NVH test data processing method, device and equipment

Abstract

The invention relates to the field of vehicle test data processing, and discloses an NVH test data processing method, device and equipment, and the method comprises the steps: obtaining a test data file, carrying out the data preprocessing of the test data file, carrying out the working condition slicing of the test data file after the data preprocessing based on a preset separator, and obtaining a working condition slice; performing classification processing on the working condition data blocks based on a plurality of preset classifiers to obtain a plurality of types of working condition arrays, determining a target working condition array in the plurality of types of working condition arrays based on category identifiers, and for each target data block of the target working condition array, performing positioning on the target data block based on a preset positioning character set and a preset positioning rule, and analyzing the data information corresponding to the target data block to generate and display an online preview table and an online preview curve of the target working condition array in a graphical user interface. According to the technical scheme provided by the invention, the accuracy, efficiency and convenience of vehicle NVH test data processing and summarization can be improved.

Description

Technical Field

[0001] This application relates to the field of vehicle test data processing, and in particular to an NVH test data processing method, apparatus and equipment. Background Technology

[0002] In vehicle development, NVH (Noise, Vibration, and Harshness) performance is one of the important indicators for measuring vehicle comfort. With the rapid development of the new energy vehicle industry, users have increasingly higher requirements for vehicle comfort, making the processing and analysis of NVH test data particularly important.

[0003] In practical applications, NVH test data typically requires complex processing and analysis to generate charts and reports for engineers' reference. Traditional data processing methods rely mainly on manual operation, involving manual data filtering, calculation, chart creation, and report generation. This is not only time-consuming and labor-intensive but also prone to errors due to human factors, making it difficult to meet the standardized, personalized, and systematic work requirements of enterprises.

[0004] Therefore, improving the efficiency and accuracy of vehicle NVH test data processing while reducing reliance on professional skills, and enhancing the efficiency and convenience of vehicle NVH test data processing and summarization, has become a current research focus in the field of vehicle test data processing. Summary of the Invention

[0005] This application provides an NVH test data processing method, apparatus, and equipment, which can automate the processing of NVH test data and improve the accuracy, efficiency, and convenience of vehicle NVH test data processing and summarization.

[0006] The first aspect of this application provides an NVH test data processing method, which involves acquiring a test data file exported from an NVH test device and preprocessing the test data file, wherein the test data file is in UNV format; responding to a data acquisition command, the preprocessed test data file is sliced ​​into multiple operating condition data blocks based on a preset delimiter, wherein the data acquisition command carries a category identifier; the operating condition data blocks are classified based on multiple preset classifiers to obtain multiple operating condition arrays, and a target operating condition array is determined in the multiple operating condition arrays based on the category identifier; for each target data block of the target operating condition array, the data information corresponding to the target data block is parsed based on a preset positioning character set and preset positioning rules, wherein the data information is used to generate and display an online preview table and an online preview curve of the target operating condition array in a graphical user interface.

[0007] In one embodiment, the data preprocessing includes encoding conversion and character conversion; the data preprocessing of the test data file includes: converting the test data file from UTF-8 encoding format to ANSI encoding format to complete the encoding conversion; reading the text string data in the test data file after the encoding conversion, and converting the text string data into byte set data to complete the character conversion.

[0008] In one embodiment, segmenting a preprocessed test data file into work condition segments based on preset delimiters includes: traversing the preprocessed test data file to detect preset delimiters and their character positions, wherein the preset delimiters are continuous byte sequences used to identify the start and end of a work condition; and determining the data content located between any two preset delimiters and the data content located after the last preset delimiter as the work condition data blocks of the test data file.

[0009] In one embodiment, the multiple operating condition arrays include a dynamic stiffness operating condition array, a vibration transfer function operating condition array, and a noise transfer function operating condition array. The preset classifiers include a first classifier, a second classifier, and a third classifier. Classifying the operating condition data blocks based on these preset classifiers to obtain the multiple operating condition arrays involves: for any given operating condition data block, sequentially detecting whether the first classifier, the second classifier, and the third classifier exist within the operating condition data block; and based on the detection results for each operating condition data block, assigning the operating condition data block to the corresponding category of the operating condition array to obtain the dynamic stiffness operating condition array, the vibration transfer function operating condition array, and the noise transfer function operating condition array.

[0010] In one implementation, classifying the operating condition data block into a corresponding category operating condition array based on the detection result of each operating condition data block includes: if the detection result indicates that the operating condition data block has the first classifier, then the operating condition data block is classified into the dynamic stiffness operating condition array; if the detection result indicates that the operating condition data block has the second classifier and does not have the third classifier, then the operating condition data block is classified into the vibration transfer function operating condition array; if the detection result indicates that the operating condition data block has the second classifier and has the third classifier, then the operating condition data block is classified into the noise transfer function operating condition array.

[0011] In one embodiment, the preset positioning character set includes paragraph separators and multiple preset positioning characters, and the data information includes coordinate data segments and multiple basic information. Querying the data information corresponding to the target data block based on the preset positioning character set and preset positioning rules includes: segmenting the target data block according to the paragraph separators to determine coordinate data segments and line break arrays within the target data block; traversing any preset positioning character in the preset positioning character set within the line break arrays, and performing data segmentation on the line break arrays based on the preset positioning characters; extracting target information according to the preset positioning rules and the data segmentation results, and determining the target information as the basic information corresponding to the preset positioning characters.

[0012] In one implementation, the data information includes curve name, project name, key information, test type, response point name, response point direction, excitation point name, excitation point direction, frequency resolution, and coordinate data segment, wherein: the curve name is determined based on the response point name, the response point direction, and the excitation point direction.

[0013] In one implementation, the data information is further used to: in response to a file output instruction, generate a report illustration of the test data file based on the data information, wherein the report illustration includes a statistical table, a line graph, and a color map; and invoke a standard report template to generate a standardized report document of the test data file based on the report illustration and the standard report template.

[0014] A second aspect of this application provides an NVH test data processing apparatus, comprising: a data acquisition unit, configured to acquire a test data file exported from an NVH test device and preprocess the test data file, wherein the test data file is in UNV format; a working condition slicing unit, configured to, in response to a data acquisition instruction, slice the preprocessed test data file into multiple working condition data blocks based on a preset delimiter to obtain multiple working condition data blocks, wherein the data acquisition instruction carries a category identifier; a working condition classification unit, configured to classify the working condition data blocks based on multiple preset classification identifiers to obtain multiple working condition arrays, and determine a target working condition array in the multiple working condition arrays based on the category identifier; and a data processing unit, configured to, for each target data block of the target working condition array, parse the data information corresponding to the target data block based on a preset positioning character set and preset positioning rules, wherein the data information is used to generate and display an online preview table and an online preview curve of the target working condition array in a graphical user interface.

