Data extraction method and device, computer equipment and storage medium

Abstract

The invention relates to a data extraction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a specified index file, the index file comprising a label structure composed of a plurality of labels, the label structure comprising a plurality of mutually associated hierarchies, the index file being used for storing address information of vehicle NVH data, the vehicle NVH data comprising noise data, vibration data and sound vibration roughness, acquiring each sub-label of a current label in the label structure, and traversing each sub-tag, extracting the address information from the current sub-tag when the current sub-tag contains the address information, and extracting the corresponding vehicle NVH data according to the address information. By adopting the method, the NVH data of the vehicle can be accurately extracted.

Description

Technical Field

[0001] The present application relates to the field of data analysis technology, and in particular, to a data extraction method, apparatus, computer device, and storage medium. Background Art

[0002] In the field of automotive NVH (Noise, Vibration, Harshness), accurately obtaining and analyzing the noise and vibration data of a vehicle during testing is crucial for optimizing automotive performance and improving ride comfort.

[0003] Specifically, in terms of automotive design, by extracting NVH data, engineers can accurately understand the noise and vibration conditions generated by various parts of the vehicle. For example, when designing the engine compartment layout, the data can be used to reasonably arrange the positions of components based on the data to avoid resonance, thereby reducing noise and vibration and improving the comfort of the vehicle. For automotive quality control, NVH data extraction is the key to measuring the quality of a vehicle. On the automotive production line, the NVH performance of each vehicle is detected by extracting data to ensure that it meets the quality standards. If the data is abnormal, problems such as component defects or improper assembly can be detected in a timely manner to ensure product quality.

[0004] NVH data is generally stored in some encoded files, and these encoded files usually have a relatively complex structure, such as a multi-level tag structure, because this structure is suitable for storing the organizational information of the data. For these encoded files, certain means are required to accurately extract the NVH data from them. However, there is currently a lack of an effective method to accurately extract the NVH data of a vehicle from a file to meet the needs of automotive NVH analysis. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide a data extraction method, apparatus, computer device, and storage medium that can accurately extract vehicle NVH data.

[0006] In a first aspect, the present application provides a data extraction method, including:

[0007] Obtain a specified index file, where the index file includes a tag structure composed of multiple tags, the tag structure includes multiple levels that are interrelated, and the index file is used to store the address information of vehicle NVH data, and the vehicle NVH data includes noise data, vibration data, and harshness;

[0008] Obtain each sub-tag of the current tag in the tag structure and traverse each sub-tag;

[0009] When the current sub-tag contains address information, extract the address information from the current sub-tag;

[0010] Extract the corresponding vehicle NVH data according to the address information.

[0011] In one embodiment, the address information includes the identification information of the target file, where the target file is a file storing vehicle NVH data, and the data extraction method further includes:

[0012] Extract the attribute information of each sub-tag;

[0013] When the attribute information of the current sub-tag contains the identification information of the target file, determine that the current sub-tag contains address information.

[0014] In one embodiment, the address information further includes the data type, data length, and offset of the vehicle NVH data. Extracting the corresponding vehicle NVH data according to the address information includes:

[0015] Read the vehicle NVH data from the target file according to the identification information, data type, data length, and offset of the target file.

[0016] In one embodiment, the data extraction method further includes:

[0017] Continue to traverse other sub-tags of the current tag;

[0018] After all sub-tags of the current tag have been traversed, traverse other tags at the same level as the current tag;

[0019] When the current sub-tag does not contain address information, continue to obtain the next-level tag of the current sub-tag and perform traversal until the entire tag structure has been traversed.

[0020] In one embodiment, obtaining each sub-tag of the current tag in the tag structure includes:

[0021] Determine each sub-tag of the current tag according to the identification information of each sub-tag recorded in the current tag.

[0022] In one embodiment, the data extraction method further includes:

[0023] Obtain the associated relationship chain where the current sub-tag is located;

[0024] Integrate the associated relationship chain with the vehicle NVH data;

[0025] Perform NVH analysis of the vehicle according to the integrated result.

[0026] In one embodiment, there are multiple tags containing address information. Extracting the corresponding vehicle NVH data according to the address information includes:

[0027] Extract the corresponding vehicle NVH data according to each address information respectively.

