Methods and systems for optimizing map strings processing in BIOS configuration files

Abstract

This application discloses a method and system for optimizing the processing of map strings in BIOS configuration files, relating to the field of computer software technology. By dynamically generating map string data, performing compression processing, index building, and configuration file generation, it effectively solves the problems of configuration file size expansion, processing latency, and low retrieval efficiency caused by static storage mode. It can reduce configuration file size, improve processing efficiency, reduce maintenance costs, and optimize system startup speed.

Description

Technical Field

[0001] This application relates to the field of computer software technology, and in particular to a method and system for optimizing the processing of map strings in BIOS configuration files. Background Technology

[0002] Map strings in the BIOS configuration file serve as a crucial bridge in converting internal system identifiers into user-readable strings. Their processing mechanism has a decisive impact on computer system startup speed and user experience. Currently, the industry commonly uses a static storage model to manage map strings, pre-deploying the complete set of strings corresponding to all language versions and configuration options in the configuration file. This static approach leads to multiple technical bottlenecks: the configuration file grows dramatically in size due to the repeated storage of multilingual strings with the same internal identifiers. Especially when supporting globalized multilingual environments, redundant data consumes a large amount of firmware storage space, causing delays in the configuration parsing process and significantly extending system startup time. The static storage structure lacks dynamic adaptability. When the BIOS firmware version iterates or user configuration parameters change, the map strings cannot be automatically adjusted, requiring manual updates to the entire string library. This not only significantly increases development and maintenance costs but also easily leads to system startup failures or abnormal interface display due to incorrect mapping relationships or missing language versions. Furthermore, the static data organization lacks effective index support. When retrieving a specific string from the massive map strings set, the system is forced to perform inefficient linear scan operations, further degrading processing performance.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method and system for optimizing the processing of map strings in BIOS configuration files, aiming to improve processing efficiency, reduce maintenance costs and optimize system startup speed.

[0005] To achieve the above objectives, this application proposes a method for optimizing the processing of map strings in BIOS configuration files, the method comprising: Obtain raw BIOS configuration information data, which includes raw data of internal identifiers, raw data of configuration options, raw data of user language settings, and raw data of BIOS version. Based on the original BIOS configuration information data, dynamic map strings data are generated through dynamic generation processing. Based on the dynamic map strings data, compressed map strings data is generated through compression processing; Based on the compressed map strings data, an index structure data is generated through index building processing; Based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data in the original BIOS configuration information data, optimized BIOS configuration file data is generated through configuration file generation processing.

[0006] In one embodiment, the step of generating dynamic mapstrings data based on the original BIOS configuration information data includes: Based on the original BIOS configuration information data, configuration data structure data is generated through configuration data structure creation and processing. Based on the configuration data structure, version adaptation rule data is generated through version adaptation rule generation processing; Based on the configuration data structure and the version adaptation rules, dynamic map strings data are generated through string generation processing.

[0007] In one embodiment, the step of generating configuration data structure data through configuration data structure creation and processing based on the original BIOS configuration information data includes: Extract internal identifiers from the original BIOS configuration information data; Extract configuration option data from the original BIOS configuration information data; Extract user language settings data from the original BIOS configuration information data; Data is extracted based on the internal identifier, the configuration options, and the user language settings, and then processed through data structure construction to generate configuration data structure data.

[0008] In one embodiment, the step of generating dynamic map strings data through string generation processing based on the configuration data structure data and the version adaptation rule data includes: Obtain internal identifier mapping relationship data from the configuration data structure data; Based on the version adaptation rule data, template data is generated by generating strings through template generation processing. Data is extracted based on the user's language settings, and language string library data is obtained from the configuration data structure. Based on the internal identifier mapping relationship data and the string generation template data, raw string data is generated through raw string generation processing; Based on the language string library data, the original string data is processed for language conversion to generate dynamic map strings data.

[0009] In one embodiment, the step of generating compressed mapstrings data through compression processing based on the dynamic map strings data includes: Based on the dynamic map strings data, cleaned map strings data is generated through data cleaning processing. Based on the cleaned map strings data, standardized map strings data are generated through data standardization processing. Based on the standardized map strings data, compressed map strings data is generated through compression encoding.

[0010] In one embodiment, the step of generating compressed map strings data through compression encoding based on the standardized map strings data includes: The standardized map strings data are subjected to text feature analysis to generate text feature analysis result data; Based on the text feature analysis results, the compression algorithm type is determined through algorithm selection processing. Based on the compression algorithm type data, compression parameter data is generated through parameter configuration processing; Based on the compression algorithm type data and the compression parameter data, the standardized map strings data are subjected to block compression processing to generate a compressed data block set data; The compressed data block set is encapsulated to generate compressed map strings data.

[0011] In one embodiment, the step of generating index structure data through index building processing based on the compressed map strings data includes: Based on the compressed map strings data, the index structure is determined and processed to generate index structure type data; Based on the compressed map strings data and the index structure type data, index mapping relationship data is generated through index mapping construction processing; Based on the index mapping relationship data, index structure data is generated through index optimization processing.

[0012] In one embodiment, the step of generating index mapping relationship data by constructing an index mapping based on the compressed map strings data and the index structure type data includes: The compressed map strings data is decompressed and preprocessed to generate parsable map strings data; The parsable map strings data is parsed to extract the internal identifier set data and string position information data; Based on the index structure type data, index container data is generated through index container creation and processing. The internal identifier set data is used as key data, and the string position information data is used as value data. These are stored in the index container data to generate index mapping relationship data.

[0013] In one embodiment, the step of generating optimized BIOS configuration file data through configuration file generation processing based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data in the original BIOS configuration information data includes: Based on the dynamic map strings data, the compressed map strings data, and the index structure data, integrated configuration data is generated through data integration processing. Based on the integrated configuration data, standard format BIOS configuration data is generated through format conversion processing; Based on the standard format BIOS configuration data and the original BIOS version data in the original BIOS configuration information data, version tag data is generated through version tagging processing; Based on the standard format BIOS configuration data and the version tag data, optimized BIOS configuration file data is generated through merging processing.

