Vehicle configuration word parsing method, system, and computer-readable medium
By generating triples to automatically parse vehicle configuration values and using lightweight machine learning to correct anomalies, the problem of poor flexibility and reliance on manual intervention in existing technologies is solved, achieving efficient and accurate configuration word parsing that adapts to document variations of different formats.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DONGFENG MOTOR GRP
- Filing Date
- 2026-03-05
- Publication Date
- 2026-06-12
AI Technical Summary
Existing vehicle configuration word parsing methods are inflexible, have limited functionality, rely on manual intervention leading to high error rates and high maintenance costs, and cannot effectively handle configuration items spanning multiple bytes.
By extracting descriptive text information from configuration documents, generating triples, parsing configuration values, and using a lightweight machine learning model to correct abnormal configurations, it supports parsing configuration items stored across bytes.
It achieves automated parsing of configuration values, reduces manual intervention, lowers the error rate, improves parsing efficiency, and adapts to the configuration word document format changes of different OEMs and vehicle models.
Smart Images
Figure CN122197859A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle data processing technology, and in particular to a method, system, and computer-readable medium for parsing vehicle configuration words. Background Technology
[0002] With the rapid development of automotive electronics technology, modern vehicle configuration management has become one of the core technologies in the automotive manufacturing industry. The vehicle configuration word, as a crucial data structure storing various functional parameters of a vehicle, carries all configuration information, from basic function settings to advanced driver assistance systems. Effective configuration word parsing technology is of great significance for the correct implementation of vehicle functions, fault diagnosis, and subsequent maintenance.
[0003] Current methods for parsing vehicle configuration characters have at least one of the following drawbacks:
[0004] Poor flexibility: Each time a new model or configuration word format is added, the parsing rules must be rewritten and debugged, resulting in a long development cycle.
[0005] Functional limitations: The existing parsing rules are based on a single-byte design and cannot effectively handle configuration items with large values that need to be stored across multiple bytes (such as 12-bit tire pressure sensor data).
[0006] Reliance on manual labor, high error rate, and low efficiency: Vehicle manufacturers release a large number of unstructured configuration word documents (such as PDF / Excel documents) every year. Currently, the industry relies heavily on manual labor to convert them into machine-readable parsing rules or code. The calculation of mask values depends entirely on developers, which is prone to errors due to negligence.
[0007] High maintenance costs: When the vehicle manufacturer updates the configuration word document (such as changing the bit field of a configuration item), the developers must manually find and modify the corresponding hard-coded value in the code, which is a cumbersome process and easy to miss. Summary of the Invention
[0008] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a vehicle configuration word parsing method, system and computer-readable medium.
[0009] In a first aspect, the present invention provides a method for parsing vehicle configuration words, comprising:
[0010] Obtain the descriptive text information corresponding to each configuration word from the configuration document;
[0011] Generate a triplet corresponding to the target configuration word based on the description text information of the target configuration word. The three elements recorded in the triplet are the start byte, start bit, and end bit of the corresponding target configuration word, respectively.
[0012] The configuration value corresponding to the target configuration word is parsed from the vehicle configuration array based on the triple.
[0013] In some embodiments, the step of generating the triplet corresponding to the target configuration word based on the description text information of the target configuration word includes:
[0014] The description text information corresponding to each configuration word is parsed to extract the configuration word name, byte position and bit range corresponding to each configuration word;
[0015] Bit field conflict detection is performed on each configuration word based on the byte position and bit range corresponding to each configuration word;
[0016] The configuration word that has passed the bit field conflict detection is taken as the target configuration word. The start byte is determined according to the byte position corresponding to the target configuration word, and the start bit and end bit are determined according to the bit range corresponding to the target configuration word, thereby obtaining the triplet corresponding to the target configuration word.
[0017] In some embodiments, the method further includes:
[0018] When there is a configuration word that fails the bit field conflict detection, the configuration word that fails the bit field conflict detection is regarded as an abnormal configuration word. The feature extraction is performed on the description text information of the abnormal configuration word to obtain the conflict feature information of the abnormal configuration word.
