Method for generating telemetry data processing information configuration file and application, computer device and storage medium

Abstract

The application relates to the technical field of space telemetry data processing, and particularly discloses a telemetry data processing information configuration file generation method and application, computer equipment and a storage medium. The method comprises the following steps: acquiring an input data source and a pre-constructed file generation engine template, and connecting the input data source to the template; the input data source is a data file containing telemetry parameter information, and the file generation engine template is used for defining a rule of extracting telemetry parameter information from the input data source and a format rule of outputting a configuration file; according to the extraction rule defined in the template, the telemetry parameter information is extracted from the input data source, and the extracted telemetry parameter information is constructed into structured telemetry metadata; and according to the output format rule defined in the template, the structured telemetry metadata is converted into a telemetry data processing information configuration file and output. Thus, the telemetry data processing information configuration file can be automatically generated, manual intervention is greatly reduced, and the delivery cycle is greatly shortened.

Description

Technical Field

[0001] This application relates to the field of aerospace telemetry data processing technology, and in particular to a method and application for generating telemetry data processing information configuration files, computer equipment, and storage media. Background Technology

[0002] Telemetry data processing is an indispensable and crucial component in the flight tests of launch vehicles, satellites, and various spacecraft. Spacecraft generate massive amounts of telemetry data during flight, which ground-based telemetry and control systems need to receive, parse, process, and display to monitor the spacecraft's flight status and operational health in real time. The configuration of telemetry data processing information—that is, establishing the mapping relationship between telemetry data frame structures and physical parameters—is the prerequisite and foundation for the operation of telemetry data processing software.

[0003] Currently, telemetry data processing software from traditional aerospace research institutes and commercial rocket companies generally adopts a customized development model, while some rocket telemetry and control data service units use a general-purpose development model. Regardless of the development model used, the configuration of data processing information for software products heavily relies on manual intervention. Existing technologies rely on documents such as telemetry channel allocation tables and parameter tables, where technicians manually sort out information such as telemetry frame structure, parameter codes, names, units, word width (bit width), byte order, range, and calibration calculation methods.

[0004] The aforementioned prior art has at least the following drawbacks: First, there is a lack of reliability. Telemetry data processing involves the extraction, organization, and input of numerous telemetry parameters. For example, a medium-sized launch vehicle typically contains hundreds or even thousands of telemetry parameters, each requiring the configuration of more than ten information items. This reliance on manual sorting of telemetry frame structures and parameter input results in a large amount of repetitive work and is prone to introducing random errors (such as incorrect parameter code input, channel position misalignment, and unit confusion). These errors directly affect the accuracy of telemetry data processing results and the stability of the software operation.

[0005] Second, the delivery cycle is long. Customized software requires repeated development or code adaptation modifications and debugging for different rocket and satellite projects, with delivery cycles measured in weeks or even months, resulting in a prolonged period of time. While general-purpose software improves code reusability to some extent, the delivery of its data processing information configuration files still takes days or even weeks. Current technologies are insufficient to meet the urgent needs of today's high-density, routine space launch missions for rapid iterative response in data processing.

[0006] Therefore, there is an urgent need for a method that can automatically extract configuration information from telemetry data processing reference files and generate configuration files with one click, so as to reduce manual intervention, shorten delivery cycle, and improve the consistency and reliability of data processing information. Summary of the Invention

[0007] Therefore, it is necessary to provide a method and application for generating telemetry data processing information configuration files, as well as computer equipment, storage media, and program products to address the above problems.

[0008] According to a first aspect of the embodiments of this application, a method for generating a telemetry data processing information configuration file is provided, comprising: The system obtains an input data source and a pre-built file generation engine template, and connects the input data source to the template. The input data source is a data file containing telemetry parameter information. The file generation engine template is used to define the rules for extracting telemetry parameter information from the input data source and the format rules for the output configuration file. Based on the extraction rules defined in the template, telemetry parameter information is extracted from the input data source, and the extracted telemetry parameter information is constructed into structured telemetry metadata. Based on the output format rules defined in the template, the structured telemetry metadata is converted into a telemetry data processing information configuration file and output.

[0009] In one embodiment, the file type of the input data source includes Word documents, Access databases, Excel spreadsheets, Text files, or XML files; the content defined in the file generation engine template includes: input data source file information, source file type, table name, header name, database table name, field name, information identification and parsing method, formatted output style, compliance check principles, output target file, and output type.