[0015] A third aspect of this application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the NVH test data processing method described in the first aspect above.

[0016] The technical solutions provided in one or more embodiments of this application construct an automated processing flow for NVH test data from data acquisition to report generation, improving the accuracy, efficiency, and convenience of test data processing and summarization. Specifically, test data files are first acquired and preprocessed to achieve data format uniformity. Then, based on preset delimiters, the preprocessed files are decomposed into multiple independent operating condition data blocks. Through structured slicing and classification processing, invalid global processing of the entire dataset is avoided, simplifying the data processing logic and improving overall processing efficiency. Furthermore, the operating condition data blocks are classified and processed according to multiple preset classifiers. The target operating condition array is determined by combining category identifiers. The data information of the target operating condition array is parsed based on preset positioning character sets and positioning rules, thereby achieving high efficiency, accuracy, and convenience in NVH test data processing. In addition, the data information is used to generate and display online preview tables and curves in a graphical user interface. Through automated parsing and visualization, testers can view and interactively analyze data in real time, quickly identify data anomalies and trends, and provide support for subsequent automatic report generation processes, enhancing the efficiency, convenience, and user experience of NVH test data summarization. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A schematic diagram illustrating the steps of an NVH test data processing method provided in this application embodiment; Figure 2(a) is a data schematic diagram of a dynamic stiffness condition data block provided in an embodiment of this application; Figure 2(b) is a data schematic diagram of a vibration transfer function operating condition data block provided in an embodiment of this application; Figure 2(c) is a data schematic diagram of a noise transfer function operating condition data block provided in an embodiment of this application; Figure 3 A schematic diagram of a graphical user interface provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of an NVH test data processing device provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0019] 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 described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Furthermore, the use of terms such as "first," "second," etc., in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of embodiments in this application, unless otherwise stated, "multiple" means two or more. Additionally, the use of "based on" or "according to" implies openness and inclusiveness, because processes, steps, calculations, or other actions "based on" or "according to" one or more of the stated conditions or values ​​may in practice be based on additional conditions or beyond the stated values.

[0021] With the development of the new energy vehicle industry, users have increasingly higher requirements for vehicle performance. NVH, as a key evaluation point for measuring vehicle comfort, is of paramount importance. Routine vehicle NVH testing requires measurements such as origin dynamic stiffness (IPI), vibration transfer function (VTF), and noise transfer function (NTF). Test data needs to be manually processed to convert it into easily viewable data and images. In the process of processing, calculating, and statistically analyzing the aforementioned test data, due to the large number of test points, data filtering and calculation at each point is tedious, time-consuming, prone to errors, and highly specialized.

[0022] In NVH development, a typical vehicle requires approximately 40-50 impact test points. Each point has three directions. Considering the vibration transfer function typically has 2-4 response points, and the noise transfer function typically has 2-4 response points, the total data to be processed from these three tests is estimated to reach 50*3 + 50*3*4 + 50*3*4 = 1350 sets. Each set of data requires corresponding calculations, statistics, and graph generation. In related technologies, test data is usually imported into specialized software, followed by manual calculations to output charts, which are then manually screenshotted and converted into PowerPoint reports or Excel spreadsheets. This method demands a high level of expertise from the user and is cumbersome, requiring the selection of data, input of calculation parameters, calculations, chart adjustments, table creation, and PowerPoint layout. The process is tedious and error-prone, failing to meet the standardized, personalized, and systematic work requirements of enterprise-level operations. Furthermore, the sheer volume of data and the lengthy processing time prevent rapid and efficient data processing and analysis.

[0023] In view of the above, this application provides one or more embodiments of an NVH test data processing method, apparatus, and device, which can solve the above problems, realize the automated processing of NVH test data, improve the efficiency and accuracy of data processing, and reduce the dependence on professional skills, thereby improving the accuracy, efficiency, and convenience of vehicle NVH test data processing and summarization.

[0024] Please see Figure 1 One embodiment of this application provides an NVH test data processing method, which may include the following steps: S1: Obtain the test data file exported from the NVH testing equipment and perform data preprocessing on the test data file, wherein the test data file is a UNV format file.

[0025] The aforementioned NVH testing equipment is an instrument used to measure physical quantities such as noise and vibration generated by a vehicle during operation. The raw data files generated during the measurement process serve as the aforementioned test data files. These test data files record the noise and vibration data of the vehicle under different operating conditions. These test data files are typically exported in UNV format, which usually uses UTF-8 encoding. Exporting the test data files from the NVH testing equipment ensures the integrity and accuracy of the data. Preprocessing the acquired UNV format test data files ensures a unified data format, facilitating subsequent automated processing such as data slicing, classification, and parsing, thereby improving overall processing efficiency. Furthermore, ensuring data format standardization and compatibility reduces problems caused by format incompatibility.

[0026] S3: In response to the data acquisition command, the test data file that has undergone data preprocessing is sliced ​​into working condition segments based on a preset delimiter to obtain multiple working condition data blocks, wherein the data acquisition command carries a category identifier.

[0027] In this embodiment, the aforementioned data processing instruction is used to trigger the data processing flow. It is typically issued by the user or the system and specifies the test data file to be processed and its related parameters (such as category identifiers). The category identifier specifies the category to which the data to be processed belongs (such as dynamic stiffness, vibration transfer function, noise transfer function, etc.). By including the category identifier in the data processing instruction, it is convenient to extract the operating condition data block corresponding to the category identifier from the test data file, allowing for targeted processing of the required operating condition data block. The category identifier guides the system to process data of specific categories, avoiding unnecessary global processing of the entire data file, thereby improving the system's convenience and efficiency. Furthermore, the data blocks after operating condition segmentation can be classified and processed according to the category identifier, providing a foundation for subsequent automated analysis and report generation.

[0028] In this embodiment, the aforementioned operating condition data block is obtained by performing an operating condition slicing operation on the test data file. The operating condition slicing operation can be understood as dividing a complete test data file into multiple independent data blocks as operating condition data blocks. Each operating condition data block contains all the data information for a specific test operating condition. In NVH testing, a test operating condition typically refers to a complete, independent measurement. For example, striking a point (excitation point) once in the X direction with a hammer while simultaneously measuring its vibration response in the Y direction at another point (response point), the data generated from this entire process constitutes an operating condition data block. A vehicle has multiple measurement points, each with multiple directions, thus generating multiple such operating condition data blocks, as well as redundant operating condition data blocks for other purposes (e.g., response data generated from non-response points). By extracting the various operating condition data blocks from the complex test data file through operating condition slicing as the basic unit for subsequent analysis and processing, this structured processing method simplifies data processing logic and improves processing efficiency.

