Audio processing process backtracking method and device, electronic equipment and vehicle

Abstract

The invention relates to an audio processing process backtracking method and device, electronic equipment and a vehicle, and is applied to the field of audio processing technology application. The audio processing process backtracking method comprises the steps of recording a track label corresponding to audio data, adding audio processing information of each processing node in the processing process into the track label in the processing process of the audio data, backtracking the processing process based on the track label, and identifying an abnormal condition in the processing process. Therefore, the track label corresponding to the audio data is recorded, information is dynamically added to each processing node, the track label completely records the full-link processing history of the audio data from input to output, a traceable data chain is formed, backtracking analysis can be autonomously carried out based on the complete track data, and the processing efficiency is improved. Abnormal links are positioned from mass process information, passive response is changed into active analysis, and the automation level and accuracy of fault diagnosis are greatly improved.

Description

Technical Field

[0001] This disclosure relates to the field of audio processing technology applications, and in particular to an audio processing process tracing method, apparatus, electronic device, and vehicle. Background Technology

[0002] In modern in-vehicle audio systems, audio data goes through multiple processing nodes from the audio source input to the speaker output, including audio source decoding, signal processing, mixing, equalization, and power amplification. When in-vehicle audio systems experience abnormal problems such as degraded sound quality, noise, or intermittent playback, it is difficult to independently identify and locate the source of the fault, and it is also impossible to provide effective data support for subsequent system maintenance and upgrades.

[0003] In existing technologies, different audio formats have different data structures and processing methods, and the in-vehicle environment has strict requirements for real-time performance, resource consumption, and reliability. Traditional logging methods cannot meet the needs of lightweight design, multi-format compatibility, and in-vehicle adaptability. In addition, related technologies struggle to record the complete audio processing trajectory. When audio quality problems occur, the system can only respond passively and cannot proactively analyze and trace the root cause of the problem, resulting in low efficiency in fault backtracking and maintenance. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides an audio processing process backtracking method, apparatus, electronic device, and vehicle.

[0005] A first aspect of this disclosure provides an audio processing backtracking method, comprising: Record the trajectory labels corresponding to the audio data, and the trajectory labels are used to record the audio processing information corresponding to the audio data; During the processing of the audio data, the audio processing information of each processing node in the processing process is added to the trajectory label; The processing procedure is traced back based on the trajectory labels to identify abnormal situations in the processing procedure.

[0006] In some embodiments of this disclosure, the trajectory label includes the following fields: Track identifier, processing node identifier, timestamp, processing type, parameter data, parameter length, status code, checksum, track label start flag, and track label end flag.

[0007] In some embodiments of this disclosure, the trajectory tags corresponding to the recorded audio data include: Embed track tags in the audio data according to the audio format type; or, Store the trajectory labels corresponding to the audio data in a log file; or... The trajectory labels corresponding to the audio data are stored in the database.

[0008] In some embodiments of this disclosure, embedding track tags in audio data according to audio format type includes at least one of the following: For pulse code modulation format audio, the trajectory tags are embedded in the audio frame gaps of the audio data; For waveform audio file formats, the trajectory labels are recorded based on extended blocks of the audio data; For the third-level format audio of the Moving Image Experts Group (MOE) audio, the trajectory tag is embedded in the reserved field of the audio frame header of the audio data; For audio in advanced audio encoding formats, the trajectory tags are recorded based on the header extension information of the audio data transport stream; For lossless audio compression encoding formats, the trajectory tags are stored in the metadata of the audio data.

[0009] In some embodiments of this disclosure, adding the audio processing information of each processing node in the processing process to the trajectory label includes: Monitor the current load status; If the current load condition is greater than the preset load threshold, then the audio processing information of the target processing node is added to the trajectory label; If the current load is less than or equal to the preset load threshold, then the audio processing information of each processing node is added to the track label.

[0010] In some embodiments of this disclosure, the method further includes: Monitor current storage space data; If the current storage space data is less than a preset threshold, the audio processing information in the trajectory tags is compressed, and trajectory tags with a storage duration greater than the preset duration are deleted.

[0011] In some embodiments of this disclosure, the method further includes: In extreme temperature environments, the audio processing information of the target processing node is added to the trajectory label, and the trajectory label is recorded in batches; Based on the trajectory identifier in the trajectory label of the audio data, the target audio data is identified from the audio data, and the storage duration of the trajectory label of the target audio data is set as the target duration.

[0012] In some embodiments of this disclosure, the step of backtracking the processing based on the trajectory tags to identify abnormal situations in the processing includes: The time consumed in each processing step is calculated based on the timestamps in the trajectory labels; Abnormal situations in the processing are identified based on the status codes in the trajectory labels; The location of the anomaly is determined based on the processing node identifier and processing type in the trajectory label; Maintenance recommendations are generated based on the time consumed in each processing step, any abnormal situations that occur during the processing, and the location of the abnormality.

[0013] A second aspect of this disclosure provides an audio processing process rewind apparatus, comprising: The storage module is used to record the trajectory tags corresponding to the audio data, and the trajectory tags are used to record the audio processing information corresponding to the audio data; An addition module is used to add audio processing information of each processing node in the audio data processing process to the track label; The backtracking module is used to backtrack the processing process based on the trajectory labels and identify abnormal situations in the processing process.

[0014] In some embodiments of this disclosure, the trajectory label includes the following fields: Track identifier, processing node identifier, timestamp, processing type, parameter data, parameter length, status code, checksum, track label start flag, and track label end flag.