[0010] In one embodiment, the step of extracting telemetry parameter information from the input data source and constructing the extracted telemetry parameter information into structured telemetry metadata includes: The processing branch is determined based on the file type of the input data source; If the input data source is a Word document, the Word document processing branch is executed; if the input data source is an Access database or an Excel spreadsheet, the database table processing branch is executed; if the input data source is a text file or an XML file, the formatted text processing branch is executed.

[0011] In one embodiment, the Word document processing branch includes: Index all tables in the input data source, match them according to the table names specified in the template, and select the successfully matched tables to the table index set; Based on the header name specified in the template, identify the telemetry parameter information in the rows or columns of each table, and extract and construct standardized telemetry metadata.

[0012] In one embodiment, the database table processing branch includes: Based on the database table names and field names specified in the template, the corresponding database is matched and parsed from the input Access database or Excel spreadsheet to extract telemetry parameter information and construct standardized telemetry metadata. The formatted text processing branch includes: Based on the parsing rules specified in the template, telemetry parameter information is matched and extracted from the input Text or XML file to construct standardized telemetry metadata.

[0013] In one embodiment, in the step of extracting telemetry parameter information from the input data source and constructing the extracted telemetry parameter information into structured telemetry metadata, the information identification and parsing method specified by the template includes one or more combinations of the following: Table mapping method: Based on the relationship between the header rows of different tables, establish a data mapping between multiple tables, and combine the telemetry parameter association information scattered in different tables into a complete metadata structure; Row information sequential extraction method: Extract telemetry parameter information horizontally row by row according to the arrangement order of data rows in the table, with fixed row intervals; Multi-column selection of row information: Selectively extract telemetry parameter information from a specified column based on the header name from the same row of data in the table.

[0014] In one embodiment, in the step of converting the structured telemetry metadata into a telemetry data processing information configuration file, the telemetry metadata is first stored as structured intermediate data according to the formatted output style specified in the template, and then the intermediate data is used to generate the configuration file according to the output pointing file and output type specified in the template.

[0015] According to a second aspect of the embodiments of this application, a method for generating telemetry data processing information configuration files is provided for updating existing telemetry parameter calibration coefficients, including: The Excel parameter calibration table containing the updated calibration coefficients is used as the input data source, and the existing telemetry data processing information configuration file is used as the data source to be updated. Based on the name or code of the parameter in the Excel parameter calibration table, perform a fuzzy search and match in the existing telemetry data processing information configuration file to locate the position of the configuration row of the corresponding parameter and obtain the configuration primary key value of the parameter. Based on the primary key value, locate the calibration information line corresponding to the parameter in the existing telemetry data processing information configuration file, and extract the original calibration coefficient; The original calibration coefficients are compared with the calibration coefficients of the corresponding parameters in the Excel parameter calibration table. Based on the difference in magnitude between the two, the calibration coefficients in the Excel table are adaptively transformed so that the transformed calibration coefficients match the magnitude of the numerical values ​​in the existing telemetry data processing information configuration file. Replace the corresponding original calibration coefficients in the existing telemetry data processing information configuration file with the calibration coefficients after adaptive value transformation to generate an updated telemetry data processing information configuration file.

[0016] According to a third aspect of the embodiments of this application, a computer device is provided, comprising: At least one processor; The at least one processor is also connected in communication with a memory, wherein the memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method for generating a telemetry data processing information configuration file as described above, and the application of the above-described generation method in updating existing telemetry parameter calibration coefficients.

[0017] According to a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the method for generating a telemetry data processing information configuration file as described above, and the application of the above-described generation method in updating existing telemetry parameter calibration coefficients.

[0018] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the method for generating a telemetry data processing information configuration file as described above, and the application of the generation method as described above in updating existing telemetry parameter calibration coefficients.

[0019] The telemetry data processing information configuration file generation method provided in this application allows the file generation engine to automatically extract telemetry parameter information and construct metadata based on a template, thereby automatically generating the telemetry data processing information configuration file. This significantly reduces manual intervention, effectively improves the processing efficiency of the configuration file, greatly shortens the delivery cycle, and significantly reduces the error rate, fundamentally eliminating random errors introduced by human negligence and ensuring the consistency of the final generated configuration file with the input data source. Furthermore, it offers high reusability; the constructed file generation engine template can be adapted to the data processing information configuration needs of different rocket and satellite projects, fully meeting the rapid iterative response requirements of high-density, routine aerospace launch data processing. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for generating a telemetry data processing information configuration file according to an embodiment of this application; Figure 2 A partial flowchart of step S400 in the method for generating a telemetry data processing information configuration file provided in an embodiment of this application; Figure 3 A partial flowchart of step S420 in the method for generating a telemetry data processing information configuration file provided in an embodiment of this application; Figure 4 A flowchart illustrating the application of a method for generating telemetry data processing information configuration files, provided in another embodiment of this application, in updating existing telemetry parameter calibration coefficients. Detailed Implementation

[0021] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application.

