Data backup management method and system for automobile data recorder

Abstract

The application discloses a data backup management method and system for a driving recorder, and relates to the technical field of data backup management.The application solves the technical problems of data storage structure and security loss, passive and inefficient storage space optimization, calculates the frequency of use through a quantitative standard, accurately divides high-frequency / middle-frequency / low-frequency scenarios, and matches differentiated backup strategies, thereby avoiding backup lag of high-frequency scenario data, reducing redundancy of low-frequency scenario, improving backup efficiency, establishing a three-level storage system, separately classifying core event data, shortening search time, setting three levels of permissions of administrators, ordinary users and temporary authorized users, effectively preventing unauthorized access, improving the privacy and security protection capability of core data, dynamically optimizing low-frequency non-important data according to data access frequency and importance, and transferring the low-frequency non-important data to cold storage, and using H.265 coding for high-frequency data to improve the utilization rate of local storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data backup management, in particular to a data backup management method and system for a driving recorder. BACKGROUND

[0002] With the continuous growth of the number of cars and the improvement of road traffic safety awareness, driving recorders have become a standard equipment for vehicles. The core value of driving recorders lies in recording daily driving trajectories, providing key evidence for traffic collisions / accidents, and meeting users' recording needs for scenery and special road conditions. Currently, driving recorders generally support 4K high-definition recording, and the data volume per hour can reach 3-5GB. In addition, users have diverse driving scenarios, and the amount of driving record data is growing explosively, which puts higher requirements on the efficiency, security and storage optimization of data management.

[0003] In the prior art, data storage is based on a single dimension of date, and lacks structured classification, resulting in mixed core event data and daily data, low retrieval efficiency, and limited duplicate checking function based on single attribute comparison such as file name and file size, which cannot identify fragment duplication or format difference duplication, and has high misjudgment rate.

[0004] Secondly, only when the local storage is full does it remind the user to manually delete, without dynamic optimization of data access frequency and importance; data recovery relies on a single backup node, and after core event data is damaged or the backup fails, there is no effective reconstruction mechanism, which easily loses the value of evidence. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a data backup management method and system for a driving recorder, which solves the problems of lack of structured data storage and security, and passive and inefficient storage space optimization.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a data backup management method for a driving recorder, which specifically includes the following steps:

[0007] Step one, counting the effective driving times in a preset period by the driving recorder, calculating the usage frequency, comparing it with a preset comparison threshold, dividing it into three categories of high-frequency use, medium-frequency use and low-frequency use, and performing corresponding backup processing to generate backup processing information corresponding to the scene;

[0008] Step two, obtaining the backup driving record data based on the backup processing information, performing hierarchical storage, obtaining the driving record data in each storage level, identifying the completely repeated data, fragment repeated data and difference repeated data through a multi-level duplicate checking method, and then forming a repeated classification set;

[0009] Step three, different repeated types in the repeated classification set are processed respectively, for the completely repeated data, the association with the abnormal event is judged, secondary confirmation or recycle bin transfer is performed according to the association result, and the storage node index is updated synchronously; for the fragment repeated data, the timeline view and key frame comparison information are generated, and the retention suggestion is generated in combination with the data integrity and key degree; for the difference repeated data, whether it is repeated data is judged according to the feature coincidence degree and time watermark consistency, and the merging or retention processing is performed;

[0010] Step four, the remaining space of each storage node is checked regularly, when the remaining space is lower than the preset threshold, the space release mechanism is triggered, the data is divided into high-frequency access data, medium-frequency access data and low-frequency access data according to the access frequency, the low-frequency access data is transferred to the cold storage device or the cloud storage low-price storage layer; the high-frequency access data is checked and converted by using a more efficient compression algorithm;

[0011] Step five, the integrity of the driving record data is checked regularly by using the checksum or hash value comparison method, if the data is damaged, the local backup or cloud backup is recovered preferentially; if the backup is not available, the data reconstruction process is started for the key data, the data recovery and reconstruction process information is recorded, and the process log is formed.