[0014] Furthermore, to achieve the above objectives, this application also proposes a system for optimizing map string processing in a BIOS configuration file. The system for optimizing map string processing in a BIOS configuration file includes: a memory, a processor, and a program for optimizing map string processing in a BIOS configuration file stored on the memory and executable on the processor. The program for optimizing map string processing in a BIOS configuration file is configured to implement the steps of the method for optimizing map string processing in a BIOS configuration file.

[0015] The method and system proposed in this application for optimizing the processing of map strings in BIOS configuration files effectively solve the problems of configuration file size expansion, processing latency and low retrieval efficiency caused by static storage mode by dynamically generating map string data, compression processing, index building and configuration file generation. It can reduce configuration file size, improve processing efficiency, reduce maintenance costs and optimize system startup speed. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

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

[0018] Figure 1 A flowchart is provided as an embodiment of the method for optimizing map strings processing in BIOS configuration files in this application; Figure 2 This is a schematic diagram of a system embodiment for optimizing map strings processing in BIOS configuration files according to this application.

[0019] Explanation of icon numbers: 10. Memory; 20. Processor.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In existing technologies, the map strings processing method in BIOS configuration files uses a static storage model, resulting in large configuration file sizes and data redundancy. This leads to wasted storage space and prolonged parsing time, especially in multi-language environments. This model lacks flexibility, failing to dynamically adapt to BIOS version updates or user configuration changes, increasing maintenance complexity and the probability of errors. Furthermore, the lack of an effective indexing mechanism results in low string retrieval efficiency, impacting the overall processing performance of the BIOS configuration file.

[0024] Based on this, embodiments of this application provide a method for optimizing map string processing in BIOS configuration files, referring to... Figure 1 The method for optimizing map strings processing in the BIOS configuration file includes steps S100 to S500, wherein: Step S100: Obtain raw BIOS configuration information data, which includes raw data of internal identifiers, raw data of configuration options, raw data of user language settings, and raw data of BIOS version. Step S200: Based on the original BIOS configuration information data, dynamic mapstrings data is generated through dynamic generation processing; Step S300: Based on the dynamic map strings data, compressed map strings data is generated through compression processing; Step S400: Based on the compressed map strings data, generate index structure data through index building processing; Step S500: Based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data in the original BIOS configuration information data, optimized BIOS configuration file data is generated through configuration file generation processing.

[0025] In this embodiment, the BIOS configuration file refers to the file that stores BIOS (Basic Input / Output System) settings and configuration information, the content of which determines the initialization and startup behavior of the computer hardware. Map strings refer to the data set in the BIOS configuration file used to map internal identifiers (such as hardware IDs, configuration item IDs) to user-readable strings (such as device names, option descriptions), typically used for multi-language support and user interface display. Raw BIOS configuration information data refers to the unprocessed initial configuration data obtained from the BIOS firmware or development tools, containing all the basic information needed to build map strings, such as internal identifiers, configuration options, user language settings, and BIOS version information. Dynamic map strings data refers to the set of map strings generated in real-time or on demand based on the raw BIOS configuration information data through a specific processing flow. Its characteristic is that it can be flexibly adjusted and generated according to different configuration or version requirements, rather than being pre-statically stored. Compressed map strings data refers to the data format obtained after compressing dynamically generated map strings data, aiming to reduce storage space usage and improve transmission efficiency. Indexed structure data refers to the data structure built to achieve fast retrieval of specific strings in compressed map strings data, typically containing a mapping relationship between internal identifiers and string storage locations or contents. Optimized BIOS configuration file data refers to BIOS configuration file data generated after processing by this method, which contains dynamically generated, compressed, and indexed map strings. Its purpose is to improve processing efficiency, reduce redundancy, and enhance dynamic adaptation capabilities.

[0026] In one implementation, raw BIOS configuration information data can be obtained through various means. For example, it can be read directly from the BIOS firmware storage medium or exported using specific BIOS development tools. This raw BIOS configuration information data typically includes raw data of internal identifiers, raw data of configuration options, raw data of user language settings, and raw data of the BIOS version. Specifically, a data acquisition module can be designed to parse the BIOS firmware image file, extract the aforementioned types of raw data, and store them in a temporary storage area for subsequent processing.

[0027] In this embodiment, dynamic map strings are generated based on the acquired raw BIOS configuration information data through dynamic generation processing. This dynamic generation process can combine preset string templates with the raw data. For example, a series of templates containing placeholders can be defined, such as "{configuration item name}:{configuration value}", and then the raw configuration option data and user language setting data from the raw data can be filled into these templates to generate map strings specific to the configuration and language. This approach avoids pre-storing all possible string combinations and achieves on-demand generation.

[0028] In this embodiment, compressed mapstrings data is then generated based on the dynamic map strings data through compression processing. This compression processing can employ general data compression algorithms; for example, the dynamic map strings data can be treated as a text stream and compressed using Huffman coding or the LZ77 algorithm. In one implementation, all the dynamic mapstrings data can be concatenated into a large string, and then that string can be compressed as a whole. This processing aims to reduce the storage size of the map strings data and lower the demand for storage resources.

[0029] Furthermore, based on this compressed map strings data, an index structure is generated through an index building process. This index building process can employ a basic key-value pair storage structure. For example, an entry can be created for each internal identifier, and it can be associated with the offset and length of the corresponding map string in the compressed data. In one implementation, a simple lookup table can be constructed, containing the mapping between internal identifiers and string positions in the compressed data. This index structure aims to improve the efficiency of subsequent retrieval of map strings.

[0030] In this embodiment, optimized BIOS configuration file data is ultimately generated based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data from the original BIOS configuration information data. This configuration file generation process can integrate the processed data into a new configuration file. For example, the compressed map strings data and index structure data can be used as the main content of the configuration file, while the original BIOS version data can be embedded as metadata or header information. This integration method aims to create a structured, efficient, and easily manageable optimized BIOS configuration file.