[0022] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0023] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] Reference Figure 1 In one embodiment, a method for generating a telemetry data processing information configuration file is provided, specifically including the following steps: Step S200: Obtain the input data source and the pre-built file generation engine template, and connect the input data source to the template. The input data source is a data file containing telemetry parameter information. The file generation engine template is used to define the rules for extracting telemetry parameter information from the input data source and the format rules for the output configuration file.

[0026] The input data source is a data file containing telemetry parameter information. This data file can be a telemetry system flight test outline, a telemetry data processing requirements and methods document, a telemetry channel allocation table, a parameter calibration data table, etc. Users can store the above data files in a storage path accessible to the file generation engine and specify the file path in the engine's operation interface.

[0027] In this embodiment, the file types of the input data source can include Word documents, Access databases, Excel spreadsheets, text files, or XML files. These file types cover the most common document and data formats in the field of telemetry data processing. According to GJB2238B-2019 "Telemetry Data Processing," telemetry data processing documents and data are usually stored electronically, and their format may vary depending on the document management standards of different units and projects. For example, telemetry system flight test outlines and data processing requirements and methods are usually in Word document (.doc / .docx) format, containing a large amount of text description and tabular data; telemetry parameter calibration data are usually stored in Excel spreadsheet (.xls / .xlsx) format for easy numerical calculation and tabular management; telemetry data communication procedures or formatted data are sometimes provided in text (.txt) or XML (.xml) format; some units manage telemetry parameter information by storing it in Access databases (.mdb / .accdb).

[0028] This application supports the above-mentioned multiple file types, which can adapt to the format differences of input data sources for different users and projects, and has strong versatility and practicality.

[0029] The document generation engine template is a set of rules pre-built by the user. It acts as a "configuration manual," informing the engine what information to extract from the input data source, how to identify that information, and the format of the final generated configuration file. Users can create or modify templates according to their actual needs using the engine's template editing function.

[0030] Once the engine obtains the input data source and the file to generate the engine template, it can first load and parse the template, reading the various rules defined in the template into memory to provide a foundation for subsequent automated data processing.

[0031] Step S400: Based on the extraction rules defined in the template, extract telemetry parameter information from the input data source, and construct the extracted telemetry parameter information into structured telemetry metadata.

[0032] Once the template is loaded, the engine can begin processing the input data source according to the extraction rules defined in the template. The extraction rules include at least location rules and identification rules. Location rules determine the specific location of the data to be extracted within the input data source (e.g., a table in a Word document, a worksheet in an Excel file). Identification rules define how to identify specific telemetry parameter information items from the located data block (e.g., which column in a table is the "parameter code," which is the "unit," etc.).

[0033] The engine iterates through the input data source according to the above rules, extracting all telemetry parameter information that conforms to the rules, and organizing the extraction results into telemetry metadata in a structured form. "Structured telemetry metadata" refers to reorganizing raw, scattered, and inconsistently formatted telemetry parameter information according to a unified data model, giving it standardized field definitions and data types for easier subsequent formatted output. For example, the original data source might record information in the form of "Parameter Code: P101, Unit: ℃". After structuring, this information can be uniformly organized into a structured data object like {ParameterCode:"P101", Unit:"℃"}.

[0034] Step S600: Based on the output format rules defined in the template, convert the structured telemetry metadata into a telemetry data processing information configuration file and output it.

[0035] Once all telemetry parameter information has been extracted and constructed into structured telemetry metadata, the engine enters the output stage. The engine can read the output format rules defined in the template, which specify how the configuration file is presented, including but not limited to: the file encoding format, the arrangement of data entries, the order and delimiters of each field, and fixed content in the file header and footer.

[0036] Following the above rules, the engine converts the structured telemetry metadata into corresponding entries in the configuration file, generating a complete telemetry data processing information configuration file, and saves it to the output path specified in the template. This provides users with a configuration file that can be directly loaded and used by telemetry data processing software.

[0037] The telemetry data processing information configuration file generation method provided in this application allows the file generation engine to automatically extract telemetry parameter information and construct metadata based on a template, thereby automatically generating the telemetry data processing information configuration file. This significantly reduces manual intervention, effectively improves the processing efficiency of the configuration file, greatly shortens the delivery cycle, and significantly reduces the error rate, fundamentally eliminating random errors introduced by human negligence and ensuring the consistency of the final generated configuration file with the input data source. Furthermore, it offers high reusability; the constructed file generation engine template can be adapted to the data processing information configuration needs of different rocket and satellite projects, fully meeting the rapid iterative response requirements of high-density, routine aerospace launch data processing.

