Test data processing method and device, equipment and storage medium

Abstract

The invention provides a test data processing method and device, equipment and a storage medium, and the method comprises the steps: obtaining multivariate heterogeneous original data generated by a test, the multivariate heterogeneous original data at least comprising a first format data file and a second format data file; respectively converting the first format data file and the second format data file into intermediate structured data files which can be uniformly read by a program; summarizing all the intermediate structured data files to form an integrated data set; performing time axis synchronization processing on the data in the integrated data set, and aligning the data from different sources with a unified time reference; based on the synchronized integrated data set, a preset test performance index is calculated and extracted through an automatic script; and generating a data visualization chart reflecting the continuous change trend of the index based on the test performance index. By adopting the method, the integrity, accuracy, efficiency and intuition of lane cruise auxiliary function test data processing are comprehensively improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and more specifically, to a test data processing method, apparatus, device, and storage medium. Background Technology

[0002] With the rapid popularization of Advanced Driver Assistance Systems (ADAS) in automobiles, lane cruise assist, as a key feature for improving driving safety and comfort, is receiving increasing attention for its track performance testing. The testing process generates various types of data, including vehicle operating status and network interactions. This data is the core basis for verifying whether the function meets design requirements and identifying potential problems. The efficiency and accuracy of data processing directly affect the progress of the testing process and the effectiveness of function optimization.

[0003] In existing technologies, the processing of test data for lane cruise assist functions mainly relies on manual operation. Engineers need to use professional tools to process and analyze test data of different formats one by one in order to obtain key parameters related to the test.

[0004] The existing technology has obvious drawbacks: test data from different sources have their own independent timestamp benchmarks, and manual processing methods make it difficult to achieve accurate synchronization of various types of data on the timeline, resulting in biases in the data analysis results and failing to objectively and truthfully reflect the actual performance of the lane cruise assist function. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a test data processing method, apparatus, device and storage medium that can improve the integrity, accuracy, efficiency and intuitiveness of test data processing.

[0006] In a first aspect, embodiments of this application provide a test data processing method, the method comprising: Obtain multi-dimensional heterogeneous raw data generated by testing, wherein the multi-dimensional heterogeneous raw data includes at least a first format data file and a second format data file; The first format data file and the second format data file are respectively converted into intermediate structured data files that can be uniformly read by the program; All the intermediate structured data files are compiled to form an integrated dataset; The data in the integrated dataset are time-axis synchronized to align data from different sources with a unified time reference. Based on the synchronized integrated dataset, preset test performance metrics are calculated and extracted through automated scripts; Based on the test performance indicators, generate data visualization charts that reflect the continuous changing trend of the indicators.

[0007] Optionally, the first format data file is a .blf format file used to record vehicle bus communication, and the second format data file is a .pcap format file used to record network data packets; The step of converting the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program includes: Convert the first format data file into an intermediate structured data file in .parquet format; Convert the second format data file into an intermediate structured data file in .csv format; The .parquet and .csv formats are intermediate structured data formats that the program can read uniformly.

[0008] Optionally, the aggregation of all intermediate structured data files to form an integrated dataset includes: The data reading script reads various intermediate structured data files. The read intermediate structured data files are converted into a unified data structure object within the program and then merged to form the integrated dataset.

[0009] Optionally, the time-axis synchronization processing of the data in the integrated dataset includes: Based on the timestamps of each data item in the integrated dataset and / or based on preset synchronization event signals, data from different sources are matched and aligned on a unified timeline.

[0010] Optionally, the step of calculating and extracting preset test performance metrics based on the synchronized integrated dataset using automated scripts includes: The data processing script is invoked to parse the synchronized integrated dataset; From the parsed data, at least one of the following performance indicators is calculated: vehicle speed, vehicle acceleration, and distance between the vehicle and the target object; Based on the calculated velocity, acceleration, or distance data, at least one of the following can be further calculated: average velocity, maximum acceleration, or minimum distance.

[0011] Optionally, generating data visualization charts reflecting the continuous changing trend of test performance indicators includes: Call the data plotting script; Plot time on the horizontal axis and one or more of the aforementioned test performance indicators on the vertical axis; Based on the time-series data of the performance indicators, a continuous line graph reflecting the dynamic changes of the performance indicators is plotted over the entire test period.

[0012] Optionally, the method further includes: The test performance metrics are automatically written into and saved as a structured spreadsheet report file; The generated data visualization charts are automatically saved as image files.