[0015] In some embodiments of this disclosure, when the storage module records the trajectory tags corresponding to the audio data, it is specifically used for: Embed track tags in the audio data according to the audio format type; or, Store the trajectory labels corresponding to the audio data in a log file; or... The trajectory labels corresponding to the audio data are stored in the database.

[0016] In some embodiments of this disclosure, when the storage module embeds track tags in the audio data according to the audio format type, it is specifically used for: For pulse code modulation format audio, the trajectory tags are embedded in the audio frame gaps of the audio data; For waveform audio file formats, the trajectory labels are recorded based on extended blocks of the audio data; For the third-level format audio of the Moving Image Experts Group (MOE) audio, the trajectory tag is embedded in the reserved field of the audio frame header of the audio data; For audio in advanced audio encoding formats, the trajectory tags are recorded based on the header extension information of the audio data transport stream; For lossless audio compression encoding formats, the trajectory tags are stored in the metadata of the audio data.

[0017] In some embodiments of this disclosure, when the adding module adds audio processing information of each processing node in the processing process to the track tag, it is specifically used for: Monitor the current load status; If the current load condition is greater than the preset load threshold, then the audio processing information of the target processing node is added to the trajectory label; If the current load is less than or equal to the preset load threshold, then the audio processing information of each processing node is added to the track label.

[0018] In some embodiments of this disclosure, the apparatus further includes: The monitoring module is used to monitor the current storage space data; if the current storage space data is less than a preset threshold, the audio processing information in the trajectory tags is compressed, and trajectory tags with a storage duration greater than the preset duration are deleted.

[0019] In some embodiments of this disclosure, the apparatus further includes: The recording module is used to add the audio processing information of the target processing node to the trajectory tag in extreme temperature environments, and to record the trajectory tag in batches. The setting module is used to identify target audio data from the audio data based on the trajectory identifier in the trajectory label of the audio data, and set the storage duration of the trajectory label of the target audio data as the target duration.

[0020] In some embodiments of this disclosure, when the backtracking module backtracks the processing based on the trajectory tags and identifies abnormal situations in the processing, it is specifically used for: The time consumed in each processing step is calculated based on the timestamps in the trajectory labels; Abnormal situations in the processing are identified based on the status codes in the trajectory labels; The location of the anomaly is determined based on the processing node identifier and processing type in the trajectory label; Maintenance recommendations are generated based on the time consumed in each processing step, any abnormal situations that occur during the processing, and the location of the abnormality.

[0021] A third aspect of this disclosure provides an electronic device, including: processor; Memory, used to store executable instructions; The processor is used to read executable instructions from memory and execute the executable instructions to implement the audio processing process backtracking method provided in the first aspect above.

[0022] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the audio processing process rewind method provided in the first aspect.

[0023] A fifth aspect of this disclosure provides a computer program product comprising a computer program or instructions that, when executed by a processor, implement the audio processing process rewinding method of the first aspect described above.

[0024] A sixth aspect of this disclosure provides a vehicle that includes electronic equipment provided in the third aspect.

[0025] The technical solution provided in this disclosure has the following advantages: The audio processing backtracking method, apparatus, electronic device, and vehicle provided in this disclosure can record trajectory tags corresponding to audio data. These trajectory tags are used to record audio processing information corresponding to the audio data. Furthermore, during the audio data processing, audio processing information for each processing node is added to the trajectory tags. The processing process is then backtracked based on the trajectory tags to identify any abnormalities. Thus, by recording trajectory tags corresponding to audio data, the previous state of no recording of the audio processing process is changed. By dynamically adding information at each processing node, the trajectory tags completely record the entire processing history of audio data from input to output, forming a traceable data chain. Based on the complete trajectory data, autonomous backtracking analysis can be performed, locating abnormal links from massive amounts of process information, transforming passive response into proactive analysis, and greatly improving the automation level and accuracy of fault diagnosis. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0027] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1This is a flowchart of an audio processing process backtracking method provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a trajectory tag provided in an embodiment of this disclosure; Figure 3 This is a flowchart of another audio processing process backtracking method provided in this embodiment of the disclosure; Figure 4 This is a flowchart of another audio processing process backtracking method provided in this disclosure embodiment; Figure 5 This is a schematic diagram of the structure of an audio processing process retracing device provided in an embodiment of this disclosure; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0029] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0030] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0031] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0033] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0034] In modern in-vehicle audio systems, audio data goes through multiple processing nodes from the audio source input to the speaker output, including audio source decoding, signal processing, mixing, equalization, and power amplification. When in-vehicle audio systems experience abnormal problems such as degraded sound quality, noise, or intermittent playback, it is difficult to independently identify and locate the source of the fault, and it is also impossible to provide effective data support for subsequent system maintenance and upgrades.

[0035] In existing technologies, different audio formats have different data structures and processing methods, and the in-vehicle environment has strict requirements for real-time performance, resource consumption, and reliability. Traditional logging methods cannot meet the needs of lightweight design, multi-format compatibility, and in-vehicle adaptability. Furthermore, related technologies struggle to record complete audio processing trajectories. When audio quality issues occur, the system can only respond passively, unable to proactively analyze and trace the root cause, leading to low efficiency in fault backtracking and maintenance. Therefore, this disclosure provides an audio processing backtracking method, which will be described below with reference to specific embodiments.

[0036] Figure 1 This is a flowchart of an audio processing process backtracking method provided in an embodiment of the present disclosure. The method can be executed by an audio processing process backtracking device, which can be implemented in software and / or hardware. The audio processing process backtracking device can be configured in an electronic device, such as a server or terminal, wherein the terminal specifically includes an in-vehicle terminal, a computer or a tablet computer, etc.