[0019] The conflict feature information of the abnormal configuration word is input as the target conflict feature information into a preset lightweight machine learning model, so that the preset lightweight machine learning model can analyze it and output correction suggestions.
[0020] The preset lightweight machine learning model is configured to identify the historical conflict cases with the highest similarity to the target conflict feature information from the historical database, and output correction suggestions for the descriptive text information of the abnormal configuration word based on the correction scheme recorded in the identified historical conflict cases. The historical database contains multiple historical conflict cases, and each historical conflict case contains conflict feature information of the corresponding case and a pre-defined correction scheme.
[0021] In some embodiments, after the preset lightweight machine learning model performs analysis and outputs correction suggestions, the method further includes:
[0022] In response to the user determining the final correction scheme for the abnormal configuration word, the conflict characteristic information of the abnormal configuration word and the final correction scheme are stored as a historical conflict case in the historical database.
[0023] In some embodiments, the step of obtaining the descriptive text information corresponding to each configuration word from the configuration document includes:
[0024] Perform text recognition on the configuration document to extract all text information;
[0025] The system identifies keywords from text information that represent any one of three types of information: configuration word name, byte position, and bit range. These keywords are then standardized to represent the information using the corresponding standardized keywords. For each standardized keyword representing a byte position, a pre-configured regular expression is used to extract the corresponding numeric portion to obtain the complete data representing the byte position. Similarly, for each standardized keyword representing a bit range, a pre-configured regular expression is used to extract the corresponding numeric portion to obtain the complete data representing the bit range.
[0026] Based on the identified configuration word names, byte positions, and bit ranges within the entire text information, and following the principle of proximity in the text context, each combination of byte positions and bit ranges is associated with the nearest preceding configuration word name. Furthermore, the entire text information is structurally divided according to the positions of the associated configuration word names, byte positions, and bit ranges, resulting in multiple descriptive text messages. Each descriptive text message contains the content of a corresponding associated set of configuration word names, byte positions, and bit ranges.
[0027] In some embodiments, the step of parsing the configuration value corresponding to the target configuration word from the vehicle configuration array based on the triple includes:
[0028] Determine whether the bit field of the target configuration word spans bytes based on the triple;
[0029] When it is determined that the bit field of the target configuration word does not span bytes, the corresponding data is extracted from the start byte according to the start bit and the end bit as the configuration value of the target configuration word.
[0030] When it is determined that the bit field of the target configuration word spans bytes, the occupied bytes of the bit field of the target configuration word and the occupied bits in each occupied byte are determined according to the triple. The corresponding sub-data is extracted from the corresponding occupied bits in each occupied byte, and all the sub-data are concatenated in order to obtain the complete data, which is used as the configuration value of the target configuration word.
[0031] In some embodiments, when it is determined that the bit field of the target configuration word only spans two adjacent bytes, the steps of determining the occupied bytes of the bit field of the target configuration word and the occupied bits in each occupied byte based on the triplet, extracting the corresponding sub-data from the corresponding occupied bits in each occupied byte, and concatenating all the sub-data in sequence to obtain complete data as the configuration value of the target configuration word include:
[0032] Based on the start byte recorded in the triplet, the occupancy bytes of the bit field of the target configuration word are determined to be the start byte and the next byte adjacent to the start byte, respectively, with the start byte being the low-order occupancy byte and the next byte adjacent to the start byte being the high-order occupancy byte.
[0033] Based on the start bit and the end bit, calculate the number of low-order valid bits L_low in the low-order occupied byte and the number of high-order valid bits L_high in the high-order occupied byte of the target configuration word, where L_low = 8 - (Ls % 8) and L_high = (Le - Ls + 1) - L_low.
[0034] The low-level mask M_low is calculated based on the number of low-level effective bits L_low, and the high-level mask M_high is calculated based on the number of high-level effective bits L_high, where M_low = (2^L_low)–1 and M_high = (2^L_high)–1.
[0035] The corresponding sub-data is extracted from the low-order occupied byte according to the low-order mask M_low as the low-order value, and the corresponding sub-data is extracted from the high-order occupied byte according to the high-order mask M_high as the high-order value. The low-order value and the high-order value are then merged and concatenated to obtain the configuration value.