[0029] In this embodiment, the test data file is segmented based on a preset delimiter. Specifically, the preset delimiter is a specific character sequence or marker defined by the UNV file format, used to identify the start and end positions of different operating conditions in the data file. According to the UNV file format specification, using its built-in, predefined data block delimiters, a mixed source file containing multiple test operating conditions can be accurately decomposed into multiple independent operating condition data blocks that can be processed one by one by subsequent programs. For example, a continuous byte sequence can be used as a preset delimiter to distinguish different operating condition data blocks. For instance, using [-1, -1, 58] as the preset delimiter, the test data file is treated as a long byte sequence. All positions where the specific sequence [-1, -1, 58] appears in this long byte sequence are scanned and located to define the data block between two preset delimiters in the test data file as a single operating condition data block.

[0030] S5: Classify the working condition data block based on multiple preset classifiers to obtain multiple working condition arrays, and determine the target working condition array in the multiple working condition arrays based on the category identifier.

[0031] In this embodiment, the multiple operating condition data blocks obtained from the segmentation are classified so that each operating condition data block is stored in the corresponding category of the operating condition array. NVH test data typically contains multiple types of operating conditions (e.g., dynamic stiffness operating condition data, vibration transfer function operating condition data, noise transfer function operating condition data, etc.). The data structure and processing method of each operating condition may be different. By classifying the data, different types of data can be processed separately, reducing the repeated scanning and parsing of all data blocks, improving the efficiency of test data processing, and the classified data structure is clear, which facilitates subsequent automated analysis and report generation. Specifically, by detecting whether there is a corresponding preset classifier in each operating condition data block, the data block is classified into the corresponding operating condition array (e.g., dynamic stiffness operating condition array, vibration transfer function operating condition array, and noise transfer function operating condition array) according to the detection results. The preset classifier can be understood as a pre-set specific tag or keyword used to distinguish different types of operating condition data blocks.

[0032] In this embodiment, based on the category identifier in the data acquisition instruction, a target working condition array requiring further processing is selected from multiple working condition arrays. These category identifiers correspond to different types of working condition data blocks. For example, if the category identifier represents the dynamic stiffness working condition data type, then the dynamic stiffness working condition array is used as the target working condition array for further targeted processing of multiple working condition data blocks under that target working condition array. By selecting a specific category of working condition array for processing based on the category identifier in user requirements or system instructions, unnecessary calculations on unnecessary data are avoided, simplifying the test data processing logic and thus improving the efficiency and convenience of test data processing. Simultaneously, this classification processing method better adapts to the processing needs of different types of data, enhancing the system's flexibility and adaptability.

[0033] S7: For each target data block of the target working condition array, based on the preset positioning character set and preset positioning rules, the data information corresponding to the target data block is parsed. The data information is used to generate and display the online preview table and online preview curve of the target working condition array in the graphical user interface.

[0034] The aforementioned preset positioning character set is a set of predefined characters or tags used to locate specific data information within a target data block, typically including delimiters and keywords. The aforementioned preset positioning rules guide how to extract and parse data information from the target data block based on the preset positioning character set. Each working condition data block has its own data information, including but not limited to coordinate data, test type, response point name, and excitation point name. Specifically, data information is located within the target data block using the preset positioning character set, and this data information is extracted and parsed according to the preset positioning rules. Using the preset positioning character set and positioning rules for data extraction automates the extraction process of test data, avoiding errors and inconsistencies from manual extraction, and ensuring the accuracy and convenience of data extraction.

[0035] In this embodiment, the aforementioned data information can be used to generate visualized online preview tables and online preview curves to present the characteristics and trends of the data in real time and intuitively. Specifically, the generated online preview tables and online preview curves allow testers to instantly view the analysis results and perform interactive operations (such as adjusting parameters, re-analyzing, etc.). By quickly understanding and analyzing the data, anomalies and trends in the data can be quickly identified, supporting more effective decision-making. The aforementioned online preview tables and online preview curves can be presented in a graphical user interface, i.e., a visual interface for user interaction with the system, enabling users to intuitively view and analyze the data, supporting real-time interaction and dynamic adjustments, and enhancing data visualization capabilities and user experience. The analyzed data information can be directly used in subsequent automated processing flows, such as report generation and data statistics. Optionally, standardized report documents can also be automatically generated based on the analysis results, reducing manual intervention and improving the accuracy, efficiency, and convenience of test data summarization.

[0036] Based on the above ideas, the technical solution provided in this embodiment of the application constructs an automated processing flow for NVH test data from data acquisition to report generation, improving the accuracy, efficiency, and convenience of test data processing. Specifically, test data files are first acquired and preprocessed to achieve data format uniformity. Then, based on preset delimiters, the preprocessed files are decomposed into multiple independent operating condition data blocks. Through structured slicing and classification processing, invalid global processing of the entire dataset is avoided, simplifying the data processing logic and improving overall processing efficiency. Furthermore, the operating condition data blocks are classified and processed according to multiple preset classifiers. The target operating condition array is determined by combining category identifiers. The data information of the target operating condition array is parsed based on preset positioning character sets and positioning rules, thereby achieving high efficiency, accuracy, and convenience in NVH test data processing. In addition, the data information is used to generate and display online preview tables and curves in a graphical user interface. Through automated parsing and visualization, testers can view and interactively analyze data in real time, quickly identify data anomalies and trends, and provide support for subsequent automatic report generation processes, enhancing the efficiency, convenience, and user experience of NVH test data summarization.

[0037] In one implementation, based on step S1 above, the data preprocessing operation for the test data file includes encoding conversion and character conversion. Specifically, the test data file is converted from UTF-8 encoding format to ANSI encoding format to complete the encoding conversion; the text string data in the encoded test data file is read and converted into byte set data to complete the character conversion.

[0038] The aforementioned test data files are typically exported in UNV format, which usually uses UTF-8 encoding. Therefore, it's necessary to convert the test data files from UTF-8 to ANSI encoding, a format more easily understood by computers. ANSI encoding offers higher parsing efficiency, accommodating subsequent data processing needs and ensuring data compatibility within the environment. Furthermore, the data in the test data files is converted from text strings to byte sequences, transforming text string data into byte set data for direct mathematical operations and data processing, avoiding the complex character encoding parsing process. The byte set data format facilitates interaction with underlying hardware or systems, significantly improving data processing efficiency and simplifying processing logic, especially when dealing with low-level programming or embedded systems.

[0039] The technical solution provided in this embodiment of the application performs preprocessing operations such as encoding conversion and character conversion on NVH test data files. By converting the encoding to the more efficient ANSI format, the compatibility of the data in subsequent processing environments is ensured. At the same time, text string data is converted into byte set data that can be directly used for mathematical operations and data processing, avoiding the complex character encoding parsing process. It can also better adapt to the interaction requirements of underlying hardware or embedded systems, further simplifying the data processing logic and improving the overall data processing efficiency.