[0012] As a further scheme of the application, the way of dividing into three types of high-frequency use, medium-frequency use and low-frequency use is:

[0013] If the use frequency is greater than P max , it is classified as high-frequency use, if the use frequency is in the comparison threshold interval, it is classified as medium-frequency use, and if the use frequency is less than P min , it is classified as low-frequency use.

[0014] As a further scheme of the application, the way of generating backup processing information corresponding to the scene is:

[0015] For the high-frequency use scene, fixed backup once a week + emergency scene immediate backup are adopted, and the high-frequency backup processing information is generated, wherein the emergency scene includes scratching and accident.

[0016] For the medium-frequency use scene, backup within twelve hours after each use + monthly comprehensive arrangement are adopted, and the medium-frequency backup processing information is generated.

[0017] For the low-frequency use scene, single trip immediate backup + final backup before parking are adopted, and the low-frequency backup processing information is generated.

[0018] As a further scheme of the application, the multi-level duplicate checking way is:

[0019] The first level is that if two data time stamps are completely same, it is determined that the data is completely repeated data; if the time stamps are partially overlapped, it is determined that the data is partially repeated data and enters the second level verification;

[0020] The second level is that if the length, file size and resolution of two data are completely same, it is determined that the data is repeated data; if any parameter is different, it is determined that the data is different repeated data and enters the third level identification;

[0021] The third level is that whether the data content is completely repeated is determined through image feature comparison or video content analysis, and then the segment repeated data and the different repeated data are distinguished.

[0022] As a further scheme of the present application, the processing details of the completely repeated data include:

[0023] If the completely repeated data is associated with an abnormal event, a user secondary confirmation pop-up window is triggered, and after the user confirms, subsequent operations are executed;

[0024] If the completely repeated data is not associated with an abnormal event, it is directly transferred to a repeated data recycling station, and the default retention time t of the recycling station is set by an operator;

[0025] After the recycling station is transferred, the indexes of the local core node, the local extension node and the cloud node are synchronously updated, and after synchronization is completed, an index verification report is generated.

[0026] As a further scheme of the present application, the processing details of the segment repeated data include:

[0027] In the time axis view, the original file is marked with red, the repeated segment is marked with blue, and the overlapping area is highlighted with yellow, 3-5 key frames of the overlapping area are automatically extracted, including the accident core frame or the road sign frame;

[0028] If the segment is an accident core frame, it is suggested to retain the original file + repeated segment and mark the associated ID; if the segment is a daily commuting ordinary segment, it is suggested to retain only the original file.

[0029] As a further scheme of the present application, the processing details of the different repeated data include:

[0030] The video key frame is converted into an 8x8 grayscale image by using a Python script, a dHash value is generated, the similarity is calculated through a Hamming distance, the similarity corresponding to the Hamming distance ≤5 is ≥80%, for the video with a dHash similarity ≥70%, 1 frame per second is sampled, and the road marking and time watermark features of each frame are extracted by using OpenCV;

[0031] If the coincidence frame proportion is ≥75% and the time watermark is completely consistent, it is determined that the data is repeated data, the repeated data is processed and retained in one, and a merging log is recorded; if the coincidence frame proportion is <75% or the time watermark is inconsistent, it is determined that the data is not repeated data, and two are retained and a difference description is added.

[0032] As a further scheme of the present application, the quantitative standard of data access frequency is:

[0033] High-frequency access data, effective access ≥ 3 times in the last 30 days;

[0034] Medium-frequency access data, effective access 1-2 times in the last 30 days;

[0035] Low-frequency access data, effective access 0 times in the last 30 days and effective access ≤ 1 time in the last 90 days.

[0036] As a further scheme of the present application, the quantitative standard of important data is:

[0037] Core important data, accident, scratch, violation related data, or user manually marked important data;

[0038] General important data, special scene record;

[0039] Unimportant data, daily commuting records without special scenes, redundant data backed up to 3 or more nodes.