[0031] In this embodiment, by dynamically generating, compressing, and indexing map strings, the problems of large size, data redundancy, and low parsing efficiency caused by static storage of map strings in traditional BIOS configuration files are effectively solved. On-demand generation and compression reduce the data volume of the configuration file, saving storage space. Simultaneously, the constructed index structure improves the retrieval speed of specific strings, enhancing the processing performance of the BIOS configuration file. Furthermore, the dynamic generation mechanism gives the system flexibility, enabling it to better adapt to BIOS version updates and user configuration changes, reducing maintenance complexity.

[0032] In one feasible implementation, the step of generating dynamic map strings data based on the original BIOS configuration information data includes: generating configuration data structure data through configuration data structure creation processing based on the original BIOS configuration information data; generating version adaptation rule data through version adaptation rule generation processing based on the configuration data structure data; and generating dynamic mapstrings data through string generation processing based on the configuration data structure data and the version adaptation rule data.

[0033] In this embodiment, based on the original BIOS configuration information data, configuration data structure data is generated through configuration data structure creation and processing. The aim is to organize the raw, scattered BIOS configuration information data (e.g., raw internal identifier data, raw configuration option data, raw user language setting data, and raw BIOS version data) into a structured and easily processed format. This can be achieved by parsing the raw BIOS configuration information data file (e.g., XML, INI, binary file, etc.), extracting key information, and mapping it to a predefined memory data structure, such as a tree structure, hash table, or object model. This data structure clearly represents the hierarchical relationships, dependencies, and attributes between configuration items, providing a unified and easily accessible data view for subsequent processing.

[0034] Based on this, version adaptation rule data is generated using the configuration data structure data and version adaptation rule generation processing. The purpose of this step is to generate a set of adaptation rules to address potential differences in configuration items, naming conventions, or functional additions / removals across different BIOS versions, ensuring that the subsequent string generation process can correctly handle data from different versions. Version adaptation rule data can be a set of rules, stored as, for example, in JSON, XML files, or database records. These rules can define the name mapping, default value, valid range, and display conditions for a specific configuration item under a particular BIOS version. The generation process can load or dynamically generate corresponding adaptation rules from a preset rule base based on the BIOS version information contained in the configuration data structure data.

[0035] In this embodiment, dynamic map strings are generated based on the configuration data structure and the version adaptation rule data through string generation processing. The string generation process iterates through the configuration data structure, and for each configuration item, determines its display name, description, and optional values ​​under the current BIOS version based on the version adaptation rule data. For example, it can search for corresponding localized strings from the language resource library based on internal identifiers and user language settings, and combine them with the current or default values ​​of the configuration options to generate the final map strings according to a preset template.

[0036] In this embodiment, the present application can systematically and structurally organize the original BIOS configuration information data and provide a flexible adaptation mechanism for different BIOS versions. This makes the generation process of dynamic map strings more accurate and efficient, avoiding the complexity and errors that may result from generating them directly from the raw data. The configuration data structure provides a unified and easily accessible data view for subsequent processing, the version adaptation rule data ensures compatibility and correctness in multi-version BIOS environments, and the string generation process can accurately generate dynamic map strings data that conforms to specific version and language requirements based on these structured data and rules. This step-by-step processing method improves the robustness and maintainability of the map strings generation process, laying a solid foundation for optimizing BIOS configuration file data.

[0037] In one feasible implementation, the step of generating configuration data structure data by creating and processing configuration data structure data based on the original BIOS configuration information data includes: extracting internal identifier extraction data from the original BIOS configuration information data; extracting configuration option extraction data from the original BIOS configuration information data; extracting user language setting extraction data from the original BIOS configuration information data; and generating configuration data structure data by constructing and processing the data structure based on the internal identifier extraction data, the configuration option extraction data, and the user language setting extraction data.

[0038] In this embodiment, internal identifier extraction data is extracted from the original BIOS configuration information data. The aim is to identify and separate the internal code or name used to uniquely identify specific configuration items or functions from the original BIOS configuration information data. This can be achieved in various ways, such as using predefined parsing rules, regular expression matching, keyword searching, or parsing based on specific file formats (such as XML, INI, or binary structures). For example, for text-formatted BIOS configurations, these identifiers can be extracted by searching for specific tags or strings at fixed positions. For binary formats, it is necessary to locate and read the specific memory region or data block storing the internal identifiers according to the BIOS firmware's structural specifications.

[0039] In this embodiment, configuration option extraction data is simultaneously extracted from the original BIOS configuration information data. The aim is to obtain the specific parameters that the user or system can set and their corresponding values ​​from the original BIOS configuration information data. This can also be achieved using structured parsing (such as parsing XML nodes or JSON objects), pattern matching (such as "key=value" pairs), or binary reading based on specific data structure offsets. For example, a set of rules can be defined to identify the name, type, allowed value range, and current value of configuration items, thereby accurately obtaining configuration option information.

[0040] In addition, extracting user language settings data from the raw BIOS configuration information aims to obtain the user's currently selected language information, such as "Simplified Chinese" or "English," from the raw BIOS configuration data. Typically, user language settings exist in the BIOS configuration as specific language codes or language name strings. The extraction process can be accomplished by searching preset language identifier fields, parsing specific language configuration blocks, or through specific key-value pairs in the configuration file.

[0041] Building upon this foundation, data is extracted based on internal identifiers, configuration options, and user language settings. This data is then processed through data structure construction to generate a configuration data structure. The data structure construction process organizes the previously extracted, scattered internal identifiers, configuration options, and user language settings into a unified, standardized, and easily processed configuration data structure according to a pre-defined logical relationship and data model. This data structure can be a tree structure, a collection of key-value pairs, an object model, or a custom array of structures, aiming to provide a clear and programmable view of the configuration. For example, it can be represented using data structures in a programming language (such as dictionaries, mappings, or custom classes), constructing a nested dictionary where the top-level key represents the internal identifier, and its value is a sub-dictionary containing configuration options (key-value pairs) and language settings. During the construction process, it is necessary to ensure the integrity, consistency, and correctness of the data; for example, by associating relevant configuration options with their corresponding internal identifiers.

