Intelligent driving data processing method

Abstract

The invention relates to an intelligent driving data processing method, and relates to the technical field of intelligent driving. The technical scheme provided by the embodiment of the invention at least has the following beneficial effects that the method is applied to two aspects, on one hand, the method is applied to a data management platform, and the method comprises the steps of receiving an uploaded intelligent driving data packet; reading the image data carrying the timestamp and the CAN bus data; sending the image data and the CAN bus data to a front-end page; and storing the synchronous data packet generated by the front-end page. On the other hand, the method is applied to a front-end page, and the method comprises the following steps: receiving image data and CAN bus data which are sent by a data management platform and carry timestamps; performing video format conversion on the image data to generate video data, and playing the video data; performing signal processing on the CAN bus data and generating a signal curve; and in a video data playing process, performing timestamp alignment on the video data and the signal curve. According to the invention, timestamp synchronization of the image data and the CAN signal data can be realized.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a method for processing intelligent driving data. Background Art

[0002] At present, with the rapid development of intelligent driving technology, a large number of sensors and cameras are used in vehicles, generating massive amounts of data. The efficient management, analysis and visualization of this data are crucial to the research and development of intelligent driving systems.

[0003] Common intelligent driving data include image data, CAN bus data, etc. These data not only play an important role in the real-time analysis of the driving process, but also provide a data basis for subsequent data mining and event backtracking. During the development and testing process of current intelligent driving R&D projects, a large number of data packets containing image data and CAN bus data need to be recorded for playback, viewing and analysis to verify the functions of the intelligent driving system and solve bugs. However, at present, the image data in the data packet can only be viewed frame by frame, and the CAN signal can only be viewed through the original broadcast packet. The two cannot be viewed synchronously for comparison and analysis, which results in a lot of time required for functional verification, analysis and problem solving, affecting the system iteration efficiency and lengthening the development cycle. Summary of the invention

[0004] Based on this, it is necessary to provide a method for processing intelligent driving data to address the above technical issues.

[0005] In a first aspect, a method for processing intelligent driving data is provided, the method being applied to a data management platform, the method comprising:

[0006] Receive smart driving data packets uploaded by users;

[0007] Reading image data carrying a timestamp in the smart driving data packet;

[0008] Read and process the CAN bus data carrying the timestamp in the smart driving data packet;

[0009] Sending the image data and the CAN bus data to the front-end page;

[0010] The synchronization data packet generated by the front-end page is stored, wherein the synchronization data packet includes the video data and the signal curve after the timestamps are aligned.

[0011] As an optional implementation manner, the reading and processing of the CAN bus data carrying the timestamp in the intelligent driving data packet includes:

[0012] Read CAN bus data in a first target format in the smart driving data packet, and convert the CAN bus data into a transmission message in a standardized message format.

[0013] As an optional implementation manner, sending the image data and the CAN bus data to the front-end page includes:

[0014] Sending the image data to the front-end page via a preset network communication protocol;

[0015] The CAN bus data converted into the standardized message format is sent to the front-end page.

[0016] As an optional implementation, the first target format is a .db3 format, and the transmission message in the standardized message format is an HTTPS message in a JSON format.

[0017] As an optional implementation, the preset network communication protocol is the WebSocket protocol.

[0018] As an optional implementation, the method further includes:

[0019] Receive the event start time and event end time input by the user;

[0020] Based on the event start time and the event end time, extract the image data and CAN bus data corresponding to the event start time and the event end time in the synchronization data packet;

[0021] A slice data packet is generated according to the image data and CAN bus data corresponding to the event start time and the event end time.

[0022] In a second aspect, a method for processing intelligent driving data is provided, the method being applied to a front-end page, the method comprising:

[0023] Receiving image data and CAN bus data with timestamps sent by the data management platform;

[0024] Convert the image data into a video format to generate video data, and play the video data;

[0025] Performing signal processing on the CAN bus data and generating a signal curve;

[0026] During the video data playback process, the video data and the signal curve are time stamp aligned.

[0027] As an optional implementation manner, converting the image data into a video format to generate video data, and playing the video data, includes:

[0028] The image data is converted into video data in a video format according to a preset number of frames, and the video data is played.