[0037] like Figure 1 As shown, the audio processing process backtracking method provided in this disclosure can be applied to the field of audio processing technology applications. For example, it can be used to manage or record audio processing trajectories. The audio processing process backtracking method may include the following steps: S110. Record the trajectory labels corresponding to the audio data. The trajectory labels are used to record the audio processing information corresponding to the audio data.

[0038] In this embodiment of the disclosure, the electronic device can record track tags corresponding to the audio data, and the track tags are used to record the audio processing information corresponding to the audio data.

[0039] Optionally, the audio data can be each frame of audio data currently being input in real time, with each frame corresponding to a track label, or it can be each segment of audio data, with each segment corresponding to a track label. Specifically, the audio data can be each frame of a song or each frame of a warning sound; there are no specific limitations. For example, when playing a song, the audio data is each frame of that song, and a track label can be embedded in each frame to record the audio processing information corresponding to that frame.

[0040] In some embodiments, S110 may include S1101, S1102, and S1103: S1101. Embed track tags in the audio data according to the audio format type; or, In this embodiment, the electronic device can embed track tags into audio data according to the audio format type. The track tags are used to record audio processing information corresponding to the audio data. Specifically, based on the format of the audio file to be processed, an embedder matching the format is invoked. The processing logic of the embedder is as follows: for audio data, the format standard of the audio data is first parsed, then the reserved, customizable, or expandable "redundant space" in the format standard is accurately located or identified, and the structured track tag data is written into these redundant spaces in a manner that conforms to the format syntax. The redundant space may include reserved bits, padding areas, extended fields, auxiliary data areas, custom blocks, and metadata blocks, etc. An embedder refers to a type of dedicated software module or hardware logic unit that can write structured track tag data into the storage structure or transport stream of the audio data itself without destroying the original encoding format of the target audio data or affecting its normal decoding and playback, thereby achieving seamless physical or logical integration between the two. The embedder can find or create a space in a specific format data structure that does not affect normal audio decoding and playback for storing track tags. Tracking labels are a reserved data structure that may initially contain only a start identifier and format information, achieving compatibility with multiple mainstream audio formats and ensuring the universality of the solution.

[0041] Optionally, the audio format type may include Pulse Code Modulation (PCM), Moving Picture Experts Group Audio Layer 3 (MP3), Advanced Audio Coding (AAC), Waveform Audio File (WAV), Lossless Audio Compression Coding (FLAC), or other format types, which are not limited here.

[0042] S1102. Store the trajectory labels corresponding to the audio data in a log file; or, Optionally, the trajectory labels corresponding to the audio data can be stored in a log file.

[0043] S1103. Store the trajectory labels corresponding to the audio data in the database.

[0044] Optionally, the trajectory labels corresponding to the audio data can be stored in a database.

[0045] Track tags can be recorded in multiple optional ways, improving the flexibility of track tag storage.

[0046] Compared to storing data in log files or databases, embedding track tags in audio data has the following advantages: 1) It can solve the problems of "data correlation" and "precise backtracking".

[0047] If processing information is recorded in a separate log or database, each log entry needs a unique identifier (such as a timestamp or task ID) to associate with the specific frame or segment of audio data being processed at that time. In a high-speed, concurrent, real-time audio processing pipeline, ensuring the absolute accuracy of this cross-system, cross-time association is extremely complex and error-prone. Once the association is incorrect, backtracking analysis becomes meaningless.

[0048] The advantages of the embedded solution: By directly writing trajectory tags into the audio data itself, it achieves "physical inseparability of data and its processing history." Whether the audio data is transmitted, cached, or ultimately stored, its "medical record" is always carried along. During backtracking analysis, no complex matching algorithms are needed; simply reading the tags within the audio file accurately reconstructs the complete processing chain, ensuring the accuracy and reliability of the diagnosis. This is unmatched by external log solutions.

[0049] 2) It can meet the stringent requirements of the vehicle environment for "real-time performance" and "low overhead".

[0050] A standalone logging system requires additional I / O operations (writing to files or databases), context switching, and potential lock contention, all of which consume valuable CPU cycles and memory bandwidth and can introduce unpredictable latency. In resource-constrained in-vehicle infotainment systems with extremely high real-time audio playback requirements, this additional overhead is unacceptable and may affect the smoothness of the main functions (audio decoding, rendering).

[0051] Advantages of the embedding scheme: Lightweight trajectory tag embedding typically utilizes reserved "free spaces" or standard "extended fields" in the audio frame format. Writing to these locations is an incidental operation for the audio processor, introducing almost no additional computational burden or I / O bottleneck, and having a minimal impact on the real-time performance of audio processing. It is essentially a "harmless addition" to the existing data stream, perfectly matching the lightweight and high-efficiency requirements of in-vehicle systems.

[0052] 3) It can guarantee "data integrity" and "anti-interference capability".

[0053] Due to the high vibration and wide temperature range of the vehicle environment, sudden power outages are possible. Independent log files or databases may be damaged, lost, or out of sync with the master data (audio files) under abnormal conditions, resulting in the loss of critical diagnostic information.

[0054] Advantages of the embedded solution: As part of the audio data, the track tag benefits from the existing verification and protection mechanisms in audio data transmission and storage (e.g., CRC checksums built into the audio file container). When uploading diagnostic data via the vehicle network, the audio file containing the track is transmitted and backed up as a whole, avoiding data inconsistencies that might occur when logs and audio files are separated. This improves the robustness of the entire diagnostic data chain.

[0055] 4) Achieve "data self-containment" and "convenient offline analysis".

