, it is preferable to extract the first and second anomalies under similar conditions from the first and second anomalies calculated every one hour before and after the collision, and compare them with each other.

[0091] The magnitude of the difference between the first and second anomalies can be evaluated based on the absolute value of the difference, as in the example above, or it can be evaluated based on the ratio or proportion of the first and second anomalies.

[0092] Furthermore, in the correction unit 41, for example, the driving characteristics calculated for the first anomaly and the driving characteristics calculated for the second anomaly are obtained by numericalizing them separately. By comparing the difference between the two with a threshold, it is determined whether the driving characteristics have been intentionally altered. As an example, the number of sudden starts and stops within a constant driving distance interval (10km) is taken as the dangerous driving rate, i.e., the driving characteristic. If the average number of sudden starts and stops within the driving distance interval (10km) before the collision is 1 time, and after the collision it is 20 times, then the difference in driving characteristics is calculated as "20 - 1 = 19". Here, assuming the threshold is set to 10 (times), in this example, it is considered that the driver intentionally performed sudden starts and stops after the collision.

[0093] In addition to such changes in driving characteristics, in order to rule out the influence of factors that are not the cause of the accident (such as driving environment such as weather, road conditions, temperature, and different drivers), it is possible to determine whether the difference in anomaly is caused by a collision using any benchmark or factor.

[0094] Furthermore, in the example above, the change in driving characteristics is evaluated separately from the difference between the first and second anomalies. However, for example, the change in driving characteristics (e.g., the difference) can also be calculated and used as a correction coefficient for the first or second anomaly, or the difference between the two, used in the comparison unit 40. That is, the change in driving characteristics can also be taken into account when evaluating the difference between the first and second anomalies.

[0095] In the event of a vehicle collision, the transmitting unit 50 sends the results of the processing described above, along with necessary information or data, to one or more display units 60. For example, it may send a list of vehicle parts requiring repair or replacement within the collision area, vehicle parts whose difference between a first anomaly and a second anomaly exceeds a threshold (i.e., vehicle parts affected by deterioration or other factors outside the collision area), and data used as the basis for judgment (behavior, photos, sound data, etc. of parts before and after the collision). Furthermore, it may also include dynamic image data from a dashcam, vehicle signals such as GPS information indicating the driving range, CAN data, image data of the vehicle's appearance or component status after the collision, and data or information in any format, such as photos of the appearance of the corresponding component or an explanatory diagram of its installation location obtained from a component list database.

[0096] In addition, data that serves as evidence for the analysis of an accident or the procedures and processing associated with it can be sent at the moment a collision is detected, such as 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 of the vehicle components before and after the collision, photographic and audio data, post-collision vehicle data used for calculating the second degree of anomaly, and accident condition data obtained by vehicle cameras (location of the accident, object, collision area or impact size, and the judgment result of whether it is a self-inflicted accident or a personal injury accident based on camera images, etc.).

[0097] The display unit 60 generates an image to be displayed based on the information sent from the transmission unit 50, and displays it on the screen. The display unit 60 may be, for example, a terminal of an insurance company or car dealership that manages a cloud system, a smartphone or personal computer held by a user, or a display screen in a vehicle.

[0098] In one embodiment, the display unit 60, which is the recipient of the transmission from the sending unit 50, may include, in addition to the above-described components, smartphones, personal computers, etc., belonging to other relevant personnel. These relevant personnel may include, for example, those involved in accident analysis or handling (police officers, etc.), those involved in legal proceedings (lawyers or agents), those responsible for arranging replacement vehicles through car rental or car rental services, dealerships or repair shops, and those responsible for towing services such as towing.

[0099] The method for displaying vehicle components affected by a collision is arbitrary, but for example, to easily understand the extent of damage caused by the collision, the components can be arranged in descending order of the difference between the first and second anomalies. Furthermore, to clearly identify vehicle components that require repair even though they are outside the collision zone, they can be classified based on collision-related information into vehicle components that were directly subjected to collision energy (in other words, vehicle components within the collision zone) and vehicle components that were indirectly subjected to collision energy (in other words, vehicle components outside the collision zone), and displayed separately for each category.