[0038] In one embodiment, the content defined in the file generation engine template includes: input data source file information, source file type, table name, header name, database table name, field name, information identification and parsing method, formatted output style, compliance check principles, output pointing file and output type.

[0039] The input data source file information includes the name and storage path of the data source file to be processed, which the engine uses to locate and open the data source file. The source file type is the file format of the input data source (Word, Access, Excel, Text, XML, etc.), and the engine uses this information to call the corresponding processing branch. Defining table names and header names allows the template to specify the target table name (e.g., "Main Frame Channel Allocation Table") and the header names of each column in the table (e.g., "Parameter Name," "Code," "Byte Length," etc.) when the input data source is a document containing tables (such as Word or Excel). The engine can locate the target table by matching the table name and then locate the target column by matching the header name. Defining database table names and field names allows the template to specify the target data table name and target field name when the input data source is a database file. Defining the information recognition and parsing method specifies the specific method used to extract information from the input data source, such as table mapping, sequential row information extraction, or multi-column row information extraction. Defining the formatted output style specifies the format of the output configuration file, such as a text "section-line-key" style. Defining compliance check principles specifies the rules for validating the extracted data, such as routine logical checks. Defining the output file and output type specifies the save path and file format for the output configuration file.

[0040] In a preferred embodiment of this application, the template can be stored in a structured text format (such as JSON or XML), and users can create and modify it visually through the template editor provided by the engine, which reduces the technical threshold for template writing.

[0041] Reference Figure 2 In one embodiment, step S400, namely the step of extracting telemetry parameter information from the input data source and constructing structured telemetry metadata from the extracted telemetry parameter information, may include: Step S410: Determine the processing branch based on the file type of the input data source; Step S420: If the input data source is a Word document, execute the Word document processing branch; if the input data source is an Access database or an Excel spreadsheet, execute the database table processing branch; if the input data source is a Text file or an XML file, execute the formatted text processing branch.

[0042] In other words, the engine can automatically identify the file type by reading the file extension and header information of the input data source, and call the corresponding processing branch for subsequent processing based on the identification result. This allows the engine to adopt the optimal parsing method for the characteristics of different file formats, ensuring both processing efficiency and accuracy of information extraction.

[0043] Reference Figure 3 In one embodiment, the Word document processing branch includes: Step S421: Index all tables in the input data source, match them according to the table names specified in the template, and select the successfully matched tables to the table index set; Step S422: Based on the header name specified in the template, identify the telemetry parameter information in the rows or columns of each table, extract and construct standardized telemetry metadata.

[0044] When the input data source is determined to be a Word document: First, the engine performs a full-text scan of the Word document, indexing all tables contained within. Tables in Word documents are the most common form of carrying telemetry parameter information; for example, telemetry channel allocation tables and parameter summary tables are usually presented in Word table format. Second, the engine matches the name of each indexed table with the table names specified in the template one by one. Tables that match successfully are selected into the table index set as targets for subsequent information extraction. Finally, for each table in the table index set, based on the header name (or column position) specified in the template, the engine identifies the telemetry parameter information items represented by each column in the table (such as parameter name column, code column, unit column, etc.), and then reads the content of the corresponding cell in each data row, thereby extracting complete telemetry parameter information and constructing it into structured telemetry metadata.

[0045] In one embodiment, the database table processing branch includes: matching and parsing the corresponding database from the input Access database or Excel spreadsheet based on the database table name and field name specified in the template, in order to extract telemetry parameter information and construct standardized telemetry metadata.

[0046] When the input data source is an Access database or an Excel spreadsheet, the engine's processing method is relatively straightforward because database tables and Excel spreadsheets have clear table structures and field definitions. Specifically, the engine can locate the target data table in the data source based on the database table name specified in the template; then, based on the field names specified in the template, it extracts the data content of the corresponding fields from that data table; finally, it constructs the extracted telemetry parameter information into structured telemetry metadata.

[0047] For Excel spreadsheets, the engine can treat each worksheet as a data table for processing; for Access databases, the engine can directly manipulate the data tables within them.

[0048] In one embodiment, the formatted text processing branch includes: matching and extracting telemetry parameter information from the input Text or XML file according to the parsing rules specified in the template, and constructing standardized telemetry metadata.