[0056] When a vehicle comes to the shop for repair, if the logs or database are stored, technicians need to obtain both the audio sample (to reproduce the problem) and the corresponding system logs, and ensure that the two correspond, which is a cumbersome process.

[0057] The advantages of the embedded solution: An audio file with a complete embedded trajectory is itself a "self-contained diagnostic report package." Repair personnel only need to copy this single file to view, offline, the entire process using analysis tools: which modules the problematic audio passed through in the vehicle, what parameters each module used, and at what point the error occurred. This greatly simplifies the process of problem reproduction, data collection, and after-sales analysis.

[0058] In some embodiments of this disclosure, the trajectory label includes the following fields: trajectory identifier, processing node identifier, timestamp, processing type, parameter data, parameter length, status code, checksum, trajectory label start flag, and trajectory label end flag.

[0059] Figure 2 This is a schematic diagram of the structure of a trajectory tag provided in an embodiment of this disclosure.

[0060] like Figure 2As shown, the track label includes a track identifier, processing node identifier, timestamp, processing type, parameter data, parameter length, status code, checksum, track label start flag, and track label end flag. The track identifier uniquely identifies a track instance; the processing node identifier indicates which hardware or software component performed the processing; the timestamp precisely records the time point of the processing for timing and duration analysis; the processing type characterizes the processing action, such as decoding, equalization, and mixing; the parameter data records key configurations or input / output values ​​during processing; the parameter length characterizes the length of the parameter data; the status code characterizes the result status of the processing (success, warning, error, etc.) and is the direct basis for anomaly identification; the checksum verifies the integrity of the recorded data, preventing misjudgments due to transmission and storage errors; the start / end flags accurately locate the start / end boundaries of the label in the audio data stream, ensuring correct parsing. This embodiment, through a standardized data structure, ensures the richness, accuracy, and parsability of the recorded information, laying a data foundation for subsequent complex analysis.

[0061] S120. During the audio data processing, the audio processing information of each processing node in the processing process is added to the track label.

[0062] In this embodiment of the disclosure, the electronic device can add audio processing information of each processing node in the audio data processing process to the track tag.

[0063] Optionally, each processing node may include a decoder, equalizer, power amplifier, or other processing nodes. After completing its task (such as decoding or adjusting the frequency), each processing node triggers a trajectory recording action. This action collects the audio processing information of the current processing node (e.g., component ID "AMP1", processing type "power amplification", timestamp, input / output parameters, status code "success 0x00", etc.) and appends the audio processing information to the end of the trajectory tag carried by the audio data according to a predetermined format. Therefore, as the processing flow progresses, the trajectory tag grows like a chain, recording the complete processing chain. This achieves frame-by-frame full recording of the processing process, providing a data foundation accurate to each step for backtracking.

[0064] S130. Based on the trajectory labels, the processing process is traced back to identify abnormal situations in the processing process.

[0065] In this embodiment, the electronic device can trace back the processing process based on trajectory tags to identify abnormal situations during processing. In some embodiments, when a problem needs to be diagnosed, such as when a user reports noise or the system performs periodic self-checks, the analysis engine reads the complete trajectory tags attached to the audio file, parses each record in timestamp order, and can reconstruct all processing steps, parameters used, time consumption, and status of the audio data, thereby identifying abnormal situations during processing, i.e., analyzing whether there are abnormalities at each processing node. By recording the trajectory tag content of each frame of audio, on the one hand, fault location can be improved from "which module may have a problem" to "which frame of data, in which processing step, and because of which specific parameter the problem occurred," shortening the vague troubleshooting of hours or even days to a precise analysis of minutes. It can capture instantaneous and intermittent faults. Each frame recording ensures that even the briefest anomaly (such as an overload of one frame) will be permanently fixed in the tag, solving the persistent problem of "sometimes working and sometimes not, and unable to be reproduced." On the other hand, the long-term accumulated data of each frame can be used to analyze the micro-trends of system performance. For example, it can be observed that the processing latency of a certain filter is slowly increasing by a few microseconds per thousand frames at high temperatures. This allows for early warning or triggering maintenance before it causes perceptible faults such as audio interruptions, enabling a leap from "fault repair" to "health assurance".

[0066] Therefore, in this embodiment, trajectory tags can be embedded in the audio data according to the audio format type. These trajectory tags record the audio processing information corresponding to the audio data. Furthermore, during the audio data processing, the audio processing information of each processing node is added to the trajectory tags. The processing process can then be traced back based on the trajectory tags to identify any abnormalities. Thus, by recording the trajectory tags corresponding to the audio data, the previous state of no recording of the audio processing process is changed. By dynamically adding information at each processing node, the trajectory tags completely record the entire processing history of the audio data from input to output, forming a traceable data chain. Based on the complete trajectory data, autonomous backtracking analysis can be performed to locate abnormal links from massive amounts of process information, transforming passive response into proactive analysis, greatly improving the automation level and accuracy of fault diagnosis.

[0067] Optionally, S110 may specifically include S1101, S1102, S1103, S1104, and S1105: S1101. For pulse code modulation format audio, embed track tags in the audio frame gaps of the audio data; In this step, for Pulse Code Modulation (PCM) format audio, which is an uncompressed raw audio data stream, a short, fixed-length track tag data is inserted at specific intervals between consecutive audio samples (e.g., after every 1024 samples). Since the player reads samples at a fixed frequency, these inserted data blocks are treated as mute or ignored, thus not affecting real-time playback.

[0068] S1102. For waveform audio file format audio, record trajectory tags based on extended blocks of audio data; In this step, for Waveform Audio Files (WAV) format audio, WAV supports custom extended blocks. An extended block can be created after the file header, storing the track tag sequence within it. Standard players will skip unrecognized blocks, resulting in excellent compatibility.

