Audio system health assessment method, electronic equipment and vehicle

Abstract

The invention relates to an audio system health assessment method, electronic equipment and a vehicle, and is applied to the field of audio processing technology application. The audio system health assessment method comprises the steps of obtaining track label data recorded in an audio processing process, calculating a health index score of each dimension in a multi-dimensional health index system based on the track label data, and performing weighted fusion based on the health index score of each dimension and the weight of each dimension, and evaluating the health state of the vehicle-mounted audio system based on the comprehensive health score, and generating an evaluation result. Therefore, the system health is comprehensively described from different dimensions, and the complex internal state is converted into the comprehensive health score and health state through weighted fusion and state evaluation, so that the operability and intuition of health management are greatly improved, and a scientific basis is provided for active operation and maintenance and accurate maintenance.

Description

Technical Field

[0001] This disclosure relates to the field of audio processing technology applications, and in particular to an audio system health assessment method, electronic devices, and vehicles. Background Technology

[0002] In the field of automotive audio systems, when audio quality deteriorates or malfunctions, the system can only detect problems through simple fault codes or output detection, and cannot gain a deeper understanding of the operational status of each link in the audio processing chain.

[0003] Existing health assessments mainly rely on external detection methods, which cannot obtain information about the internal status of audio processing. Furthermore, the operating status of each processing node is isolated, making it difficult to achieve refined assessments. They can only determine whether the system is normal or abnormal, but cannot perform quantitative assessments. This leads to difficulties in maintaining the audio system and a lack of preventative measures, making it difficult to achieve accurate fault location and proactive maintenance. Summary of the Invention

[0004] To address the aforementioned technical issues, this disclosure provides an audio system health assessment method, an electronic device, and a vehicle.

[0005] A first aspect of this disclosure provides a method for assessing the health of an audio system, comprising: Obtain trajectory label data recorded during audio processing; Based on the trajectory label data, calculate the health indicator scores for each dimension in the multi-dimensional health indicator system; A comprehensive health score is obtained by weighting and fusing the health indicator scores of each dimension and the weights of each dimension. The health status of the in-vehicle audio system is assessed based on the comprehensive health score, and an assessment result is generated.

[0006] In some embodiments of this disclosure, the trajectory label data includes the following fields: Track identifier, tag header, processing node identifier, timestamp, processing type, parameter data, parameter length, resource usage information, status code, checksum, priority, tag tail; The trajectory identifier is used to distinguish trajectory labels, the label header is used to indicate the start position of the label trajectory, the processing node identifier is used to distinguish each processing node, the timestamp is used to record the processing time of each processing node, the processing type is used to characterize the audio processing category, the parameter data is used to store processing parameters, the parameter length is used to characterize the length of the stored processing parameters, the resource usage information is used to characterize the resource usage of each processing node, the status code is used to record the processing result status, the checksum is used to verify data integrity, the priority is used to characterize the importance of each processing node, and the label tail is used to indicate the end position of the label trajectory.

[0007] In some embodiments of this disclosure, before acquiring the trajectory tag data recorded during audio processing, the method further includes: A multi-dimensional health indicator system is constructed, which includes health indicators for processing timeliness, processing reliability, parameter stability, and resource utilization.

[0008] In some embodiments of this disclosure, the calculation of health indicator scores for each dimension of the multi-dimensional health indicator system based on the trajectory label data includes: Based on the trajectory label data, extract the health indicator data corresponding to each dimension; Based on the health indicator data corresponding to each dimension, calculate the health indicator score for each dimension.

[0009] In some embodiments of this disclosure, the extraction of health indicator data corresponding to each dimension based on the trajectory label data includes: Based on the timestamp interval in the trajectory label data, the average processing time, maximum delay time, delay variance, and number of delay anomaly events of each processing node are calculated to obtain the health indicator data corresponding to the processing timeliness health indicator. Based on the status codes in the trajectory label data, the abnormal state occurrence rate and error state count of each processing node are calculated to obtain the health indicator data corresponding to the processing reliability health indicator. Based on the parameter data in the trajectory label data, the number of parameter changes, parameter drift degree and number of abnormal parameter adjustment events of each processing node are calculated to obtain the health index data corresponding to the parameter stability health index. Based on the resource usage information in the trajectory label data, the average resource utilization rate, peak resource usage, and load value of each processing node are calculated to obtain the health indicator data corresponding to the resource utilization health indicator.

[0010] In some embodiments of this disclosure, calculating the health indicator score for each dimension based on the health indicator data corresponding to each dimension includes: Based on the average processing time, the maximum delay time, the delay variance, and the number of delay anomalies, calculate the health index score of the processing timeliness health index; Based on the occurrence rate of the abnormal state and the number of error states, calculate the health index score of the processing reliability health index; Based on the number of parameter changes, the parameter drift, and the number of abnormal parameter adjustment events, calculate the health index score of the parameter stability health index; Based on the average resource utilization rate, the peak resource utilization rate, and the load value, a health index score for the resource utilization health index is calculated.

[0011] In some embodiments of this disclosure, the step of assessing the health status of the in-vehicle audio system based on the comprehensive health score and generating assessment results includes: Based on the comprehensive health score, the health level of the in-vehicle audio system is determined; The health status of the in-vehicle audio system is assessed based on its corresponding health level, and an assessment result is generated.