[0036] In some embodiments, the step of extracting the corresponding sub-data from the low-order occupied byte according to the low-order mask M_low as the low-order value, and extracting the corresponding sub-data from the high-order occupied byte according to the high-order mask M_high as the high-order value, and merging the low-order value and the high-order value to obtain the configuration value includes:
[0037] The low-order occupied byte is shifted to the right by the starting bit, and then the corresponding sub-data is extracted from the low-order byte that has completed the right shift as the low-order value using the binary number of the low-order mask.
[0038] The corresponding sub-data is extracted from the high-order occupied byte using the binary number of the high-order mask, and then the extracted sub-data is left-shifted by the number of low-order effective bits L_low bits to obtain the high-order value;
[0039] A bitwise OR operation is performed on the low-order value and the high-order value to merge and concatenate the low-order value and the high-order value to obtain the configuration value.
[0040] In a second aspect, the present invention also provides a vehicle configuration word parsing system, wherein the system is configured to implement the method provided in the first aspect, the system comprising:
[0041] The module is configured to retrieve the descriptive text information corresponding to each configuration word from the configuration document;
[0042] The generation module is configured to generate a triplet corresponding to the target configuration word based on the description text information of the target configuration word. The three elements recorded in the triplet are the start byte, start bit, and end bit of the corresponding target configuration word, respectively.
[0043] The parsing module is configured to parse the configuration value corresponding to the target configuration word from the vehicle configuration array based on the triple.
[0044] Thirdly, the present invention also provides a computer-readable medium on which a computer program is stored, which, when executed by a processor, implements the steps of the method provided in the first aspect.
[0045] The technical solution of the present invention has the following beneficial technical effects:
[0046] 1) Eliminate bottlenecks and errors in manual transcoding: Completely solve the problems of inefficiency and human error that occur when manually converting unstructured configuration documents into parsing rules.
[0047] 2) Overcoming parsing limitations: Providing a method that can automatically identify and parse configuration item values stored across bytes.
[0048] 3) Achieve system adaptability: Enable the parsing system to adapt to changes in configuration word document formats of different vehicle manufacturers and vehicle models, and have the ability to learn and optimize itself. Attached Figure Description
[0049] Figure 1 A flowchart illustrating a vehicle configuration word parsing method provided in an embodiment of the present invention;
[0050] Figure 2 This is a flowchart of an optional implementation method of step S1 in this invention;
[0051] Figure 3 This is a flowchart of an optional implementation method of step S2 in this invention;
[0052] Figure 4This is a flowchart of an optional implementation method of step S3 in this invention;
[0053] Figure 5 This is a data processing logic diagram corresponding to the vehicle configuration word parsing method provided by the present invention;
[0054] Figure 6 This is a structural block diagram of a vehicle configuration word parsing system provided in an embodiment of the present invention;
[0055] Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0056] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.
[0057] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.
[0058] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.
[0059] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.
[0060] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.
[0061] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.
[0062] To address at least one of the technical problems existing in the above-mentioned background art, the present invention provides a vehicle configuration word parsing method. Figure 1 This is a flowchart illustrating a vehicle configuration word parsing method provided in an embodiment of the present invention. Figure 1 As shown, the vehicle configuration word parsing method includes:
[0063] Step S1: Obtain the description text information corresponding to each configuration word from the configuration document.
[0064] Vehicle configuration documents refer to configuration word description documents released by vehicle manufacturers, which are not fixed in format. They are usually in PDF or Excel format and contain text information such as configuration word names, byte positions, and bit positions.
[0065] The descriptive text information corresponding to a configuration word refers to the information in the configuration document used to describe the attributes of the corresponding configuration word. It generally includes information such as the name and storage location of the configuration word.
[0066] Step S2: Generate a triplet corresponding to the target configuration word based on the description text information of the target configuration word. The three elements recorded in the triplet are the start byte, start bit, and end bit of the corresponding target configuration word.
[0067] In this invention, the descriptive text information of the target configuration word is extracted from an unstructured document, and the descriptive text is converted into a corresponding triple, which can be directly read and used by a computer system (such as a vehicle configuration word parsing system).