[0029] As an optional implementation manner, the performing signal processing on the CAN bus data and generating a signal curve includes:

[0030] Filter out CAN signal data corresponding to a target topic from the data corresponding to each topic in the CAN bus data;

[0031] The timestamp is extracted as the X-axis, and the CAN signal data with the same signal name is used as the Y-axis, and the CAN signal data is plotted into the signal curve.

[0032] As an optional implementation manner, during the video data playback process, aligning the video data and the signal curve with the timestamps includes:

[0033] During the video data playback process, extracting the timestamp of the video data;

[0034] Based on the principle of closest matching between the timestamp of the video data and the timestamp of the signal curve, calibrating the synchronization point;

[0035] The video data and the signal curve after the timestamps are aligned are stored as a synchronization data packet and stored in the data management platform.

[0036] In a third aspect, a system for processing intelligent driving data is provided, the system comprising: a data management platform as described in any one of the first aspect and a front-end page as described in any one of the second aspect.

[0037] In a fourth aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method steps described in any one of the first aspect or the second aspect are implemented.

[0038] In a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method steps as described in any one of the first aspect or the second aspect are implemented.

[0039] The present application provides a method for processing intelligent driving data. The technical solution provided by the embodiment of the present application brings at least the following beneficial effects: the method is applied to a data management platform, and the method includes: receiving an intelligent driving data packet uploaded by a user; reading image data with a timestamp in the intelligent driving data packet; reading and processing CAN bus data with a timestamp in the intelligent driving data packet; sending the image data and the CAN bus data to a front-end page; storing a synchronization data packet generated by the front-end page, and the synchronization data packet includes video data and signal curves after timestamp alignment. The method is applied to a front-end page, and the method includes: receiving image data and CAN bus data with a timestamp sent by the data management platform; performing video format conversion on the image data to generate video data, and playing the video data; performing signal processing on the CAN bus data and generating a signal curve; and performing timestamp alignment on the video data and the signal curve during the video data playback process. The present application can achieve timestamp synchronization of image data and CAN signal data, so that data analysts can intuitively observe the association between image and signal changes, and provide support for event analysis and anomaly detection in intelligent driving scenarios.

[0040] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of the structure of a system for processing intelligent driving data provided in an embodiment of the present application;

[0043] Figure 2 A flowchart of a method for processing intelligent driving data provided in an embodiment of the present application;

[0044] Figure 3 A flowchart of a method for interaction between a data management platform and a front-end page provided in an embodiment of the present application;

[0045] Figure 4 A flowchart of another method for processing intelligent driving data provided in an embodiment of the present application;

[0046] Figure 5A flowchart of another method for processing intelligent driving data provided in an embodiment of the present application;

[0047] Figure 6 A flowchart of a method for processing CAN bus data provided in an embodiment of the present application;

[0048] Figure 7 A flowchart of a method for processing video data and signal curves provided in an embodiment of the present application;

[0049] Figure 8 A flowchart of an example of a method for processing intelligent driving data provided in an embodiment of the present application;

[0050] Fig. 9 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The method for processing intelligent driving data provided in the embodiment of the present application can be applied to an intelligent driving data processing system, such as Figure 1 As shown, the system includes a data management platform 110 and a front-end page 120. The data management platform 110 and the front-end page 120 can be connected through a network communication protocol. The data management platform 110 serves as a back-end processing module, and the front-end page 120 serves as a user operation and data display interface to realize two-way interaction of data. The user uploads the smart driving data packet through the data management platform 110. After the data management platform 110 decodes and parses the data, it sends the image data and standardized CAN signal data to the front-end page 120. After receiving the data, the front-end page 120 plays the image data as a video, and draws the CAN signal data as a signal curve. During the video playback process, based on timestamp alignment, the front-end page 120 realizes the synchronous display of the video and the signal curve. The user can generate slices or add tags for specific time periods through the front-end page 120 to complete the in-depth analysis of the data.