[0013] Secondly, embodiments of this application provide a test data processing apparatus, the apparatus comprising: The raw data acquisition module is used to acquire multi-dimensional heterogeneous raw data generated by the test, wherein the multi-dimensional heterogeneous raw data includes at least a first format data file and a second format data file; The data format conversion module is used to convert the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program. The dataset building module is used to aggregate all the intermediate structured data files to form an integrated dataset; The synchronization processing module is used to perform time axis synchronization processing on the data in the integrated dataset to align data from different sources with a unified time reference; The test performance metric calculation module is used to calculate and extract preset test performance metrics based on the synchronized integrated dataset through automated scripts. The visualization chart generation module is used to generate data visualization charts that reflect the continuous changing trend of the test performance indicators.

[0014] Optionally, the first format data file is a .blf format file used to record vehicle bus communication, and the second format data file is a .pcap format file used to record network data packets; The step of converting the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program includes: Convert the first format data file into an intermediate structured data file in .parquet format; Convert the second format data file into an intermediate structured data file in .csv format; The .parquet and .csv formats are intermediate structured data formats that the program can read uniformly.

[0015] Optionally, the aggregation of all intermediate structured data files to form an integrated dataset includes: The data reading script reads various intermediate structured data files. The read intermediate structured data files are converted into a unified data structure object within the program and then merged to form the integrated dataset.

[0016] Optionally, the time-axis synchronization processing of the data in the integrated dataset includes: Based on the timestamps of each data item in the integrated dataset and / or based on preset synchronization event signals, data from different sources are matched and aligned on a unified timeline.

[0017] Optionally, the step of calculating and extracting preset test performance metrics based on the synchronized integrated dataset using automated scripts includes: The data processing script is invoked to parse the synchronized integrated dataset; From the parsed data, at least one of the following performance indicators is calculated: vehicle speed, vehicle acceleration, and distance between the vehicle and the target object; Based on the calculated velocity, acceleration, or distance data, at least one of the following can be further calculated: average velocity, maximum acceleration, or minimum distance.

[0018] Optionally, generating data visualization charts reflecting the continuous changing trend of test performance indicators includes: Call the data plotting script; Plot time on the horizontal axis and one or more of the aforementioned test performance indicators on the vertical axis; Based on the time-series data of the performance indicators, a continuous line graph reflecting the dynamic changes of the performance indicators is plotted over the entire test period.

[0019] Optionally, the device further includes a post-processing module for: The test performance metrics are automatically written into and saved as a structured spreadsheet report file; The generated data visualization charts are automatically saved as image files.

[0020] Thirdly, embodiments of this application provide a computer device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the test data processing method described in any of the optional embodiments of the first aspect are performed.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the test data processing method described in any of the optional embodiments of the first aspect.

[0022] The technical solution provided in this application includes, but is not limited to, the following beneficial effects: The steps of acquiring diverse and heterogeneous raw data generated from testing enable the comprehensive collection of two key types of core data during the lane cruise assist function testing process, ensuring the integrity and comprehensiveness of the data sources and providing sufficient and necessary basic data support for subsequent data processing, analysis, and functional performance evaluation.

[0023] The step of converting raw data in different formats into intermediate structured data files that can be read uniformly by the program breaks down the compatibility barriers between different data formats, enabling heterogeneous data that originally needed to be processed separately to be processed uniformly, and providing format assurance for the smooth progress of subsequent data aggregation and analysis processes.

[0024] The step of summarizing all intermediate structured data files to form an integrated dataset achieves centralized integration of scattered data, avoids the cumbersome operations caused by scattered data storage and separate processing, and allows various related data to be presented in the same dataset, improving the centralization and convenience of data processing.

[0025] The step of synchronizing the integrated dataset with the timeline unifies the time base of different source data, ensuring that data from different format files are accurately aligned in the time dimension. This eliminates the data analysis bias caused by inconsistent time bases at the source, laying a crucial foundation for the accuracy of subsequent performance indicator calculations.

[0026] The automated scripts calculate and extract preset test performance indicators, replacing the traditional manual extraction method. This not only significantly reduces the errors that may be caused by human operation, but also significantly improves the efficiency of indicator extraction, enabling the rapid and accurate acquisition of the core parameters required to evaluate functional performance.

[0027] The steps of generating data visualization charts based on test performance indicators transform abstract indicator data into intuitive charts that reflect continuous changing trends. This clearly presents the dynamic performance changes of a function within a complete test cycle, facilitating the rapid capture of key information and understanding of function performance, and providing intuitive support for the formation of test conclusions.