[0040] In one implementation, based on step S3 above, the preprocessed test data file is segmented into operating condition blocks based on preset delimiters and their character positions. Specifically, the preprocessed test data file is traversed to detect preset delimiters and their character positions. The preset delimiters are continuous byte sequences used to identify the start and end of an operating condition, serving as a start marker for one operating condition data segment and an end marker for the previous operating condition data segment. Further, the data content between any two preset delimiters, and the data content after the last preset delimiter, are defined as operating condition data blocks in the test data file. For example, [-1, -1, 58] is used as the preset delimiter, and the data content between two preset delimiters is defined as one operating condition data block. By quickly locating operating condition boundaries using preset delimiters, the complexity of data parsing is reduced, thereby improving the accuracy and convenience of NVH test data processing.

[0041] The technical solution provided in this embodiment of the application achieves precise segmentation of the test data file based on preset delimiters, building upon step S3. Specifically, the pre-processed test data file is traversed to detect preset delimiters and their character positions. Data content between any two preset delimiters and after the last preset delimiter is identified as the operating condition data block. The operating condition boundary is quickly located using preset delimiters, effectively reducing data parsing complexity and improving the accuracy and convenience of NVH test data processing.

[0042] In one implementation, based on step S5 above, the aforementioned multi-category operating condition arrays include a dynamic stiffness operating condition array, a vibration transfer function operating condition array, and a noise transfer function operating condition array. The aforementioned preset classifiers include a first classifier, a second classifier, and a third classifier. The operating condition data blocks are classified based on the multiple preset classifiers to obtain the aforementioned multi-category operating condition arrays. Specifically, for any operating condition data block, the presence of the first classifier, the second classifier, and the third classifier is sequentially detected in the operating condition data block. Further, based on the detection results of each operating condition data block, the operating condition data block is assigned to the corresponding category of the operating condition array to obtain the dynamic stiffness operating condition array, the vibration transfer function operating condition array, and the noise transfer function operating condition array.

[0043] In NVH development, origin dynamic stiffness testing, vibration transfer function testing, and noise transfer function testing are three important testing methods. Origin dynamic stiffness, measured at the excitation point, reflects the structure's stiffness characteristics at a specific frequency and is often used to evaluate the dynamic performance of vehicle attachment points. Vibration transfer function describes the transmission of vibration from the excitation point to the response point, used to analyze vibration transmission paths and structural vibration characteristics. Noise transfer function describes the transmission of noise from the excitation point to the response point, used to analyze noise transmission paths and acoustic performance. Correspondingly, the data blocks under these three test conditions are divided into multiple test condition arrays: dynamic stiffness test condition array, vibration transfer function test condition array, and noise transfer function test condition array.

[0044] In this embodiment, the presence of a first classifier, a second classifier, and a third classifier is sequentially detected in each working condition data block. Based on the detection results of each preset classifier, the working condition data block is assigned to the corresponding category of the working condition array. Specifically, this includes the following three scenarios: Scenario 1: If the test results indicate that the working condition data block has a first classifier, then the working condition data block will be classified into the dynamic stiffness working condition array.

[0045] For example, the first classifier is the field 'Dynamic Stiffness'. If the field 'Dynamic Stiffness' is detected in a certain working condition data block, it is determined that the working condition data block belongs to the dynamic stiffness working condition array and the working condition data block is classified into the dynamic stiffness working condition array. If the field 'Dynamic Stiffness' is not detected, the determination is made based on the detection results of the second and third classifiers.

[0046] Scenario 2: If the test results indicate that the working condition data block has a second classifier but no third classifier, then the working condition data block will be classified into the vibration transfer function working condition array.

[0047] For example, the second classifier is the field 'FRF for' and the third classifier is the field ':S / '. Based on the determination that the first classifier does not exist, if the field 'FRF for' is detected in the working condition data block and the field ':S / ' does not exist, then the working condition data segment is determined to belong to the vibration transfer function working condition array, and the working condition data block is classified into the vibration transfer function working condition array.

[0048] Scenario 3: If the test results indicate that the working condition data block has a second classifier and a third classifier, then the working condition data block will be classified into the noise transfer function working condition array.

[0049] For example, the second classifier is the field 'FRF for' and the third classifier is the field ':S / '. Based on the determination that the first classifier does not exist, if the field 'FRF for' and the field ':S / ' are detected in the operating condition data block, then the operating condition data segment is determined to belong to the noise transfer function operating condition array, and the operating condition data block is classified into the noise transfer function operating condition array.

[0050] The technical solution provided in this embodiment of the application classifies operating condition data blocks based on multiple preset classifiers to obtain multi-category operating condition arrays corresponding to NVH test types. Specifically, for any operating condition data block, the operating condition data block is assigned to the corresponding category operating condition array according to the detection results of each preset classifier, realizing accurate classification of operating condition data blocks. This ensures that the classified data arrays correspond one-to-one with the three types of test operating conditions in the NVH development process, thereby efficiently distinguishing different types of NVH test data, ensuring the relevance and accuracy of data processing, and providing a clear and structured data foundation for subsequent automated analysis and report generation, significantly improving the efficiency and accuracy of NVH test data processing.

[0051] In one implementation, based on step S7 above, the preset positioning character set includes paragraph separators and multiple preset positioning characters, and the data information includes coordinate data segments and multiple basic information segments. The multiple basic information segments are located within the basic information segment. The coordinate data segments and basic information segments can be determined by the paragraph separators, and the basic information segments can be determined by the preset positioning characters. Based on the preset positioning character set and preset positioning rules, the data information corresponding to the target data block is queried. Specifically, the following steps are performed: S61: The target data block is segmented according to the paragraph separator to determine the coordinate data segment and line break array in the target data block.

[0052] S63: Traverse any preset positioning character in the preset positioning character set in the line array, and perform data segmentation on the line array according to the preset positioning character.

[0053] S65: Extract target information based on preset positioning rules and data segmentation results, and determine the target information as the basic information corresponding to the preset positioning character.

[0054] In this embodiment, the operating condition data segment is first segmented according to paragraph separators. The paragraph separator is a preset character or character sequence used to identify the boundaries of different paragraphs or data regions within the data block. Specifically, the paragraph separator and its corresponding line are located within the operating condition data segment. Lines above the paragraph separator line are designated as basic information segments, and lines below the paragraph separator line are designated as coordinate data segments. The basic information segments include multiple pieces of basic information describing the attribute portion of the test data, typically containing specific attribute content of the test results (such as project name, response point name, excitation point name, etc.). The coordinate data segments describe the numerical portion of the test data, typically containing specific numerical values ​​of the test results (such as frequency, amplitude, etc.). Segmenting the data into independent paragraphs using paragraph separators allows for clearer identification and processing of different types of fields, reduces repetitive scanning and parsing of the entire data block, and improves data processing efficiency.