[0049] When the input data source is a text file or an XML file, because these files lack the structured organization of tables or databases, the engine needs to process them according to the parsing rules defined in the template. For text files, parsing rules typically use regular expressions or keyword location methods, such as locating and extracting information based on fixed line formats, delimiters (e.g., commas, tabs), or keyword identifiers (e.g., "parameter code:"). For XML files, parsing rules can use XPath or similar data query languages, locating and extracting information by specifying node paths in the XML document. Based on these parsing rules, the engine can match and extract telemetry parameter information from the text or XML file, constructing structured telemetry metadata.

[0050] In one embodiment, in step S400, which involves extracting telemetry parameter information from the input data source and constructing structured telemetry metadata from the extracted telemetry parameter information, the information identification and parsing method specified by the template includes one or more combinations of the following: Table mapping method: Based on the relationship between the header rows of different tables, establish a data mapping between multiple tables, and combine the telemetry parameter association information scattered in different tables into a complete metadata structure; Row information sequential extraction method: Extract telemetry parameter information horizontally row by row according to the arrangement order of data rows in the table, with fixed row intervals; Multi-column selection of row information: Selectively extract telemetry parameter information from a specified column based on the header name from the same row of data in the table.

[0051] Table mapping is suitable for scenarios where telemetry parameter information is scattered across multiple tables and needs to be combined through the relationships between these tables. In actual telemetry data processing documentation, a complete set of telemetry parameter information is often not concentrated in a single table, but rather distributed across multiple hierarchical tables. For example, a "Main Frame Channel Allocation Table" describes the overall structure of a telemetry data frame, with some fields pointing to the "GNSS Frame Data Summary Table" and the "1553B Station Address Allocation Table," the latter of which further points to the more detailed "Message Parameter Table." The core of table mapping lies in establishing a data mapping between multiple tables based on the relationships between their header rows or key fields. The engine can first identify the correspondence between the main table and the sub-tables (e.g., through the same key fields such as "Frame Identifier" or "Message ID"), and then combine each record in the main table with the associated records in the sub-tables to form a metadata structure containing complete information. Through table mapping, the engine can integrate telemetry parameter association information scattered across different tables into a complete metadata structure, avoiding data fragmentation and incompleteness caused by information dispersion.

[0052] The row information sequential extraction method is suitable for scenarios where telemetry parameter information is arranged in a table with fixed row intervals. In some telemetry parameter tables, data rows are not arranged continuously, but are interspersed with separator rows, header rows, or blank rows between valid data rows. For example, there may be a separator row between every two rows of data, or a comment row after each row of data. The principle of the row information sequential extraction method is: the engine follows the physical arrangement of the data rows in the table, starting from the first valid data row below the table header, and traverses downwards row by row at the row intervals specified in the template (e.g., every other row), extracting telemetry parameter information horizontally from each column of each valid data row. The row interval value is predefined by the extraction rules in the template, and users can flexibly configure it according to the actual table format.

[0053] The row information multi-column extraction method is suitable for scenarios where a table contains multiple columns of information, but only a subset of those columns needs to be extracted. In real-world data tables, a single table may contain multiple categories of information columns, such as "parameter name," "code," "byte length," "parameter type," "proportion coefficient," and "remarks." Users may only need to extract the "parameter name," "code," and "byte length" columns, ignoring the others. The principle behind the row information multi-column extraction method is as follows: the engine locates the target columns in the first row (header row) of the table based on the header names specified in the template. Then, for each data row, it extracts only the cell content from these target columns, ignoring other columns. In this way, the engine achieves on-demand extraction of table information, improving processing efficiency while avoiding interference from irrelevant information.

[0054] In practical applications, the three methods described above can be combined depending on the specific format of the data source. For example, in a specific example, the extraction of main frame channel table parameters uses the row information sequential extraction method, the extraction of sub-frame parameters and bus message parameters uses the row information multi-column selection method, and the construction of the overall data structure uses the table mapping method.

[0055] After the telemetry metadata is built, before outputting the configuration file, a compliance check can be performed on the built telemetry metadata. Specifically, the engine can read the compliance check principles defined in the template and verify each item of the structured telemetry metadata. The specific verification content includes, but is not limited to: Data integrity check: Determines whether all necessary fields are completely filled in for each telemetry parameter. For example, does each parameter have core information such as parameter name, parameter code, and byte length? If a parameter is missing a necessary field, the engine marks it as "data missing".

[0056] Format correctness check: Determines whether the information format conforms to specifications. For example, whether the byte length is a reasonable value such as 1, 2, 4, or 8, and whether the parameter code follows naming conventions. For data with incorrect format, the engine marks it as "format incorrect".