[0100] Figure 3This is an explanatory diagram showing an example of a display in the display unit 60 for users or insurance companies, etc. In this example, the text message "Damage caused by the accident has been detected outside the collision area in area 101 of the upper part of the screen. Please inspect it during repair" is displayed. The details of the damage are displayed in area 102 on the left side of the screen, including items such as "Abnormality Category", "Faulty Part", "Maintenance Content", and "Required Cost". In this example, the text states that in addition to replacing the bumper, there is an engine misfire, the injector and engine assembly need to be replaced, and the cost is 150,000 yen. In area 103 on the lower left of the screen, photos of the damaged parts, namely the injector and engine assembly, which are not clearly visible, are displayed along with a photo of the engine compartment. These photos are obtained from the aforementioned parts list database.

[0101] In addition, one embodiment of the data analysis device preferably has, as an additional function, the ability to cooperate with a maintenance and repair database (not shown) for each vehicle component, and, with reference to the maintenance and repair database, determine an appropriate maintenance policy for vehicle components for which the difference between a first anomaly and a second anomaly is above a threshold, and calculate an estimated repair price according to the maintenance policy. Figure 3 The "Maintenance Content" and "Corresponding Costs" are displayed in accordance with these maintenance policies and estimated repair prices.

[0102] Additionally, in area 104 at the top right of the screen, as evidence of ejector malfunction, there is a bar chart comparing the degree of ejector malfunction before and after the collision.

[0103] In area 105 at the bottom right of the screen, a list of parts to be replaced is displayed in order of "damage level," specifically divided into "damage caused by collision" and "damage other than the collision site." The former represents "front bumper," and the latter represents "injector" and "engine ASSY." As a result of the damage level order, "injector" is listed above "engine ASSY."

[0104] Figure 4This section presents another example of a display on the display unit 60 intended for users or repair shops. In this example, the text message "The injector's injection volume has decreased due to an accident. Please take a picture of the data after the accident and send it. Please consult your dealer if the vehicle is not working." is displayed in area 201 at the top of the screen. In area 202 on the left side of the screen, photos showing the fuel injection status of the injector are displayed as evidence of injector malfunction. The photo after the accident is recorded in area 202a, and the photo before the accident is recorded in area 202b. The photo in area 202a is displayed because the user or other party sends photo data based on the aforementioned text message, and is currently blank. In the photo before the accident 202b, the date and time of the shooting are marked as "Data acquisition date and time before the accident: 12 / 24 12:01" and "Data acquisition source: Data taken during vehicle inspection."

[0105] In area 203 at the bottom left of the screen, there is a description titled "Positional Relationship between Collision Site and Corresponding Components," which is consistent with the top-view description of the vehicle. Figure 1 The image includes photographs of the damaged parts, namely the injectors, whose appearance is not clearly visible. The top-view explanatory diagram shows the location of the injectors and the point of impact. Additionally, in area 204 on the right side of the image, a bar graph comparing the degree of anomalousness of the injectors before and after the impact is included as evidence of injector malfunction.

[0106] Figure 5 This is an example of a display in the display unit 60 when the correction unit 41 determines that the driver intentionally changed driving characteristics after a collision, including sudden acceleration and sudden braking. In this example, the text message "Because intentional deterioration driving was detected, insurance for the damaged parts only impacted is applied" is displayed in area 301 at the top of the screen. In area 302 on the left side of the screen, a map is displayed along with the title "Location of Sudden Braking and Acceleration" showing the driving range considered to have been engaged in sudden braking and acceleration, and the location of the sudden braking and acceleration is also displayed on the map. In addition, the text message "[Confirmation Index] Number of Sudden Braking and Accelerations" and "[Before the Accident] 1 time → [After the Accident] 6 times" is displayed in area 303 at the top right of the screen. Moreover, the text message "[Confirmation Index] Number of Sudden Braking and Accelerations" is displayed in area 304 at the bottom right of the screen. Figure 3 The same damage order of the parts replacement list, and in area 305 below it, with Figure 4 Similarly, the description is the same as the top view showing the collision area. Figure 1 The document contains photos of the front bumper as a replacement part.