[0055] In this embodiment, the basic information of the current operating condition data block is determined in the basic information segment based on multiple preset positioning characters. These preset positioning characters are a set of predefined characters or tags. Specifically, each character line in the basic information segment is treated as an array element, and the basic information segment is divided into a row array, with each row containing specific data information for easy line-by-line processing and analysis. The row array is traversed to find each preset positioning character in the preset positioning character set. The row array is then segmented into smaller units based on these preset positioning characters, and the required target information is extracted from the segmented data according to preset positioning rules as the basic information. The use of preset positioning rules and preset positioning character sets reduces errors from manual data extraction and improves the accuracy and reliability of NVH test data processing. It should be noted that the preset positioning rules and preset positioning character sets can be pre-adjusted according to different data formats, enhancing the system's flexibility and adaptability.

[0056] In one embodiment, please refer to Figure 2(a), each working condition data block in the dynamic stiffness working condition array is processed as follows: '0 0 0 0 NONE NONE' is used as the paragraph separator, the character paragraph below the paragraph separator is used as the coordinate data segment, and the character paragraph above is used as the basic information segment. A line array is constructed based on the characters of each line of the basic information segment, so as to perform data cutting and extraction on the line array according to the preset positioning character.

[0057] Furthermore, the fifth element of the row array is split with '\' to form an array with two elements. The first element of this array is extracted, and its data content serves as basic information, representing the 'test type' as 'DynamicStiffness', indicating that this data block belongs to the dynamic stiffness test condition.

[0058] Furthermore, the character segments containing 9 consecutive spaces and 6 consecutive spaces in the 7th element of the line array are deleted. Then, the two positions where the spaces were deleted are filled with two space characters respectively to generate a new array. After deleting all leading and trailing spaces from the 3rd element of this array, its data content is used as basic information to represent 'frequency resolution', which is '1'.

[0059] Furthermore, the fifth element of the row array is split with ':' to form an array with two elements. The second element of this array is extracted, and its data content is used as basic information to represent the 'excitation point direction', which is '+Z'.

[0060] Furthermore, the first element of the line-by-line array is split using a space character to form an array 'a' with multiple elements. The third element of this array is extracted and split using a colon (':') to form a new array 'b'. The first element of this array is extracted, and its data content is used as basic information to represent the 'response point name', which is 'compressor bracket'. The second element of array 'b' is split using a forward slash (' / ') to generate a new array 'c'. The first element of array 'c' is extracted, and leading and trailing spaces are removed. Its data content is used as basic information to represent the 'response point direction', which is '+Z'.

[0061] Furthermore, the fourth element of the line break array is separated by quotation marks to form a new array. The second element of this array is extracted, and its data content is used as basic information to represent 'section', which is 'NTF&VTF'. The fourth element of this array is extracted, and its data content is used as basic information to represent 'run', which is 'compressor bracket + Z'. The content containing the excitation point direction in 'run' is deleted, and its data content is used as basic information to represent 'excitation point name', which is 'compressor bracket'.

[0062] In this embodiment, the preset positioning characters include: '\', spaces of a specified number and position, ':', ' / ', quotation marks, etc. The extracted basic information includes test type, frequency resolution, excitation point direction, response point name, response point direction, section (test group), run (test sub-item, representing a specific test item in the test group), etc. Among them, for the working condition data block in the dynamic stiffness working condition array, the extracted excitation point name should be completely consistent with the response point name.

[0063] In one embodiment, please refer to Figure 2(b), each working condition data block in the vibration transfer function group is processed as follows: '0 0 0 0 NONE NONE' is used as the paragraph separator, the character paragraph below the paragraph separator is used as the coordinate data segment, and the character paragraph above is used as the basic information segment. A line array is constructed based on the characters of each line of the basic information segment, so as to perform data segmentation and extraction on the line array according to the preset positioning character.

[0064] Furthermore, the fifth element of the row array is split with '\' to form an array with two elements. The first element of this array is extracted, and its data content serves as basic information, representing the 'test type' as 'FRF', indicating that this working condition data block belongs to the vibration transfer function working condition data.

[0065] Furthermore, the character segments containing 9 consecutive spaces and 6 consecutive spaces in the 7th element of the line array are deleted. Then, the two positions where the spaces were deleted are filled with two space characters respectively to generate a new array. After deleting all leading and trailing spaces from the 3rd element of this array, its data content is used as basic information to represent 'frequency resolution', which is '1'.

[0066] Furthermore, the fifth element of the row array is split with ':' to form an array with two elements. The second element of this array is extracted, and its data content is used as basic information to represent the 'excitation point direction', which is '-Z'.

[0067] Furthermore, the first element of the line-by-line array is split using a space character to form an array 'a' with multiple elements. The third element of this array is extracted and split using a colon (':') to form a new array 'b'. The first element of this array is extracted, and its data content is used as basic information to represent the 'response point name', which is 'seat rail'. The second element of array 'b' is split using a forward slash (' / ') to generate a new array 'c'. The first element of array 'c' is extracted, and leading and trailing spaces are removed. Its data content is used as basic information to represent the 'response point direction', which is '-Y'.

[0068] Furthermore, the fourth element of the line break array is separated by quotation marks to form a new array. The second element of this array is extracted, and its data content is used as basic information to represent 'section', which is 'NTF&VTF'. The fourth element of this array is extracted, and its data content is used as basic information to represent 'run', which is 'left front shock absorber-Z'. The content containing the excitation point direction in 'run' is deleted, and its data content is used as basic information to represent 'excitation point name', which is 'left front shock absorber'.

[0069] In this embodiment, the preset positioning characters include: '\', spaces of a specified number and position, ':', ' / ', quotation marks, etc. The extracted basic information includes test type, frequency resolution, excitation point direction, response point name, response point direction, section, run, etc. Among them, for the working condition data block in the vibration transfer function working condition array, the extracted excitation point name should not be the same as the response point name.

[0070] In one embodiment, referring to Figure 2(c), each working condition data block in the noise transfer function group is processed as follows: '0 0 0 0 NONE NONE' is used as the paragraph separator, the character paragraph below the paragraph separator is used as the coordinate data segment, and the character paragraph above is used as the basic information segment. A line array is constructed based on the characters of each line of the basic information segment, so as to perform data segmentation and extraction on the line array according to the preset positioning character.