[0068] Step S3: Parse the configuration value corresponding to the target configuration word from the vehicle configuration array based on the triple.
[0069] The vehicle configuration array refers to an array (usually a binary array, including multiple bytes) used to store various functional status information of the vehicle. It is generally stored in the vehicle's microcontroller (MCU). Different bits or combinations of bits (bit fields) in the vehicle configuration array represent different vehicle functional parameters (such as steering wheel position, drive type, air conditioning type, etc.).
[0070] As can be seen from the above, the technical solution of the present invention can automatically convert unstructured configuration documents into configuration word parsing rules (existing in the form of triples that can be directly understood by the system) without manual intervention, and automatically parse the corresponding configuration values from the vehicle configuration array according to the configuration word parsing rules, thereby effectively improving the parsing efficiency of vehicle configuration words and avoiding human parsing errors.
[0071] Figure 2 This is a flowchart of an optional implementation method of step S1 in this invention. For example... Figure 2 As shown, in some embodiments, step S1 includes:
[0072] Step S101: Perform text recognition on the configuration document to extract all text information.
[0073] The system receives configuration documents in PDF or Excel format and uses an OCR (Optical Character Recognition) engine or table parsing interface to extract all text information from the configuration documents.
[0074] Step S102: Identify keywords from the text information that represent any one of the three types of information: configuration word name, byte position, and bit range, and standardize the identified keywords to represent them using the corresponding standardized keywords; and for each standardized keyword representing the byte position, extract the corresponding numeric part of the standardized keyword according to a pre-configured regular expression to obtain the complete data representing the byte position; and for each standardized keyword representing the bit range, extract the corresponding numeric part of the standardized keyword according to a pre-configured regular expression to obtain the complete data representing the bit range.
[0075] Step S102 includes the following two processes:
[0076] 1) Text Analysis: Parse the extracted text and identify keywords containing configuration item names (such as "steering wheel position"), byte positions (such as "Byte 26"), and bit ranges (such as "Bit3-Bit12"). For example, "steering wheel position" is a keyword that represents the configuration item name, "Byte" is a keyword that represents the byte position, and "Bit" is a keyword that represents the bit range.
[0077] 2) Element Recognition and Semantic Matching: A professional semantic knowledge base covering multiple vehicle models and languages is pre-established, containing standard names and common variations of keywords for each category (e.g., "steering wheel position" corresponds to "Steering WheelPosition," "SWP," etc., and "Byte" corresponds to "byte," "byte," "BYTE," etc.). The system uses this knowledge base to perform fuzzy matching on the identified text, standardizing each keyword into standardized keywords; during this process, keywords belonging to "configuration word names" can be identified and standardized uniformly. Then, rule matching and regular expressions are used to extract numerical information from the text, determining byte positions (also known as byte indices) and bit ranges.
[0078] Step S103: Based on the position of the identified configuration word name, byte position, and bit range in all text information, according to the principle of proximity in the text context, associate each combination of byte position and bit range with the nearest configuration word name in front of it, and perform structured division of all text information according to the position of the configuration word name, byte position, and bit range that constitute the association, to obtain multiple descriptive text information. Each descriptive text information records the content of the corresponding set of configuration word names, byte positions, and bit ranges that constitute the association.
[0079] Step S103 can be used to structurally divide the text information into multiple descriptive text messages, each of which corresponds to a configuration word.
[0080] Figure 3 This is a flowchart of an optional implementation method of step S2 in this invention. For example... Figure 3 As shown, in some embodiments, step S2 includes:
[0081] Step S201: Parse the description text information corresponding to each configuration word to extract the configuration word name, byte position and bit range corresponding to each configuration word.
[0082] Step S202: Perform bit field conflict detection on each configuration word according to the byte position and bit range corresponding to each configuration word.
[0083] In this embodiment of the invention, bit-field conflict detection mainly includes two types of detection:
[0084] 1) Bit range validity check: This mainly checks whether the values of the start and end bits in the bit range are valid. For example, the end and start bits should meet the following conditions: (1) The end bit must be greater than the start bit; (2) The value of the end bit should be less than or equal to n*8-1, where n is the total number of bytes occupied by the configuration word. When both of the above conditions are met, the bit range is detected as valid; otherwise, it is invalid.