[0028] In summary, the steps of this application are sequentially connected and work synergistically, forming a complete test data processing workflow from data acquisition, format conversion, and aggregation to time synchronization, indicator extraction, and visualization. This workflow effectively solves the problems of connecting heterogeneous data processing, time synchronization, and data presentation, comprehensively improving the completeness, accuracy, efficiency, and intuitiveness of lane cruise assist function test data processing, and providing reliable data support for functional performance evaluation and optimization.

[0029] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 A flowchart of a test data processing method provided in Embodiment 1 of this application is shown; Figure 2 A flowchart of an integrated dataset construction method provided in Embodiment 1 of this application is shown; Figure 3 A flowchart of a test performance index calculation method provided in Embodiment 1 of this application is shown; Figure 4 A flowchart of a data visualization chart generation method provided in Embodiment 1 of this application is shown; Figure 5 A flowchart of a data visualization chart output method provided in Embodiment 1 of this application is shown; Figure 6 This illustration shows a schematic diagram of the structure of a test data processing device provided in Embodiment 2 of this application; Figure 7 A schematic diagram of the structure of a computer device provided in Embodiment 3 of this application is shown. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0033] Example 1 To facilitate understanding of this application, the following is combined with... Figure 1The flowchart illustrating a test data processing method provided in Embodiment 1 of this application will be described in detail for Embodiment 1 of this application.

[0034] See Figure 1 As shown, Figure 1 A flowchart of a test data processing method provided in Embodiment 1 of this application is shown, wherein the method includes steps S101 to S103: S101: Obtain the multi-variable heterogeneous raw data generated by the test, wherein the multi-variable heterogeneous raw data includes at least a first format data file and a second format data file.

[0035] Specifically, the multi-source heterogeneous raw data comes from the lane cruise assist function field performance test and is the core data collected during the test for verification. It is mainly used for test result verification and subsequent problem analysis. Among them, the first format data file is a .blf format file used to record vehicle bus communication, and the second format data file is a .pcap format file used to record network data packets.

[0036] In existing technologies, engineers need to manually open these two file formats using different professional tools such as CANoe (for .blf files) and Wireshark (for .pcap files), and observe and record data at key time points by eye. The operation is cumbersome and difficult to learn.

[0037] S102: Convert the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program.

[0038] Specifically, the conversion operation is performed using existing tools, which involves converting the first format data file (.blf format) recording vehicle bus communication into an intermediate structured data file in .parquet format, and converting the second format data file (.pcap format) recording network data packets into an intermediate structured data file in .csv format.

[0039] This conversion step primarily addresses the technical obstacle of "inability to analyze data simultaneously due to different data formats" in existing technologies. Previously, .blf and .pcap formats required different professional tools to open, making it impossible to summarize and analyze them on the same platform. However, .parquet and .csv formats can both be read by the program uniformly, laying the foundation for subsequent data integration.

[0040] S103: Summarize all the intermediate structured data files to form an integrated dataset.

[0041] Specifically, the data aggregation script reads the converted .parquet and .csv intermediate structured data files, and internally converts the read data into dataframe format for merging, ensuring that data from different sources can be integrated under the same data structure.

[0042] This step addresses the pain points of existing technologies, namely "inefficiency and inability to process in batches"—traditional manual processing requires operating on individual files one by one, and the large amount of data generated in each test cannot be quickly summarized. This step, however, automates the summarization through scripts, enabling efficient integration of batch data and laying the foundation for subsequent batch processing.

[0043] S104: Perform timeline synchronization processing on the data in the integrated dataset to align data from different sources with a unified time reference.

[0044] Specifically, the synchronization process relies on a timeline synchronization script. The synchronization is based on two aspects: first, the timestamps of each data item in the integrated dataset; and second, the preset synchronization event signals (such as the activation signal jump point of the lane cruise assist function). Through these two methods, different source data from .blf files and .pcap files are accurately matched and aligned on a unified timeline.

[0045] This step solves the core problem of "difficult data synchronization" in existing technologies. Previously, due to different data sources and different timestamp benchmarks, manual comparison and synchronization was not only cumbersome but also prone to errors. This step, however, achieves reproducible and accurate alignment through programmatic synchronization, providing a time benchmark guarantee for subsequent accurate data analysis.

[0046] S105: Based on the synchronized integrated dataset, preset test performance metrics are calculated and extracted through automated scripts.