[0028] In a second aspect, the present application provides a data extraction device, including:

[0029] An acquisition module, configured to acquire a specified index file, where the index file includes a tag structure composed of multiple tags, the tag structure includes multiple levels that are interconnected, the index file is used to store address information of vehicle NVH data, and the vehicle NVH data includes noise data, vibration data, and sound quality;

[0030] A traversal module, configured to acquire each sub-tag of the current tag in the tag structure and traverse each sub-tag;

[0031] A first extraction module, configured to extract address information from the current sub-tag when the current sub-tag contains address information;

[0032] A second extraction module, configured to extract corresponding vehicle NVH data according to the address information.

[0033] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the data extraction method provided in any one of the embodiments of the first aspect of the present application are implemented.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data extraction method provided in any one of the embodiments of the first aspect of the present application are implemented.

[0035] The above data extraction method, device, computer device, and storage medium use a recursive algorithm to traverse the tag structure of the index file storing vehicle NVH data. Utilizing the correlation between the multi-level tag structures of the index file, traversing layer by layer, it is determined whether each layer of tags contains the address information of vehicle NVH data. When it is traversed that the current sub-tag contains the address information of vehicle NVH data, the address information of the vehicle NVH data therein is extracted, and the corresponding vehicle NVH data is extracted based on the address information of the vehicle NVH data. The vehicle NVH data in the present application specifically includes the noise data, vibration data, and sound quality of the vehicle. Extracting these data can contribute to the NVH analysis of the vehicle. It can be seen that the present application can achieve rapid and accurate extraction of data for vehicle NVH analysis according to the index file. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a schematic flowchart of the data extraction method in some embodiments;

[0037] Figure 2 is a schematic structural diagram of the tag structure in some embodiments;

[0038] Figure 3 is the structural block diagram of the data extraction device in some embodiments;

[0039] Figure 4 is the internal structure diagram of the computer device in some embodiments. Detailed implementation manners

[0040] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

[0041] In the first aspect, the present application provides a data extraction method, as Figure 1 shown, taking the application of this method to a server as an example for illustration, including the following steps:

[0042] Step S11, obtain a specified index file. The index file includes a tag structure composed of multiple tags. The tag structure includes multiple levels that are interconnected. The index file is used to store the address information of vehicle NVH data, and the vehicle NVH data includes noise data, vibration data, and sound vibration roughness.

[0043] Among them, the index file is a file used to store the address information of specific data. In the present application, it is used to store the address information of vehicle NVH data. Similar to the table of contents of a book, through it, the specific location where the corresponding vehicle NVH data is stored can be quickly found, facilitating subsequent operations such as calling and querying of these data.

[0044] In a possible design, the index file of the present application can be an ATFX (ASAM Transfer Format XML) file. The ATFX file is a general standard format, specifically used to store vibration data and its common forms (such as time domain, frequency domain, etc.). In the field of automotive NVH, it can also store the frequency domain and time domain information of vibration acceleration, sound pressure of noise, as well as the storage address information of the detailed data of this information, playing a role similar to an index directory, facilitating finding the location of the corresponding data.

[0045] Specifically, the ATFX file may include the following information:

[0046] (1) The vehicle condition during the experiment, including states such as idling, accelerating, cruising, and decelerating

[0047] (1) The physical information of the data, including sound pressure data and vibration data

[0048] (3) The type of data, including frequency domain data and time domain data

[0049] (4) Units of data, including megapascals, acceleration G, etc.

[0050] (5) Types of detailed data, including integer type, floating-point type, imaginary type

[0051] (5) Address information of vehicle NVH data.

[0052] Furthermore, the tag structure in this application is composed of multiple tags, and these tags have multiple levels of interrelationships. It can be imagined as a hierarchical classification system. For example, the first-level tags may be divided according to the major categories of vehicles (such as sedans), the second-level tags may be further subdivided according to the types of NVH data (such as noise, vibration, and roughness), and the next level may be further divided according to specific test conditions, etc. Through this multi-level and interrelated tag structure, the address information of vehicle NVH data can be organized and managed more accurately and systematically in the index file, facilitating quick positioning to the desired data address.