[0057] Logical consistency check: This checks whether the information for the same parameter is consistent across different tables or locations. For example, whether the name and code of a parameter are consistent in the main frame channel allocation table and the parameter summary table. Inconsistent information is flagged as a "logical anomaly" by the engine.

[0058] For all the above-mentioned validation anomalies, the engine can generate corresponding alarm messages, clearly informing the user of the anomaly type, location, and content, for manual review and correction. Alarm messages can be displayed as a list in the engine interface or marked as annotations in the output file.

[0059] In one embodiment, in step S600, which is to convert the structured telemetry metadata into a telemetry data processing information configuration file, the telemetry metadata is first stored as structured intermediate data according to the formatted output style specified in the template, and then the intermediate data is used to generate the configuration file according to the output pointing file and output type specified in the template.

[0060] Specifically, the engine can read the formatted output style defined in the template and organize the structured telemetry metadata into structured intermediate data with a specified format according to that style. The formatted output style includes, but is not limited to, the field order of each metadata entry, the delimiters between fields, the indentation method of the hierarchical structure, and the data block division method. In a specific example, the formatted output style is a "section-line-key" text style, meaning the top-level data is divided into "sections," each section contains several "lines," and each line consists of several "key-value" pairs. Afterward, the engine can read the output file and output type specified in the template and write the structured intermediate data to a file at the specified path, generating a configuration file that can be directly loaded by telemetry data processing software.

[0061] In one embodiment, after outputting the configuration file, its validity can be verified. First, the engine can load the output configuration file into the telemetry data processing software (or the engine's built-in simulation parsing module). Second, a simulated telemetry data stream is input into the data processing software. This simulated data stream can be test data generated according to the telemetry data frame structure standard, containing various typical telemetry parameter values, and its expected parsing results are known. Next, the data processing software can be run to parse the simulated data stream using the configuration file and view the parsing results. Assuming the generated configuration file correctly defines the frame structure, the accurate location of each telemetry parameter can be extracted from the simulated data stream, and the parsed value of each telemetry parameter matches the expected value. Based on this, the configuration file can be verified. If the verification passes, the configuration file is correct and valid and can be used. If the verification fails, there is an error in the configuration file, requiring the error location to be located and the configuration file to be iteratively modified. After modification, closed-loop verification is performed again until verification passes.

[0062] By performing closed-loop verification of the configuration file, it can be ensured that the final delivered configuration file meets the usage requirements in terms of both correctness and completeness, further improving the reliability of the configuration information.

[0063] In one embodiment, the method for generating telemetry data processing information configuration files provided in this embodiment further includes a process visualization and orchestration step, that is, users can edit and orchestrate the engine's workflow in a visual manner.

[0064] Specifically, the engine provides a graphical workflow orchestration interface, which consists of two parts: a workflow orchestration visualization area and an operation object area. The operation object area lists all available functional nodes, such as template loading nodes, data source access nodes, information identification and extraction nodes, metadata construction nodes, compliance check nodes, and document output nodes. Users can drag and drop the required functional nodes from the operation object area to the workflow orchestration visualization area, arranging them sequentially according to actual needs to form a complete workflow diagram. Users can also set specific parameter values ​​for nodes by clicking on them.

[0065] In addition, one embodiment also provides an Optical Character Recognition (OCR) preprocessing function. In actual aerospace telemetry data processing, some historical data or data provided by external units may only exist in paper or photocopy form (such as PDF scans), the content of which cannot be directly recognized and processed by a computer. To address this, before inputting the data into the document generation engine template, the engine can first call the OCR module to scan and recognize the paper or photocopy documents, converting the text content and table structure into editable electronic files (such as Word documents or Excel spreadsheets). The converted electronic files can then be input into the constructed document generation engine template for subsequent data processing. This expands the range of input data sources and increases the application scope of this application.

[0066] Based on the same inventive concept, and referring to Figure 4 In another embodiment, a method for generating telemetry data processing information configuration files is also provided for updating existing telemetry parameter calibration coefficients, including: Step S100': Use the Excel parameter calibration table containing the updated calibration coefficients as the input data source, and use the existing telemetry data processing information configuration file as the data source to be updated.