[0085] 2) Bit range conflict detection: This mainly detects whether the bit field of the current configuration word overlaps with the bit field of other configuration words; if there is an overlap, a conflict is detected; if there is no overlap, no conflict is detected.
[0086] The configuration word is detected as having passed bit field conflict detection only if it passes both the bit range validity detection and bit range conflict detection mentioned above; the configuration word is detected as having failed bit field conflict detection if it fails at least one of the bit range validity detection and bit range conflict detection mentioned above.
[0087] Specifically, for configuration words that pass the bit field conflict detection, step S203 is executed; for configuration words that fail the bit field conflict detection, step S204 is executed.
[0088] Step S203: Take the configuration word that has passed the bit field conflict detection as the target configuration word, determine the start byte according to the byte position corresponding to the target configuration word, and determine the start bit and end bit according to the bit range corresponding to the target configuration word, so as to obtain the triplet corresponding to the target configuration word.
[0089] Step S204: The configuration words that fail the bit field conflict detection are regarded as abnormal configuration words. The feature of the description text information of the abnormal configuration words is extracted to obtain the conflict feature information of the abnormal configuration words.
[0090] Step S205: Input the conflict feature information of the abnormal configuration word as the target conflict feature information into the preset lightweight machine learning model, so that the preset lightweight machine learning model can analyze and output correction suggestions.
[0091] The default lightweight machine learning model is configured to identify historical conflict cases with the highest similarity to the target conflict feature information from the historical database, and output correction suggestions for the descriptive text information of the abnormal configuration words based on the correction scheme recorded in the identified historical conflict cases. The historical database contains multiple historical conflict cases, and each historical conflict case contains the conflict feature information of the corresponding case and the pre-labeled correction scheme.
[0092] Unlike deep learning models, this invention uses a lightweight machine learning model and a historical database, which can quickly output corresponding correction suggestions for abnormal configuration words with low computational cost.
[0093] In some embodiments, step S205 is followed by step S206.
[0094] Step S206: In response to the user determining the final correction scheme for the abnormal configuration word, the conflict characteristic information of the abnormal configuration word and the final correction scheme are stored as a historical conflict case in the historical database.
[0095] In this invention, when the bit-field conflict detection module detects an anomaly (such as overlapping bit ranges due to document annotation errors), it extracts conflict features (vehicle model, configuration item, byte position, etc.). These features are fed into a lightweight machine learning model for analysis and compared with historical data. The system generates correction suggestions (such as automatically adjusting the bit range of a configuration item), which are then confirmed and updated in the historical database (and, if necessary, the aforementioned semantic knowledge base can also be updated). These technical means enable the system to possess a feedback mechanism capable of self-learning and optimization based on historical conflict data. This mechanism allows the system to continuously adapt to new document formats and correct potential errors in vehicle manufacturer documents.
[0096] Figure 4 This is a flowchart of an optional implementation method of step S3 in this invention. For example... Figure 4 As shown, step S3 includes:
[0097] Step S301: Determine whether the bit field of the target configuration word spans bytes based on the triplet.
[0098] If it is determined that the bit field of the target configuration word does not span bytes, then step S302 is executed; if it is determined that the bit field of the target configuration word spans bytes, then step S303 is executed.
[0099] Step S302: Extract the corresponding data from the start byte according to the start bit and end bit as the configuration value of the target configuration word.
[0100] Step S303: Determine the occupied bytes of the bit field of the target configuration word and the occupied bits in each occupied byte based on the triplet. Extract the corresponding sub-data from the corresponding occupied bits in each occupied byte. Concatenate all the sub-data in order to obtain the complete data, which is used as the configuration value of the target configuration word.
[0101] The technical solution of this invention supports adaptive parsing of cross-byte bit fields.
[0102] In some embodiments, when it is determined that the bit field of the target configuration word spans only two adjacent bytes, step S303 includes:
[0103] S3031. Based on the starting byte recorded in the triplet, determine the occupancy bytes of the bit field of the target configuration word as the starting byte and the next adjacent byte of the starting byte, with the starting byte as the low-order occupancy byte and the next adjacent byte as the high-order occupancy byte.