[0053] Among them, tags are the basic units that make up the tag structure. These tags are used to classify and organize the address information of vehicle NVH data. They have multiple levels and are interrelated, and each tag may represent different vehicle attributes or categories related to NVH data characteristics. For example, one tag may represent the brand of the vehicle, another tag may represent the vehicle model series, and there may also be tags representing categories such as NVH data types (such as noise data, vibration data, and roughness). Through this multi-level tag combination, the address information of vehicle NVH data can be accurately located and managed.

[0054] That the tag structure includes multiple levels of interrelationships specifically means that the tag structure includes multiple levels, each level includes multiple tags, and each level has an associated relationship with its next lower level.

[0055] To further illustrate the specific structure of the tag structure, please refer to Figure 2 , Figure 2 which is a schematic diagram of a tag structure. In Figure 2 , the tag structure includes multiple layers. The first layer is the item (root node), the second layer includes Node 1 and Node 2. Node 1 includes two child nodes, namely Working Condition 1 and Working Condition 2. Node 2 includes two child nodes, namely Node 3 and Node 4, and so on. Among them, each node represents a tag.

[0056] Furthermore, the vehicle NVH data in this application refers to the noise conditions (such as engine operation noise, wind noise, tire noise, etc.), vibration conditions (such as body vibration, engine vibration transmitted into the vehicle, etc.), and sound-vibration roughness (a quantitative description of the combined uncomfortable feeling of vibration and noise by humans) generated during vehicle operation. These data are very important for evaluating aspects such as vehicle comfort and quality.

[0057] Specifically, the noise data refers to the internal and external noises generated by the vehicle under various working conditions such as driving, accelerating, decelerating, and idling. For example, the sound pressure level data of engine operation noise, noise generated by the friction between the tire and the ground, wind noise, etc. (which can be time-domain sound waveform data or frequency-domain spectrum data).

[0058] The vibration data refers to the vibration conditions of vehicle components and the whole vehicle. Such as the vibration amplitude and frequency of the engine, the vibration of the chassis suspension system, the vibration acceleration of the body during driving (including different directions, such as longitudinal, lateral, and vertical). These data can be measured in the time domain (reflecting the change of vibration over time) and the frequency domain (reflecting the frequency components of vibration).

[0059] Sound-vibration roughness mainly involves data related to the subjective feelings of users about vibration and noise. For example, the feedback values of consumers on the comfort of in-vehicle sound and vibration obtained through some subjective evaluation tests. These values can be used to evaluate the comfort of vehicle rides.

[0060] The address information of vehicle NVH data is the guiding information for locating the storage location of vehicle NVH data.

[0061] Specifically, the server creates an index file, and in this file, the address information of vehicle NVH data is stored through a hierarchical and interrelated tag structure. The advantage of doing this is that when it is necessary to find specific vehicle NVH data (such as the noise data of a certain sedan under specific working conditions), according to the tag structure in the index file, the address of this data can be quickly located from the multi-level tag classification, so as to efficiently obtain the desired vehicle NVH data for subsequent analysis, vehicle performance optimization and other related work.

[0062] Step S12, obtain each sub-tag of the current tag in the tag structure and traverse each sub-tag.

[0063] Among them, the current tag refers to one of the tags currently being processed in the tag structure. The subtag of the current tag refers to a finer division of the current tag, and is a tag at the next level of the current tag. In a multi-level tag structure, the tag at the next level of the current tag is its subtag. For example, if the current tag is "Car Brand A", its subtags may be different "car models series" under the brand, such as "Sedan Series". These subtags inherit certain classification attributes of the current tag and are further subdivided on this basis to more accurately organize and locate the address information of vehicle NVH data.

[0064] Specifically, the server may obtain each sub-tag of the current tag according to the association relationship between levels.

[0065] Further, traversing each sub-tag may include:

[0066] First, check the label name of each sub-label. The label name is used to determine the specific category represented by the sub-label, such as a certain vehicle model, a certain NVH data type (such as noise in a specific frequency range), etc.

[0067] Second, check the identifier (ID) of each sub-tag: If a tag has a unique identifier, this ID can be used to accurately locate and manage the tag in a database or complex data structure, as well as to establish associations with other related tags or data.