[0067] In telemetry data processing, parameter calibration coefficients (including sensor slope, sensor intercept, transducer slope, transducer intercept, etc.) need to be updated periodically or as required by the task. For example, after the onboard measurement device is recalibrated, new calibration coefficient data will be generated. The traditional update method is to manually search and modify the parameter coefficients one by one in the configuration file, which is inefficient and prone to omissions. In this application, an Excel parameter calibration table containing updated calibration coefficients can be obtained. This table includes at least the parameter name (or code) and the corresponding updated calibration coefficient value. At the same time, an existing telemetry data processing information configuration file (i.e., the configuration file to be updated) can be obtained. Then, the obtained Excel parameter calibration table and the existing telemetry data processing information configuration file are input into the engine.

[0068] Step S300': Based on the name or code of the parameter in the Excel parameter calibration table, perform a fuzzy search and match in the existing telemetry data processing information configuration file to locate the position of the configuration row of the corresponding parameter and obtain the configuration primary key value of the parameter.

[0069] The engine can target each parameter in the Excel parameter calibration table, searching for it in existing configuration files using its parameter name or code. Because the naming of the same parameter may vary slightly between different files (e.g., "P_101" vs. "P101", "temperature_1" vs. "temperature1"), the engine uses a fuzzy search algorithm for matching, rather than requiring exact character consistency, to avoid matching failures caused by differences in naming rules. When a match is successful, the engine locates the parameter's configuration line position in the configuration file and obtains its configuration primary key value (i.e., the parameter's unique identifier in the configuration file).

[0070] Step S500': Based on the primary key value, locate the calibration information row corresponding to the parameter in the existing telemetry data processing information configuration file, and extract the original calibration coefficient.

[0071] Based on the obtained primary key value, the engine can further locate the corresponding calibration information line in the configuration file. Calibration information typically includes sensor calibration information lines and converter calibration information lines, recording the sensor slope and intercept, and the converter slope and intercept, respectively. The engine can extract the original calibration coefficient values ​​from these lines.

[0072] Step S700': Compare the original calibration coefficient with the calibration coefficient of the corresponding parameter in the Excel parameter calibration table, and perform adaptive value transformation on the calibration coefficient in the Excel table according to the difference in magnitude between the two, so that the transformed calibration coefficient matches the numerical magnitude of the existing telemetry data processing information configuration file.

[0073] In practical applications, the magnitude of coefficient values ​​in the Excel parameter calibration table may differ from that in the configuration file. For example, the Excel table might record values ​​as "Ba=0.5," while the corresponding coefficient in the configuration file is "500," a difference of 1000 times. This difference in magnitude stems from different recording habits in different documents or systems (such as unit conversions, scaling factors, etc.), but if manual processing forgets to perform these conversions, it will lead to serious errors in the data parsing results. To address this, this application can perform adaptive magnitude transformation. Specifically, the engine compares the original coefficient values ​​extracted from the configuration file with the corresponding coefficient values ​​in the Excel table, calculating the magnitude ratio between the two (e.g., if the original coefficient is 500 and the Excel coefficient is 0.5, the magnitude ratio is 1000). Then, the engine performs adaptive multiplication and division operations on the coefficients in the Excel table based on this magnitude ratio, ensuring that the transformed calibration coefficients match the magnitude of the values ​​in the configuration file. After the transformation is complete, standard metadata containing the updated calibration coefficients is generated.

[0074] Step S700': Replace the corresponding original calibration coefficients in the existing telemetry data processing information configuration file with the calibration coefficients after adaptive value transformation, and generate an updated telemetry data processing information configuration file.

[0075] The engine can replace the original calibration coefficient values ​​in the configuration file with the aforementioned adaptively transformed calibration coefficients, while keeping the other content and format of the configuration file unchanged, to generate an updated telemetry data processing information configuration file. During the replacement process, for parameters that fail to match the positioning (e.g., exist in the Excel sheet but cannot be found in the configuration file), or parameters whose differences between the old and new coefficient values ​​exceed a preset threshold, the engine can specially mark the parameter in Excel (e.g., highlight it with a specific color) to prompt technicians for manual review.

[0076] Based on the same inventive concept, in another embodiment, a computer device is provided, including at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for generating telemetry data processing information configuration files and the application of the above-described generation method in updating existing telemetry parameter calibration coefficients.

[0077] The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores various types of data related to the method for generating the telemetry data processing information configuration file described above, as well as the application of this method in updating existing telemetry parameter calibration coefficients. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the method for generating the telemetry data processing information configuration file described above, and its application in updating existing telemetry parameter calibration coefficients.

[0078] Based on the same inventive concept, in another embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described method.

[0079] Based on the same inventive concept, in another embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the method described above.