[0047] Specifically, the automated script here is a data processing script. It first parses the synchronized integrated dataset, automatically filtering out valid data related to the performance of the lane cruise assist function (such as vehicle operating parameters, target object interaction parameters, etc.) while eliminating redundant information to avoid interference from invalid data in the calculation.

[0048] From the parsed valid data, several basic test performance indicators can be calculated, including at least the vehicle's speed, vehicle acceleration, and distance between the vehicle and the target object. These indicators are the core parameters for evaluating the performance of the lane cruise assist function in the field. The data comes directly from the synchronized vehicle bus data (original .blf file) and network data packet data (original .pcap file).

[0049] After the basic indicators are calculated, further calculations will be performed to obtain key statistical indicators, including at least average speed (the average speed over the entire test period), maximum acceleration (the peak acceleration during the test), and minimum distance (the shortest distance between the vehicle and the target object). This process is completed automatically by the program, avoiding the problems of misreading or missing data when processing data manually, and ensuring the accuracy of indicator extraction.

[0050] S106: Generate a data visualization chart reflecting the continuous changing trend of the test performance indicators.

[0051] Specifically, the generated visualization charts are continuous data line charts over the entire test period, covering the dynamic changes of various core indicators, such as speed-time curves, acceleration-time curves, and distance-time curves between the vehicle and the target object, which can intuitively present the fluctuation trend of the indicators throughout the test conditions.

[0052] This step addresses the pain points of existing technologies, namely "insufficient depth of data analysis and lack of visualization"—traditional manual processing can only extract scattered key data points (such as maximum / minimum values), and cannot draw complete and continuous curves, resulting in empty and unintuitive test reports; while the line chart generated by this step makes the test results easier to interpret and provides support for a comprehensive analysis of the system's dynamic performance.

[0053] In an optional implementation, the first format data file is a .blf format file for recording vehicle bus communication, and the second format data file is a .pcap format file for recording network data packets.

[0054] Specifically, .blf format files mainly store real-time operating data transmitted by the vehicle bus (such as CAN bus), including parameters related to the vehicle's own operating status such as vehicle speed, acceleration, and steering signals. They are the core data source for analyzing the control logic of the lane cruise assist function. .pcap format files, on the other hand, store data packets generated by network interactions during the test, including communication data between the vehicle and the test equipment, and between the vehicle and the target detection module. They are the key basis for verifying the accuracy of functional interactions.

[0055] In existing technologies, these two types of files need to be opened and processed separately using specialized tools—.blf files require CANoe software, and .pcap files require Wireshark software. Engineers need to switch between different software to operate, which is cumbersome and difficult to learn, resulting in low data processing efficiency.

[0056] The step of converting the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program includes: Convert the first format data file into an intermediate structured data file in .parquet format.

[0057] Specifically, the .parquet format is a columnar storage format with features of high-efficiency compression and fast reading. It can completely preserve the original attributes of vehicle bus data in the .blf file (such as data precision and timestamp correlation), while supporting efficient reading by programming languages ​​such as Python, and adapting to the data processing logic in subsequent programs.

[0058] The conversion process is completed using existing tools, eliminating the need to redevelop format conversion algorithms. Batch conversion can be achieved simply by calling the interfaces of mature tools, reducing the technical difficulty of implementation while ensuring the integrity of the converted data.

[0059] Convert the second format data file into an intermediate structured data file in .csv format.

[0060] Specifically, the .csv format is a general text data format with strong compatibility and easy parsing. It can organize complex network data packets in .pcap files into structured data according to the "field name-field value" structure, which facilitates subsequent filtering of key parameters (such as network communication latency and data interaction frequency).

[0061] The conversion process automatically extracts the core valid data from the .pcap file, removes irrelevant content such as network protocol headers and redundant check information, reduces the amount of computation in subsequent data processing, and improves the overall process efficiency.

[0062] The .parquet and .csv formats are intermediate structured data formats that the program can read uniformly.

[0063] Specifically, both formats support reading and manipulating data through Python scripts by calling data processing libraries such as pandas, and can be uniformly converted into a dataframe data structure. This structure can organize data from different sources by rows (time dimension) and columns (parameter dimension), which facilitates subsequent operations such as timeline synchronization and indicator calculation.

[0064] Choosing these two formats as intermediate formats takes into account both data storage efficiency (.parquet has a high compression rate) and data compatibility (.csv is easy to use), effectively balancing processing efficiency and ease of use, and solving the previous technical obstacle of "the inability to summarize data in two different formats".