[0071] Furthermore, the fifth element of the row array is split with '\' to form an array with two elements. The first element of this array is extracted, and its data content serves as basic information, representing the 'test type' as 'CrossPowers', indicating that this data block belongs to the noise transfer function test condition.

[0072] Furthermore, the character segments containing 9 consecutive spaces and 6 consecutive spaces in the 7th element of the line array are deleted. Then, the two positions where the spaces were deleted are filled with two space characters respectively to generate a new array. After deleting all leading and trailing spaces from the 3rd element of this array, its data content is used as basic information to represent 'frequency resolution', which is '1'.

[0073] Furthermore, the fifth element of the row array is split with ':' to form an array with two elements. The second element of this array is extracted, and its data content is used as basic information to represent the 'excitation point direction', which is '-Y'.

[0074] Furthermore, the first element of the line-by-line array is split using a space character to form an array 'a' with multiple elements. The third element of this array is extracted and split using a colon (':') to form a new array 'b'. The first element of this array is extracted, and its data content is used as basic information to represent the 'response point name', which is 'DRE'. The second element of array 'b' is split using a forward slash (' / ') to generate a new array 'c'. The first element of array 'c' is extracted, and leading and trailing spaces are removed. Its data content is used as basic information to represent the 'response point direction', which is 'S'.

[0075] Furthermore, the fourth element of the line break array is separated by quotation marks to form a new array. The second element of this array is extracted, and its data content is used as basic information to represent 'section', which is 'NTF&VTF'. The fourth element of this array is extracted, and its data content is used as basic information to represent 'run', which is 'front subframe front point - Y'. The content containing the excitation point direction in 'run' is deleted, and its data content is used as basic information to represent 'excitation point name', which is 'front subframe front point'.

[0076] In this embodiment, the preset positioning characters include: '\', spaces of a specified number and position, ':', ' / ', quotation marks, etc. The extracted basic information includes test type, frequency resolution, excitation point direction, response point name, response point direction, section, run, etc. Among them, for the operating condition data block in the noise transfer function operating condition array, the extracted excitation point name should not be the same as the response point name, and the response point direction should be S, that is, the sound pressure is a scalar, not a vector in Cartesian coordinates.

[0077] In one embodiment of the above examples, the data information includes curve name, project name, key information, test type, response point name, response point direction, excitation point name, excitation point direction, frequency resolution, and coordinate data segment. The curve name is determined based on the response point name, response point direction, and excitation point direction. For example, the format of the curve name can be: response point name + ':' + response direction + ' / H:' + excitation point direction.

[0078] The technical solution provided in this embodiment of the application efficiently parses target data blocks to extract key data information through a preset positioning character set and preset positioning rules. Specifically, the target data block is first segmented according to paragraph separators to determine coordinate data segments and line arrays composed of characters from each line of basic information segments. Then, preset positioning characters are traversed in the line arrays and data is cut. Finally, basic information such as test type, frequency resolution, and names and directions of excitation and response points are extracted based on preset positioning rules and the cutting results. The above segmentation, cutting, and extraction operations are performed on the working condition data blocks in the three types of working condition arrays: dynamic stiffness, vibration transfer function, and noise transfer function. This technical solution achieves rapid segmentation of data through paragraph separators, reducing repeated scanning and parsing of the entire data block. It achieves accurate extraction of data information with the help of preset positioning characters and preset positioning rules, effectively reducing the error rate of manual data extraction. At the same time, the preset positioning rules and character sets can be flexibly adjusted according to different data formats, significantly improving the efficiency, accuracy, and convenience of NVH test data processing, and providing standardized and structured data support for subsequent data analysis.

[0079] In one implementation, the aforementioned data is used to generate an online preview table and online preview curves of the target operating condition array within a graphical visualization interface. Please refer to [link / reference]. Figure 3 , Figure 3 This application provides an example of a graphical user interface for real-time interaction between the user and the system. Exemplarily, the interface includes a file selection box, an IPI setting area, a VTF setting area, an NTF setting area, a basic information area, an online preview table area, and an online preview curve area. The IPI, VTF, and NTF setting areas have modifiable setting parameter options, such as retrieval bandwidth and target values. The file selection box is used to retrieve the NVH test data file to be processed. Clicking the corresponding buttons (IPI button, VTF button, NTF button) in each setting area serves as a data acquisition instruction, enabling data processing and extraction for the corresponding test conditions. The obtained data is displayed online in tabular form in the online preview table area, and the corresponding data is displayed online in curve form in the online preview curve area.

[0080] In one embodiment of this implementation, to generate an online preview table of the dynamic stiffness condition array and an online preview curve of each condition data block in the dynamic stiffness condition array, it is necessary to extract the coordinate data segments of each condition data block and process the data block as follows: extract the coordinate data segments of all condition data blocks, and for any condition data segment, delete all newline characters, and then calculate the total number of characters. Split the entire string into Group, numbered as ,but The corresponding frequency is where f is the frequency resolution, the obtained That is the point Coordinates, denoted as And so on, each group of strings corresponds to a group of complex numbers. The first 13 characters of a string converted to a numerical value are... The last 13 characters are converted into numerical values. Then the y-coordinate is... And so on. correspond , correspond , ..., correspond This constitutes a set of scattered coordinates for the data segment of this working condition. All dynamic stiffness curve coordinates are extracted using the method described above and stored as a dynamic stiffness coordinate array.

[0081] In this embodiment, the user can modify the valley value retrieval bandwidth in the IPI settings area according to their needs; otherwise, the software's default settings will be used. After the settings are completed, the system will start from all... Find the mean, trough, and corresponding frequency information in the coordinate system. When the user clicks the IPI button, the system will process and extract data to display information such as run, test type, stimulus point name, stimulus point direction, response point name, response point direction, mean, trough, and corresponding frequency in a table. Each row represents the data information of one operating condition data block. The project name, section, frequency resolution, and bandwidth will be displayed in the basic information box. The online table area can be configured with "Plotting" and "Output" column checkboxes. Checking the "Plotting" column checkbox for the corresponding row will display an online preview graph of the corresponding operating condition data block on the interface. Checking the "Output" column checkbox will output only the online preview table containing the corresponding operating condition data block. This graph offers functions such as moving, zooming, converting to a data table, and downloading.

[0082] In one embodiment of this implementation, to generate an online preview table of the vibration transfer function working condition array and an online preview curve of each working condition data block of the vibration transfer function working condition array, it is necessary to extract the coordinate data segment of each working condition data block and process the data block as follows: extract the coordinate data segment of all working condition data blocks, and for any working condition data segment, delete all newline characters, then calculate the total number of characters T, divide the entire string into T / 26 groups, and number them as follows. ,but The corresponding frequency is where f is the frequency resolution, the obtained That is the point Coordinates, denoted as And so on, each group of strings corresponds to a group of complex numbers. The first 13 characters of a string converted to a numerical value are... The last 13 characters are converted into numerical values. Then the y-coordinate is... And so on. correspond , correspond , ..., correspond This constitutes a set of scattered coordinates for the data segment of this working condition. All dynamic stiffness curve coordinates are extracted using the method described above and stored as a dynamic stiffness coordinate array.