[0042] In this embodiment, by refining the raw BIOS configuration information data into internal identifier extraction data, configuration option extraction data, and user language setting extraction data, this application can achieve accurate parsing and classification of the raw BIOS configuration information data. This step-by-step extraction method avoids the confusion and errors that may result from directly constructing complex data structures from the raw data, ensuring that each type of key information can be accurately identified and obtained. Based on this, through data structure construction processing, these classified and extracted data are integrated into a unified and standardized configuration data structure, greatly improving the efficiency and accuracy of data processing. This provides high-quality, structured input for subsequent version adaptation rule generation and dynamic map string generation, thereby effectively improving the stability and reliability of the map string processing method in the entire optimized BIOS configuration file.

[0043] In one feasible implementation, the step of generating dynamic map strings data based on the configuration data structure data and the version adaptation rule data through string generation processing includes: obtaining internal identifier mapping relationship data from the configuration data structure data; generating string generation template data based on the version adaptation rule data through template generation processing; extracting data based on the user language settings and obtaining language string library data from the configuration data structure data; generating raw string data based on the internal identifier mapping relationship data and the string generation template data through raw string generation processing; and performing language conversion processing on the raw string data based on the language string library data to generate dynamic map strings data.

[0044] In this embodiment, obtaining internal identifier mapping data from the configuration data structure refers to extracting the association information between internal system identifiers (such as hardware component IDs, software module IDs, configuration item key values, etc.) and their corresponding basic string descriptions from the pre-built configuration data structure. This association information can exist in the form of key-value pairs, lookup tables, or more complex structures, and its function is to provide the core semantic correspondence for subsequent string generation. For example, it can be a mapping table that maps "CPU_FREQ_ID" to "Processor Frequency".

[0045] In this embodiment, generating string template data based on version adaptation rule data means dynamically selecting or constructing templates for generating strings according to the adaptation rules corresponding to the current BIOS version. These templates can be predefined text fragments containing placeholders for dynamic data, or scripts or code describing the string generation logic. The version adaptation rule data guides the template generation process to select the most suitable set of templates for the current BIOS version to ensure that the generated strings conform to the display requirements and format specifications of the specific version. For example, the display format of a certain configuration item may be slightly different for different BIOS versions, and the template generation process will select the corresponding template according to the version rules.

[0046] In this embodiment, extracting data based on the user's language setting and retrieving language string library data from the configuration data structure means retrieving the corresponding language string resource set from the configuration data structure based on the user's currently selected language setting (e.g., "en-US" for American English and "zh-CN" for Simplified Chinese). This language string library data typically contains translated versions of various strings and is crucial for achieving multilingual support. For example, if the user's language is set to "zh-CN", a string library containing all Chinese translations will be retrieved.

[0047] In this embodiment, generating raw string data based on internal identifier mapping data and string generation template data involves combining the acquired internal identifier mapping data with the version-adapted string generation template data. This is done by filling in placeholders in the template or executing logic defined within the template to generate raw string data that has not been localized to the desired language. This raw string data is determined based on internal identifiers and version rules, but has not yet been converted to the user's selected language. For example, if the template is "{ID} is {Value}", and the internal identifier mapping data provides the ID and Value, the raw string data might be "Processor Frequency is 3.0 GHz".

[0048] In this embodiment, language conversion processing of the original string data based on language string library data to generate dynamic map strings data refers to using the previously acquired language string library data to search and replace each element of the generated original string data, converting it into a string in the user-specified language. If a corresponding translation exists in the language string library data, the original string data is replaced with the translated string; otherwise, the original string data can be retained or a string in the default language can be used. The final generated dynamic map strings data is fully localized and version-adapted.

[0049] In this embodiment, the generation process of dynamic map strings is refined into multiple collaborative steps, improving the flexibility and accuracy of the processing. First, by obtaining internal identifier mapping data from the configuration data structure, the basic semantic accuracy of the generated strings is ensured. Second, string generation template data is generated based on version adaptation rule data, enabling the generated strings to accurately adapt to the specific requirements of different BIOS versions. Simultaneously, language string library data is extracted based on user language settings, providing necessary resources for subsequent localization processing. The original string data is generated collaboratively with the internal identifier mapping data and the string generation template data, ensuring content accuracy and version consistency. Finally, language conversion processing is performed on the original string data using the language string library data, achieving precise localization of the map strings, ensuring that the final generated dynamic map strings fully conform to the user's language preferences. This step-by-step and refined processing flow not only improves the efficiency and reliability of dynamic map string generation but also greatly simplifies the maintenance of BIOS configuration files in multi-version and multi-language environments, thereby enhancing the user experience.

[0050] In one feasible implementation, the step of generating compressed map strings data based on the dynamic map strings data includes: generating cleaned map strings data through data cleaning based on the dynamic map strings data; generating standardized map strings data through data standardization based on the cleaned map strings data; and generating compressed map strings data through compression encoding based on the standardized map strings data.

[0051] In this embodiment, data cleaning aims to identify, correct, or remove errors, inconsistencies, duplicates, or incomplete information in dynamic map strings data. Its purpose is to improve data quality and reliability, providing better input for subsequent data standardization and compression. For example, data cleaning may include removing duplicate string entries, correcting spelling errors, handling null values ​​or invalid characters, standardizing string encoding (e.g., converting all to UTF-8 or ASCII), and removing unnecessary whitespace characters or special symbols. Each string in the dynamic map strings data can be traversed, using regular expressions to match and remove character sequences that do not conform to preset specifications, or a hash table can be used to detect and remove identical duplicate strings.