[0107] In this example, because of the intentional sudden braking and acceleration, for example... Figure 3In the example, the part outside the collision zone, namely the injector, was excluded from the list of replacement parts, and only the front bumper that directly applied the collision energy was loaded.

[0108] In this way, based on the comparison of the first degree of anomaly before the collision and the second degree of anomaly after the collision, specific vehicle parts that are affected by certain deterioration or internal damage, even though they are outside the collision area, are clearly indicated to users or insurance companies, thereby making it easier to resolve insurance claims and other issues between the parties involved.

[0109] Figure 2 The processing flow in the vehicle data analysis apparatus of the first embodiment is illustrated by a flowchart. First, various types of vehicle data are acquired (step 1), and these vehicle data are associated with each vehicle component (association) (step 2). Next, based on the vehicle data acquired in step 1, a first anomaly degree is calculated for each vehicle component (step 3). This first anomaly degree is repeatedly calculated during driving. As described above, its progression can also be predicted.

[0110] Next, collision detection is performed in step 4. Before a collision is detected, the calculation of the first anomaly degree continues. After a collision is detected, the process proceeds to step 5, where vehicle data is acquired during post-collision autonomous driving. If the vehicle cannot drive autonomously, in step 5, instead of acquiring vehicle data based on driving, post-collision vehicle data is acquired from past accident data or accident simulation data stored in the vehicle information database 90 as described above, for similar collisions. Then, in step 6, the post-collision anomaly degree, i.e., the second anomaly degree, is calculated for each vehicle component in the same manner as the first anomaly degree.

[0111] Next, in step 7, the first and second anomalies of each vehicle component are compared. In step 8, it is determined whether there are any vehicle components whose second anomaly is greater than the first anomaly by a predetermined value, i.e., the difference between the two is greater than a predetermined threshold. If the determination in step 8 is "NO", then proceed to step 9, and only specific faulty components (damaged components) at the collision site (collision area) are selected. Then, proceed to step 10, and send the necessary information to be displayed to the display unit 60 (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) and display it.

[0112] On the other hand, if the determination in step 8 is "YES", then the process proceeds to step 11, where the driving characteristics at the time of the first anomaly calculation and the driving characteristics at the time of the second anomaly calculation 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, then there is no intentional deterioration of the second anomaly, and the process proceeds to step 13. In step 13, vehicle parts whose second anomaly is greater than the first anomaly by a predetermined value are identified as vehicle parts that will be affected by deterioration or internal damage even outside the collision zone. Then, the process proceeds to step 10, where the necessary information to be displayed (part information, the behavior of the corresponding part before and after the accident, photos, sound data, etc.) is sent to the display unit 60 and displayed.

[0113] If, in step 12, it is determined that the difference between the two driving characteristics is large, then there is an intentional deterioration of the second degree of abnormality, and the process proceeds to step 9. In this case, as described above, only the specific faulty component (damaged component) at the collision site (collision area) is displayed.

[0114] Next, a second embodiment of the present invention will be described. In the second embodiment, even if a vehicle component experiences a significant increase in the second degree of anomalousness following a collision, it is excluded from insurance claims if the increase is solely due to the loosening of fastening components, such as fastening nuts. That is, since the deterioration of the anomalousness caused by the loosening of fastening nuts is not an inherent deterioration or malfunction of the vehicle component itself, it is treated differently.

[0115] Figure 6 This is a functional block diagram of the data analysis device according to the second embodiment. The data analysis device is the same as that in the first embodiment, and includes a data acquisition unit 10, an anomaly 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.

[0116] The data acquisition unit 10, the anomaly 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.