[0068] Third, determine whether the address information is included. Check whether the sub-tag contains the address information of the vehicle NVH data. This is the focus. If it is included, the data storage location can be extracted from it to obtain the actual NVH data.

[0069] Fourth, check the association between the sub-tag and the next-level tag. Specifically, the server checks whether there are any sub-tags under this sub-tag. If so, it may be necessary to further recursively traverse these sub-tags to fully explore the entire tag structure and mine the address information of all possible vehicle NVH data.

[0070] Before facilitating each sub-tag, the server first checks whether the current tag contains the address information of the vehicle NVH data. If not, it executes the step of traversing each sub-tag of the current tag.

[0071] Step S13: when the current sub-tag contains address information, extract the address information from the current sub-tag.

[0072] The current sub-tag refers to a sub-tag that is currently traversed among all sub-tags of the current tag.

[0073] The address information refers to the address information of NVH data. In this application, the address information of NVH is included in the tag structure of the index file. By traversing each level of the tag structure, the address information of the corresponding NVH data is obtained, and then the corresponding NVH data is obtained according to the address information.

[0074] Specifically, when the server determines through judgment that the current sub-tag contains the address information of the vehicle NVH data, the address information is extracted from the current sub-tag. In this way, the corresponding vehicle NVH data can be accurately found through the extracted address information, and subsequent operations such as viewing and analyzing these data can be performed.

[0075] Step S14, extract the corresponding vehicle NVH data according to the address information.

[0076] This application can extract the corresponding vehicle NVH data from the file indicated by the address information. After extraction, operations such as format conversion and verification can be performed on the data.

[0077] In one of the embodiments, the address information includes the identification information of the target file, where the target file is the file storing the vehicle NVH data. The data extraction method further includes: extracting the attribute information of each sub-tag, and when the attribute information of the current sub-tag contains the identification information of the target file, it is determined that the current sub-tag contains the address information.

[0078] Among them, the attribute information of the tag is a set of information used to describe the characteristics and associated content of the tag itself. These attributes may include the name, number, related data type, hierarchical relationship, etc. of the tag. For example, the name of a tag may be "noise data of a certain vehicle model", the number is "001", and the hierarchical level is "NVH data type - noise", and these are all its attribute information.

[0079] Specifically, during the process of obtaining the vehicle NVH data, the address information contains the identification information of the target file (the file storing the vehicle NVH data). First, the server extracts the attribute information of each sub-tag. When it is checked that the attribute information of the current sub-tag contains the identification information of the target file, it can be determined that the current sub-tag contains the address information. Among them, the identification information of the target file can be the name or ID of the target file.

[0080] Exemplarily, assume that the identification information of the target file is a specific number "NVH-001". The sub-tags are like boxes with information. Extracting the attribute information of the sub-tags is like checking the label content on the boxes. If "NVH-001" is seen on the label of one of the boxes (the current sub-tag), it means that this box (the current sub-tag) contains the address information of the target file. In this way, the location clue of the file storing the vehicle NVH data can be found.

[0081] Among them, the target file here can specifically be a BTF (Binary Trace Forma) file. The BTF file is a metadata format encoding relevant debugging information such as BPF (Berkeley Packet Filter) programs and map structures, and can encode metadata data types, function information, line information, etc. into a compact format. In this application, as a binary file, the BTF file stores the actual noise and vibration time-domain and frequency-domain data.

[0082] In the field of automotive NVH, it is used in combination with the ATFX file to store the experimental data of the automotive NVH discipline. In particular, it can store the detailed data such as the frequency-domain and time-domain information of vibration acceleration and noise sound pressure pointed to by the ATFX file. The ATFX file is like an index directory, indicating the situation of relevant data (such as vibration, noise, etc.) and the address of the detailed content of these data in the BTF file. Through this address, the corresponding more detailed debugging information and other content can be found in the BTF file. The BTF file is the format specifically carrying the detailed data information indexed by the ATFX file. The combination of the two can completely store the experimental data of the automotive NVH discipline. Figuratively speaking, if the ATFX file is similar to the index directory of a dictionary, the BTF file is similar to the corresponding specific content under the dictionary index directory.