[0012] In some embodiments of this disclosure, the method further includes: Based on comprehensive health score data over a historical period, a linear regression algorithm is used to predict changes in health scores within a preset future timeframe; or... Based on comprehensive health score data over a historical period, a moving average method is used to smooth short-term fluctuations in the comprehensive health score, thus obtaining the expected changes in the health score over a predetermined future period; or, Based on comprehensive health score data over a historical period, the exponential smoothing method is used to capture the long-term development trend of the comprehensive health score and predict the changes in the health score within a preset future time period.

[0013] A second aspect of this disclosure provides an audio system health assessment apparatus, comprising: The acquisition module is used to acquire trajectory label data recorded during audio processing; The calculation module is used to calculate the health indicator scores of each dimension in the multi-dimensional health indicator system based on the trajectory label data. The fusion module is used to perform weighted fusion based on the health indicator scores of each dimension and the weights of each dimension to obtain a comprehensive health score; The assessment module is used to assess the health status of the in-vehicle audio system based on the comprehensive health score and generate assessment results.

[0014] In some embodiments of this disclosure, the trajectory label data includes the following fields: Track identifier, tag header, processing node identifier, timestamp, processing type, parameter data, parameter length, resource usage information, status code, checksum, priority, tag tail; The trajectory identifier is used to distinguish trajectory labels, the label header is used to indicate the start position of the label trajectory, the processing node identifier is used to distinguish each processing node, the timestamp is used to record the processing time of each processing node, the processing type is used to characterize the audio processing category, the parameter data is used to store processing parameters, the parameter length is used to characterize the length of the stored processing parameters, the resource usage information is used to characterize the resource usage of each processing node, the status code is used to record the processing result status, the checksum is used to verify data integrity, the priority is used to characterize the importance of each processing node, and the label tail is used to indicate the end position of the label trajectory.

[0015] In some embodiments of this disclosure, the apparatus further includes: The module is used to construct a multi-dimensional health indicator system, which includes health indicators for processing timeliness, processing reliability, parameter stability, and resource utilization.

[0016] In some embodiments of this disclosure, when the calculation module calculates the health indicator scores for each dimension of the multi-dimensional health indicator system based on the trajectory label data, it is specifically used for: Based on the trajectory label data, extract the health indicator data corresponding to each dimension; Based on the health indicator data corresponding to each dimension, calculate the health indicator score for each dimension.

[0017] In some embodiments of this disclosure, when the calculation module extracts health indicator data corresponding to each dimension based on the trajectory label data, it is specifically used for: Based on the timestamp interval in the trajectory label data, the average processing time, maximum delay time, delay variance, and number of delay anomaly events of each processing node are calculated to obtain the health indicator data corresponding to the processing timeliness health indicator. Based on the status codes in the trajectory label data, the abnormal state occurrence rate and error state count of each processing node are calculated to obtain the health indicator data corresponding to the processing reliability health indicator. Based on the parameter data in the trajectory label data, the number of parameter changes, parameter drift degree and number of abnormal parameter adjustment events of each processing node are calculated to obtain the health index data corresponding to the parameter stability health index. Based on the resource usage information in the trajectory label data, the average resource utilization rate, peak resource usage, and load value of each processing node are calculated to obtain the health indicator data corresponding to the resource utilization health indicator.

[0018] In some embodiments of this disclosure, when the calculation module calculates the health indicator scores for each dimension based on the health indicator data corresponding to each dimension, it is specifically used for: Based on the average processing time, the maximum delay time, the delay variance, and the number of delay anomalies, calculate the health index score of the processing timeliness health index; Based on the occurrence rate of abnormal states and the number of error states, calculate the health index score of the processing timeliness health index. Based on the number of parameter changes, the parameter drift, and the number of abnormal parameter adjustment events, calculate the health index score of the processing timeliness health index; Based on the average resource utilization rate, the peak resource utilization rate, and the load value, a health indicator score for the processing timeliness health indicator is calculated.

[0019] In some embodiments of this disclosure, when the evaluation module assesses the health status of the in-vehicle audio system based on the comprehensive health score and generates the evaluation result, it is specifically used for: Based on the comprehensive health score, the health level of the in-vehicle audio system is determined; The health status of the in-vehicle audio system is assessed based on its corresponding health level, and an assessment result is generated.

[0020] In some embodiments of this disclosure, the apparatus further includes: The prediction module is used to predict changes in health scores within a preset future timeframe based on comprehensive health score data from historical periods, using a linear regression algorithm; or, Based on comprehensive health score data over a historical period, a moving average method is used to smooth short-term fluctuations in the comprehensive health score, thus obtaining the expected changes in the health score over a predetermined future period; or, Based on comprehensive health score data over a historical period, the exponential smoothing method is used to capture the long-term development trend of the comprehensive health score and predict the changes in the health score within a preset future time period.

[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 system health assessment 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 system health assessment 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 system health assessment 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 system health assessment method, electronic device, and vehicle provided in this disclosure can acquire trajectory tag data recorded during audio processing. Further, based on the trajectory tag data, health indicator scores for each dimension in a multi-dimensional health indicator system are calculated. Weighted fusion is then performed based on the health indicator scores and their respective weights to obtain a comprehensive health score. The health status of the in-vehicle audio system is then assessed based on this comprehensive health score, generating an assessment result. Thus, by comprehensively characterizing system health from different dimensions and utilizing trajectory tags recorded during audio processing, the assessment can penetrate deep into the system's internal workings. Finally, through weighted fusion and status assessment, the complex internal state is transformed into an intuitive, quantifiable, and comparable comprehensive health score and health status, greatly improving the operability and intuitiveness of health management and providing a scientific basis for proactive operation and precise maintenance. 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 1 This is a flowchart of an audio system health assessment 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 3This is a flowchart of another audio system health assessment method provided in this disclosure embodiment; Figure 4 This is a flowchart of another audio system health assessment method provided in this disclosure embodiment; Figure 5 This is a schematic diagram of the structure of an audio system health assessment 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 the field of automotive audio systems, when audio quality deteriorates or malfunctions, the system can only detect problems through simple fault codes or output detection, and cannot gain a deeper understanding of the operational status of each link in the audio processing chain.