[0040] The data backup management system for a car recorder comprises:

[0041] A scene classification calculation module is configured to automatically count the number of effective vehicle uses in a time period through a car recorder supporting APP, and calculate the use frequency according to the formula use frequency = effective vehicle use number / time period, match the use frequency with a preset comparison threshold, divide the use frequency into high-frequency use, medium-frequency use and low-frequency use scenes, and transmit the classified use scenes to a data backup processing module;

[0042] The data backup processing module is configured to perform corresponding backup processing according to different use scenes, the high-frequency use scene adopts fixed weekly backup + emergency scene immediate backup, the medium-frequency use scene adopts backup within 12 hours after the end of vehicle use + monthly overall arrangement, and the low-frequency use scene adopts immediate backup after a single trip ends + final backup before parking, generate backup processing information corresponding to the scenes, and transmit the backup processing information to a hierarchical storage module;

[0043] The hierarchical storage module is configured to acquire car recording data based on the backup processing information, store the data in a three-level hierarchy of year-month-event type, set access permissions for each level, and identify complete duplicate data, fragment duplicate data and differential duplicate data through a hierarchical process of time stamp comparison, time length / file size / resolution verification and data content identification, form a duplicate classification set, and transmit the duplicate classification set to a duplicate data processing module;

[0044] The repeated data processing module is used for type processing of the repeated classification set, and the complete repeated data is subjected to secondary confirmation or recycle station transfer according to the association with the abnormal event; a fragment repeated data generation timeline view and key frame comparison information are generated, a retention suggestion is generated and the associated relationship is persisted; difference repeated data is subjected to dHash algorithm and OpenCV frame feature extraction to determine whether it is repeated or not, and is subjected to merging or retention processing, and repeated data processing information is generated and transmitted to the space optimization module;

[0045] The space optimization module is used for periodically checking the remaining space of the storage node, triggering space release when the remaining space is lower than a preset threshold, transferring non-important data in the low-frequency access data to cold storage when the data is over the preset period, and using an efficient compression algorithm / encoding format conversion for high-frequency access data.

[0046] The application provides a data backup management method and system for a driving recorder.

[0047] The application calculates the use frequency by a quantitative standard, accurately divides high-frequency / middle-frequency / low-frequency scenarios, and matches a differentiated backup strategy, thereby avoiding backup lag of high-frequency scenario data, reducing redundancy of low-frequency scenario data, improving backup efficiency, establishing a three-level storage system, classifying core event data separately, shortening search time, setting three levels of permissions for administrators, ordinary users and temporary authorized users, effectively preventing unauthorized access, improving core data privacy security protection capability, dynamically optimizing according to data access frequency and importance, transferring low-frequency non-important data to cold storage, and using H.265 encoding for high-frequency data, thereby improving local storage utilization. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The figure is a step method of the application;

[0049] Figure 2 The figure is a system block of the application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0051] First embodiment

[0052] Please refer to Figure 1 The application provides a data backup management method for a driving recorder, which specifically includes the following steps:

[0053] Step one, automatically counting the number of effective vehicle use times in the time period T through the supporting APP of the driving recorder, and the effective vehicle use times represent the number of times from vehicle ignition to engine off, and the driving time is greater than or equal to 5 minutes, and the usage frequency is calculated according to the formula usage frequency = effective vehicle use times / time period, and the usage frequency is calculated to one decimal place, and the usage frequency is matched with the comparison threshold preset by the operator, and the comparison threshold is the interval value composed of P min and P max If the usage frequency is greater than P max , it is classified as high frequency use, if the usage frequency is in the comparison threshold interval, it is classified as medium frequency use, and if the usage frequency is less than P min , it is classified as low frequency use.

[0054] Backup processing is carried out based on different frequency usage, and the backup processing includes backup frequency and backup mode. For high frequency usage, the mode of fixed backup once a week + emergency scene instant backup is adopted, emergency scenes include scratching and accidents, and high frequency backup processing information is generated. For medium frequency usage, the mode of backup within twelve hours after each vehicle use + monthly comprehensive arrangement is adopted, and medium frequency backup processing information is generated. For low frequency usage, the mode of immediate backup after single trip + final backup before parking is adopted, and low frequency backup processing information is generated.