[0065] In an optional implementation, see Figure 2 As shown, Figure 2The flowchart of an integrated dataset construction method provided in Embodiment 1 of this application is shown, wherein the step of summarizing all intermediate structured data files to form an integrated dataset includes steps S201-S202: S201: Read the intermediate structured data files using the data reading script.

[0066] Specifically, the data reading script is developed based on the Python programming language and integrates the pandas library's read_parquet() function (for reading .parquet files) and read_csv() function (for reading .csv files). It can read multiple intermediate structured data files simultaneously through batch path configuration, eliminating the need for manual file-by-file operations.

[0067] The script also has a data verification function. During the reading process, it will automatically check the integrity of the file (such as whether there is missing data or corrupted format). If an anomaly is found, it will output a prompt message to avoid subsequent analysis errors due to file problems. It is more reliable than traditional manual reading (which cannot be quickly verified).

[0068] S202: The read intermediate structured data files are converted into a unified data structure object within the program and then merged to form the integrated dataset.

[0069] Specifically, the unified data structure object is the dataframe object in the pandas library. During the conversion process, the vehicle bus data fields (such as "speed_kmh" and "acceleration_m / s²") in the .parquet file and the network data fields (such as "communication delay_ms" and "target detection distance_m") in the .csv file are mapped and integrated according to column names to ensure that data with the same time dimension can be correlated.

[0070] The merging operation uses timestamps as the core association basis, merging data lines from the same point in time from two intermediate files into a complete data record, forming an integrated dataset covering all dimensions of "vehicle operation-network interaction". This process is fully automated, avoiding omissions and errors in traditional manual recording, and the merging efficiency is much higher than that of manual merging - it takes more than 30 minutes to merge one set of data manually, while this step only takes 1-2 seconds.

[0071] In an optional implementation, the time-axis synchronization processing of the data in the integrated dataset includes: Based on the timestamps of each data item in the integrated dataset and / or based on preset synchronization event signals, data from different sources are matched and aligned on a unified timeline.

[0072] Specifically, if the timestamps inherent in the data items are used as the basis, the script will first unify the timestamp format (e.g., convert all timestamps to the format "YYYY-MM-DD HH:MM:SS.ms"), and then use an interpolation algorithm (e.g., linear interpolation) to fill the time gaps between different source data, ensuring that there is a corresponding data record for every 1ms time node, and achieving millisecond-level precision alignment.

[0073] If the preset synchronization event signal is used as the basis, the key state change points of the lane cruise assist function will be selected as the synchronization reference, such as "the moment when the function activation signal jumps from 0 to 1" or "the moment when the target object appears and the signal is triggered". These moments are set as the origin (or reference point) of the time axis, and the timestamps of the two source data are recalibrated relative to the reference point to ensure that the data analysis corresponding to the key events is without deviation.

[0074] This synchronization process solves the problem of "no mature solution for timeline matching" in existing technologies. Previously, manual synchronization relied on visually finding key time points, with an accuracy of at most the second level, and was prone to errors due to differences in individual judgment. However, this solution improves the accuracy to the millisecond level through programmatic synchronization, and the synchronization results are reproducible, providing a guarantee for the accurate calculation of performance indicators in the future.

[0075] In an optional implementation, see Figure 3 As shown, Figure 3 The flowchart of a test performance index calculation method provided in Embodiment 1 of this application is shown. The step of calculating and extracting preset test performance indexes based on the synchronized integrated dataset using an automated script includes steps S301-S303: S301: Call the data processing script to parse the synchronized integrated dataset.

[0076] Specifically, the data processing script first reads the preset "indicator requirement configuration table", which defines the type of indicator to be extracted, the calculation logic, and the data source field (such as "speed" corresponding to the "speed_kmh" field in the integrated dataset). The script automatically locates the required data column according to the configuration table, without the need for manual filtering.

[0077] During the analysis process, data cleaning is also performed to automatically remove outliers (such as speed values ​​and distance values ​​that exceed the reasonable range due to sensor fluctuations). The "3σ principle" is used to identify abnormal data and replace it with the mean of adjacent valid data to ensure the accuracy of subsequent calculations. Traditional manual processing is difficult to fully identify outliers, which can easily lead to deviations in index calculations.

[0078] S302: Calculate at least one of the following performance indicators from the parsed data: vehicle speed, vehicle acceleration, and distance between the vehicle and the target object.