[0052] Building upon this foundation, data standardization aims to transform the cleaned map strings data into a unified format or representation. Its purpose is to eliminate data heterogeneity, enabling strings with the same semantics but different expressions to be processed uniformly, thus laying the foundation for efficient compression encoding. For example, data standardization can include converting all strings to lowercase or uppercase, standardizing date or time formats, unifying numerical representations to a specific precision, or mapping synonyms or near-synonyms to a unified, standardized vocabulary. This can be achieved through predefined mapping rule tables or semantic analysis-based normalization algorithms.

[0053] Subsequently, compression encoding aims to encode the standardized map strings data using specific compression algorithms to reduce its storage footprint. The goal is to minimize the size of the optimized BIOS configuration file while ensuring data integrity, thereby accelerating loading speed and reducing storage costs. Various mature compression algorithms can be employed, such as LZ77, LZ78, Huffman coding, Run-Length Encoding (RLE), or more advanced algorithms like Zlib and Brotli. These algorithms convert the original data into a more compact binary representation by identifying recurring patterns, counting character frequencies, or using dictionary encoding.

[0054] In this embodiment, before compressing the dynamic map strings data, data cleaning is performed first. This effectively removes redundant, erroneous, or inconsistent information from the data, ensuring its purity. Subsequently, data standardization is applied to unify the cleaned data into a standardized format, further eliminating heterogeneity and ensuring that strings with the same semantics have a consistent expression. This preprocessing improves the data's regularity and compressibility. Based on this, compression encoding is then performed, which more efficiently identifies and utilizes repetitive patterns and structural features in the data, thereby achieving a higher compression ratio and generating more compact compressed map strings data. This not only significantly reduces the amount of data in the optimized BIOS configuration file, lowering storage and transmission overhead, but also improves the efficiency and accuracy of subsequent index building and configuration file generation, ensuring the optimization effect of the BIOS configuration file and system performance.

[0055] In one feasible implementation, the step of generating compressed map strings data through compression encoding based on the standardized map strings data includes: performing text feature analysis on the standardized map strings data to generate text feature analysis result data; determining the compression algorithm type data through algorithm selection based on the text feature analysis result data; generating compression parameter data through parameter configuration based on the compression algorithm type data; performing block compression on the standardized map strings data based on the compression algorithm type data and the compression parameter data to generate a compressed data block set data; and performing data encapsulation on the compressed data block set data to generate compressed map strings data.

[0056] In this embodiment, text feature analysis is performed on the standardized map strings data to generate text feature analysis result data. This step aims to gain a deeper understanding of the inherent characteristics of the standardized map strings data, providing a basis for subsequent compression algorithm selection and parameter configuration. Specifically, text feature analysis processing may include, but is not limited to: statistical character frequency, word frequency, N-gram analysis, entropy calculation, duplicate substring detection, and data type identification. For example, the system can analyze whether there are a large number of repeated string fragments in the data, or whether it contains structured information with specific patterns (such as XML or JSON format substrings). Through these analyses, text feature analysis result data can be generated, which can quantitatively describe the compressibility, repeatability, and structure of the map strings data.

[0057] In this embodiment, after obtaining the text feature analysis results, the compression algorithm type is determined based on these results through algorithm selection processing. The algorithm selection process intelligently determines the most suitable compression algorithm for the current data based on these features. This method can preset multiple compression algorithms. For example, for highly repetitive data, dictionary-based LZ series algorithms (such as DEFLATE and GZIP) can be selected; for low-entropy data, statistical coding algorithms (such as Huffman coding and arithmetic coding) can be selected; or customized coding can be used for specific structured data. The algorithm selection process can be based on predefined rule sets, decision trees, or machine learning models to ensure that the selected compression algorithm type can maximize compression efficiency or meet specific performance requirements.

[0058] In this embodiment, once the compression algorithm type data is determined, compression parameter data is generated based on the compression algorithm type data through parameter configuration processing. Parameter configuration processing further optimizes the specific operating parameters of the algorithm. These parameters are crucial to the compression effect. For example, for the DEFLATE algorithm, different compression levels can be configured (e.g., from 1 to 9, representing a trade-off between speed and compression ratio); for dictionary encoding, the dictionary size or window size can be adjusted. Parameter configuration processing can dynamically adjust based on text feature analysis results and preset performance targets (e.g., prioritizing compression ratio, prioritizing decompression speed, or balancing both), thereby generating optimal compression parameter data.

[0059] In this embodiment, to improve compression efficiency and data access flexibility, the standardized map strings data are subjected to block compression processing based on the compression algorithm type data and the compression parameter data, generating a set of compressed data blocks. Block compression processing can employ strategies such as fixed-size blocks, content boundary-based blocks, or dynamically adjusted block sizes. Each data block will be independently compressed using the aforementioned determined compression algorithm type data and compression parameter data. This block processing method not only supports parallel compression, shortening processing time, but also allows for the decompression of specific data blocks as needed during subsequent decompression, avoiding complete decompression of the entire compressed data, thereby improving data access efficiency.

[0060] In this embodiment, the compressed data block set is then encapsulated to generate compressed mapstrings data. The encapsulation process integrates all the compressed data blocks and necessary metadata to form the final compressed map strings data. Encapsulation may include adding metadata to the header of the compressed data, such as the overall compression algorithm type, global compression parameters, the starting offset and size of each data block, and the specific algorithm and parameters that may be used for each data block (if different from the global settings). This encapsulation ensures the integrity and parsability of the compressed map strings data and provides the necessary information for subsequent decompression and index construction.

[0061] In this embodiment, by performing text feature analysis on standardized map strings data, the system can intelligently identify the inherent characteristics of the data and adaptively select the most suitable compression algorithm and optimize its parameters. This dynamically adjusted compression strategy, compared to using a single fixed algorithm, can improve the compression ratio, reduce storage space usage, and optimize compression and decompression speeds, thereby improving overall system performance. Block compression further enhances processing efficiency and data access flexibility, allowing for on-demand decompression and reducing resource consumption. Furthermore, data encapsulation ensures the integrity and parsability of the compressed data. Combined with the aforementioned data cleaning and standardization processes, this solution ensures the quality of the input data, enabling subsequent text feature analysis and adaptive compression to be more accurate and efficient, thus generating more optimized BIOS configuration file data.