[0055] Step two, based on the generated backup processing information, storage management is carried out, the backup driving record data is obtained, and the driving record data is stored in the corresponding level storage space according to the annual-month-event type hierarchical storage of the driving record data. Among them, the year is the highest level, which is used to distinguish the driving records of different years; the month is the intermediate level, which further subdivides the driving records of different months in the same year; the event type is the lowest level, which classifies and stores the driving records in each month according to specific event types such as normal driving, scratching and accidents. At the same time, the corresponding access permission is set for each storage level to ensure the security and privacy of the data, and only authorized users are allowed to access the data of a specific level.

[0056] Then the data in the storage space is acquired, and the data is subjected to a duplicate checking process. The specific duplicate checking process is to acquire the corresponding data timestamp. If the corresponding data timestamp is completely the same, it indicates that the data is duplicate data. If the data timestamp partially overlaps, it indicates that the data is partially duplicate. Then, the time length, file size, and resolution are verified and analyzed. If the time length, file size, and resolution are completely consistent, it indicates that the data is duplicate data. If any of the three is different, it indicates that the data is different duplicate. Then, the corresponding data content is identified. If the data content is completely duplicated, it indicates that the data is duplicate data. In this way, completely duplicated, partially duplicated, and different duplicate data are identified, and are classified according to the duplicate type to obtain a duplicate classification set. The classification specifically indicates that all completely duplicated data is classified, and the partially duplicated data is classified, and the classified data includes the duplicate data and the corresponding original data.

[0057] Step three, processing the obtained duplicate classification set, for the completely duplicated data category, judging the association between the completely duplicated data and the abnormal event. If the two are associated, a user secondary confirmation pop-up window is triggered. If the two are not associated, the duplicate data is directly moved to the recycle bin, and a default buffer period of t is retained. The value of t is set by the operator. After the recycle bin is transferred, the indexes of all storage nodes are updated synchronously. After synchronization is completed, an index verification report is generated, and the index update state of each node is displayed.

[0058] For the partially duplicated data category, the original file is marked in red, the duplicate segment is marked in blue, and the overlapping area is highlighted in yellow. A timeline view is generated, and 3-5 key frames of the overlapping area are automatically extracted. According to the data integrity and keyness, a corresponding retention suggestion is generated. If the segment is an accident core frame, the original file and the duplicate segment are suggested to be retained, and the associated ID is marked. If the segment is a routine commuting ordinary segment, only the original file is suggested to be retained, and the duplicate segment is deleted. The association between the original file and the duplicate segment is stored in the local database and the cloud backup. The associated ID, original file path, segment path, overlapping information, and user retention selection are recorded.

[0059] For differentially repeated data categories, a Python script converts video keyframes into 8x8 grayscale images, generates dHash values, calculates similarity through Hamming distance, and for videos with dHash similarity ≥ 70%, samples at 1 second per frame. Using OpenCV, extract unique features corresponding to road markings and time watermarks, and count the percentage of overlapping frames. If the overlapping frames ≥ 75% and the time watermarks are completely consistent, further confirm content duplication, then consider the differentially repeated data as repeated data, perform merging processing, retain one complete data, and record the merging log, including merging time, merging reason, original data path, and retained data path; If the overlapping frames are less than 75% or the time watermarks are not consistent, it is determined as non-repeated data, and the two data are retained, and the difference is added to the data description for subsequent retrieval and utilization.

[0060] Step four, after completing the repeated data processing, the storage space is optimized and managed, the remaining space of each storage node is checked regularly, and the remaining space is compared with the preset threshold value. When the remaining space is lower than the preset threshold value, the space release mechanism is automatically triggered, and a space release analysis signal is generated;