[0083] The beneficial effect of this embodiment is that by using the recursive algorithm to parse the ATFX file, the association relationship between the ATFX file and the BTF file is parsed, and then the parsing result, that is, the address information, is obtained. Using the address information, the corresponding vehicle NVH data is found from the corresponding BTF file, realizing the accurate extraction of vehicle NVH data.

[0084] In one of the embodiments, the address information further includes the data type, data length, and offset of the vehicle NVH data. Extracting the corresponding vehicle NVH data according to the address information includes: reading the vehicle NVH data from the target file according to the identification information, data type, data length, and offset of the target file.

[0085] Among them, the data type of vehicle NVH data refers to the format category of the data. For example, noise data may be numerical data in decibels (dB), and the data type can be floating-point type; if vibration data records the amplitude and frequency of vibration, the amplitude may be floating-point type and the frequency may be integer type. Different data types determine how the computer stores, reads, and interprets these data.

[0086] In this application, the data length includes the total length of the data and the length of a single data. The total length of the data represents the storage space size occupied by the vehicle NVH data. For data stored in a file, it can be measured in bytes. For example, for a sequence of 100 floating-point noise data, if each floating-point data occupies 4 bytes, then the data length of this part of the noise data is 400 bytes. The data length helps to accurately locate and extract the complete data content.

[0087] The offset refers to the position offset of the vehicle NVH data relative to the beginning of the file or a certain reference position in the target file. It is like the starting page number of a certain chapter content in a book. Suppose in a target file, the file header stores some basic information of the vehicle, which occupies 100 bytes, then starting from the 101st byte, the NVH data is stored, and the offset of the NVH data is 100 bytes.

[0088] Specifically, the server locates the corresponding target file according to the address information, further opens the target file, and reads the corresponding vehicle NVH data according to the data type, the total length of the data, the length of a single data, and the offset.

[0089] When extracting vehicle NVH data, only having the identification information of the target file (such as file name, file path, etc.) is not enough. Because the target file may contain various types of data, and the positions and sizes of these data in the file are different. After finding the target file through the address information, it is necessary to use the data type to correctly interpret the data. For example, after knowing that it is floating-point data, the computer can read and convert the data according to the storage format rules of the floating-point type.

[0090] The data length is used to ensure the complete extraction of data and avoid reading too much or too little data. For example, knowing that the data length is 200 bytes, the 200-byte NVH data can be accurately read from the file.

[0091] The offset helps to locate the starting position of the data in the file. Combining the offset, data type, and data length, the required vehicle NVH data can be accurately read from the target file. For example, according to the given offset, find the data starting position, and read the vehicle NVH data with a length of the specified data length according to the rules of the data type.

[0092] In one embodiment, the data extraction method may further include: continuing to traverse other sub - tags of the current tag. After all sub - tags of the current tag have been traversed, traverse other tags at the same level as the current tag. When the current sub - tag does not contain address information, continue to obtain the next - level tag of the current sub - tag and perform traversal until the entire tag structure has been traversed.

[0093] Among them, the present application specifically uses a recursive algorithm to traverse the tag structure of the index file. The specific traversal steps may include:

[0094] The first step: Obtain and traverse the sub - tags of the current tag. First, focus on a certain current tag in the tag structure, obtain its various sub - tags, and then traverse the information of these sub - tags one by one.

[0095] The second step: Judge and extract address information. During the process of traversing each sub - tag, check whether the current sub - tag being viewed (the current sub - tag) contains the address information of the vehicle NVH data. If it contains, extract the address information from this sub - tag.

[0096] The third step: Extract data according to the address information. Once the address information is extracted from the sub - tag, extract the corresponding vehicle NVH data based on this address information, so that the vehicle NVH data that can actually be used for analysis and other operations can be obtained.

[0097] The fourth step: Continue to traverse other sub - tags of the current tag. After completing the operations related to the current sub - tag (the sub - tag containing the address information), continue to traverse the remaining other sub - tags of the current tag. This is to ensure that all sub - tags that may contain data address information under the current tag are checked without missing any potentially useful information. That is to say, there may be more than one tag containing address information.

[0098] The fifth step: Traverse other tags at the same level. After all sub - tags of the current tag have been traversed, transfer to other tags at the same level as the current tag and continue to perform the same operations. That is to say, continue to traverse the sibling tags (tags at the same level as the current tag) of the current tag.