[0069] S1103. For the third-level format audio of the Dynamic Image Experts Group audio, embed trajectory tags in the reserved field of the audio frame header of the audio data. In this step, for MP3 (Multi-Level Audio) format audio from the Moving Picture Experts Group, the MP3 frame header contains some reserved bits or reserved fields, and trajectory tags can be embedded in the reserved fields of the audio frame header of the audio data.

[0070] S1104. For audio in advanced audio coding formats, a trajectory tag is recorded based on the header extension information of the audio data transport stream. In this step, for Audio with Advanced Audio Coding (AAC) format, the Audio Data Transport Stream (ADTS) header contains an optional Extended Information field. Tracking tag information can be encoded and placed within this extended field.

[0071] S1105. For lossless audio compression encoding formats, store track tags in the metadata of the audio data.

[0072] In this step, for lossless audio compression encoding (FLAC) format audio, the FLAC format supports rich metadata blocks, and a metadata block can be created to store track tags in the metadata block of the audio data.

[0073] Therefore, in this embodiment, low-intrusion tag embedding strategies are designed for commonly used audio formats. These strategies all follow the public standards of each format and utilize their reserved fields, extended regions, or frame structure characteristics to ensure that the embedded track tags do not affect the normal operation of any standard decoder or player, thereby improving the universal applicability of this method in different audio source scenarios.

[0074] Figure 3 This is a flowchart of another audio processing process backtracking method provided in this embodiment of the disclosure.

[0075] like Figure 3 As shown, this audio processing backtracking method may include the following steps: S310. Embed track tags in the audio data according to the audio format type. Track tags are used to record the audio processing information corresponding to the audio data.

[0076] Specifically, the implementation process and principle of S310 and S1101 are the same, and will not be repeated here.

[0077] S320, monitor the current load status.

[0078] Specifically, the electronic device continuously monitors the current load. Optionally, the current load includes memory usage and CPU usage.

[0079] S330. If the current load is greater than the preset load threshold, add the audio processing information of the target processing node to the track label.

[0080] In this step, if the electronic device determines that the current load exceeds a preset load threshold, it adds the audio processing information of the target processing node to the track tag. Specifically, the preset load threshold can be 80%, without any specific limitation. If the current load exceeds the preset load threshold, it is determined to be in a high-load state. At this time, to avoid track recording increasing the system load and affecting the real-time performance of audio processing, the target processing node recording mode is activated, that is, only key processing nodes are recorded. This mode predefines a series of target processing nodes, such as audio source input, final output, and nodes involving safety alarms. Track information addition is only performed at these nodes, and the processing information of non-key nodes is skipped.

[0081] S340. If the current load is less than or equal to the preset load threshold, add the audio processing information of each processing node to the track label.

[0082] In this step, if the electronic device determines that the current load is less than or equal to a preset load threshold, it adds the audio processing information of each processing node to the track tag. Specifically, if the current load is less than or equal to the preset load threshold, indicating sufficient resources, it performs complete track information addition for each processing node, achieving full recording.

[0083] This embodiment achieves an adaptive balance between recording granularity and system load. When the system is busy, some recording details are sacrificed to ensure smooth audio playback; when the system is idle, the complete trajectory is recorded to obtain maximum diagnostic information, demonstrating the real-time performance and robustness of the vehicle system design.

[0084] S350: Based on trajectory labels, the processing process is traced back to identify abnormal situations during the processing.

[0085] Specifically, the implementation process and principle of S350 and S130 are the same, and will not be repeated here.

[0086] S360: Monitor current storage space data.

[0087] To address the issue of limited onboard storage space, a lifecycle management strategy for trajectory data was designed. Specifically, electronic devices monitor the remaining capacity of the storage partition used to store trajectory data.

[0088] S370. If the current storage space data is less than the preset threshold, the audio processing information in the track tags is compressed, and the track tags with a storage duration greater than the preset duration are deleted.

[0089] Specifically, the preset threshold can be 10%, without limitation. If the current storage space data is less than the preset threshold, storage is considered tight, triggering two levels of management: 1) Compression: Compressing uncompressed historical trajectory tag files (e.g., using the LZ4 algorithm) to reduce space usage. 2) Cleanup: Deleting old trajectory data files with the earliest storage time that have exceeded the preset basic retention period (e.g., 7 days) based on timestamps to free up space. This effectively avoids storage overflow issues caused by continuous recording, ensuring the long-term stability of the system. Through compression and time-based cleanup strategies, the retention time of effective data is extended as much as possible within limited space.

[0090] This embodiment embeds trajectory tags into audio data according to the audio format type. These trajectory tags record audio processing information corresponding to the audio data and monitor the current load. Further, if the current load exceeds a preset load threshold, the audio processing information of the target processing node is added to the trajectory tags; if the current load is less than or equal to the preset load threshold, the audio processing information of each processing node is added to the trajectory tags. The processing process is then traced back based on the trajectory tags to identify anomalies. Next, the current storage space data is monitored. If the current storage space data is less than a preset threshold, the audio processing information in the trajectory tags is compressed, and trajectory tags with a storage duration exceeding a preset duration are deleted. Thus, when system computing resources are strained, it automatically switches to a critical node recording mode, recording only core node information affecting the audio backbone function or security, prioritizing the smoothness of audio playback. When resources are abundant, full recording is performed to obtain the most detailed diagnostic data. This dynamic adjustment mechanism intelligently balances the relationship between diagnostic needs and system real-time performance and stability. When storage space is critically low, the system proactively releases space by compressing historical data and cleaning up expired data, effectively avoiding the risk of storage exhaustion and system crashes caused by the continuous accumulation of trajectory data, and ensuring that the system can operate stably for a long time.