[0117] The fastening confirmation unit 70 confirms whether the fastening components of fixed vehicle parts (especially vehicle parts judged by the comparison unit 40 to have a second anomaly degree greater than a predetermined value than the first anomaly degree) have become loose due to a collision. For example, the technology of monitoring the tightening torque of fastening components such as bolts and nuts and communicating it to external devices is known (Japanese Patent Application Laid-Open No. 2006-346784, etc.), and using such technology, information on the tightening torque is obtained for each vehicle part. Moreover, for vehicle parts where the second anomaly degree is greater than a predetermined value than the first anomaly degree, for example, if the tightening torque is below a reference value, it is determined that the fastening component is loose, and a notification or instruction to perform tightening work or inspection is displayed to the user or repair shop, for example, via the sending unit 50 and one or more display units 60. In addition, if the vehicle continues to be driven in such a state of loose fastening components, the anomaly of the vehicle part may further deteriorate or cause unexpected malfunctions. Therefore, the user is notified via the sending unit 50 and the display unit 60 to control driving and perform inspection. Furthermore, even vehicle parts whose second anomaly level is greater than a specified value than the first anomaly level are distinguished and displayed as vehicle parts not subject to insurance claims if the tightening torque is below the benchmark value. These vehicle parts are essentially those that can be easily tightened simply by fastening bolts, nuts, and other fasteners.

[0118] Figure 8 This example illustrates a situation where the tightening torque of a fastening component is below a reference value, as shown on a user's display unit 60 (e.g., a user's smartphone or a vehicle's display). In this example, in area 401 at the top of the screen, a text message is displayed: "There is a section with insufficient tightening torque. Please control the vehicle and check immediately. The section to be tightened is outside the insurance coverage." In area 402 on the left side of the screen, the text indicating that the tightening torque is below the reference value is displayed as "Section to be tightened: Engine ASSY." In area 403 below this, a photograph of the engine assembly, which is the section requiring tightening, is displayed along with a photograph of the engine compartment. These photographs are obtained from the aforementioned component list database. Additionally, in area 404 at the top right of the screen, a bar graph is displayed comparing the pre-collision anomaly level (i.e., the first anomaly level) and the post-collision anomaly level (i.e., the second anomaly level) of the relevant engine assembly. The same display can also be shown at terminals such as insurance companies or repair shops.

[0119] By displaying these details, users can identify loose bolts, nuts, and other parts caused by a collision, allowing them to have them inspected and repaired at a repair shop before an unforeseen malfunction occurs. Furthermore, by explicitly indicating items not covered by insurance claims, disputes between parties are reduced.

[0120] then, Figure 7The processing flow in the vehicle data analysis device of the second embodiment is illustrated by a flowchart. Similar to the first embodiment, except for confirming the tightening torque of the fastening components, various vehicle data are first acquired (step 1), and these vehicle data are then associated with each of the vehicle components (association) (step 2). Next, based on the vehicle data acquired in step 1, a first anomaly degree is calculated for each vehicle component (step 3).

[0121] Next, collision detection is performed in step 4. After a collision is detected, the process proceeds to step 5, where vehicle data is acquired during post-collision autonomous driving. If the vehicle cannot drive autonomously, in step 5, instead of acquiring driving-based vehicle data, post-collision vehicle data is obtained from the vehicle information database 90 based on past accident data, etc. In step 6, the post-collision anomaly degree, i.e., the second anomaly degree, is calculated for each vehicle component, similar to the first anomaly degree calculation.

[0122] Next, in step 7, the first and second anomalies of each vehicle component are compared. In step 8, it is determined whether there are any vehicle components whose second anomaly is greater than the first anomaly by a predetermined value, i.e., the difference between the two is greater than a predetermined threshold. If the determination in step 8 is "no", then proceed to step 9, and only specific faulty components (damaged components) at the collision site (collision area) are selected. Then, proceed to step 10, and send the necessary information to be displayed to the display unit 60 (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) and display it.

[0123] On the other hand, if the determination in step 8 is "yes", then proceed to step 11 to calculate the driving characteristics at the time of the first anomaly calculation and the driving characteristics at the time of the second anomaly calculation, and compare them. Then, in step 12, determine whether the difference between these two driving characteristics is small. That is, determine whether there is an intentional deterioration of the second anomaly.