[0035] Existing health assessments primarily rely on external detection methods, failing to acquire internal status information of audio processing. Furthermore, the operational status of each processing node is isolated, hindering refined assessment; they can only determine normal or abnormal operation, lacking quantitative evaluation. This results in difficulties in audio system maintenance and a lack of preventative measures, making accurate fault location and proactive maintenance challenging. Therefore, this disclosure provides an audio system health assessment method, which will be described below with reference to specific embodiments.

[0036] Figure 1 This is a flowchart of an audio system health assessment method provided in an embodiment of the present disclosure. The method can be executed by an audio system health assessment device, which can be implemented in software and / or hardware. The audio system health assessment 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 system health assessment method provided in this disclosure can be applied to the field of audio processing technology. For example, it can be used to perform a health assessment on an audio system. The audio system health assessment method may include the following steps: S120. Obtain the trajectory label data recorded during audio processing.

[0038] Electronic devices can embed track tags into audio data. These track tags are used to record audio processing information corresponding to the audio data. In this embodiment, the electronic device can acquire track tag data recorded during audio processing for subsequent health assessment. A track tag is metadata containing timestamps, status codes, parameters, and other information, appended in real-time by each processing node (such as a decoder or equalizer) as the audio data flows through it. For example, all track tag data generated within a certain period (e.g., the past hour) can be collected.

[0039] In some embodiments of this disclosure, the trajectory label data includes the following fields: Track identifier, tag header, processing node identifier, timestamp, processing type, parameter data, parameter length, resource usage information, status code, checksum, priority, tag tail; The system includes: a trajectory identifier to distinguish trajectory labels; a label header to indicate the start position of the label trajectory; a processing node identifier to distinguish each processing node; a timestamp to record the processing time of each processing node; a processing type to characterize the audio processing category; parameter data to store processing parameters; parameter length to characterize the length of the stored processing parameters; resource usage information to characterize the resource usage of each processing node; a status code to record the processing result status; a checksum to verify data integrity; a priority to characterize the importance of each processing node; and a label tail to indicate the end position of the label trajectory.

[0040] Figure 2 This is a schematic diagram of the structure of a trajectory tag provided in an embodiment of this disclosure.

[0041] like Figure 2 As shown, the track label includes a track identifier, label header, processing node identifier, timestamp, processing type, parameter data, parameter length, resource usage information, status code, checksum, priority, and label tail. It may also include reserved fields and padding bytes, which are not specifically limited here. The track identifier uniquely identifies a track instance; the processing node identifier indicates which hardware or software component performed the processing; the timestamp accurately records the time point of the processing for analyzing timing and duration; 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 this processing (success, warning, error, etc.), serving as a direct basis for anomaly identification; the checksum verifies the integrity of the recorded data, preventing misjudgments due to transmission and storage errors; the priority characterizes the importance of each processing node; the label header / label tail accurately locates 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.

[0042] Specifically, (1) Tag header (4 bytes), fixed value: 0x54524143 ('TRAC'), function: to identify the starting position of the track tag.

[0043] (2) Processing Node ID (4 bytes), format: ASCII character encoding, example: "AMP1" - power amplifier module 1, "DSP1" - digital signal processing 1, function: uniquely identifies the processing component.

[0044] (3) Timestamp (8 bytes), format: Unix timestamp (millisecond level), precision: ±1 millisecond, function: to record the processing time node.

[0045] (4) Processing type (2 bytes), encoding definition: 0x0001: decoding processing, 0x0002: encoding processing, 0x0003: mixing processing, 0x0004: equalization processing, 0x0005: compression processing, 0x0006: format conversion, 0x0007: effects processing, etc. It may also include other processing nodes, without specific limitations.

[0046] (5) Parameter length (2 bytes), range: 0-1024 bytes, function: indicates the length of parameter data.

[0047] (6) Status code (1 byte), defined as: 0x00: normal completion, 0x01: warning status, 0x02: error status, 0x03: initialization status, 0x04: standby status, etc. Other statuses may also be included, but no specific limitation is made here.

[0048] (7) Priority (1 byte), range: 0-255, 0-50: Debug level (optional record), 51-150: Normal level (regular record), 151-200: Important level (required record), 201-255: Urgent level (critical record).

[0049] (8) Parameter data (variable length), dynamically adjusted according to the processing type, including key parameter values ​​during processing, and supports KV key-value pair format storage.

[0050] (9) Check code (4 bytes), algorithm: CRC-32, function: to verify the integrity of tag data.

[0051] (10) Tag end (4 bytes), fixed value: 0x454E44 ('END'), function: to identify the end position of the track tag.

[0052] S130. Calculate the health indicator scores for each dimension in the multi-dimensional health indicator system based on trajectory label data.