[0091] Figure 4 This is a flowchart of another audio processing process backtracking method provided in this embodiment.

[0092] like Figure 4 As shown, this audio processing backtracking method may include the following steps: S410. Embed track tags in the audio data according to the audio format type. Track tags are used to record the audio processing information corresponding to the audio data.

[0093] Specifically, the implementation process and principle of S410 and S1101 are the same, and will not be repeated here.

[0094] S420. During the audio data processing, the audio processing information of each processing node in the processing process is added to the track label.

[0095] Specifically, the implementation process and principle of S420 and S120 are the same, and will not be repeated here.

[0096] S430. In extreme temperature environments, add the audio processing information of the target processing node to the trajectory label and record the trajectory labels in batches.

[0097] Specifically, extreme temperature environments can be ≥65℃ or ≤-20℃, without limitation. Under extreme temperature conditions, the performance of electronic devices and storage reliability may degrade. Electronic devices can switch to high / low temperature protection modes: 1) Reduce recording frequency: Record only critical processing nodes, reducing write operations. 2) Batch recording: Temporarily store and merge information processed multiple times in memory, then write it to storage all at once. Specifically, the audio processing information corresponding to multiple frames of audio data is not added to the track tags initially, but temporarily stored in memory. Then, the temporarily stored audio processing information in memory is batch-written to the track tags, reducing frequent erasure and rewriting of the storage chip and improving the data write success rate and chip lifespan under extreme conditions.

[0098] S440. Based on the trajectory identifier in the trajectory label of the audio data, identify the target audio data from the audio data, and set the storage duration of the trajectory label of the target audio data to the target duration.

[0099] Specifically, electronic devices can identify target audio data based on the trajectory identifier field in the trajectory tag. Target audio data refers to audio processing data related to safety-related processes, such as pedestrian warning sound generation and collision warning audio synthesis. For this target audio data, the trajectory tag storage strategy has been upgraded, with the storage duration set to a longer target duration of 3 years or marked as permanent retention to prevent deletion by conventional cleanup strategies. Through complete recording of key audio processing, remote in-depth analysis, problem reproduction, and OTA optimization of the audio system are supported, improving the overall safety and maintainability of intelligent vehicles.

[0100] Long-term storage of safety-related audio ensures the traceability of critical safety-related data. This data can prove whether the warning sound synthesis and playback system was functioning correctly at the time of the incident. If not, it can pinpoint the specific stage of the malfunction (synthesis algorithm, mixer, or amplifier). This provides a solid and objective data foundation for incident investigation, liability determination, and subsequent safety improvements. It also provides valuable data for algorithm optimization and OTA upgrades based on real-world scenario data. For example, if abnormalities occur in safety-related audio processing data, such as collision warning sounds, the processing nodes of the audio system can be optimized and upgraded via OTA based on the trajectory tags of the target audio data.

[0101] Long-term stored audio data with complete trajectory tags constitutes a "failure time capsule." Relevant personnel can retrieve audio files from the time period in question and, by analyzing the trajectory tags within them, accurately reconstruct the processing flow at that time. Even if the failure is sporadic, intermittent, or occurred a long time ago, it's possible to "reproduce the historical failure scene," enabling precise post-incident diagnosis and accountability. Furthermore, the accumulated trajectory tag data allows for analysis of performance degradation trends at each processing node, predicting potential failures.

[0102] This embodiment enhances the reliability and durability of the solution in harsh vehicle environments, embodies the functional safety concept, and ensures the traceability of key data that are strongly related to safety.

[0103] In some embodiments, the retention period for track tags can be automatically adjusted based on storage capacity, ranging from 7 to 30 days. Optionally, a circular buffer can be used for cyclic storage management to avoid storage overflow.

[0104] In some embodiments, the track tags may be stored in real time to ensure that the audio track data is written to storage before the system loses power. Alternatively, redundant storage may be used to ensure the integrity of the audio track data.

[0105] S450: Calculate the time consumed for each processing step based on the timestamp in the trajectory label.

[0106] In this step, the timestamps of consecutive records in the track labels can be parsed to calculate the processing latency, i.e., the time consumed, for each stage such as "decoding → equalization" and "equalization → mixing". An abnormal increase in the time consumed may indicate a performance degradation or blockage of a certain component.

[0107] S460. Identify abnormal situations during the processing based on the status codes in the trajectory labels.

[0108] Specifically, electronic devices can filter all records with abnormal status codes and quickly locate the processing steps for errors or warnings.

[0109] S470. Determine the location of the anomaly based on the processing node identifier and processing type in the trajectory label.

[0110] Specifically, electronic devices can combine processing node identifiers and processing types to accurately pinpoint the location of an anomaly, i.e., the abnormal hardware or software module.

[0111] S480. Based on the time consumed in each processing step, abnormal situations during the processing, and the location of the abnormality, generate maintenance suggestions.

[0112] In this step, the electronic device can generate maintenance suggestions based on the time consumed in each processing stage, abnormal situations during the processing, and the location of the abnormality. Specifically, the analysis engine in the electronic device integrates the above analysis, calls the preset rule base or knowledge graph, and generates actionable maintenance suggestions described in natural language. For example, the diagnostic result: the mixer output gain is set too high, causing the post-amplifier to be continuously overloaded; the maintenance suggestion is: check the audio source input level or reduce the mixer gain setting by 3dB. This embodiment transforms trajectory data into intuitive, actionable diagnostic conclusions and maintenance suggestions, greatly improving after-sales maintenance efficiency and user experience, realizing a value leap from recording to empowerment.