[0104] S3032. Based on the start bit Ls and the end bit Le, calculate the number of low-order significant bits L_low in the low-order byte occupied by the target configuration word and the number of high-order significant bits L_high in the high-order byte occupied by the target configuration word. L_low = 8 - (Ls % 8), L_high = (Le - Ls + 1) - L_low.
[0105] S3033. Calculate the low-bit mask M_low based on the number of low-bit effective bits L_low, and calculate the high-bit mask M_high based on the number of high-bit effective bits L_high, M_low=(2^L_low)–1, M_high=(2^L_high)-1;
[0106] S3034. Extract the corresponding sub-data from the low-order occupied byte according to the low-order mask M_low as the low-order value, and extract the corresponding sub-data from the high-order occupied byte according to the high-order mask M_high as the high-order value. Merge and concatenate the low-order value and the high-order value to obtain the configuration value.
[0107] In some embodiments, step S3034 includes: shifting the low-order occupied byte to the right by a starting bit, and then using the binary number of the low-order mask to extract the corresponding sub-data from the right-shifted low-order byte as the low-order value; using the binary number of the high-order mask to extract the corresponding sub-data from the high-order occupied byte, and then shifting the extracted sub-data to the left by a number of low-order effective bits L_low bits to obtain the high-order value; performing a bitwise OR operation on the low-order value and the high-order value to merge and concatenate the low-order value and the high-order value to obtain the configuration value.
[0108] As an example:
[0109] Assume that the configuration word obtained in step S1 is "Tire Pressure Monitoring Range" and its description text information is: the tire pressure monitoring range is stored in bytes 30 to 31 and bits 3 to 12. Then, step S2 can be used to obtain the triplet corresponding to "Tire Pressure Monitoring Range" as [30, 3, 12], that is, the starting byte is Byte26, the starting bit is Bit3, and the ending bit is Bit12.
[0110] The parameters are calculated as follows:
[0111] The total length of the bit field L = Le - Ls + 1 = 12 - 3 + 1 = 10 bits.
[0112] The starting bit is offset by 3 within the byte.
[0113] The number of significant low-order bits L_low = 8 - 3 = 5 bits (corresponding to Bit 3 to Bit 7 of byte 30).
[0114] The number of significant high-order bits L_high = 10 - 5 = 5 bits (corresponding to Bit 0 to Bit 4 of byte 31).
[0115] The low-order mask M_low = 2^5 - 1 = 31 (binary 00011111).
[0116] The high-order mask M_high = 2^5 - 1 = 31 (binary 00011111)
[0117] Data extraction and merging are as follows:
[0118] Low-order value = (vehicle configuration array
[30] >> 3) & 31
[0119] High-order value = (vehicle configuration array
[31] & 31) << 5
[0120] Tire pressure monitoring value = High value | Low value
[0121] Among them, vehicle configuration array
[30] represents the 30th byte (i.e., the low-order byte) in the vehicle configuration array, vehicle configuration array
[31] represents the 31st byte (i.e., the high-order byte) in the vehicle configuration array, >> represents right shift, << represents left shift, "&31" in the low-order value formula represents data extraction based on the low-order mask, "&31" in the high-order value formula represents data extraction based on the high-order mask, and "|" in the tire pressure monitoring value formula represents bitwise OR operation.
[0122] In the above scheme, when extracting low-order bits, shifting is a "preliminary step," requiring a right shift to adjust the position of the valid bits before filtering with a mask. Conversely, when extracting high-order bits, shifting is a "subsequent step," requiring a mask to filter irrelevant bits before a left shift to adjust the merging position. This process effectively ensures that the high and low valid bits do not overlap during subsequent merging.
[0123] It should be noted that the above-mentioned scheme for dynamic calculation and parsing based on the parsing parameters L_low, L_high, M_low, and M_high is only one optional implementation scheme in this invention and does not limit the technical solution of this invention.