[0061] According to the data access frequency, the data is divided into high-frequency access data, medium-frequency access data and low-frequency access data, and the specific quantitative standard is that the access ≥ 3 times in the past 30 days is classified as high-frequency access data, the access 1-2 times in the past 30 days is classified as medium-frequency access data, and the access 0 times in the past 30 days and the access ≤ 1 times in the past 90 days is classified as low-frequency access data. For low-frequency access data, if its storage time exceeds the preset period, and the value of the preset period is set by the operator, and it is not marked as important data, and the quantitative standard of important data is that core important data includes accident, scratch, violation related data or user manually marked important data, general important data includes special scene record, non-important data includes daily commuting record without special scene, redundant data backed up to 3 or more nodes, then it is transferred to the cold storage device or the low-price storage layer of the cloud storage to release the local storage space. At the same time, for high-frequency access data, check whether there is a more efficient compression algorithm or encoding format, if there is, then perform data conversion, specifically, video data prefers H.265 / HEVC encoding instead of H.264 / AVC, old device adapts H.264 / AVC encoding, log / text data uses 7-Zip (LZMA2 algorithm) or ZIP64 compression.

[0062] Step five, establish a data backup recovery mechanism to ensure that data can be quickly recovered when data is lost or damaged, and regularly check the integrity of all stored driving record data, using checksum or hash value comparison to ensure data integrity and consistency. If the data is found to be damaged, first try to recover the data from the local backup or cloud backup, if the local backup and cloud backup are not available, according to the importance of the data, start the data reconstruction process, for key data such as accident records, through the log file of the driving recorder, vehicle sensor data and associated external camera data, data reconstruction is performed to restore the original data content as much as possible. At the same time, record the process of data recovery and reconstruction, including recovery time, recovery data volume, recovery success rate, etc. for subsequent analysis and improvement.

[0063] Second embodiment

[0064] Please refer to Figure 2 The application provides a data backup management system for a driving recorder, which comprises a scene classification calculation module, a data backup processing module, a hierarchical storage module, a duplicate data processing module and a space optimization module, and combines Figure 1 It can be known that the information between the above-mentioned functional modules is one-way transmission;

[0065] The scene classification calculation module is used for automatically counting the number of effective vehicle uses in a time period through the driving recorder supporting APP, and calculating the use frequency according to the formula use frequency = effective vehicle use number / time period, and matching the use frequency with a preset comparison threshold to divide the use frequency into high-frequency use, medium-frequency use and low-frequency use scenes, and simultaneously transmitting the classified use scenes to the data backup processing module;

[0066] The data backup processing module is used for executing corresponding backup processing according to different use scenes, and the high-frequency use scene adopts weekly fixed backup + emergency scene instant backup, the medium-frequency use scene adopts backup within twelve hours after the end of vehicle use + monthly comprehensive arrangement, and the low-frequency use scene adopts immediate backup after the end of single trip + final backup before parking, generates backup processing information corresponding to the scenes, and transmits the backup processing information to the hierarchical storage module;

[0067] The hierarchical storage module is used for obtaining driving record data based on the backup processing information, storing the data in a three-level hierarchy of year-month-event type, setting access permissions for each level, and identifying complete duplicate data, fragment duplicate data and differential duplicate data through a hierarchical process of time stamp comparison, time length / file size / resolution verification and data content identification, forming a duplicate classification set, and transmitting the duplicate classification set to the duplicate data processing module;

[0068] The repeated data processing module is configured to type process the repeated classification set, perform secondary confirmation or recycle bin transfer on the complete repeated data according to the relevance with the abnormal event, generate a timeline view and key frame comparison information of the fragment repeated data, generate a retention suggestion and persist the associated relationship, determine whether the difference repeated data is repeated or not through a dHash algorithm and OpenCV frame feature extraction, perform merging or retention processing, generate repeated data processing information, and transmit the repeated data processing information to the space optimization module.

[0069] The space optimization module is configured to periodically check the remaining space of the storage node, trigger space release when the remaining space is lower than a preset threshold, transfer non-important data that is lower than a preset period and is in low-frequency access data to cold storage, and use an efficient compression algorithm / encoding format conversion on high-frequency access data.