[0083] In this embodiment, the user can modify the valley value retrieval bandwidth in the VTF settings area according to their needs; otherwise, the software's default settings will be used. After setting, the system will start from all... The system retrieves the mean, troughs, and corresponding frequencies from the coordinates. When the user clicks the VTF button, the system processes and extracts data to display information such as run, test type, stimulus point name, stimulus point direction, response point name, response point direction, mean, trough, and corresponding frequency in a table. Each row represents a data block for a specific operating condition. The project name, section, frequency resolution, and bandwidth are displayed in the basic information box. The online table area includes a "Plot" column checkbox. Checking the "Plot" column checkbox for the corresponding row displays an online preview graph of the corresponding operating condition data block. This graph offers zooming, conversion to a data table, and downloading functions.

[0084] In one embodiment of this implementation, to generate an online preview table of the noise transfer function operating condition array and an online preview curve of each operating condition data block of the noise transfer function operating condition array, it is necessary to extract the coordinate data segment of each operating condition data block and process the data block as follows: extract the coordinate data segment of all operating condition data blocks, and for any operating condition data segment, delete all newline characters, and then calculate the total number of characters. Split the entire string into Group, numbered as ,but The corresponding frequency is where f is the frequency resolution, the obtained That is the point Coordinates, denoted as And so on, each group of strings corresponds to a group of complex numbers. The first 13 characters of a string converted to a numerical value are... The last 13 characters are converted into numerical values. Then the y-coordinate is... And so on. correspond , correspond , ..., correspond This constitutes a set of scattered coordinates for the data segment of this working condition. All dynamic stiffness curve coordinates are extracted using the method described above and stored as a dynamic stiffness coordinate array.

[0085] In this embodiment, the user can modify the valley value retrieval bandwidth in the NTF settings area according to their needs; otherwise, the software's default settings will be used. After setting, the system will find the mean, valley values, and corresponding frequencies from all y-coordinates. When the user clicks the NTF button, the system will process and extract data information to display data such as run, test type, stimulus point name, stimulus point direction, response point name, response point direction, mean, valley value, and valley value corresponding frequency in a table. Each row represents the data information of a working condition data block, and the project name, section, frequency resolution, and bandwidth will be displayed in the basic information box. The online table area can have a "plot" column checkbox. Checking the "plot" column checkbox for the corresponding row will display an online preview curve of the corresponding working condition data block on the interface. This curve provides functions such as zooming, converting to a data table, and downloading.

[0086] It should be noted that the above description is merely an example of a graphical visualization interface provided in the specification and is not intended to limit the present invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0087] The technical solution provided in this embodiment generates online preview tables and curves of the target operating condition array on a graphical visualization interface based on the extracted operating condition data block information. Specifically, for the three types of operating condition arrays—dynamic stiffness, vibration transfer function, and noise transfer function—data information and coordinate data segments of their respective operating condition data blocks are extracted. The extracted data information is displayed on the interface in the form of an online preview table. Based on the extracted coordinate data segments and data information, online preview curves are generated and displayed on the interface. The graphical visualization interface enables online preview and interactive operation of NVH test data, significantly improving the intuitiveness of data display and the convenience of interaction. This facilitates users in quickly analyzing the characteristic patterns of the operating condition data and provides an efficient and visualized support method for the subsequent analysis and application of NVH test data.

[0088] In one implementation, the aforementioned data information is also used to generate standardized report documents that users can directly apply or report. Specifically, in response to a file output command, a report illustration of the test data file is generated based on the data information. This report illustration includes statistical tables, graphs, and color maps. A standard report template is invoked, and a standardized report document of the test data file is generated based on the report illustration and the standard report template. The statistical tables display statistical information of the data in tabular form, such as mean, peak, and trough values. The graphs graphically display the changing trends of the data, such as frequency response curves and vibration curves. The color maps use color changes to display the distribution and comparison of the data, such as heat maps and spectrum diagrams.

[0089] In this embodiment, the aforementioned report diagrams are output as a standardized report document by reading the system's built-in standard report template. Specifically, the system loads a predefined standard report template, which defines the report's layout and style. The aforementioned report diagrams are inserted into the standard report template to generate a standardized report document. For example, a document processing library (such as the matplotlib library) is used to draw the report diagrams, which are then inserted into the report document. The generated report document can be saved to a user-specified path. Optionally, the data can be compared with target values ​​based on the corresponding IPI, VTF, and NTF settings, and the comparison results can be output to the report diagrams. The aforementioned standardized report document includes, but is not limited to, Excel documents, PowerPoint documents, and Word documents.

[0090] The technical solution provided in this application generates standardized report documents that users can directly apply or report based on extracted operating condition data block information. Specifically, it generates report illustrations including statistical tables, graphs, and color mapping maps based on data information, and finally generates standardized report documents in formats such as Excel, PPT, and Word that can be saved to a user-specified path. This achieves standardized processing of NVH test data from extraction and analysis to report output. The generated report documents directly meet application and reporting needs, effectively reducing the workload of manual report compilation, improving the efficiency and standardization of report generation, and providing more comprehensive and clear presentation of the core characteristics of the test data through diverse report illustrations and data comparison content, offering precise and intuitive support for technical communication and decision-making. Compared to the manual drawing of report illustrations and production of report documents in traditional technologies, this significantly improves the efficiency, accuracy, and convenience of NVH test data summarization.

[0091] Please see Figure 4 This application also provides an NVH test data processing device, the device comprising: The data acquisition unit 100 is used to acquire test data files exported by NVH testing equipment and to perform data preprocessing on the test data files, wherein the test data files are in UNV format. The working condition slicing unit 200 is used to respond to the data acquisition instruction and slice the pre-processed test data file into working condition slices based on a preset delimiter to obtain multiple working condition data blocks, wherein the data acquisition instruction carries a category identifier. The working condition classification unit 300 is used to classify the working condition data block based on multiple preset classifiers to obtain multiple working condition arrays, and to determine the target working condition array in the multiple working condition arrays based on the category identifier. The data processing unit 400 is used to parse the data information corresponding to each target data block of the target working condition array based on a preset positioning character set and preset positioning rules. The data information is used to generate and display an online preview table and an online preview curve of the target working condition array in a graphical user interface.