[0124] If it is determined in step 12 that the difference between the two driving characteristics is small, then proceed from step 12 to step 13, and assume that the vehicle component whose second anomaly degree is greater than the first anomaly degree by a specified value is a vehicle component that will be affected by deterioration or internal damage even outside the collision area.

[0125] In the second embodiment, the process then proceeds to step 21, where the tightening torque of the fastening components for each vehicle component is confirmed. That is, the tightening torque is compared with a reference value.

[0126] Then, in step 22, it is determined whether there is a vehicle component whose second anomaly degree is greater than a predetermined value by a specified amount and whose tightening torque is less than a reference value. If the answer is "no," then step 23 is performed, and the vehicle component whose second anomaly degree is greater than a predetermined value by a specified amount is ultimately identified as a vehicle component that would be affected by deterioration or internal damage even outside the collision zone. Then, step 10 is performed, and the necessary information to be displayed (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) is sent to the display unit 60 and displayed. The display at this time is consistent with... Figure 3 and Figure 4 The example shown is the same.

[0127] On the other hand, in step 22, if it is determined that there is a vehicle component with a second anomaly degree greater than the first anomaly degree by a predetermined value and a tightening torque less than the reference value, step 24 is entered, and an inspection and tightening instruction is notified to the repair shop (e.g., a dealership), and in step 25, the user is notified to control the operation of the vehicle. Then, in step 26, vehicle components with a second anomaly degree greater than the first anomaly degree are finally identified as vehicle components that will be affected by deterioration or internal damage even outside the collision area, in the form of excluding vehicle components with insufficient tightening torque from the insurance claim object. Finally, in step 10, the necessary information to be displayed (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) is sent to the display unit 60 and displayed. In this case, as follows Figure 8 The display shown is shown below.

[0128] Next, a third embodiment of the present invention will be described. In the third embodiment, vehicle parts whose second degree of anomalousness increases due to foreign objects (such as stones, fragments of parts, water, etc., scattered by the impact of an accident) are mixed into or enter the vehicle during a collision and are excluded from the scope of insurance claims. That is, the deterioration of the degree of anomalousness caused by the mixing of foreign objects is not an inherent deterioration or malfunction of the vehicle part itself, so it is treated differently.

[0129] Figure 9 This is a functional block diagram of the data analysis device according to the third embodiment. This data analysis device is the same as that in the first embodiment, comprising a data acquisition unit 10, an anomaly calculation unit 20, a collision detection unit 30, a comparison unit 40, a transmission unit 50, and a display unit 60, and further comprising a foreign object detection unit 80.

[0130] The data acquisition unit 10, the anomaly 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.

[0131] The foreign object detection unit 80 detects the intrusion of foreign objects into various parts of the vehicle during a collision, particularly those related to the abnormality of vehicle components whose second abnormality is determined by the comparison unit 40 to be greater than a predetermined value than the first abnormality. For example, it uses techniques such as object detection based on an onboard camera, distance measurement based on lasers, sonar, etc., or foreign object detection based on brightness values ​​to detect foreign objects intruding into specific parts of the vehicle. Similar to collision detection, detection can also be based on methods outside the vehicle, such as information transmitted from dashcams of vehicles before and after the accident. When foreign object intrusion is detected, the unit 50 displays a notification or instruction to remove or inspect the foreign object on one or more display units 60, for example, to the user or a repair shop. Furthermore, if driving continues with foreign objects present, the abnormality of vehicle components may worsen or unexpected malfunctions may occur; therefore, the unit 50 and display units 60 notify the user to control driving and perform an inspection. Furthermore, even vehicle parts whose second anomaly level is greater than the first anomaly level by a specified value are distinguished as vehicle parts not subject to insurance claims if foreign objects are involved. These vehicle parts are essentially those that can be eliminated simply by removing the foreign objects.