[0053] In this embodiment of the disclosure, the electronic device can calculate health indicator scores for each dimension of a multi-dimensional health indicator system based on trajectory tag data; that is, it analyzes the trajectory tag data and scores each dimension. Optionally, each dimension will receive a raw score (e.g., 0-100 points).

[0054] S140. A comprehensive health score is obtained by weighting and integrating the health indicator scores and weights of each dimension.

[0055] In this embodiment, the electronic device can perform weighted fusion based on health indicator scores across various dimensions and the weights of each dimension to obtain a comprehensive health score. Since different dimensions have varying importance—for example, reliability is more important than resource utilization—a weight is preset for each dimension, such as reliability weight 0.4, timeliness weight 0.3, parameter stability weight 0.2, and resource utilization weight 0.1. Then, the scores of each dimension are multiplied by their respective weights and summed to obtain a comprehensive health score between 0 and 100. For example, (Timeliness 85 points × 0.3) + (Reliability 90 points × 0.4) + (Stability 70 points × 0.2) + (Resource Utilization 80 points × 0.1) = Comprehensive Health Score 83.

[0056] Specifically, the overall health score = W1×D + W2×E + W3×P + W4×R. Where D represents the processing timeliness score, E represents the processing reliability score, P represents the parameter stability score, R represents the resource utilization score, and W1, W2, W3, and W4 represent the weight coefficients of each dimension, which can be adjusted according to actual needs.

[0057] S150: Assess the health status of the in-vehicle audio system based on a comprehensive health score and generate assessment results.

[0058] In this embodiment of the disclosure, the electronic device can assess the health status of the in-vehicle audio system based on a comprehensive health score and generate an assessment result. Based on the comprehensive health score, it is possible to clearly determine the current health status of the system, whether it is excellent, good, average, poor, or even dangerous, providing a clear basis for whether maintenance is required.

[0059] Therefore, in this embodiment, trajectory tag data recorded during audio processing can be obtained. Further, health indicator scores for each dimension of the multi-dimensional health indicator system are calculated based on the trajectory tag data. Weighted fusion is then performed based on these scores and their respective weights to obtain a comprehensive health score. The health status of the in-vehicle audio system is then assessed based on this comprehensive health score, generating an evaluation result. Thus, by comprehensively characterizing system health from different dimensions and utilizing the trajectory tags embedded in the audio data, the evaluation can penetrate deep into the system's internal workings. Finally, through weighted fusion and status evaluation, the complex internal state is transformed into an intuitive, quantifiable, and comparable comprehensive health score and health status, greatly improving the operability and intuitiveness of health management and providing a scientific basis for proactive operation and precise maintenance.

[0060] Based on the above embodiments, the audio system health assessment method further includes steps A, B, and C: Step A: Based on comprehensive health score data within a historical time period, use a linear regression algorithm to predict changes in health scores within a preset future timeframe; or, Suppose we have stored the comprehensive health scores for each day over the past 30 days. Using linear regression for prediction: the algorithm fits these 30 data points to a straight line and extends this line to predict the future. For example, if the line shows the score is decreasing by 0.5 points per day, and the current score is 80, it predicts that in 7 days it will be approximately 76.5, potentially falling from "good" to the edge of "average." Electronic devices can then use this information to issue early warnings of performance degradation.

[0061] Step B: Based on the comprehensive health score data within a historical time period, use the moving average method to smooth short-term fluctuations in the comprehensive health score, and obtain the changes in the health score within a preset future time period; or, The moving average method is used: the average score of the most recent 7 days (e.g., 82 points) is taken as the predicted health score for the next day. This method can smooth out occasional abnormally low or high scores on a particular day, providing a more stable short-term prediction.

[0062] Step C: Based on the comprehensive health score data within a historical time period, the long-term development trend of the comprehensive health score is captured by the exponential smoothing method, and the changes in the health score within a preset time period are predicted.

[0063] Exponential smoothing: This method gives higher weight to recent data. If the score drops significantly in recent days, even if the 30-day average score is still acceptable, exponential smoothing will be more sensitive to predicting the downward trend, making it suitable for capturing inflection points.

[0064] This disclosure adds the ability to predict the future to health assessments, which is central to enabling preventative maintenance. By analyzing historical trends in health scores, performance degradation can be predicted in advance, allowing for proactive intervention before problems cause failures or impact user experience, thereby significantly improving system reliability and user satisfaction.

[0065] Figure 3 This is a flowchart of another audio system health assessment method provided in this disclosure embodiment.

[0066] like Figure 3 As shown, the audio system health assessment method may include the following steps: S310. Construct a multi-dimensional health indicator system, which includes health indicators for processing timeliness, processing reliability, parameter stability, and resource utilization.

[0067] In this embodiment, a multi-dimensional health indicator system is constructed, pre-defining the dimensions from which health is measured. Optionally, the multi-dimensional health indicator system includes processing timeliness health indicators, processing reliability health indicators, parameter stability health indicators, and resource utilization health indicators. Processing timeliness health indicators measure reaction speed, assessing how quickly audio is processed at each processing node; processing reliability health indicators measure work accuracy, assessing the frequency of errors during processing; parameter stability health indicators measure parameter stability, assessing the volatility of system parameters; and resource utilization health indicators measure resource utilization, assessing whether the use of resources such as CPU and memory is reasonable. These four dimensions together constitute a multi-dimensional health indicator system. By constructing a multi-dimensional health indicator system, the dimensions for measuring health can be clearly defined, improving the accuracy of health assessments.