[0124] Based on the same inventive concept, the present invention also provides a vehicle configuration character parsing system. Figure 6 This is a structural block diagram of a vehicle configuration word parsing system provided in an embodiment of the present invention. Figure 6As shown, the vehicle configuration word parsing system can implement the vehicle configuration word parsing method provided in the previous embodiment. The vehicle configuration word parsing system includes: an acquisition module, a generation module, and a parsing module.
[0125] The module configuration is obtained by retrieving the descriptive text information corresponding to each configuration word from the configuration document;
[0126] The generation module is configured to generate a triplet corresponding to the target configuration word based on the description text information of the target configuration word. The three elements recorded in the triplet are the start byte, start bit, and end bit of the corresponding target configuration word, respectively.
[0127] The parsing module is configured to parse the configuration value corresponding to the target configuration word from the vehicle configuration array based on the triple.
[0128] For a detailed description of each of the above functional modules, please refer to the corresponding content in the previous embodiments, which will not be repeated here.
[0129] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 7 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the vehicle configuration word parsing methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory, configured to enable information interaction between the processors and the memory.
[0130] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).
[0131] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.
[0132] In some embodiments, the one or more processors 101 include a field-programmable gate array.
[0133] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the vehicle configuration word parsing methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.
[0134] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described vehicle configuration word parsing method.
[0135] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).
[0136] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.
[0137] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.
[0138] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.
[0139] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.
[0140] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should 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-readable program instructions.
[0141] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.
[0142] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.
[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0144] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.
Claims
1. A method for parsing vehicle configuration characters, wherein, include: Obtain the descriptive text information corresponding to each configuration word from the configuration document; Generate a triplet corresponding to the target configuration word based on the description text information of the target configuration word. The three elements recorded in the triplet are the start byte, start bit, and end bit of the corresponding target configuration word, respectively. The configuration value corresponding to the target configuration word is parsed from the vehicle configuration array based on the triple.
2. The method according to claim 1, wherein, The steps of generating the triplet corresponding to the target configuration word based on the description text information of the target configuration word include: The description text information corresponding to each configuration word is parsed to extract the configuration word name, byte position and bit range corresponding to each configuration word; Bit field conflict detection is performed on each configuration word based on the byte position and bit range corresponding to each configuration word; The configuration word that has passed the bit field conflict detection is taken as the target configuration word. The start byte is determined according to the byte position corresponding to the target configuration word, and the start bit and end bit are determined according to the bit range corresponding to the target configuration word, thereby obtaining the triplet corresponding to the target configuration word.
3. The method according to claim 2, wherein, Also includes: When there is a configuration word that fails the bit field conflict detection, the configuration word that fails the bit field conflict detection is regarded as an abnormal configuration word. The feature extraction is performed on the description text information of the abnormal configuration word to obtain the conflict feature information of the abnormal configuration word. The conflict feature information of the abnormal configuration word is input as the target conflict feature information into a preset lightweight machine learning model, so that the preset lightweight machine learning model can analyze it and output correction suggestions. The preset lightweight machine learning model is configured to identify the historical conflict cases with the highest similarity to the target conflict feature information from the historical database, and output correction suggestions for the descriptive text information of the abnormal configuration word based on the correction scheme recorded in the identified historical conflict cases. The historical database contains multiple historical conflict cases, and each historical conflict case contains conflict feature information of the corresponding case and a pre-defined correction scheme.
4. The method according to claim 1, wherein, After the preset lightweight machine learning model performs analysis and outputs correction suggestions, the method further includes: In response to the user determining the final correction scheme for the abnormal configuration word, the conflict characteristic information of the abnormal configuration word and the final correction scheme are stored as a historical conflict case in the historical database.