[0099] The sixth step: Handle the case where the current sub - tag does not contain address information. If it is found during the traversal process that the current sub - tag does not contain address information, continue to obtain the next - level tag of the current sub - tag (if any), and then use the next - level tag as the current tag and return to the step of obtaining and traversing the sub - tags of the current tag. Repeat this process until all tags at all levels in the entire tag structure have been traversed.

[0100] Specifically, taking the index file as an ATFX file and the target file as a BTF file as an example, with the address information being the BTF file information, the code implementation logic of the recursive algorithm of this application can include:

[0101] First, initialize the parsing-related objects and parse the ATFX file.

[0102] Call the parseAtfxAndReadBTF method. The parseAtfxAndReadBTF method is the entry method for the entire parsing process, used to start parsing the specified ATFX file and find and process the tags containing BTF file information during the parsing process. In the parseAtfxAndReadBTF method, create a DocumentBuilder instance through the DocumentBuilderFactory to parse the ATFX file. Use the created DocumentBuilder to parse the ATFX file at the specified path to obtain a Document object representing the entire ATFX file structure, and then obtain its root element.

[0103] Second, recursively traverse the element nodes of the atfx file.

[0104] Call the parseRecursively method. The function of the parseRecursively method is to traverse the xml structure of the ATFX file recursively, find the tags containing BTF file information, and perform corresponding processing on the found tags (read and parse the corresponding BTF file data). For other non-target tags, continue to recursively search for their subordinate nodes.

[0105] In the parseRecursively method: For the incoming element (the current tag), obtain its list of all child nodes, traverse the list of child nodes, and for each child node, determine whether it is an element node. If so, convert it to the Element type, and then determine whether this element is a tag containing BTF file information. If so, proceed to the next step; if not, continue to recursively traverse the child nodes of this element. Among them, the element node represents a complete tag, and converting it to the element type means converting this tag into data that can be recognized and processed by the computer.

[0106] Third, determine whether it is a tag containing BTF file information.

[0107] Call the isBTFInfoElement method. The function of the isBTFInfoElement method is to determine whether a given element is a tag containing BTF file information, and the basis for judgment is whether the element has specific attributes (such as BTF file name, data type, and offset). In the isBTFInfoElement method: By checking whether the element has the three attributes of BTF file name, data type, and offset at the same time, to determine whether it is a tag containing BTF file information.

[0108] Fourth, read and parse the BTF file.

[0109] Call the readAndParseBTF method. The function of the readAndParseBTF method is to implement the specific logic of reading the BTF file and parsing the file data according to the information such as the incoming data type and offset.

[0110] In the readAndParseBTF method: Obtain the BTF file name, data type, offset and other information from the tag containing BTF file information, read the corresponding BTF file, and parse the data in the file according to the requirements of the corresponding data type and offset.

[0111] Through such a repetitive and in-depth traversal process, this application comprehensively and systematically searches for and extracts vehicle NVH data in the entire tag structure, ensuring that no place where vehicle NVH data may be stored is missed, and achieving accurate extraction of vehicle NVH data.

[0112] In one embodiment, obtain each sub-tag of the current tag in the tag structure, including: Determine each sub-tag of the current tag according to the identification information of each sub-tag recorded in the current tag.

[0113] In this application, each tag has its own ID, just like assigning a unique "ID card number" to each tag to accurately identify this tag. At the same time, each tag also contains the IDs of its sub-tags, that is to say, this tag not only knows who it is, but also knows who each sub-tag at the next level below it is (identified by the ID of the sub-tag).

[0114] Specifically, the server will accurately locate and find each sub-tag of the current tag based on the identification information of each sub-tag recorded in the current tag. Moreover, in this process, the association relationships between different tags will also be recorded.

[0115] For example, the tag structure includes two tags: LocalColumn tag and ExternalComponent tag. The LocalColumn tag contains the sub-tag ExternalComponentId, and the sub-tag ID is 20559. Based on this value, check whether the ID value in the ExternalComponent tag is 20559. If so, the association between the two unit tags is completed.