[0068] S320. Obtain the trajectory label data recorded during audio processing.

[0069] Specifically, the implementation process and principle of S320 and S110 are the same, and will not be repeated here.

[0070] S330: Extract health indicator data corresponding to each dimension based on trajectory label data.

[0071] In this step, the electronic device can extract health indicator data corresponding to various dimensions from the trajectory label data. For example, in order to assess the timeliness of processing, it is necessary to filter out all records belonging to processing nodes from the trajectory labels, and then extract the timestamp of each record to prepare for further calculation of indicators such as average delay.

[0072] In some embodiments of this disclosure, S330 may specifically include S3301, S3302, S3303, and S3304: S3301. Based on the timestamp interval in the trajectory label data, calculate the average processing time, maximum delay time, delay variance, and number of delay anomalies for each processing node to obtain the health indicator data corresponding to the processing timeliness health indicator.

[0073] In this step, the electronic device analyzes the difference between consecutive timestamps. For example, it calculates the time interval between processing one frame of data and starting processing the next frame in the "audio decoding" node. During audio processing, the statistics show that the average processing time for each processing node is 4ms, but the maximum delay reaches 50ms, the delay variance is relatively large at 0.6, and there are 5 abnormal events with processing timeouts (>30ms). These 4ms, 50ms, variance of 0.6, and 5 instances are the extracted indicator data.

[0074] S3302. Based on the status codes in the trajectory label data, calculate the abnormal state occurrence rate and error state count of each processing node to obtain the health indicator data corresponding to the processing reliability health indicator.

[0075] In this step, the status code field is iterated. For example, statistics show that the "Network Audio Buffer" node has encountered 50 "Insufficient Buffer" warnings (status code 0x01) and 2 "Decoding Failure" errors (status code 0x02) in the past 10,000 processing cycles. Therefore, the abnormal state occurrence rate = (50+2) / 10000 = 0.52%, and the number of error states = 2. These are the reliability metrics.

[0076] S3303. Based on the parameter data in the trajectory label data, calculate the number of parameter changes, parameter drift, and number of abnormal parameter adjustment events for each processing node to obtain the health indicator data corresponding to the parameter stability health indicator.

[0077] In this step, the electronic device analyzes changes in the same parameter across adjacent track labels. For example, it monitors the target volume value for "Automatic Volume Control (AGC)". It finds that when the user is not interacting with the device, this value fluctuates slightly three times per minute (number of changes), with the fluctuation range around the center value ±2dB (drift), and once suddenly jumps by 10dB (abnormal adjustment event). These "3 times", "±2dB", and "1 jump" are stability indicator data.

[0078] S3304. Based on the resource usage information in the trajectory label data, calculate the average resource utilization rate, peak resource usage and load value of each processing node to obtain the health indicator data corresponding to the resource utilization health indicator.

[0079] In this step, the electronic device reads resource usage information fields. For example, it calculates that the CPU utilization of the "main audio thread" averaged 30% over the past minute, peaking at 75%, and the current load score is assessed as 60 based on storage I / O activity patterns. These 30%, 75%, and 60 points are the resource utilization metrics.

[0080] This embodiment defines a specific, quantifiable, and calculable set of sub-indicators for each health dimension. These sub-indicators meticulously characterize the state of that dimension from different perspectives, making the assessment results more comprehensive and accurate.

[0081] S340. Calculate the health indicator scores for each dimension based on the corresponding health indicator data for each dimension.

[0082] In this step, the electronic device calculates health indicator scores for each dimension based on the corresponding health indicator data. In some embodiments, scoring can be performed according to preset rules. These preset rules can be a scoring lookup table or a function. For example, the preset rules might stipulate: an average latency <5ms earns 100 points, 5-10ms earns 80 points, and >20ms earns 0 points. Based on the extracted average latency of 5.2ms, the system calculates an initial score of 80 points for the "processing timeliness" dimension. Further deductions may be applied based on the "maximum latency of 15ms," ultimately yielding the health indicator score for that timeliness dimension. In other embodiments, the health indicator data corresponding to each dimension can be weighted to obtain the health indicator score for each dimension. The scoring for other dimensions is similar to that for the timeliness dimension and will not be elaborated further here.

[0083] This embodiment divides the extraction of indicator data and the calculation of scores into two steps, making the entire calculation process logically clear, easy to understand and maintain.

[0084] S350: A comprehensive health score is obtained by weighting and integrating the health indicator scores and weights of each dimension.

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

[0086] S360 determines the health level of the in-vehicle audio system based on a comprehensive health score.

[0087] In this step, a health level comparison table is predefined. For example: 90-100 points is "Excellent" (green), indicating perfect system operation; 75-89 points is "Good" (blue), indicating normal system operation; 60-74 points is "Average" (yellow), indicating minor problems; 40-59 points is "Poor" (orange), indicating numerous problems; and 0-39 points is "Dangerous" (red), indicating serious problems requiring attention. Different levels can be associated with different warning colors, maintenance suggestions, and handling strategies, enabling hierarchical management and making maintenance responses more accurate and efficient. After obtaining the comprehensive health score, the electronic device can determine the corresponding health level of the in-vehicle audio system based on the comprehensive health score. For example, comparing the calculated comprehensive health score (e.g., 83 points) with the comparison table determines that it falls within the "75-89 points" range, corresponding to a "Good" health level.

[0088] S370. Assess the health status of the in-vehicle audio system based on the corresponding health level and generate assessment results.