[0113] This embodiment embeds trajectory tags into audio data according to the audio format type. These trajectory tags record audio processing information corresponding to the audio data. During audio data processing, audio processing information for each processing node is added to the trajectory tags. Furthermore, in extreme temperature environments, the audio processing information of the target processing node is added to the trajectory tags, and these trajectory tags are recorded in batches. Based on the trajectory identifiers in the audio data's trajectory tags, the target audio data is identified from the audio data, and the storage duration of the target audio data's trajectory tags is set to the target duration. Next, the time consumed by each processing step is calculated based on the timestamps in the trajectory tags. Abnormal situations during the processing are identified based on the status codes in the trajectory tags, and the location of the abnormality is determined based on the processing node identifiers and processing types in the trajectory tags. Finally, maintenance suggestions are generated based on the time consumed by each processing step, the abnormal situations during the processing, and the location of the abnormality. Therefore, by making full use of the structured data of trajectory tags, cross-analysis is performed from multiple dimensions such as time consumption (time stamp), abnormal conditions (status code), and abnormal occurrence location (node ​​identifier), so that the diagnostic results can locate the problem and generate maintenance suggestions. The complex time series and status data are transformed into guidance solutions that maintenance personnel or users can directly understand and execute, completing the value loop from data recording to knowledge output, which greatly improves the practicality and user experience of the technology.

[0114] Figure 5 This is a schematic diagram of the structure of an audio processing process tracing device provided in an embodiment of this disclosure.

[0115] In this embodiment, the audio processing process rewind device can be located within an electronic device and is understood as a functional module within the aforementioned electronic device. Specifically, the electronic device can be a server or a terminal, wherein the terminal specifically includes an in-vehicle terminal, a computer, or a tablet computer, etc., and is not limited thereto.

[0116] like Figure 5 As shown, the audio processing process backtracking device 700 may include a storage module 710, an addition module 720, and a backtracking module 730.

[0117] Storage module 710 can be used to record trajectory tags corresponding to audio data, and the trajectory tags are used to record audio processing information corresponding to the audio data; The adding module 720 can be used to add audio processing information of each processing node in the audio data processing process to the track label; The backtracking module 730 can be used to backtrack the processing process based on the trajectory label and identify abnormal situations in the processing process.

[0118] In this embodiment, trajectory tags corresponding to audio data can be recorded. These trajectory tags are used to record audio processing information corresponding to the audio data. Furthermore, during the processing of the audio data, audio processing information for each processing node is added to the trajectory tags. The processing process can then be traced back based on the trajectory tags to identify any abnormalities. Thus, by recording trajectory tags corresponding to the audio data, the previous state of no recording of the audio processing process is changed. By dynamically adding information at each processing node, the trajectory tags completely record the entire processing history of the audio data from input to output, forming a traceable data chain. Based on the complete trajectory data, autonomous backtracking analysis can be performed to locate abnormal links from massive amounts of process information, transforming passive response into proactive analysis, greatly improving the automation level and accuracy of fault diagnosis.

[0119] In some embodiments of this disclosure, the trajectory label includes the following fields: Track identifier, processing node identifier, timestamp, processing type, parameter data, parameter length, status code, checksum, track label start flag, and track label end flag.

[0120] In some embodiments of this disclosure, when the storage module 710 records the trajectory tags corresponding to the audio data, it is specifically used for: Embed track tags in the audio data according to the audio format type; or, Store the trajectory labels corresponding to the audio data in a log file; or... The trajectory labels corresponding to the audio data are stored in the database.

[0121] In some embodiments of this disclosure, when the storage module 710 embeds track tags in the audio data according to the audio format type, it is specifically used for: For pulse code modulation format audio, the trajectory tags are embedded in the audio frame gaps of the audio data; For waveform audio file formats, the trajectory labels are recorded based on extended blocks of the audio data; For the third-level format audio of the Moving Image Experts Group (MOE) audio, the trajectory tag is embedded in the reserved field of the audio frame header of the audio data; For audio in advanced audio encoding formats, the trajectory tags are recorded based on the header extension information of the audio data transport stream; For lossless audio compression encoding formats, the trajectory tags are stored in the metadata of the audio data.

[0122] In some embodiments of this disclosure, when the adding module 720 adds audio processing information of each processing node in the processing process to the track tag, it is specifically used for: Monitor the current load status; If the current load condition is greater than the preset load threshold, then the audio processing information of the target processing node is added to the trajectory label; If the current load is less than or equal to the preset load threshold, then the audio processing information of each processing node is added to the track label.

[0123] In some embodiments of this disclosure, the device 700 further includes: The monitoring module 740 is used to monitor the current storage space data; if the current storage space data is less than a preset threshold, the audio processing information in the trajectory tag is compressed, and the trajectory tag with a storage duration greater than the preset duration is deleted.

[0124] In some embodiments of this disclosure, the device 700 further includes: The recording module 750 is used to add the audio processing information of the target processing node to the trajectory tag in an extreme temperature environment, and to record the trajectory tag in batches. The setting module 760 is used to identify target audio data from the audio data based on the trajectory identifier in the trajectory label of the audio data, and set the storage duration of the trajectory label of the target audio data as the target duration.