[0132] Figure 11 This describes a display example on a user's display unit 60 (e.g., a user's smartphone or a vehicle's display) when a foreign object is detected. In this example, in area 501 at the top of the screen, the text message "Foreign object present. Please control the vehicle and inspect immediately. The foreign object is outside the insurance coverage." is displayed. In area 502 on the left side of the screen, the location where the foreign object was detected is displayed as "Foreign object present at turbocharger." In area 503 at the bottom left of the screen, the text "Please remove the foreign object if possible. Please send a photo of the affected part and indicate whether it has been removed." is displayed. Between areas 502 and 503, along with a title indicating "After the accident," there is area 504 where an image is displayed if the user or others send image data as instructed in area 503. Additionally, in area 505 to the right of area 504, there are buttons 505a and 505b labeled "Implemented" and "Not Implemented" along with the title "Foreign Object Removal." Users can select either one according to the instructions in area 503. Furthermore, in area 506 at the upper right of the screen, there is a bar graph comparing the degree of anomalousness of the turbocharger before the collision (i.e., the first anomalousness) and the degree of anomalousness after the collision (i.e., the second anomalousness). This same display can also be shown on terminals in insurance companies or repair shops.

[0133] By displaying this information, users can identify areas where foreign objects may have entered, allowing for inspection and repair at a repair shop before any unexpected malfunction occurs. Furthermore, by clearly indicating items not covered by insurance claims, disputes between parties are reduced.

[0134] then, Figure 10 The process flow in the vehicle data analysis device of the third embodiment is illustrated by a flowchart. Similar to the first embodiment, except for confirming the presence of foreign matter, various vehicle data are first acquired (step 1), and these vehicle data are then associated with each of the vehicle components (association) (step 2). Next, based on the vehicle data acquired in step 1, a first anomaly degree is calculated for each vehicle component (step 3).

[0135] Next, collision detection is performed in step 4. After a collision is detected, the process proceeds to step 5, where vehicle data is acquired during post-collision autonomous driving. If the vehicle cannot drive autonomously, in step 5, instead of acquiring driving-based vehicle data, post-collision vehicle data is obtained from the vehicle information database 90 based on past accident data, etc. In step 6, the post-collision anomaly degree, i.e., the second anomaly degree, is calculated for each vehicle component, similar to the first anomaly degree calculation.

[0136] Next, in step 7, the first and second anomalies of each vehicle component are compared. In step 8, it is determined whether there are any vehicle components whose second anomaly is greater than the first anomaly by a predetermined value, i.e., the difference between the two is greater than a predetermined threshold. If the determination in step 8 is "no", then proceed to step 9, and only specific faulty components (damaged components) at the collision site (collision area) are selected. Then, proceed to step 10, and send the necessary information to be displayed to the display unit 60 (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) and display it.

[0137] On the other hand, if the determination in step 8 is "yes", then proceed to step 11 to calculate the driving characteristics at the time of the first anomaly calculation and the driving characteristics at the time of the second anomaly calculation, and compare them. Then, in step 12, determine whether the difference between these two driving characteristics is small. That is, determine whether there is an intentional deterioration of the second anomaly.

[0138] If it is determined in step 12 that the difference between the two driving characteristics is small, then proceed from step 12 to step 13, and assume that the vehicle component whose second anomaly degree is greater than the first anomaly degree by a specified value is a vehicle component that will be affected by deterioration or internal damage even outside the collision area.

[0139] In the third embodiment, the next step is to proceed to step 31, whereby the vehicle components are checked to see if any foreign objects have been mixed in.

[0140] Then, in step 32, it is determined whether there is a vehicle component whose second anomaly degree is greater than a predetermined value than the first anomaly degree and foreign matter intrusion is detected. If the answer is "no," then step 33 is performed, and the vehicle component whose second anomaly degree is greater than the first anomaly degree is ultimately identified as a vehicle component that would be affected by deterioration or internal damage even outside the collision zone. Then, step 10 is performed, and the necessary information to be displayed (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) is sent to the display unit 60 and displayed. The display at this time is consistent with... Figure 3 and Figure 4 The example shown is the same.