[0089] In this step, the electronic device can assess the health status of the in-vehicle audio system based on its corresponding health level and generate an assessment result. For example, the assessment result could be simple text: "Health Status: Good," or more detailed: "Overall score 83 points, level 'Good,' system is running stably, regular monitoring is recommended." This result can be displayed on the vehicle's infotainment screen, sent to a mobile app, or sent to the backend maintenance center. Converting continuous scores into semantically clear health levels greatly simplifies the communication and understanding of health status.

[0090] This embodiment of the disclosure constructs a multi-dimensional health indicator system, acquires trajectory label data recorded during audio processing, and extracts health indicator data corresponding to each dimension based on the trajectory label data. Further, based on the health indicator data corresponding to each dimension, a health indicator score for each dimension is calculated. A weighted fusion of these scores and their respective weights yields a comprehensive health score. Next, based on the comprehensive health score, the health level of the in-vehicle audio system is determined. Then, based on this health level, the health status of the in-vehicle audio system is evaluated, generating an evaluation result. Thus, by filtering and aggregating the trajectory label data according to the requirements of the four evaluation dimensions, health indicator data is extracted. The data extraction and score calculation are separated into two steps, making the entire calculation process logically clear, easy to understand, and easy to maintain. Converting continuous scores into discrete, semantically clear health levels greatly simplifies the evaluation and understanding of health status.

[0091] Figure 4 This is a flowchart of another audio system health assessment method provided in this disclosure.

[0092] like Figure 4 As shown, the audio system health assessment method may include the following steps: S410. Construct a multi-dimensional health indicator system, which includes health indicators for processing timeliness, processing reliability, parameter stability, and resource utilization.

[0093] Specifically, the implementation process and principle of S410 and S310 are the same, and will not be repeated here.

[0094] S420. Obtain the trajectory label data recorded during audio processing.

[0095] Specifically, the implementation process and principle of S420 and S110 are the same, and will not be repeated here.

[0096] S430: Extract health indicator data corresponding to each dimension based on trajectory label data.

[0097] Specifically, the implementation process and principle of S430 and S330 are the same, and will not be repeated here.

[0098] S440. Calculate the health indicator score for processing timeliness health indicators based on average processing time, maximum delay time, delay variance, and number of delay anomalies.

[0099] In this step, the electronic device can calculate a health indicator score for processing timeliness based on average processing time, maximum latency, latency variance, and the number of latency anomalies. In some embodiments, the preset scoring rules for processing timeliness are as follows: base score 100 points. Average processing time of 4ms is in the excellent range (<5ms), no points are deducted. Maximum latency of 50ms is severely exceeded (>20ms), deducting 30 points. A latency variance of 0.6 is relatively large, deducting 10 points. Five latency anomalies occur, deducting 2 points for each, for a total deduction of 10 points. Finally, the processing timeliness health indicator score = 100 - 30 - 10 - 10 = 50 points. In some embodiments, the average processing time, maximum latency, latency variance, and the number of latency anomalies can be weighted and calculated to obtain the processing timeliness health indicator score. The scoring calculation logic for other dimensions is similar, all based on the extracted indicator data and their respective scoring rules for comprehensive calculation.

[0100] S450. Based on the occurrence rate of abnormal states and the number of error states, calculate the health index score of the processing reliability health index.

[0101] In this step, the electronic device can calculate a health index score for processing reliability health indicators based on the occurrence rate of abnormal states and the number of error states. In some embodiments, the occurrence rate of abnormal states and the number of error states can be weighted to obtain the health index score for processing reliability health indicators.

[0102] S460. Based on the number of parameter changes, parameter drift, and the number of abnormal parameter adjustment events, calculate the health index score of the parameter stability health index.

[0103] In this step, the electronic device can calculate a health index score for the parameter stability health index based on the number of parameter changes, parameter drift, and the number of abnormal parameter adjustment events. In some embodiments, the number of parameter changes, parameter drift, and the number of abnormal parameter adjustment events can be weighted to obtain the health index score for the parameter stability health index.

[0104] S470. Calculate the health index score of the resource utilization health index based on the average resource utilization rate, peak resource utilization, and load value.

[0105] In this step, the electronic device can calculate a health index score for the resource utilization health indicator based on average resource utilization, peak resource utilization, and load value. In some embodiments, the health index score for the resource utilization health indicator can be obtained by weighting the average resource utilization, peak resource utilization, and load value.

[0106] This embodiment provides a calculation method from specific indicator data to scores for each dimension. The score for each dimension is not determined by a single sub-indicator, but rather by a combination of multiple sub-indicators under that dimension, so that the score results can better reflect the overall health level of that dimension and improve the accuracy of the assessment.

[0107] S480: A comprehensive health score is obtained by weighting and integrating the health indicator scores and weights of each dimension.

[0108] Specifically, the implementation process and principle of S480 and S130 are the same, and will not be repeated here.

[0109] S490: Assess the health status of the in-vehicle audio system based on a comprehensive health score and generate assessment results.

[0110] Specifically, the implementation process and principle of S490 and S140 are the same, and will not be repeated here.