[0070] Some data in the above formula are dimensionless for numerical calculation, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0071] The above embodiments are only used to illustrate the technical method of the present application and are not limiting. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A data backup management method for a car video recorder, characterized in that, The method specifically includes the following steps: Step 1: Count the number of valid vehicle uses within a preset period using the dashcam and calculate the usage frequency. Compare the frequency with a preset comparison threshold to classify the usage into three categories: high-frequency, medium-frequency, and low-frequency. Then, perform corresponding backup processing and generate backup processing information for each scenario. Step 2: Based on the backup processing information, obtain the backed-up driving record data, perform hierarchical storage, and obtain the driving record data from each storage level. Through multi-level deduplication methods, identify completely duplicate data, fragmented duplicate data, and differentially duplicate data, thereby forming a duplicate classification set. The specific identification method is as follows: Obtain the corresponding data timestamps. If the corresponding data timestamps are exactly the same, it is considered completely duplicate data. Conversely, if the data timestamps partially overlap, it is considered partially duplicate. Then, it is necessary to combine the duration, file size, and resolution for verification and analysis. If the duration, file size, and resolution are completely the same, it is considered duplicate data. Conversely, if any of the three are different, it is considered differentially duplicate data. Then, it is considered differentially duplicate data. Combine the corresponding data content for identification. If the data content is completely duplicate, it is considered duplicate data. And so on, to identify completely duplicate, fragmented duplicate, and differentially duplicate data. Step 3: Process the different duplicate types in the duplicate classification set separately. For completely duplicate data, determine its correlation with abnormal events, and perform secondary confirmation or recycle bin transfer based on the correlation results, while updating the storage node index synchronously. For fragmented duplicate data, generate a timeline view and keyframe comparison information, and generate retention suggestions based on data integrity and criticality. For differential duplicate data, determine whether it is duplicate data based on feature overlap and time watermark consistency, and perform merging or retention processing. Step 4: Regularly check the remaining space of each storage node. When the remaining space is lower than the preset threshold, trigger the space release mechanism. According to the access frequency, divide the data into high-frequency access data, medium-frequency access data, and low-frequency access data. For low-frequency access data, transfer it to cold storage devices or low-cost cloud storage tiers; for high-frequency access data, check and use more efficient compression algorithms to transform the data. Step 5: Regularly perform integrity checks on driving record data using checksum or hash value comparison. If data corruption is found, prioritize recovery from local or cloud backups. If backups are unavailable, initiate a data reconstruction process for critical data, record data recovery and reconstruction process information, and form a process log.

2. The data backup management method for a car video recorder according to claim 1, characterized in that, The method for classifying usage scenarios into three categories—high-frequency, mid-frequency, and low-frequency—is as follows: If the frequency of use is greater than P max , it is classified as high frequency use, if the frequency of use lies within the comparison threshold interval, it is classified as medium frequency use, and if the frequency of use is less than P min , it is classified as low frequency use.

3. The data backup management method for a car video recorder according to claim 1, characterized in that, The method for generating backup processing information for the corresponding scenario is as follows: For high-frequency usage scenarios, a fixed backup is performed once a week, plus real-time backups for emergency scenarios. Emergency scenarios include scratches and accidents, generating high-frequency backup processing information. For mid-frequency usage scenarios, backups are performed within twelve hours after each vehicle use, plus a comprehensive monthly cleanup, generating mid-frequency backup processing information. For low-frequency usage scenarios, a backup is performed immediately after each trip, plus a final backup before parking, to generate low-frequency backup processing information.

4. The data backup management method for a car video recorder according to claim 1, characterized in that, The details of handling completely duplicate data include: If the complete duplicate data is associated with abnormal events, trigger a user secondary confirmation pop-up window, and execute the subsequent operation after the user confirms; If the complete duplicate data is not associated with abnormal events, directly transfer to the duplicate data recycle bin, and the default retention period t of the recycle bin is set by the operator; After the recycle bin is transferred, the indexes of the local core node, local extension node, and cloud node are updated synchronously, and an index verification report is generated after synchronization is completed.

5. The data backup management method for a car video recorder according to claim 1, characterized in that, The processing details of the segment duplicate data include: In the timeline view, the original file is marked in red, the duplicate segment is marked in blue, and the overlapping area is highlighted in yellow. Automatically extract 3-5 key frames in the overlapping area, including accident core frames or road sign frames; If the segment is an accident core frame, it is recommended to keep the original file + duplicate segment and mark the associated ID. If the segment is a routine commuting ordinary segment, it is recommended to keep only the original file.