[0092] in, In one embodiment, the data acquisition unit 100 is specifically used to acquire a test data file exported by the NVH test equipment, perform data preprocessing on the test data file, convert the test data file from UTF-8 encoding format to ANSI encoding format to complete the encoding conversion, read the text string data in the encoded test data file, and convert the text string data into byte set data to complete the character conversion.

[0093] In one embodiment, the working condition slicing unit 200 is specifically used to traverse the preprocessed test data file to detect preset delimiters and their character positions in the test data file. The preset delimiters are continuous byte sequences used to identify the start and end of a working condition. The data content located between any two preset delimiters and the data content located after the last preset delimiter are determined as working condition data blocks of the test data file to obtain multiple working condition data blocks.

[0094] In one embodiment, the working condition classification unit 300 is specifically used to detect, for any working condition data block, whether a first classifier, a second classifier, and a third classifier exist in the working condition data block in sequence, and based on the detection result of each working condition data block, classify the working condition data block into the working condition array of the corresponding category to obtain a dynamic stiffness working condition array, a vibration transfer function working condition array, and a noise transfer function working condition array, and determine the target working condition array in the multiple working condition arrays based on the category identifier.

[0095] In one embodiment, the data processing unit 400 is specifically used to segment the target data block according to the paragraph separator, so as to determine the coordinate data segment and the line break array in the target data block, traverse any preset positioning character in the preset positioning character set in the line break array, perform data cutting on the line break array according to the preset positioning character, extract target information according to the preset positioning rule and the data cutting result, determine the target information as the basic information corresponding to the preset positioning character, and determine the coordinate data segment and the basic information as data information.

[0096] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0097] An NVH test data processing device in this application embodiment is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, or other devices that can provide the above functions.

[0098] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application, such as... Figure 5As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0099] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0100] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0101] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0103] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0104] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0105] The above embodiments illustrate devices, modules, or units that can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0106] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0107] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer devices. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer devices according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] It should also be noted that 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 limitation, 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.

[0112] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0113] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

[0114] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for processing NVH test data, characterized in that, The method includes: Obtain the test data file exported from the NVH testing equipment, and perform data preprocessing on the test data file, wherein the test data file is a UNV format file; In response to a data acquisition command, the pre-processed test data file is sliced ​​into working condition segments based on a preset delimiter to obtain multiple working condition data blocks, wherein the data acquisition command carries a category identifier; The working condition data block is classified based on multiple preset classifiers to obtain multiple working condition arrays, and the target working condition array is determined in the multiple working condition arrays based on the category identifier. For each target data block of the target operating condition array, based on a preset positioning character set and preset positioning rules, the data information corresponding to the target data block is parsed. The data information is used to generate and display an online preview table and an online preview curve of the target operating condition array in a graphical user interface.

2. The method according to claim 1, characterized in that, The data preprocessing includes encoding conversion and character conversion; the data preprocessing of the test data file includes: The test data file is converted from UTF-8 encoding format to ANSI encoding format to complete the encoding conversion; Read the text string data from the test data file after the encoding conversion, and convert the text string data into byte set data to complete the character conversion.

3. The method according to claim 1, characterized in that, Slicing preprocessed test data files into operating condition segments based on preset delimiters includes: Traverse the preprocessed test data file to detect a preset delimiter and the character position of the preset delimiter in the test data file, wherein the preset delimiter is a continuous byte sequence used to identify the start and end of the working condition; The data content located between any two of the preset delimiters, and the data content located after the last preset delimiter, are determined as the working condition data blocks of the test data file.

4. The method according to claim 1, characterized in that, The multi-condition array includes a dynamic stiffness condition array, a vibration transfer function condition array, and a noise transfer function condition array, and the preset classifier includes a first classifier, a second classifier, and a third classifier; The operating condition data blocks are classified based on multiple preset classifiers to obtain multiple operating condition arrays, including: For any of the aforementioned operating condition data blocks, the presence of the first classifier, the second classifier, and the third classifier is sequentially detected in the operating condition data block; Based on the detection results of each working condition data block, the working condition data block is classified into the corresponding category of working condition array to obtain the dynamic stiffness working condition array, the vibration transfer function working condition array, and the noise transfer function working condition array.

5. The method according to claim 4, characterized in that, Based on the detection results of each working condition data block, classifying the working condition data block into the corresponding category of the working condition array includes: If the detection result indicates that the working condition data block contains the first classifier, then the working condition data block is classified into the dynamic stiffness working condition array. If the detection result indicates that the working condition data block has the second classifier and does not have the third classifier, then the working condition data block is classified into the vibration transfer function working condition array. If the detection result indicates that the operating condition data block has the second classifier and the third classifier, then the operating condition data block is classified into the noise transfer function operating condition array.

6. The method according to claim 1, characterized in that, The preset positioning character set includes paragraph separators and multiple preset positioning characters, and the data information includes coordinate data segments and multiple basic information. Based on a preset positioning character set and preset positioning rules, querying the data information corresponding to the target data block includes: The target data block is segmented according to the paragraph separator to determine the coordinate data segment and line break array in the target data block; Traverse any preset positioning character in the preset positioning character set in the line array, and perform data segmentation on the line array according to the preset positioning character; Target information is extracted based on preset positioning rules and data segmentation results, and the target information is determined as the basic information corresponding to the preset positioning character.

7. The method according to claim 1 or 6, characterized in that, The data information includes curve name, project name, key information, test type, response point name, response point direction, excitation point name, excitation point direction, frequency resolution, and coordinate data segment, wherein: The curve name is determined based on the response point name, the response point direction, and the excitation point direction.

8. The method according to claim 1, characterized in that, The data information is also used for: In response to a file output command, a report illustration of the test data file is generated based on the data information, wherein the report illustration includes a statistical table, a line graph, and a color map; The standard report template is invoked, and a standardized report document for the test data file is generated based on the report illustration and the standard report template.

9. An NVH test data processing device, characterized in that, The device includes: The data acquisition unit is used to acquire test data files exported by NVH testing equipment and to perform data preprocessing on the test data files, wherein the test data files are in UNV format. The working condition slicing unit is used to respond to the data acquisition command and slice the pre-processed test data file into working condition slices based on a preset delimiter to obtain multiple working condition data blocks, wherein the data acquisition command carries a category identifier; The working condition classification unit is used to classify the working condition data block based on multiple preset classifiers to obtain multiple working condition arrays, and to determine the target working condition array in the multiple working condition arrays based on the category identifier. The data processing unit is used to parse the data information corresponding to each target data block of the target working condition array based on a preset positioning character set and preset positioning rules. The data information is used to generate and display an online preview table and an online preview curve of the target working condition array in a graphical user interface.

10. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform an NVH test data processing method according to any one of claims 1 to 8.

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