[0080] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0081] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating a telemetry data processing information configuration file, characterized in that, The generation method includes: The system obtains an input data source and a pre-built file generation engine template, and connects the input data source to the template. The input data source is a data file containing telemetry parameter information. The file generation engine template is used to define the rules for extracting telemetry parameter information from the input data source and the format rules for the output configuration file. Based on the extraction rules defined in the template, telemetry parameter information is extracted from the input data source, and the extracted telemetry parameter information is constructed into structured telemetry metadata. Based on the output format rules defined in the template, the structured telemetry metadata is converted into a telemetry data processing information configuration file and output.

2. The method for generating a telemetry data processing information configuration file according to claim 1, characterized in that, The file types of the input data source include Word documents, Access databases, Excel spreadsheets, Text files, or XML files; the content defined in the file generation engine template includes: input data source file information, source file type, table name, header name, database table name, field name, information recognition and parsing method, formatted output style, compliance check principles, output target file, and output type.

3. The method for generating a telemetry data processing information configuration file according to claim 2, characterized in that, The step of extracting telemetry parameter information from the input data source and constructing the extracted telemetry parameter information into structured telemetry metadata includes: The processing branch is determined based on the file type of the input data source; If the input data source is a Word document, the Word document processing branch is executed; if the input data source is an Access database or an Excel spreadsheet, the database table processing branch is executed; if the input data source is a text file or an XML file, the formatted text processing branch is executed.

4. The method for generating a telemetry data processing information configuration file according to claim 3, characterized in that, The Word document processing branch includes: Index all tables in the input data source, match them according to the table names specified in the template, and select the successfully matched tables to the table index set; Based on the header name specified in the template, identify the telemetry parameter information in the rows or columns of each table, and extract and construct standardized telemetry metadata.

5. The method for generating a telemetry data processing information configuration file according to claim 3, characterized in that, The database table processing branch includes: Based on the database table names and field names specified in the template, the corresponding database is matched and parsed from the input Access database or Excel spreadsheet to extract telemetry parameter information and construct standardized telemetry metadata. The formatted text processing branch includes: Based on the parsing rules specified in the template, telemetry parameter information is matched and extracted from the input Text or XML file to construct standardized telemetry metadata.

6. The method for generating a telemetry data processing information configuration file according to claim 2, characterized in that, In the step of extracting telemetry parameter information from the input data source and constructing the extracted telemetry parameter information into structured telemetry metadata, the information identification and parsing method specified by the template includes one or more combinations of the following: Table mapping method: Based on the relationship between the header rows of different tables, establish a data mapping between multiple tables, and combine the telemetry parameter association information scattered in different tables into a complete metadata structure; Row information sequential extraction method: Extract telemetry parameter information horizontally row by row according to the arrangement order of data rows in the table, with fixed row intervals; Multi-column selection of row information: Selectively extract telemetry parameter information from a specified column based on the header name from the same row of data in the table.

7. The method for generating a telemetry data processing information configuration file according to claim 2, characterized in that, In the step of converting the structured telemetry metadata into a telemetry data processing information configuration file, the telemetry metadata is first stored as structured intermediate data according to the formatted output style specified in the template, and then the intermediate data is used to generate the configuration file according to the output file and output type specified in the template.

8. The application of the method for generating telemetry data processing information configuration files as described in any one of claims 1 to 7 in updating existing telemetry parameter calibration coefficients, characterized in that, include: The Excel parameter calibration table containing the updated calibration coefficients is used as the input data source, and the existing telemetry data processing information configuration file is used as the data source to be updated. Based on the name or code of the parameter in the Excel parameter calibration table, perform a fuzzy search and match in the existing telemetry data processing information configuration file to locate the position of the configuration row of the corresponding parameter and obtain the configuration primary key value of the parameter. Based on the primary key value, locate the calibration information line corresponding to the parameter in the existing telemetry data processing information configuration file, and extract the original calibration coefficient; The original calibration coefficients are compared with the calibration coefficients of the corresponding parameters in the Excel parameter calibration table. Based on the difference in magnitude between the two, the calibration coefficients in the Excel table are adaptively transformed so that the transformed calibration coefficients match the magnitude of the numerical values ​​in the existing telemetry data processing information configuration file. Replace the corresponding original calibration coefficients in the existing telemetry data processing information configuration file with the calibration coefficients after adaptive value transformation to generate an updated telemetry data processing information configuration file.

9. A computer device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method for generating a telemetry data processing information configuration file as claimed in any one of claims 1 to 7 and the application of the generation method as claimed in claim 8 in updating existing telemetry parameter calibration coefficients.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for generating the telemetry data processing information configuration file as described in any one of claims 1 to 7, and the application of the generation method as described in claim 8 in updating existing telemetry parameter calibration coefficients.

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