[0116] In one of the embodiments, the data extraction method may further include: obtaining the association relationship chain where the current sub-tag is located, integrating the association relationship chain with the vehicle NVH data, and performing NVH analysis of the vehicle based on the integrated result.

[0117] Among them, the association chain refers to a link formed by connecting multiple related tags starting from one tag according to a certain hierarchical order and association rules through the association relationship between tags. It shows the hierarchical structure and mutual connection between different tags, and is used to trace and locate all tag information related to a specific tag (such as the current subtag).

[0118] Specifically, the association relationship chain may include the following information:

[0119] Information about the label itself: includes unit labels at all levels, which have their own attributes. For example, it may include vehicle brand labels, model labels, NVH data type labels (such as noise, vibration), etc. Each label has its own attributes.

[0120] Association information between tags: mainly reflected by the tag ID. For example, in the association chain, you can see how a certain vehicle model tag is associated with its brand tag through a specific ID, and how it is associated with the NVH data type tag of the next level through other IDs. This association information can clarify the positional relationship of different tags in the hierarchical structure.

[0121] Specifically, the server sorts out the associations between each tag and other tags based on the IDs of the subtags recorded by each tag. The server can start from the current subtag and continuously search for tags at the previous and next levels related to it, so as to build a chain of associations. This chain contains the path information starting from a certain tag and passing through each related level of tags, showing how these tags are related to each other, and finally fully presenting the position of the current subtag in the entire multi-level tag structure and its connection with other tags.

[0122] Further, the server integrates this association relationship chain with the corresponding vehicle NVH data. That is, it combines the vehicle NVH data with the associated path information of its corresponding tags, so that the data is no longer isolated, but carries the "identity" and "location" information in the tag system.

[0123] Finally, vehicle NVH analysis is performed based on the integrated result. At this time, the analyzed data not only has specific NVH numerical content (such as noise value, vibration amplitude, etc.), but also has its associated information in the tag system. In this way, the NVH characteristics of the vehicle can be analyzed more comprehensively and deeply from different dimensions (such as different vehicle models, different working conditions, etc. corresponding to different tag situations), such as which vehicle models have better NVH performance under specific working conditions, and which factors affect the NVH data of which links, so as to provide more targeted basis for the optimization of vehicle NVH performance, etc.

[0124] In one embodiment, there are multiple tags containing address information. Extracting the corresponding vehicle NVH data according to the address information includes: extracting the corresponding vehicle NVH data according to each address information respectively.

[0125] In this application, there may be multiple sub-tags that all contain the address information of vehicle NVH data. For example, the NVH data corresponding to different test scenarios and different vehicle components may be recorded at the positions pointed to by different sub-tags respectively.

[0126] When there are multiple tags containing address information, for the operation of extracting vehicle NVH data, it is necessary to extract the corresponding vehicle NVH data according to each different address information respectively. That is to say, it is not enough to only extract the NVH data corresponding to one address information, but it is necessary to accurately find the corresponding storage location according to each address information, and then extract all the corresponding NVH data from it, so as to completely obtain all the vehicle NVH data related to the current tag for subsequent comprehensive analysis, research and other work.

[0127] In a second aspect, this application provides a data extraction device, as Figure 3 shown, the data extraction device includes: an acquisition module 31, a traversal module 32, a first extraction module 33 and a second extraction module 34, where:

[0128] The acquisition module 31 is used to acquire a specified index file. The index file includes a tag structure composed of multiple tags. The tag structure includes multiple levels that are interconnected. The index file is used to store the address information of vehicle NVH data. The vehicle NVH data includes noise data, vibration data and sound vibration roughness;

[0129] The traversal module 32 is used to obtain each sub-tag of the current tag in the tag structure and traverse each sub-tag;

[0130] The first extraction module 33 is used to extract the address information from the current sub-tag when the current sub-tag contains address information;

[0131] The second extraction module 34 is used to extract the corresponding vehicle NVH data according to the address information.

[0132] In one embodiment, the address information includes the identification information of the target file, and the target file is a file storing vehicle NVH data. The acquisition module 31 can also: extract the attribute information of each sub-tag, and when the attribute information of the current sub-tag contains the identification information of the target file, determine that the current sub-tag contains address information.