[0111] This disclosure constructs a multi-dimensional health indicator system, acquires trajectory label data recorded during audio processing, and extracts health indicator data corresponding to each dimension based on the trajectory label data. Then, based on average processing time, maximum latency, latency variance, and the number of latency anomalies, a health indicator score for processing timeliness is calculated; based on the occurrence rate of abnormal states and the number of error states, a health indicator score for processing reliability is calculated. Furthermore, based on the number of parameter changes, parameter drift, and the number of abnormal parameter adjustment events, a health indicator score for parameter stability is calculated; and based on average resource utilization, peak resource utilization, and load value, a health indicator score for resource utilization is calculated. Further, based on the health indicator scores of each dimension and their respective weights, a weighted fusion is performed to obtain a comprehensive health score. This comprehensive health score is then used to assess the health status of the in-vehicle audio system and generate an evaluation result. Therefore, a method for calculating scores from specific indicator data to various dimensions is provided. The score for each dimension is not determined by a single sub-indicator but rather integrates multiple sub-indicators under that dimension, making the score result more reflective of the overall health level of that dimension.

[0112] Figure 5 This is a schematic diagram of the structure of an audio system health assessment device provided in an embodiment of this disclosure.

[0113] In this embodiment, the audio system health assessment device can be installed 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., without limitation.

[0114] like Figure 5 As shown, the audio system health assessment device 700 may include an acquisition module 710, a calculation module 720, a fusion module 730, and an assessment module 740.

[0115] The acquisition module 710 is used to acquire trajectory label data recorded during audio processing; The calculation module 720 is used to calculate the health indicator scores of each dimension in the multi-dimensional health indicator system based on the trajectory label data; The fusion module 730 is used to perform weighted fusion based on the health indicator scores of each dimension and the weights of each dimension to obtain a comprehensive health score; The assessment module 740 is used to assess the health status of the in-vehicle audio system based on the comprehensive health score and generate assessment results.

[0116] In some embodiments of this disclosure, the trajectory label data includes the following fields: Track identifier, tag header, processing node identifier, timestamp, processing type, parameter data, parameter length, resource usage information, status code, checksum, priority, tag tail; The trajectory identifier is used to distinguish trajectory labels, the label header is used to indicate the start position of the label trajectory, the processing node identifier is used to distinguish each processing node, the timestamp is used to record the processing time of each processing node, the processing type is used to characterize the audio processing category, the parameter data is used to store processing parameters, the parameter length is used to characterize the length of the stored processing parameters, the resource usage information is used to characterize the resource usage of each processing node, the status code is used to record the processing result status, the checksum is used to verify data integrity, the priority is used to characterize the importance of each processing node, and the label tail is used to indicate the end position of the label trajectory.

[0117] In some embodiments of this disclosure, the device 700 further includes: Module 750 is used to construct a multi-dimensional health indicator system, which includes health indicators for processing timeliness, processing reliability, parameter stability, and resource utilization.

[0118] In some embodiments of this disclosure, when the calculation module 720 calculates the health indicator scores for each dimension of the multi-dimensional health indicator system based on the trajectory label data, it is specifically used for: Based on the trajectory label data, extract the health indicator data corresponding to each dimension; Based on the health indicator data corresponding to each dimension, calculate the health indicator score for each dimension.

[0119] In some embodiments of this disclosure, when the calculation module 720 extracts health indicator data corresponding to each dimension based on the trajectory label data, it is specifically used for: Based on the timestamp interval in the trajectory label data, the average processing time, maximum delay time, delay variance, and number of delay anomaly events of each processing node are calculated to obtain the health indicator data corresponding to the processing timeliness health indicator. Based on the status codes in the trajectory label data, the abnormal state occurrence rate and error state count of each processing node are calculated to obtain the health indicator data corresponding to the processing reliability health indicator. Based on the parameter data in the trajectory label data, the number of parameter changes, parameter drift degree and number of abnormal parameter adjustment events of each processing node are calculated to obtain the health index data corresponding to the parameter stability health index. Based on the resource usage information in the trajectory label data, the average resource utilization rate, peak resource usage, and load value of each processing node are calculated to obtain the health indicator data corresponding to the resource utilization health indicator.

[0120] In some embodiments of this disclosure, when the calculation module 720 calculates the health indicator scores for each dimension based on the health indicator data corresponding to each dimension, it is specifically used for: Based on the average processing time, the maximum delay time, the delay variance, and the number of delay anomalies, calculate the health index score of the processing timeliness health index; Based on the occurrence rate of abnormal states and the number of error states, calculate the health index score of the processing timeliness health index. Based on the number of parameter changes, the parameter drift, and the number of abnormal parameter adjustment events, calculate the health index score of the processing timeliness health index; Based on the average resource utilization rate, the peak resource utilization rate, and the load value, a health indicator score for the processing timeliness health indicator is calculated.

[0121] In some embodiments of this disclosure, when the evaluation module 740 evaluates the health status of the in-vehicle audio system based on the comprehensive health score and generates the evaluation result, it is specifically used for: Based on the comprehensive health score, the health level of the in-vehicle audio system is determined; The health status of the in-vehicle audio system is assessed based on its corresponding health level, and an assessment result is generated.

[0122] In some embodiments of this disclosure, the device 700 further includes: Prediction module 760 is used to predict changes in health scores within a preset future time period based on comprehensive health score data from historical time periods, using a linear regression algorithm; or, Based on comprehensive health score data over a historical period, a moving average method is used to smooth short-term fluctuations in the comprehensive health score, thus obtaining the expected changes in the health score over a predetermined future period; or, Based on comprehensive health score data over a historical period, the exponential smoothing method is used to capture the long-term development trend of the comprehensive health score and predict the changes in the health score within a preset future time period.