[0062] In one feasible implementation, the step of generating index structure data through index construction processing based on the compressed map strings data includes: generating index structure type data through index structure determination processing based on the compressed map strings data; generating index mapping relationship data through index mapping construction processing based on the compressed map strings data and the index structure type data; and generating index structure data through index optimization processing based on the index mapping relationship data.

[0063] In this embodiment, based on compressed map strings data, an index structure type is generated through an index structure determination process. The aim is to select the most suitable index structure type based on the specific characteristics of the compressed map strings data and the expected query patterns. For example, if the compressed map strings data is large and fast, accurate searching is required, a hash table or B-tree can be considered as the index structure type; if prefix matching or range queries are required, a Trie tree or suffix tree can be considered. This process can analyze the metadata of the compressed map strings data, such as total size, average string length, and number of unique strings, and recommend or automatically select a suitable index type based on a preset rule set or through intelligent algorithms to ensure the index's adaptability and efficiency.

[0064] Building upon this foundation, based on compressed map strings data and index structure type data, index mapping relationship data is generated through index mapping construction. This process establishes a mapping relationship from internal identifiers to the actual string positions or offsets in the compressed map strings data, according to the determined index structure type. During construction, it may be necessary to pre-parse or partially decompress the compressed map strings data to extract the internal identifiers and their corresponding string content or logical position information within the compressed data. For example, if a hash table is chosen as the index structure type, the internal identifier is used as the key, and the string's starting offset and length in the compressed data are used as values, stored in the hash table; if a B-tree is chosen, a B-tree structure is constructed, with the internal identifier used as the index key, pointing to the string data. This process ensures that each internal identifier can be quickly mapped to its corresponding compressed string data, thereby achieving efficient location.

[0065] Furthermore, based on the index mapping relationship data, index structure data is generated through index optimization processing. This processing aims to further optimize the constructed index mapping relationship to improve query performance, reduce storage space, or adapt to specific hardware / software environments. Optimization strategies may include locality optimization of index data to make it more compact in memory and reduce cache misses; index balancing to ensure uniform query path length and avoid performance degradation in extreme cases; index compression to reduce its storage overhead; or adjustments based on actual query patterns, such as preloading or specially marking frequently queried index items. In addition, index persistence strategies can be considered to ensure that the index can be quickly recovered after a system restart, thus forming the final index structure data.

[0066] In this embodiment, the above technical solution effectively solves the problem of low retrieval efficiency for compressed map strings data. First, through index structure determination processing, the most suitable index type is selected based on the characteristics of the compressed data, ensuring the index's adaptability and efficiency. Second, the index mapping construction process establishes a direct association between internal identifiers and the location of compressed string data, avoiding linear scanning of the entire compressed data. Finally, index optimization processing further improves the index's query performance and storage efficiency. Overall, this solution shortens the lookup time for map strings in the BIOS configuration file, reduces system resource consumption, and thus improves the loading and processing speed of the BIOS configuration file. Its performance advantage is particularly pronounced in scenarios requiring frequent access and updates to configuration information.

[0067] In one feasible implementation, the step of generating index mapping relationship data through index mapping construction based on the compressed map strings data and the index structure type data includes: decompressing and preprocessing the compressed map strings data to generate parsable map strings data; parsing the parsable map strings data to extract internal identifier set data and string position information data; generating index container data through index container creation based on the index structure type data; and storing the internal identifier set data as key data and the string position information data as value data in the index container data to generate index mapping relationship data.

[0068] In this embodiment, the compressed map strings data undergoes decompression preprocessing to generate parsable map strings data. This step aims to restore the compressed map strings data to a readable or parsable state. Since compressed data is typically stored in a compact binary format, it cannot be directly analyzed for content recognition and structure; therefore, it needs to be restored using a suitable decompression algorithm. The specific decompression method can be determined based on the algorithm used in the previous compression process. For example, if general compression algorithms such as LZ77, Huffman coding, or RLE are used, then their reverse decompression process is employed. The result of the decompression preprocessing is the generation of parsable map strings data. This data is typically a raw, uncompressed collection of strings, or an intermediate format, but it already possesses a clear structure, facilitating subsequent parsing operations.

[0069] In this embodiment, after obtaining the parsable map strings data, the parsable map strings data is parsed to extract internal identifier set data and string position information data. This step is responsible for identifying and extracting the key information required to build the index. Internal identifiers are symbols or codes used in BIOS configuration to uniquely identify specific configuration items, while string position information refers to the starting offset or length of the string corresponding to the identifier in the data stream. Parsing can be implemented using various techniques, such as scanning the parsable map strings data through predefined syntax rules, regular expression matching, delimiter parsing, or a custom parser. For example, if the map strings data is organized in the form of "ID=Value", the parser will identify "ID" as the internal identifier and record the position of "Value" or the entire "ID=Value" entry in the data. Finally, this step outputs internal identifier set data (e.g., a list or array) and the corresponding string position information data.

[0070] In this embodiment, index container data is subsequently generated through an index container creation process based on the index structure type data. This step initializes a container suitable for storing index mapping relationships according to the predetermined index structure type data. The index structure type data may indicate various index implementation methods, such as hash tables, B-trees, Trie trees, or skip lists. Depending on the selected type, the system allocates corresponding memory space and initializes the data structure. For example, if the index structure type data is specified as a hash table, an empty hash table instance is created; if it is specified as a B-tree, an empty B-tree root node is initialized. The index container data is the skeleton structure used to carry subsequent index mapping relationships.