[0079] Specifically, the vehicle speed index is extracted directly from the "speed_kmh" field of the integrated dataset. This field data comes from the vehicle bus data in the original .blf file, with a sampling frequency of 10Hz (i.e., recorded once every 100ms), which can reflect the real-time changes in vehicle operating speed.

[0080] The vehicle acceleration index is calculated by dividing the speed difference between two adjacent time points by the time interval (e.g., a=(v2-v1) / (t2-t1)). The time interval is uniformly 100ms (matching the speed sampling frequency) to ensure the continuity and accuracy of acceleration calculation.

[0081] The distance index between the vehicle and the target object is extracted from the "target detection distance_m" field of the integrated dataset. This field data comes from the network data packets of the original .pcap file (transmitted by the target detection module), with a sampling frequency of 5Hz (recorded once every 200ms), which can reflect the real-time distance change between the vehicle and the target object in front.

[0082] S303: Based on the calculated velocity, acceleration, or distance data, further calculate at least one of the average velocity, maximum acceleration, or minimum distance.

[0083] Specifically, the average speed is calculated by the arithmetic mean of all speed data over the entire test cycle (e.g., average speed = Σ speed value / number of data points). It reflects the overall smoothness of the vehicle's operation under test conditions and is a key indicator for evaluating the speed control accuracy of the lane cruise assist function.

[0084] The maximum acceleration is obtained by selecting the maximum value from all acceleration data throughout the entire test cycle. Abnormal peak values ​​at the moment of start-up need to be excluded (which have been processed through data cleaning). This indicator can reflect whether the vehicle's acceleration performance meets the design requirements and avoid passenger discomfort due to excessive acceleration.

[0085] The minimum distance is obtained by filtering the minimum value from all distance data throughout the entire test cycle. This indicator reflects the closest safe distance between the vehicle and the target object and is the core indicator for evaluating the collision avoidance safety of the lane cruise assist function. Traditional manual processing can only record the distance values ​​of a few key points and cannot accurately find the minimum distance, while this solution can accurately locate it through full-cycle data calculation.

[0086] In an optional implementation, see Figure 4 As shown, Figure 4 The flowchart illustrates a data visualization chart generation method provided in Embodiment 1 of this application, wherein the step of generating a data visualization chart reflecting the continuous changing trend of test performance indicators includes steps S401 to S403: S401: Call the data graph drawing script.

[0087] Specifically, the data plotting script is developed based on Python's matplotlib or seaborn library, supports batch generation of multiple sets of charts, and allows customization of chart styles through configuration parameters (such as chart size, color, and legend position) to adapt to the format requirements of experimental reports.

[0088] The script also has an automatic naming function, and the generated chart files will be named in the format of "test date_operating condition name_indicator type.png" (such as "20251010_following the vehicle_speed-time curve.png"), which is convenient for subsequent file management and reference in test reports.

[0089] S402: Use time as the horizontal axis and one or more of the test performance indicators as the vertical axis.

[0090] Specifically, the horizontal axis (time axis) covers the entire test cycle. For example, if the test cycle for a certain vehicle-following condition is 300 seconds, then the horizontal axis range is set to 0-300 seconds, and the scale interval is set to 30 seconds to ensure the clarity of the time dimension.

[0091] The vertical axis can be selected as a single indicator or multiple indicators as needed: if a single indicator is selected (such as speed only), the vertical axis range is set according to the actual data range of the indicator (such as 0-120km / h); if multiple indicators are selected (such as speed + distance), a dual vertical axis design is adopted (the left vertical axis is speed and the right vertical axis is distance), and different indicators are distinguished by lines of different colors to avoid chart confusion.

[0092] S403: Based on the time sequence data of the performance index, draw a continuous line graph reflecting the dynamic changes of the performance index over the entire test cycle.

[0093] Specifically, the line graph will retain the connection relationship of each data point. For example, the speed curve will connect the speed data points every 100ms in the whole cycle to form a continuous and smooth curve, which can intuitively show the speed change process of the vehicle from starting, following at a constant speed to stopping.

[0094] The charts will also automatically label key nodes. For example, the time point corresponding to "maximum acceleration" will be labeled "maximum acceleration: 2.5m / s² (t=50s)", and the time point corresponding to "minimum distance" will be labeled "minimum distance: 15m (t=200s)", which makes it easy to quickly locate key performance data.

[0095] This step solves the problem that traditional manual processing "cannot draw continuous data curves within a complete working condition test cycle"—previously, test reports could only present results through text descriptions or scattered data tables, which were empty and not intuitive; while the line graphs generated by this step can make the test results more vivid and greatly enrich the presentation of test report content.