[0141] On the other hand, in step 32, if it is determined that a vehicle component has a second anomaly degree that is greater than a predetermined value than the first anomaly degree and foreign matter has been detected, step 34 is initiated, and an instruction to inspect and remove the foreign matter is sent to a repair shop (e.g., a dealership). In step 35, the user is notified to control the vehicle's operation. Then, in step 36, vehicle components with a second anomaly degree greater than the first anomaly degree are excluded from insurance claims, and are ultimately designated as vehicle components that would be affected by deterioration or internal damage even outside the collision zone. Finally, in step 10, the necessary information to be displayed (component information, the behavior of the corresponding component before and after the accident, photos, sound data, etc.) is sent to the display unit 60 and displayed. In this case, the following steps are performed: Figure 11 The display shown is shown below.

[0142] The present invention has been described above as an embodiment, but the present invention is not limited to the above embodiment and various modifications can be made. For example, the above example of suspension-related anomalies is merely an example for illustration. In the present invention, anomalies can also be grasped in any known form.

Claims

1. A vehicle data analysis method, wherein, Data about vehicle components is acquired while the vehicle is in motion, and based on this data, the degree of anomaly of the vehicle components as they change with vehicle use is repeatedly calculated during driving. Detecting vehicle collisions, By predicting the anomaly degree as a series of values ​​calculated up to the moment of collision, the anomaly degree at a certain comparison moment from the moment of collision is estimated and used as the first anomaly degree. Data about the vehicle components after the collision is acquired during the vehicle's self-driving process. Based on this data, the anomaly degree of the vehicle components at the comparison time is calculated and used as a second anomaly degree. Compare the first and second anomalies.

2. The vehicle data analysis method as described in claim 1, wherein, When the difference between the first anomaly and the second anomaly is above a predetermined threshold, information about the vehicle component that is affected by the collision is output.

3. The vehicle data analysis method as described in claim 1, wherein, A comparison of the first and second anomalies was performed on multiple vehicle components.

4. The vehicle data analysis method as described in claim 1, wherein, The first and second anomalies are corrected based on vehicle location data, weather data, and driver driving characteristic data.

5. The vehicle data analysis method as described in claim 2, wherein, The system acquires driver characteristic data before and after the collision. When it determines that there is an intentional change in driving characteristics, it does not perform a comparison of the first and second anomalies or output vehicle component information.

6. The vehicle data analysis method as described in claim 1, wherein, Data on the tightening torque of the fastening components that secure the vehicle parts is obtained, and the tightening torque after the collision is compared with a reference value.

7. The vehicle data analysis method as described in claim 6, wherein, If the tightening torque after a collision is below the baseline value, send an inspection request to the vehicle's repair shop.

8. The vehicle data analysis method as described in claim 6, wherein, If the difference between the first and second anomalies exceeds a specified threshold, the vehicle component will be included in the insurance claim candidate. If the tightening torque after a collision is below the baseline value, the vehicle part is excluded from the insurance claim pool.

9. The vehicle data analysis method as described in claim 6, wherein, If the tightening torque after a collision is below the baseline value, the vehicle user should be notified to take control of the vehicle and conduct an inspection.

10. The vehicle data analysis method as described in claim 1, wherein, Obtain information related to the mixing of foreign objects into vehicle components caused by a collision.

11. The vehicle data analysis method as described in claim 10, wherein, When a foreign object is detected mixed into a vehicle component, an inspection request is sent to the vehicle's repair shop.

12. The vehicle data analysis method as described in claim 10, wherein, If the difference between the first and second anomalies exceeds a specified threshold, the vehicle component will be included in the insurance claim candidate. If a foreign object is detected mixed into a vehicle component, that vehicle component will be excluded from the insurance claim pool.

13. The vehicle data analysis method as described in claim 10, wherein, When a foreign object is detected mixed into a vehicle component, the vehicle user is notified to control driving and conduct an inspection.