[0125] In some embodiments of this disclosure, when the backtracking module 730 backtracks the processing based on the trajectory tags and identifies abnormal situations in the processing, it is specifically used for: The time consumed in each processing step is calculated based on the timestamps in the trajectory labels; Abnormal situations in the processing are identified based on the status codes in the trajectory labels; The location of the anomaly is determined based on the processing node identifier and processing type in the trajectory label; Maintenance recommendations are generated based on the time consumed in each processing step, any abnormal situations that occur during the processing, and the location of the abnormality.

[0126] It should be noted that, Figure 5 The audio processing process backtracking device 700 shown can execute each step in the above method embodiments and realize each process and effect in the above method embodiments, which will not be elaborated here.

[0127] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0128] In this embodiment of the disclosure, Figure 6 The electronic device shown can be a server or a terminal. Specifically, the terminal includes in-vehicle terminals, computers, or tablets, etc., without limitation.

[0129] like Figure 6 As shown, the electronic device may include a processor 810 and a memory 820 storing computer program instructions.

[0130] Specifically, the processor 810 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this disclosure.

[0131] Memory 820 may include mass storage for information or instructions. For example, and not limitingly, memory 820 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 820 may include removable or non-removable (or fixed) media. Where appropriate, memory 820 may be internal or external to the integrated gateway device. In a particular embodiment, memory 820 is non-volatile solid-state memory. In a particular embodiment, memory 820 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (Electrically Programmable ROM, EPROM), an electrically erasable programmable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0132] The processor 810 reads and executes computer program instructions stored in the memory 820 to perform the steps of the audio processing process rewind method provided in this embodiment of the disclosure.

[0133] In one example, the electronic device may also include a transceiver 830 and a bus 840. Wherein, as... Figure 6 As shown, the processor 810, memory 820 and transceiver 830 are connected via bus 840 and communicate with each other.

[0134] Bus 840 may include hardware, software, or both. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 840 may include one or more buses.

[0135] This disclosure also provides a computer-readable storage medium that can store a computer program that, when executed by a processor, enables the processor to implement the audio processing process rewind method provided in this disclosure.

[0136] The aforementioned storage medium may, for example, include a memory 820 containing computer program instructions, which can be executed by a processor 810 of an electronic device to complete the audio processing process rewind method provided in this embodiment. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as read-only memory (ROM), random access memory (RAM), external cache memory, compact disc ROM (CD-ROM), magnetic tape, floppy disk, flash memory, and optical data storage devices. By way of illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM).

[0137] This disclosure also provides a vehicle that includes electronic devices that can implement the various processes and effects described in the above embodiments of this disclosure, which will not be elaborated here.

[0138] This disclosure also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the audio processing process backtracking method provided in this disclosure and can achieve the various processes and effects in the above embodiments of this disclosure, which will not be elaborated here.

[0139] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for tracing back the audio processing process, characterized in that, The method includes: Record the trajectory labels corresponding to the audio data, and the trajectory labels are used to record the audio processing information corresponding to the audio data; During the processing of the audio data, the audio processing information of each processing node in the processing process is added to the trajectory label; The processing procedure is traced back based on the trajectory labels to identify abnormal situations in the processing procedure.

2. The method according to claim 1, characterized in that, The trajectory label includes the following fields: Track identifier, processing node identifier, timestamp, processing type, parameter data, parameter length, status code, checksum, track label start flag, and track label end flag.

3. The method according to claim 1, characterized in that, The trajectory labels corresponding to the recorded audio data include: Embed track tags in the audio data according to the audio format type; or, Store the trajectory labels corresponding to the audio data in a log file; or... The trajectory labels corresponding to the audio data are stored in the database.

4. The method according to claim 3, characterized in that, The method of embedding trajectory tags in audio data according to audio format type includes at least one of the following: For pulse code modulation format audio, the trajectory tags are embedded in the audio frame gaps of the audio data; For waveform audio file formats, the trajectory labels are recorded based on extended blocks of the audio data; For the third-level format audio of the Moving Image Experts Group (MOE) audio, the trajectory tag is embedded in the reserved field of the audio frame header of the audio data; For audio in advanced audio encoding formats, the trajectory tags are recorded based on the header extension information of the audio data transport stream; For lossless audio compression encoding formats, the trajectory tags are stored in the metadata of the audio data.

5. The method according to claim 1, characterized in that, Adding the audio processing information of each processing node in the processing process to the trajectory label includes: Monitor the current load status; If the current load condition is greater than the preset load threshold, then the audio processing information of the target processing node is added to the trajectory label; If the current load is less than or equal to the preset load threshold, then the audio processing information of each processing node is added to the track label.

6. The method according to claim 1, characterized in that, The method further includes: Monitor current storage space data; If the current storage space data is less than a preset threshold, the audio processing information in the trajectory tags is compressed, and trajectory tags with a storage duration greater than the preset duration are deleted.

7. The method according to claim 1, characterized in that, The method further includes: In extreme temperature environments, the audio processing information of the target processing node is added to the trajectory label, and the trajectory label is recorded in batches; Based on the trajectory identifier in the trajectory label of the audio data, the target audio data is identified from the audio data, and the storage duration of the trajectory label of the target audio data is set as the target duration.

8. The method according to claim 1, characterized in that, The step of backtracking the processing based on the trajectory labels to identify abnormal situations in the processing includes: The time consumed in each processing step is calculated based on the timestamps in the trajectory labels; Abnormal situations in the processing are identified based on the status codes in the trajectory labels; The location of the anomaly is determined based on the processing node identifier and processing type in the trajectory label; Maintenance recommendations are generated based on the time consumed in each processing step, any abnormal situations that occur during the processing, and the location of the abnormality.

9. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-7.

10. A vehicle, characterized in that, Including the electronic device as described in claim 9.

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