[0096] In an optional implementation, see Figure 5 As shown, Figure 5 The flowchart illustrates a data visualization chart output method provided in Embodiment 1 of this application, wherein the method further includes steps S501-S503: S501: Automatically write and save the test performance indicators as a structured spreadsheet report file.

[0097] Specifically, the spreadsheet report is in Excel format. The data is organized into sheets according to the structure of "Operating Condition Name - Basic Indicators - Derivative Indicators - Indicator Evaluation Standards". For example, the "Following Vehicle Operating Condition" sheet will list the specific values ​​of indicators such as "Real-time Speed ​​(10Hz)", "Average Speed", "Maximum Acceleration", and "Minimum Distance". At the same time, the design threshold (such as the allowable deviation of average speed ±5km / h) is marked in the "Indicator Evaluation Standards" column to facilitate quick judgment of whether the indicators are qualified.

[0098] The output process is fully automated. The script will automatically generate the corresponding sheet according to the test conditions, without the need for manual data entry. Compared with the traditional manual compilation of Excel reports (which takes 1-2 hours per test condition), this solution can complete the report output for a test condition in just 10 seconds, which greatly shortens the test report compilation time.

[0099] S502: Automatically save the generated data visualization chart as an image file.

[0100] Specifically, the image file format is .png, and the resolution is set to 300dpi (dots per inch) to ensure that the charts are clear and unblurry when printed or inserted into reports. At the same time, this format supports cross-platform opening (compatible with both Windows and macOS) without the need for additional software installation.

[0101] The output image files will be stored in the same folder as the Excel report file and managed according to the same naming rules (such as "20251010_Following Condition_Speed-Time Curve.png"), which facilitates the compilation of subsequent test reports. Engineers can directly insert the .png images into the Word format test report without having to readjust the format, which solves the problem of "excessively long report delivery cycle" in traditional manual processing and shortens the overall delivery cycle from an average of more than a week to 1 day.

[0102] Example 2 See Figure 6 As shown, Figure 6 This illustration shows a schematic diagram of a test data processing apparatus according to Embodiment 2 of this application, wherein the apparatus includes: The raw data acquisition module 601 is used to acquire multi-dimensional heterogeneous raw data generated by the test, wherein the multi-dimensional heterogeneous raw data includes at least a first format data file and a second format data file; The data format conversion module 602 is used to convert the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program. The dataset construction module 603 is used to aggregate all the intermediate structured data files to form an integrated dataset; The synchronization processing module 604 is used to perform time axis synchronization processing on the data in the integrated dataset to align data from different sources with a unified time reference. The test performance index calculation module 605 is used to calculate and extract preset test performance indexes based on the synchronized integrated dataset through automated scripts. The visualization chart generation module 606 is used to generate data visualization charts reflecting the continuous changing trend of the test performance indicators.

[0103] In an optional implementation, the first format data file is a .blf format file for recording vehicle bus communication, and the second format data file is a .pcap format file for recording network data packets; The step of converting the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program includes: Convert the first format data file into an intermediate structured data file in .parquet format; Convert the second format data file into an intermediate structured data file in .csv format; The .parquet and .csv formats are intermediate structured data formats that the program can read uniformly.

[0104] In an optional implementation, the aggregation of all intermediate structured data files to form an integrated dataset includes: The data reading script reads various intermediate structured data files. The read intermediate structured data files are converted into a unified data structure object within the program and then merged to form the integrated dataset.

[0105] In an optional implementation, the time-axis synchronization processing of the data in the integrated dataset includes: Based on the timestamps of each data item in the integrated dataset and / or based on preset synchronization event signals, data from different sources are matched and aligned on a unified timeline.

[0106] In an optional implementation, the calculation and extraction of preset test performance metrics based on the synchronized integrated dataset via automated scripts includes: The data processing script is invoked to parse the synchronized integrated dataset; From the parsed data, at least one of the following performance indicators is calculated: vehicle speed, vehicle acceleration, and distance between the vehicle and the target object; Based on the calculated velocity, acceleration, or distance data, at least one of the following can be further calculated: average velocity, maximum acceleration, or minimum distance.

[0107] In an optional implementation, generating data visualization charts reflecting the continuous changing trend of test performance metrics includes: Call the data plotting script; Plot time on the horizontal axis and one or more of the aforementioned test performance indicators on the vertical axis; Based on the time-series data of the performance indicators, a continuous line graph reflecting the dynamic changes of the performance indicators is plotted over the entire test period.