[0053] The following will describe in detail a method for processing intelligent driving data provided by an embodiment of the present application in combination with a specific implementation method. Figure 2 A flowchart of a method for processing intelligent driving data provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method is applied to the data management platform, and the specific steps are as follows:

[0054] Step 201, receiving a smart driving data packet uploaded by a user.

[0055] In practice, the smart driving data packet contains the image data and CAN bus data collected by the user. By receiving these data packets, the subsequent data processing process can be started. The data packet should conform to the predefined format specification and contain timestamp information for subsequent time synchronization.

[0056] As an optional implementation, the image data and CAN bus data can be stored in pre-established image data folders and CAN bus data folders respectively according to preset naming rules to form a complete intelligent driving data package, and uploaded through the data management platform.

[0057] Step 202: read the image data carrying the timestamp in the smart driving data packet.

[0058] In practice, image data can be stored as a continuous frame sequence, each frame with timestamp information. The data management platform can obtain the change trajectory of image data over time by reading the image data and combining it with the timestamp.

[0059] Step 203, read and process the CAN bus data carrying the timestamp in the smart driving data packet.

[0060] In practice, CAN bus data records various sensor signals of the vehicle, such as throttle, speed, steering angle and other information. These signal data are accompanied by timestamps. The data management platform can analyze vehicle behavior and status information by reading CAN bus data and combining it with timestamps.

[0061] As an optional implementation, the specific method of reading and processing the CAN bus data carrying the timestamp in the smart driving data packet in step 203 is:

[0062] Read CAN bus data in a first target format within an intelligent driving data packet, and convert the CAN bus data into a transmission message in a standardized message format.

[0063] In practice, the CAN bus data in the smart driving data packet can be stored in a specific format, such as .db3, .csv or binary data format. The data management platform can read the CAN bus data in the target format to extract the timestamp and information of various sensors.

[0064] Step 204, sending the image data and CAN bus data to the front-end page.

[0065] In implementation, the data management platform can send image data and CAN bus data to the front-end page in real time to draw signal curves or display data. The data management platform can send these data to the front-end page via a communication protocol (such as WebSocket or HTTP).

[0066] As an optional implementation, Figure 3 A flowchart of a method for interaction between a data management platform and a front-end page provided in an embodiment of the present application, such as Figure 3 As shown, the specific steps of sending the image data and CAN bus data to the front-end page in step 204 are as follows:

[0067] Step 301, sending the image data to the front-end page via a preset network communication protocol.

[0068] In practice, image data is stored in the form of a frame sequence and can be sent to the front-end page for display via a network communication protocol (such as WebSocket or HTTP). WebSocket is a two-way communication protocol suitable for real-time transmission scenarios. For example, binary image data can be encoded in Base64 format to adapt to text transmission protocols. Image data is sent to the front-end page via the WebSocket protocol.

[0069] As an optional implementation, the preset network communication protocol is the WebSocket protocol.

[0070] Step 302: Send the CAN bus data converted into a standardized message format to the front-end page.

[0071] In practice, CAN bus data is stored as time series signals and usually needs to be converted into a standardized format (such as JSON) to achieve compatibility between modules. The JSON data can be sent to the front-end page via the HTTP protocol so that it can be used to draw signal curves or other visual displays.

[0072] Step 205: store the synchronization data packet generated by the front-end page, where the synchronization data packet includes the video data and signal curve after the timestamps are aligned.

[0073] In practice, after receiving the image data and CAN data, the front-end page can generate a synchronous data packet from the video data and signal curve after time stamp alignment and store it in the data management platform. Technicians can use the data management platform data packet to perform data analysis and event backtracking of vehicle status.

[0074] As an optional implementation, Figure 4 A flowchart of another method for processing intelligent driving data provided in an embodiment of the present application is shown in FIG. Figure 4As shown, the specific steps are as follows:

[0075] Step 401: receiving an event start time and an event end time input by a user.

[0076] In implementation, users can input the event start time and event end time of the event of interest to the data management platform to mark the time period of interest. The data management platform locates the corresponding data interval for subsequent processing based on the input time range.

[0077] Step 402 : extracting image data and CAN bus data corresponding to the event start time and the event end time from the synchronization data packet based on the event start time and the event end time.