[0071] In this embodiment, the internal identifier set data is finally stored as key data and the string position information data as value data in the index container data to generate index mapping relationship data. This is the core step in constructing the index mapping. In this step, the internal identifier set data obtained from the previous parsing is used as the "key" of the index, and the corresponding string position information data is used as the "value" of the index. The system traverses the internal identifier set data and the string position information data, and inserts each key-value pair into the created index container data. For example, in a hash table, each internal identifier is hashed and stored in a specific location, associated with its string position information. In this way, a direct mapping relationship is established between the internal identifier and its actual storage location. Finally, the filled index container data constitutes the index mapping relationship data, which enables quick location of the corresponding string data through the internal identifier.

[0072] In this embodiment, the above technical solution effectively solves the problem of difficulty in directly extracting index information when constructing index mapping relationships due to the compression of map strings data. First, the compressed map strings data is decompressed and preprocessed to generate parsable map strings data, ensuring the accuracy and efficiency of subsequent parsing. Then, the parsable map strings data is parsed to accurately extract the internal identifier set data and string position information data, providing reliable raw data for index construction. Based on this, suitable index container data is created based on the index structure type data, and the extracted internal identifier set data is used as key data, while the string position information data is used as value data, thereby efficiently and accurately constructing the index mapping relationship data. This step-by-step processing method avoids complex and inefficient lookup and parsing operations directly on compressed data, improving the efficiency and accuracy of index construction and providing a solid foundation for subsequent rapid retrieval and access to map strings in BIOS configuration information. Combined with the above index construction process, it ensures that even with compressed data, an efficient and usable index structure can be established, thereby optimizing the processing performance of BIOS configuration files and improving the system's access speed to configuration information.

[0073] In one feasible implementation, the step of generating optimized BIOS configuration file data through configuration file generation processing based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data in the original BIOS configuration information data includes: generating integrated configuration data through data integration processing based on the dynamic map strings data, the compressed map strings data, and the index structure data; generating standard format BIOS configuration data through format conversion processing based on the integrated configuration data; generating version tag data through version tagging processing based on the standard format BIOS configuration data and the original BIOS version data in the original BIOS configuration information data; and generating optimized BIOS configuration file data through merging processing based on the standard format BIOS configuration data and the version tag data.

[0074] In this embodiment, during data integration processing, dynamic map strings, compressed map strings, and index structure data are logically associated and unified. This may include creating a unified data structure, such as a hierarchical data object, an internal database structure, or a serialized data packet, to ensure that the relationship between the original strings, their compressed forms, and the corresponding index lookup information is maintained. For example, each dynamic map string can be associated with its position / offset in its corresponding compressed data block and its entry in the index structure, thereby forming a logically complete and traceable dataset. This step aims to provide a structured and consistent data source for subsequent formatting operations.

[0075] In this embodiment, based on the integrated configuration data, standard-format BIOS configuration data is then generated through format conversion processing. This step aims to convert the integrated data into a specific standard format that the target BIOS system can recognize and parse. This standard format can be a binary format defined by the BIOS vendor, a UEFI variable format, or other text formats such as INI, XML, JSON, etc., depending on the architecture and specifications of the target BIOS. The format conversion process ensures that all data elements, including map strings, compressed data, index information, and any related metadata, are structured according to the BIOS's expected input specifications, such as the correct use of delimiters, data type encoding, and field order. This may involve data serialization, byte packing, or specific text encoding processes.

[0076] Based on this, version tagging data is generated from the original BIOS version data in the standard format BIOS configuration data and the raw BIOS configuration information data through version tagging processing. BIOS configuration files typically need to contain version information to ensure compatibility and correct loading with different BIOS firmware versions. Version tagging processing embeds the raw BIOS version data into the standard format BIOS configuration data. This can be achieved by adding a dedicated version header, a specific metadata field, or by calculating a checksum / hash value containing version information in the configuration file. The generated version tagging data ensures that the final configuration file carries its associated BIOS version information, effectively avoiding compatibility issues that may result from version mismatches.

[0077] In this embodiment, optimized BIOS configuration file data is generated through a merging process based on standard-format BIOS configuration data and version tag data. This final step integrates and encapsulates all necessary components—standard-format BIOS configuration data and version tag data—to form a complete, deployable, optimized BIOS configuration file. The merging process ensures that all information is correctly assembled into a single file, conforming to the BIOS loading mechanism and file structure requirements. The output is a complete, optimized, and version-aware BIOS configuration file that can be directly used for the deployment and use of the BIOS system.

[0078] In this embodiment, through the above technical solution, this application provides a systematic and reliable mechanism for generating optimized BIOS configuration files. First, through data integration processing, dynamic map strings data, compressed map strings data, and index structure data are logically associated and unified, solving the problem of coordination between different data sources and laying the foundation for subsequent formatting. Second, format conversion processing ensures that the integrated data can adapt to the standard format required by the target BIOS system, avoiding loading failures due to format incompatibility. Third, version marking processing embeds the original BIOS version information into the configuration data, giving the generated configuration file a clear version identifier and effectively preventing compatibility issues caused by version mismatch. Finally, through merging processing, all necessary information is encapsulated into a complete optimized BIOS configuration file, ensuring the integrity, availability, and deployability of the configuration file. This step-by-step and refined generation process improves the reliability and compatibility of the optimized BIOS configuration file, ensuring that the BIOS system can correctly and efficiently load and use this optimized configuration information, thereby improving system startup speed and overall performance.

[0079] In the embodiments of this application, the method for optimizing the processing of map strings in the BIOS configuration file effectively solves the problems of configuration file size expansion, processing latency and low retrieval efficiency caused by static storage mode by dynamically generating map string data, compression processing, index building and configuration file generation. It can reduce the configuration file size, improve processing efficiency, reduce maintenance costs and optimize system startup speed.

[0080] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method of optimizing the map strings processing in the BIOS configuration file. Any simple modifications based on this technical concept are within the protection scope of this application.