14. The vehicle data analysis method as described in claim 1, wherein, Upon detection of a collision, the driving data at the time of the collision, information related to the pre-collision and post-collision states of the vehicle components of the object, and the data that forms the basis for calculating the second anomaly are sent to at least one of the following parties: insurance personnel, police personnel, and legal representatives.

15. The vehicle data analysis method as described in claim 1, wherein, Calculate the difference between the first and second anomalies for multiple vehicle components. Vehicle components are displayed on the display unit in descending order of their differences.

16. The vehicle data analysis method as described in claim 1, wherein, Among multiple vehicle components, extract those whose difference between the first and second anomalies is above a specified threshold. Information related to the collision is obtained, and based on this information, the vehicle components are classified into those that were directly subjected to collision energy and those that were indirectly subjected to collision energy. The two are distinguished and displayed on the display section.

17. The vehicle data analysis method as described in claim 1, wherein, For vehicle components where the difference between the first and second anomalies exceeds a specified threshold, an appropriate maintenance policy is determined. Calculate the estimated repair price according to this maintenance policy. Display them on the display panel.

18. The vehicle data analysis method as described in claim 1, wherein, The system acquires driver characteristic data before and after a collision, and displays the corresponding driving range on the display unit when it determines that there is an intentional change in driving characteristics.

19. A vehicle data analysis device, comprising: The data acquisition department acquires data that forms the basis for calculating the anomalies of vehicle components during operation; The collision detection unit detects vehicle collisions. Anomaly calculation unit, for the vehicle component of the object, estimates the anomaly from the time of collision to a certain comparison time by predicting the shift of the anomaly calculated up to the time of collision, and uses it as the first anomaly. It calculates the anomaly of the vehicle component at the comparison time based on the driving data obtained during the self-driving of the vehicle after the collision, and uses it as the second anomaly. The comparison section compares the first anomaly score with the second anomaly score.

20. A vehicle data analysis method, wherein, Data about vehicle components during the driving process before the collision is acquired, and the anomaly degree of the vehicle component is calculated based on this data as the first anomaly degree. Detecting vehicle collisions, The anomaly degree of the vehicle components after the collision is calculated as the second anomaly degree. Compare the first and second anomalies. If the difference between the first and second anomalies exceeds a specified threshold, the vehicle component will be included in the insurance claim candidate. This allows us to obtain information related to the current vehicle components. When the abnormality of at least some of the vehicle components falls under the pre-defined abnormality category, The vehicle part was excluded from the insurance claim pool.

21. The vehicle data analysis method as described in claim 20, wherein, The pre-defined abnormal situation refers to an abnormal situation that can be eliminated without replacing the vehicle parts.

22. The vehicle data analysis method as described in claim 21, wherein, Data on the fastening torque of the fastening components that secure the vehicle parts is obtained, and the fastening torque after the collision is compared with a reference value.

23. The vehicle data analysis method as described in claim 22, wherein, If the tightening torque after a collision is below the baseline value, send an inspection request to the vehicle's repair shop.

24. The vehicle data analysis method as described in claim 22, wherein, If the tightening torque after a collision is below the baseline value, the vehicle part is excluded from the insurance claim pool.

25. The vehicle data analysis method as described in claim 22, wherein, If the tightening torque after a collision is below the baseline value, the vehicle user should be notified to take control of the vehicle and conduct an inspection.

26. The vehicle data analysis method as described in claim 21, wherein, Obtain information related to the mixing of foreign objects into vehicle components caused by a collision.

27. The vehicle data analysis method as described in claim 26, wherein, When a foreign object is detected mixed into a vehicle component, an inspection request is sent to the vehicle's repair shop.

28. The vehicle data analysis method as described in claim 26, wherein, If a foreign object is detected mixed into a vehicle component, that vehicle component will be excluded from the insurance claim pool.

29. The vehicle data analysis method as described in claim 26, wherein, When foreign objects are detected mixed into vehicle components, the vehicle user is notified to control driving and conduct an inspection.

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