[0078] In the implementation, the synchronization data packet contains image data (with timestamp) and CAN bus data (with timestamp). Through the time matching algorithm, all relevant data between the event start time and the event end time are extracted. For example: the data management platform can parse the timestamp in the synchronization data packet and find the data index that matches the user-specified time period. Then, according to the time period, the records of all image frames and CAN bus data are extracted.

[0079] Step 403 , generating a slice data packet according to the image data and CAN bus data corresponding to the event start time and the event end time.

[0080] In implementation, the slice data packet is a repackaging of the data extracted within the event time range for subsequent separate analysis or storage. The slice data packet should contain: all image frame files within the event time period, all CAN signal records within the event time period, and metadata used to indicate the start and end time of the slice.

[0081] As an optional implementation, the data management platform can receive the target time corresponding to the event of interest and the label name of the target time input by the user. The data management platform marks the event corresponding to the target time with an ID and stores it in a pre-established database, so that the user can view and delete the label of the event through a unique ID index.

[0082] The present application also provides a method for processing intelligent driving data. Figure 5 A flowchart of another method for processing intelligent driving data provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the method is applied to the front-end page. The specific steps are as follows:

[0083] Step 501: Receive image data and CAN bus data with timestamps sent by a data management platform.

[0084] In implementation, image data and CAN bus data are transmitted from the data management platform to the front-end page through network communication protocols (such as HTTP, WebSocket). Each piece of data is timestamped to identify its acquisition time and ensure the synchronization of subsequent data processing. The front-end page can verify the integrity of the data when receiving it, and store the data in the cache or memory for processing.

[0085] Step 502: Convert the image data into a video format to generate video data, and play the video data.

[0086] In implementation, image data is transmitted in the form of frames, and the front-end page can encode the frame data into a video stream at a fixed frame rate through video decoding technology. Video format conversion can be completed using a standard media processing library (such as FFmpeg or the browser's MediaSource interface). Video plug-ins can be called during playback. For example: the front-end page can combine the received continuous image frames (JPEG or PNG format) into an MP4 or H.264 video stream. Video plug-ins can be used to display video streams, and support pause, fast forward and other functions according to user needs.

[0087] As an optional implementation, the specific method of converting the image data into a video format to generate video data and playing the video data in step 502 is as follows:

[0088] The image data is converted into video data in a video format according to a preset number of frames, and the video data is played.

[0089] In practice, image data is provided in the form of frames, which are arranged in sequence at a fixed frame rate (e.g., 30 frames per second) and packaged into a video file format (e.g., MP4) using video encoding technology (e.g., H.264 encoding). The key to video format conversion is the sequential arrangement of image frames, the alignment of timestamps, and the encoding rules that comply with the target format. The converted video file can be played through a video player plug-in or a browser native player.

[0090] Step 503: perform signal processing on the CAN bus data and generate a signal curve.

[0091] In practice, CAN bus data contains multiple signal channels, such as vehicle speed, rotation speed, etc. The front-end page can parse the CAN data in JSON format to extract the signal channel value and the corresponding timestamp. It can also perform operations such as denoising and smoothing on the CAN bus data, and then use data visualization tools (such as ECharts) to generate signal curves.

[0092] As an optional implementation, Figure 6 A flowchart of a method for processing CAN bus data provided by an embodiment of the present application is shown in FIG. Figure 6 As shown, the specific steps of performing signal processing on the CAN bus data and generating a signal curve in step 503 are as follows:

[0093] Step 601: Filter out CAN signal data corresponding to a target topic from data corresponding to each topic in CAN bus data.

[0094] In practice, the CAN bus data contains multiple "topics", each of which represents a type of signal data (such as vehicle speed, throttle position, steering angle, etc.). The screening process matches the topic name or identifier to find the user-specified target topic and extract its corresponding CAN signal data.

[0095] Step 602 , extract the timestamp as the X-axis, the CAN signal data with the same signal name as the Y-axis, and draw the CAN signal data into a signal curve.

[0096] In implementation, the front-end page uses the timestamp in the filtered target topic data as the X-axis and the CAN signal data with the same signal name as the Y-axis. These points are plotted as signal curves through data visualization tools (such as ECharts). The signal curve shows the changes of the target signal over time, which can help users intuitively analyze the vehicle status and behavior.