[0081] This application also provides a system for optimizing map strings processing in BIOS configuration files, see reference. Figure 2The system for optimizing map strings processing in the BIOS configuration file includes: a memory 10, a processor 20, and a program for optimizing map strings processing in the BIOS configuration file stored on the memory 10 and executable on the processor 20. The program for optimizing map strings processing in the BIOS configuration file is configured to implement the steps of the method for optimizing map strings processing in the BIOS configuration file.

[0082] The system for optimizing map strings processing in BIOS configuration files provided in this application, employing the method described in the above embodiments, can improve processing efficiency, reduce maintenance costs, and optimize system startup speed. Compared with the prior art, the beneficial effects of the system for optimizing map strings processing in BIOS configuration files provided in this application are the same as those of the method described in the above embodiments, and other technical features of the system for optimizing map strings processing in BIOS configuration files are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0083] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0084] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method for optimizing map string processing in BIOS configuration files, characterized in that, The method includes: Obtain raw BIOS configuration information data, which includes raw data of internal identifiers, raw data of configuration options, raw data of user language settings, and raw data of BIOS version. Based on the original BIOS configuration information data, dynamic map strings data are generated through dynamic generation processing. Based on the dynamic map strings data, compressed map strings data is generated through compression processing; Based on the compressed map strings data, an index structure data is generated through index building processing; Based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data in the original BIOS configuration information data, optimized BIOS configuration file data is generated through configuration file generation processing.

2. The method for optimizing map strings processing in BIOS configuration files as described in claim 1, characterized in that, Based on the original BIOS configuration information data, the steps for generating dynamic map strings data through dynamic generation processing include: Based on the original BIOS configuration information data, configuration data structure data is generated through configuration data structure creation and processing. Based on the configuration data structure, version adaptation rule data is generated through version adaptation rule generation processing; Based on the configuration data structure and the version adaptation rules, dynamic map strings data are generated through string generation processing.

3. The method for optimizing map strings processing in BIOS configuration files as described in claim 2, characterized in that, Based on the original BIOS configuration information data, the steps for generating configuration data structure data through configuration data structure creation and processing include: Extract internal identifiers from the original BIOS configuration information data; Extract configuration option data from the original BIOS configuration information data; Extract user language settings data from the original BIOS configuration information data; Data is extracted based on the internal identifier, the configuration options, and the user language settings, and then processed through data structure construction to generate configuration data structure data.

4. The method for optimizing map strings processing in BIOS configuration files as described in claim 2, characterized in that, Based on the configuration data structure and the version adaptation rule data, the steps for generating dynamic mapstrings data through string generation processing include: Obtain internal identifier mapping relationship data from the configuration data structure data; Based on the version adaptation rule data, template data is generated by generating strings through template generation processing. Data is extracted based on the user's language settings, and language string library data is obtained from the configuration data structure. Based on the internal identifier mapping relationship data and the string generation template data, raw string data is generated through raw string generation processing; Based on the language string library data, the original string data is processed for language conversion to generate dynamic mapstrings data.

5. The method for optimizing map strings processing in BIOS configuration files as described in claim 1, characterized in that, The steps for generating compressed map strings data based on the dynamic map strings data include: Based on the dynamic map strings data, cleaned map strings data is generated through data cleaning processing. Based on the cleaned map strings data, standardized map strings data are generated through data standardization processing. Based on the standardized map strings data, compressed map strings data is generated through compression encoding.

6. The method for optimizing map strings processing in BIOS configuration files as described in claim 5, characterized in that, Based on the standardized map strings data, the steps for generating compressed map strings data through compression encoding include: The standardized map strings data are subjected to text feature analysis to generate text feature analysis result data; Based on the text feature analysis results, the compression algorithm type is determined through algorithm selection processing. Based on the compression algorithm type data, compression parameter data is generated through parameter configuration processing; Based on the compression algorithm type data and the compression parameter data, the standardized map strings data are subjected to block compression processing to generate a compressed data block set data; The compressed data block set is encapsulated to generate compressed map strings data.

7. The method for optimizing map strings processing in BIOS configuration files as described in claim 1, characterized in that, Based on the compressed map strings data, the steps for generating index structure data through index building include: Based on the compressed map strings data, the index structure is determined and processed to generate index structure type data; Based on the compressed map strings data and the index structure type data, index mapping relationship data is generated through index mapping construction processing; Based on the index mapping relationship data, index structure data is generated through index optimization processing.

8. The method for optimizing map strings processing in BIOS configuration files as described in claim 7, characterized in that, Based on the compressed map strings data and the index structure type data, the steps for generating index mapping relationship data through index mapping construction include: The compressed map strings data is decompressed and preprocessed to generate parsable map strings data; The parsable map strings data is parsed to extract the internal identifier set data and string position information data; Based on the index structure type data, index container data is generated through index container creation and processing. The internal identifier set data is used as key data, and the string position information data is used as value data. These are stored in the index container data to generate index mapping relationship data.

9. The method for optimizing map strings processing in BIOS configuration files as described in claim 1, characterized in that, Based on the dynamic map strings data, the compressed map strings data, the index structure data, and the original BIOS version data in the original BIOS configuration information data, the steps for generating optimized BIOS configuration file data through configuration file generation processing include: Based on the dynamic map strings data, the compressed map strings data, and the index structure data, integrated configuration data is generated through data integration processing. Based on the integrated configuration data, standard format BIOS configuration data is generated through format conversion processing; Based on the standard format BIOS configuration data and the original BIOS version data in the original BIOS configuration information data, version tag data is generated through version tagging processing; Based on the standard format BIOS configuration data and the version tag data, optimized BIOS configuration file data is generated through merging processing.

10. A system for optimizing map string processing in BIOS configuration files, characterized in that, The system for optimizing map strings processing in the BIOS configuration file includes: a memory, a processor, and a program for optimizing map strings processing in the BIOS configuration file stored on the memory and executable on the processor, wherein the program for optimizing map strings processing in the BIOS configuration file is configured to implement the steps of the method for optimizing map strings processing in the BIOS configuration file as described in any one of claims 1 to 9.

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