[0133] In one embodiment, the address information further includes the data type, data length, and offset of the vehicle NVH data. The second extraction module 34 can read the vehicle NVH data from the target file according to the identification information, data type, data length, and offset of the target file.

[0134] In one embodiment, the traversal module 32 can continue to traverse other sub-tags of the current tag. After all sub-tags of the current tag are traversed, traverse other tags at the same level as the current tag. When the current sub-tag does not contain address information, continue to obtain the next-level tag of the current sub-tag and perform traversal until the entire tag structure is traversed.

[0135] In one embodiment, the traversal module 32 can determine each sub-tag of the current tag according to the identification information of each sub-tag recorded in the current tag.

[0136] In one embodiment, the traversal module 32 can also obtain the associated relationship chain where the current sub-tag is located, integrate the associated relationship chain with the vehicle NVH data, and perform NVH analysis of the vehicle according to the integrated result.

[0137] In one embodiment, there are multiple tags containing address information, and the second extraction module 34 can extract the corresponding vehicle NVH data according to each address information respectively.

[0138] In a third aspect, the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the data extraction method provided in any one of the embodiments of the first aspect of the present application.

[0139] In one embodiment, the computer device can be a server, and its internal structure diagram can be as Figure 4As shown in the figure. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, it realizes the data extraction method.

[0140] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the steps of the data extraction method provided in any one of the embodiments of the first aspect of the present application.

[0141] The computer-readable storage medium may be Figure 4 the computer-readable storage medium in the computer device shown in the figure.

[0142] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The above computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application may include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0143] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0144] The above embodiments only represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several variations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A data extraction method, characterized in that: The method comprises: Obtain a specified index file, the index file including a tag structure consisting of a plurality of tags, the tag structure including a plurality of mutually related levels, the index file being used to store address information of vehicle NVH data, the vehicle NVH data including noise data, vibration data, and acoustic vibration roughness; Obtain each sub-tag of the current tag in the tag structure, and traverse each sub-tag; When the current sub-tag contains the address information, extracting the address information from the current sub-tag; The corresponding vehicle NVH data is extracted according to the address information.

2. The method according to claim 1, characterized in that The address information includes identification information of a target file, and the target file is a file storing the vehicle NVH data. The method further includes: Extracting attribute information of each of the sub-tags; When the attribute information of the current subtag includes the identification information of the target file, it is determined that the current subtag includes the address information.

3. The method according to claim 2, characterized in that The address information also includes the data type, data length and offset of the vehicle NVH data. The extracting corresponding vehicle NVH data according to the address information includes: The vehicle NVH data is read from the target file according to the identification information, data type, data length and offset of the target file.

4. The method according to claim 1, characterized in that: The method further comprises: Continue to traverse other sub-tags of the current tag; After all sub-tags of the current tag are traversed, other tags of the same level of the current tag are traversed; When the current sub-tag does not contain the address information, continue to obtain the next level tag of the current sub-tag and perform traversal until the traversal of the tag structure is completed.

5. The method according to claim 1, characterized in that: The obtaining of each sub-tag of the current tag in the tag structure includes: The sub-tags of the current tag are determined according to the identification information of the sub-tags recorded in the current tag.

6. The method according to claim 1, characterized in that The method further comprises: Get the association relationship chain where the current sub-tag is located; Integrating the association relationship chain with the vehicle NVH data; Perform vehicle NVH analysis based on the integrated results.

7. The method according to claim 1, characterized in that The tags containing the address information include a plurality of tags, and the extracting corresponding vehicle NVH data according to the address information includes: The corresponding vehicle NVH data is extracted according to each address information.

8. A data extraction device, characterized in that: The device comprises: An acquisition module, used to acquire a specified index file, wherein the index file includes a tag structure composed of a plurality of tags, wherein the tag structure includes a plurality of mutually related levels, and the index file is used to store address information of vehicle NVH data, wherein the vehicle NVH data includes noise data, vibration data, and acoustic vibration roughness; A traversal module, used for obtaining each sub-tag of the current tag in the tag structure and traversing each sub-tag; A first extraction module, configured to extract the address information from the current sub-tag when the current sub-tag contains the address information; The second extraction module is used to extract corresponding vehicle NVH data according to the address information.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images