[0097] Step 504: During the video data playback process, the video data and the signal curve are time stamp aligned.

[0098] In practice, timestamp alignment can find the closest time point by comparing the timestamps of the video frame and the CAN signal data, ensuring the synchronization of video playback and signal curve drawing. The key to synchronization is to update the video time in real time during playback and send the current video time to the signal curve module to locate the time point of the curve.

[0099] As an optional implementation, Figure 7 A flowchart of a method for processing video data and signal curves provided in an embodiment of the present application, such as Figure 7 As shown, in step 504, during the video data playback process, the specific steps of aligning the time stamps of the video data and the signal curve are as follows:

[0100] Step 701: extract the timestamp of the video data during the video data playback process.

[0101] In implementation, video data contains embedded timestamp information, which corresponds to the display time of each frame in the video stream. By decoding the video frames, the timestamp of each frame is extracted. For example, for a video with a frame rate of 30fps, its timestamp increases by 1 / 30 per second. Timestamp extraction provides the basis for subsequent synchronization with the signal curve.

[0102] Step 702: calibrate the synchronization point based on the principle of closest match between the timestamp of the video data and the timestamp of the signal curve.

[0103] In practice, during video playback, the front-end page can obtain the current frame timestamp and compare it with the timestamp of the signal curve to find the nearest matching point. The matching logic is based on the "closest principle", that is, the minimum time difference between the video frame timestamp and the signal timestamp. After matching, the time point is marked on the signal curve.

[0104] Step 703: The video data and signal curves after the time stamps are aligned are stored as a synchronization data packet and stored in a data management platform.

[0105] In implementation, after the timestamps are aligned, the front-end page can generate a synchronization data packet containing video data and signal curves. The data packet structure includes the corresponding relationship between the video frame and the signal curve on the time axis. The synchronization data packet can be used for playback or further analysis. The data management platform is responsible for storing and managing the synchronization data packet for users to retrieve or download.

[0106] As an optional implementation, Figure 8 A flowchart of an example of a method for processing intelligent driving data provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the specific steps are as follows:

[0107] Step 801, smart driving data package upload: the image data and CAN bus data are stored in designated folders respectively to form a complete data package, and uploaded through the data management platform.

[0108] Step 802, read image data: the data management platform identifies the image data folder in the data packet, reads the image data stored in frames, and sends the image data to the front-end page in real time via the WebSocket protocol.

[0109] Step 803, image to video conversion: after receiving the image data transmitted by WebSocket, the front-end page converts the image data into a video format and implements the video playback function through a video plug-in.

[0110] Step 804, read CAN bus data: the data management platform identifies the CAN bus data folder in the data packet, reads the .db3 file therein, converts the CAN bus data into an HTTPS message in JSON format and sends it to the front-end page.

[0111] Step 805, signal processing and curve drawing: After the front end receives the HTTPS message in JSON format, it obtains all topic data, filters out CAN signal data, extracts timestamps as the X-axis, reorganizes data with the same signal name as the Y-axis, and uses ECharts to draw all data into signal curves.

[0112] Step 806, timestamp synchronization: During video playback, the video module will synchronously send timestamp information to the signal drawing module. The signal drawing module matches the timestamp with the timestamp of the CAN signal, finds the closest time point, and marks and draws at the time point to achieve synchronous display of the signal and video.

[0113] Step 807, data slicing: The user can click the "Slice" button on the platform to record the start and end timestamps of a specific event, and name and classify the time period. The platform extracts the corresponding image and CAN bus data based on the timestamp.

[0114] Step 808, generate a slice data packet: to support subsequent individual playback and downloading.

[0115] Step 809, labeling: When an event at a certain moment needs to be labeled, click the "label" button to record the timestamp and name the label.

[0116] Step 810, record tag point information: the tag will be stored in the database, indexed by tag ID, and support tag addition, deletion, modification and query functions.