[0108] In an optional implementation, the apparatus further includes a post-processing module for: The test performance metrics are automatically written into and saved as a structured spreadsheet report file; The generated data visualization charts are automatically saved as image files.

[0109] Example 3 Based on the same application concept, see [link / reference] Figure 7 As shown, Figure 7 This illustration shows a structural schematic diagram of a computer device provided in Embodiment 3 of this application, wherein, as shown... Figure 7 As shown, the computer device 700 provided in Embodiment 3 of this application includes: The computer device 700 includes a processor 701, a memory 702, and a bus 703. The memory 702 stores machine-readable instructions that can be executed by the processor 701. When the computer device 700 is running, the processor 701 communicates with the memory 702 through the bus 703. The machine-readable instructions are executed by the processor 701 to perform the steps of the test data processing method shown in Embodiment 1 above.

[0110] Example 4 Based on the same concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the test data processing method described in any of the above embodiments.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0112] The computer program product for processing test data provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0113] The test data processing device provided in this application embodiment can be specific hardware on a device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in this application embodiment are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0114] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0117] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0119] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A test data processing method, characterized in that, The method includes: Obtain multi-dimensional heterogeneous raw data generated by testing, wherein the multi-dimensional heterogeneous raw data includes at least a first format data file and a second format data file; The first format data file and the second format data file are respectively converted into intermediate structured data files that can be uniformly read by the program; All the intermediate structured data files are compiled to form an integrated dataset; The data in the integrated dataset are time-axis synchronized to align data from different sources with a unified time reference. Based on the synchronized integrated dataset, preset test performance metrics are calculated and extracted through automated scripts; Based on the test performance indicators, generate data visualization charts that reflect the continuous changing trend of the indicators.

2. The method according to claim 1, characterized in that, The first format data file is a .blf format file used to record vehicle bus communication, and the second format data file is a .pcap format file used to record network data packets; The step of converting the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program includes: Convert the first format data file into an intermediate structured data file in .parquet format; Convert the second format data file into an intermediate structured data file in .csv format; The .parquet and .csv formats are intermediate structured data formats that the program can read uniformly.

3. The method according to claim 1, characterized in that, The aggregation of all intermediate structured data files forms an integrated dataset, including: The data reading script reads various intermediate structured data files. The read intermediate structured data files are converted into a unified data structure object within the program and then merged to form the integrated dataset.

4. The method according to claim 1, characterized in that, The time-axis synchronization processing of data in the integrated dataset includes: Based on the timestamps of each data item in the integrated dataset and / or based on preset synchronization event signals, data from different sources are matched and aligned on a unified timeline.

5. The method according to claim 1, characterized in that, The integrated dataset after synchronization is used to calculate and extract preset test performance metrics through automated scripts, including: The data processing script is invoked to parse the synchronized integrated dataset; From the parsed data, at least one of the following performance indicators is calculated: vehicle speed, vehicle acceleration, and distance between the vehicle and the target object; Based on the calculated velocity, acceleration, or distance data, at least one of the following can be further calculated: average velocity, maximum acceleration, or minimum distance.

6. The method according to claim 1, characterized in that, The generation of data visualization charts reflecting the continuous changing trend of test performance indicators includes: Call the data plotting script; Plot time on the horizontal axis and one or more of the aforementioned test performance indicators on the vertical axis; Based on the time-series data of the performance indicators, a continuous line graph reflecting the dynamic changes of the performance indicators is plotted over the entire test period.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: The test performance metrics are automatically written into and saved as a structured spreadsheet report file; The generated data visualization charts are automatically saved as image files.

8. A test data processing device, characterized in that, The device includes: The raw data acquisition module is used to acquire multi-dimensional heterogeneous raw data generated by the test, wherein the multi-dimensional heterogeneous raw data includes at least a first format data file and a second format data file; The data format conversion module is used to convert the first format data file and the second format data file into intermediate structured data files that can be uniformly read by the program. The dataset building module is used to aggregate all the intermediate structured data files to form an integrated dataset; The synchronization processing module is used to perform time axis synchronization processing on the data in the integrated dataset to align data from different sources with a unified time reference; The test performance metric calculation module is used to calculate and extract preset test performance metrics based on the synchronized integrated dataset through automated scripts. The visualization chart generation module is used to generate data visualization charts that reflect the continuous changing trend of the test performance indicators.

9. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the test data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the test data processing method as described in any one of claims 1 to 7.

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