6. The data backup management method for a car video recorder according to claim 1, characterized in that, The processing details of the differential duplicate data include: Use Python script to convert video key frames to 8x8 grayscale images, generate dHash value, calculate similarity through Hamming distance, and Hamming distance ≤5 corresponds to similarity ≥80%. For videos with dHash similarity ≥70%, sample at 1 frame per second, and use OpenCV to extract road markings and time watermark features of each frame; If the feature coincidence frame ratio ≥75% and the time watermark is completely consistent, it is determined as duplicate data, merged and kept 1 complete data, and the merge log is recorded. If the coincidence frame ratio is <75% or the time watermark is inconsistent, it is determined as non-duplicate data, and two copies are kept and the difference is explained.

7. The data backup management method for a car video recorder according to claim 1, characterized in that, The quantitative standard for data access frequency is: High-frequency access data, effective access ≥3 times in the last 30 days; Medium-frequency access data, effective access 1-2 times in the last 30 days; Low-frequency access data, effective access 0 times in the last 30 days and effective access ≤1 time in the last 90 days.

8. The data backup management method for a car video recorder according to claim 1, characterized in that, The quantitative standard for important data is: Core important data, accident, scratch, and illegal related data, or important data manually marked by the user; General important data, special scene record; Non-important data, daily commuting records without special scenes, and redundant data backed up to 3 or more nodes.

9. A data backup management system for a vehicle event data recorder for performing the backup management method according to any one of claims 1 to 8, characterized by It includes: Scene classification calculation module, used to automatically count the number of effective vehicle use in a time period through the supporting APP of the dashcam, and calculate the usage frequency according to the formula usage frequency = effective vehicle use number / time period. Match the usage frequency with the preset comparison threshold to divide it into high-frequency use, medium-frequency use, and low-frequency use scenarios. At the same time, the classified use scenarios are transmitted to the data backup processing module; Data backup processing module, used to perform corresponding backup processing according to different use scenarios. High-frequency use scenario adopts weekly fixed backup + emergency scenario immediate backup, medium-frequency use scenario adopts backup within twelve hours after use + monthly comprehensive arrangement, and low-frequency use scenario adopts immediate backup after single trip and final backup before parking. Generate backup processing information corresponding to the scene, and transmit it to the hierarchical storage module; The hierarchical storage module is used for obtaining the driving record data based on backup processing information, storing the data in a three-level hierarchy of year-month-event type, setting access permissions for each level, and identifying complete duplicate data, fragment duplicate data and difference duplicate data through a hierarchical process of timestamp comparison, time length / file size / resolution verification and data content identification, forming a duplicate classification set and transmitting it to the duplicate data processing module. The identification method is as follows: The corresponding data timestamp is obtained. If the corresponding data timestamp is completely the same, it represents complete duplicate data. Otherwise, if the data timestamp partially overlaps, it represents partial duplicate data, which needs to be verified and analyzed in combination with time length, file size and resolution. If the time length, file size and resolution are completely consistent, it represents duplicate data. Otherwise, if any of them is different, it represents difference duplicate data. Then, the corresponding data content is identified. If the data content is completely duplicated, it represents duplicate data. In this way, complete duplicate, fragment duplicate and difference duplicate data are identified. The duplicate data processing module is used for processing the duplicate classification set by type. Complete duplicate data is subjected to secondary confirmation or recycle bin transfer according to its association with abnormal events. Fragment duplicate data generates timeline view and key frame comparison information, generates retention suggestions and persists the associated relationship. Difference duplicate data is subjected to dHash algorithm and OpenCV frame feature extraction to determine whether it is duplicated. If it is duplicated, it is subjected to merging or retention processing. At the same time, duplicate data processing information is generated and transmitted to the space optimization module. The space optimization module is used for periodically checking the remaining space of the storage node. When the remaining space is lower than the preset threshold, space release is triggered. Non-important data in low-frequency access data that is beyond the preset period is transferred to cold storage. High-frequency access data is subjected to efficient compression algorithm / encoding format conversion.

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