[0117] The embodiment of the present application provides a method for processing intelligent driving data. The technical solution provided by the embodiment of the present application brings at least the following beneficial effects: On the one hand, the method is applied to a data management platform, and the method includes: receiving an intelligent driving data packet uploaded by a user. Reading image data with a timestamp in the intelligent driving data packet. Reading and processing CAN bus data with a timestamp in the intelligent driving data packet. Sending image data and CAN bus data to a front-end page. Storing a synchronization data packet generated by the front-end page, the synchronization data packet contains video data and signal curves after timestamp alignment. On the other hand, the method is applied to a front-end page, and the method includes: receiving image data and CAN bus data with a timestamp sent by a data management platform. Converting the image data into a video format to generate video data, and playing the video data. Signal processing is performed on the CAN bus data and a signal curve is generated. During the video data playback process, the video data and the signal curve are timestamp aligned. The present application can achieve timestamp synchronization of image data and CAN signal data, so that data analysts can intuitively observe the association between image and signal changes, and provide support for event analysis and anomaly detection in intelligent driving scenarios.

[0118] It should be understood that although Figures 2 to 8 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 2 to 8 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0119] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0120] In one embodiment, a computer device is provided, such as Fig. 9 As shown, it includes a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor implements the method steps for processing the above-mentioned intelligent driving data when executing the computer program.

[0121] In one embodiment, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for processing intelligent driving data.

[0122] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0123] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0124] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0125] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0126] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for processing intelligent driving data, characterized in that: The method is applied to a data management platform, and the method comprises: Receive smart driving data packets uploaded by users; Reading image data carrying a timestamp in the smart driving data packet; Read and process the CAN bus data carrying the timestamp in the smart driving data packet; Sending the image data and the CAN bus data to the front-end page; The synchronization data packet generated by the front-end page is stored, wherein the synchronization data packet includes the video data and the signal curve after the timestamps are aligned.

2. The method according to claim 1, characterized in that The reading and processing of the CAN bus data carrying the timestamp in the intelligent driving data packet includes: Read CAN bus data in a first target format in the smart driving data packet, and convert the CAN bus data into a transmission message in a standardized message format.

3. The method according to claim 2, characterized in that The sending of the image data and the CAN bus data to the front-end page includes: Sending the image data to the front-end page via a preset network communication protocol; The CAN bus data converted into the standardized message format is sent to the front-end page.

4. The method according to claim 2, characterized in that: The first target format is a .db3 format, and the transmission message in the standardized message format is an HTTPS message in a JSON format.

5. The method according to claim 3, characterized in that: The preset network communication protocol is the WebSocket protocol.

6. The method according to claim 1, characterized in that The method further comprises: Receive the event start time and event end time input by the user; Based on the event start time and the event end time, extract the image data and CAN bus data corresponding to the event start time and the event end time in the synchronization data packet; A slice data packet is generated according to the image data and CAN bus data corresponding to the event start time and the event end time.

7. A method for processing intelligent driving data, characterized in that: The method is applied to a front-end page, and the method includes: Receiving image data and CAN bus data with timestamps sent by the data management platform; Convert the image data into a video format to generate video data, and play the video data; Performing signal processing on the CAN bus data and generating a signal curve; During the video data playback process, the video data and the signal curve are time stamp aligned.

8. The method according to claim 7, characterized in that The converting the image data into a video format to generate video data, and playing the video data, comprises: The image data is converted into video data in a video format according to a preset number of frames, and the video data is played.

9. The method according to claim 7, characterized in that: The step of performing signal processing on the CAN bus data and generating a signal curve comprises: Filter out CAN signal data corresponding to a target topic from the data corresponding to each topic in the CAN bus data; The timestamp is extracted as the X-axis, and the CAN signal data with the same signal name is used as the Y-axis, and the CAN signal data is plotted into the signal curve.

10. The method according to claim 7, characterized in that During the video data playback process, aligning the video data and the signal curve with the timestamps includes: During the video data playback process, extracting the timestamp of the video data; Based on the principle of closest matching between the timestamp of the video data and the timestamp of the signal curve, calibrating the synchronization point; The video data and the signal curve after the timestamps are aligned are stored as a synchronization data packet and stored in the data management platform.

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