[0123] It should be noted that, Figure 5 The audio system health assessment device 700 shown can perform the various steps in the above method embodiments and achieve the various processes and effects in the above method embodiments, which will not be elaborated here.

[0124] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure.

[0125] 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.

[0126] like Figure 6 As shown, the electronic device may include a processor 810 and a memory 820 storing computer program instructions.

[0127] 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.

[0128] Memory 820 may include a large-capacity storage for information or instructions. For example, and not limitingly, memory 820 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a 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 a 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.

[0129] The processor 810 reads and executes computer program instructions stored in the memory 820 to perform the steps of the audio system health assessment method provided in this disclosure embodiment.

[0130] 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.

[0131] 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.

[0132] This disclosure also provides a computer-readable storage medium that can store a computer program that, when executed by a processor, causes the processor to implement the audio system health assessment method provided in this disclosure.

[0133] When the computer program is executed by the processor, it can perform the following steps: acquire trajectory label data recorded during audio processing; calculate health indicator scores for each dimension in a multi-dimensional health indicator system based on the trajectory label data; perform weighted fusion based on the health indicator scores for each dimension and the weights of each dimension to obtain a comprehensive health score; evaluate the health status of the in-vehicle audio system based on the comprehensive health score and generate an evaluation result.

[0134] 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 system health assessment 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).

[0135] 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.

[0136] 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 system health assessment 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.

[0137] 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 health assessment of an audio system, characterized in that, The method includes: Obtain trajectory label data recorded during audio processing; Based on the trajectory label data, calculate the health indicator scores for each dimension in the multi-dimensional health indicator system; A comprehensive health score is obtained by weighting and fusing the health indicator scores of each dimension and the weights of each dimension. The health status of the in-vehicle audio system is assessed based on the comprehensive health score, and an assessment result is generated.

2. The method according to claim 1, characterized in that, The trajectory label data includes the following fields: Track identifier, tag header, processing node identifier, timestamp, processing type, parameter data, parameter length, resource usage information, status code, checksum, priority, tag tail; The trajectory identifier is used to distinguish trajectory labels, the label header is used to indicate the start position of the label trajectory, the processing node identifier is used to distinguish each processing node, the timestamp is used to record the processing time of each processing node, the processing type is used to characterize the audio processing category, the parameter data is used to store processing parameters, the parameter length is used to characterize the length of the stored processing parameters, the resource usage information is used to characterize the resource usage of each processing node, the status code is used to record the processing result status, the checksum is used to verify data integrity, the priority is used to characterize the importance of each processing node, and the label tail is used to indicate the end position of the label trajectory.

3. The method according to claim 1, characterized in that, Before acquiring the trajectory label data recorded during audio processing, the method further includes: A multi-dimensional health indicator system is constructed, which includes health indicators for processing timeliness, processing reliability, parameter stability, and resource utilization.

4. The method according to claim 3, characterized in that, The calculation of health indicator scores for each dimension in the multi-dimensional health indicator system based on the trajectory label data includes: Based on the trajectory label data, extract the health indicator data corresponding to each dimension; Based on the health indicator data corresponding to each dimension, calculate the health indicator score for each dimension.

5. The method according to claim 4, characterized in that, The extraction of health indicator data corresponding to each dimension based on the trajectory label data includes: Based on the timestamp interval in the trajectory label data, the average processing time, maximum delay time, delay variance, and number of delay anomaly events of each processing node are calculated to obtain the health indicator data corresponding to the processing timeliness health indicator. Based on the status codes in the trajectory label data, the abnormal state occurrence rate and error state count of each processing node are calculated to obtain the health indicator data corresponding to the processing reliability health indicator. Based on the parameter data in the trajectory label data, the number of parameter changes, parameter drift degree and number of abnormal parameter adjustment events of each processing node are calculated to obtain the health index data corresponding to the parameter stability health index. Based on the resource usage information in the trajectory label data, the average resource utilization rate, peak resource usage, and load value of each processing node are calculated to obtain the health indicator data corresponding to the resource utilization health indicator.

6. The method according to claim 5, characterized in that, The calculation of health indicator scores for each dimension based on the health indicator data corresponding to each dimension includes: Based on the average processing time, the maximum delay time, the delay variance, and the number of delay anomalies, calculate the health index score of the processing timeliness health index; Based on the occurrence rate of the abnormal state and the number of error states, calculate the health index score of the processing reliability health index; Based on the number of parameter changes, the parameter drift, and the number of abnormal parameter adjustment events, calculate the health index score of the parameter stability health index; Based on the average resource utilization rate, the peak resource utilization rate, and the load value, a health index score for the resource utilization health index is calculated.

7. The method according to claim 1, characterized in that, The assessment of the health status of the in-vehicle audio system based on the comprehensive health score generates assessment results, including: Based on the comprehensive health score, the health level of the in-vehicle audio system is determined; The health status of the in-vehicle audio system is assessed based on its corresponding health level, and an assessment result is generated.

8. The method according to claim 1, characterized in that, The method further includes: Based on comprehensive health score data over a historical period, a linear regression algorithm is used to predict changes in health scores within a preset future timeframe; or... Based on comprehensive health score data over a historical period, a moving average method is used to smooth short-term fluctuations in the comprehensive health score, thus obtaining the expected changes in the health score over a predetermined future period; or, Based on comprehensive health score data over a historical period, the exponential smoothing method is used to capture the long-term development trend of the comprehensive health score and predict the changes in the health score within a preset future time period.

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 1-8.

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

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