5. The method according to claim 1, wherein, The steps to obtain the descriptive text information corresponding to each configuration word from the configuration document include: Perform text recognition on the configuration document to extract all text information; The system identifies keywords from text information that represent any one of three types of information: configuration word name, byte position, and bit range. These keywords are then standardized to represent the information using the corresponding standardized keywords. For each standardized keyword representing a byte position, a pre-configured regular expression is used to extract the corresponding numeric portion to obtain the complete data representing the byte position. Similarly, for each standardized keyword representing a bit range, a pre-configured regular expression is used to extract the corresponding numeric portion to obtain the complete data representing the bit range. Based on the identified configuration word names, byte positions, and bit ranges within the entire text information, and following the principle of proximity in the text context, each combination of byte positions and bit ranges is associated with the nearest preceding configuration word name. Furthermore, the entire text information is structurally divided according to the positions of the associated configuration word names, byte positions, and bit ranges, resulting in multiple descriptive text messages. Each descriptive text message contains the content of a corresponding associated set of configuration word names, byte positions, and bit ranges.
6. The method according to claim 1, wherein, The steps for parsing the configuration value corresponding to the target configuration word from the vehicle configuration array based on the triple include: Determine whether the bit field of the target configuration word spans bytes based on the triple; When it is determined that the bit field of the target configuration word does not span bytes, the corresponding data is extracted from the start byte according to the start bit and the end bit as the configuration value of the target configuration word. When it is determined that the bit field of the target configuration word spans bytes, the occupied bytes of the bit field of the target configuration word and the occupied bits in each occupied byte are determined according to the triple. The corresponding sub-data is extracted from the corresponding occupied bits in each occupied byte, and all the sub-data are concatenated in order to obtain the complete data, which is used as the configuration value of the target configuration word.
7. The method according to claim 6, wherein, When it is determined that the bit field of the target configuration word only spans two adjacent bytes, the steps of determining the occupied bytes of the bit field of the target configuration word and the occupied bits in each occupied byte based on the triplet, extracting the corresponding sub-data from the corresponding occupied bits in each occupied byte, and concatenating all the sub-data in sequence to obtain complete data as the configuration value of the target configuration word include: Based on the start byte recorded in the triplet, the occupancy bytes of the bit field of the target configuration word are determined to be the start byte and the next byte adjacent to the start byte, respectively, with the start byte being the low-order occupancy byte and the next byte adjacent to the start byte being the high-order occupancy byte. Based on the start bit Ls and the end bit Le, calculate the number of low-order valid bits L_low in the low-order occupied byte and the number of high-order valid bits L_high in the high-order occupied byte of the target configuration word, where L_low = 8 - (Ls % 8) and L_high = (Le - Ls + 1) - L_low. The low-level mask M_low is calculated based on the number of low-level effective bits L_low, and the high-level mask M_high is calculated based on the number of high-level effective bits L_high, where M_low = (2^L_low)–1 and M_high = (2^L_high)–1. The corresponding sub-data is extracted from the low-order occupied byte according to the low-order mask M_low as the low-order value, and the corresponding sub-data is extracted from the high-order occupied byte according to the high-order mask M_high as the high-order value. The low-order value and the high-order value are then merged and concatenated to obtain the configuration value.
8. The method according to claim 7, wherein, The steps of extracting the corresponding sub-data from the low-order occupied byte according to the low-order mask M_low as the low-order value, and extracting the corresponding sub-data from the high-order occupied byte according to the high-order mask M_high as the high-order value, and merging the low-order value and the high-order value to obtain the configuration value include: The low-order occupied byte is shifted to the right by the starting bit, and then the corresponding sub-data is extracted from the low-order byte that has completed the right shift as the low-order value using the binary number of the low-order mask. The corresponding sub-data is extracted from the high-order occupied byte using the binary number of the high-order mask, and then the extracted sub-data is left-shifted by the number of low-order effective bits L_low bits to obtain the high-order value; A bitwise OR operation is performed on the low-order value and the high-order value to merge and concatenate the low-order value and the high-order value to obtain the configuration value.
9. A vehicle configuration character parsing system, wherein, The system is configured to implement the method as described in any one of claims 1 to 8, the system comprising: The module is configured to retrieve the descriptive text information corresponding to each configuration word from the configuration document; The generation module is configured to generate a triplet corresponding to the target configuration word based on the description text information of the target configuration word. The three elements recorded in the triplet are the start byte, start bit, and end bit of the corresponding target configuration word, respectively. The parsing module is configured to parse the configuration value corresponding to the target configuration word from the vehicle configuration array based on the triple.
10. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.