Meteorological NC structured data extraction and analysis method and device

Abstract

The invention discloses a meteorological NC structured data extraction and analysis method and device. The method comprises the following steps: constructing a feature description file; generating a structured file containing meteorological semantic configuration items, space-time screening conditions and multi-source data compatible configuration; an analysis engine reads the feature description file, converts business terms into technical parameters according to a built-in meteorological semantic dictionary, and identifies an application scene; automatically associating coordinate system information of meteorological data according to space-time screening conditions in the feature description file, and calculating a slice index of target data; calling a bottom layer analysis library to load a target NC file, positioning a target variable in combination with the calculated slice index, and executing data type verification, unit conversion and dimension alignment operations; and outputting an extraction result according to a specified format. According to the method, the extraction rule and the analysis logic are separated, and meteorological data characteristics and service scene self-adaptive extension are combined, so that the problem of weak emergency demand response capability is solved.

Description

Technical Field

[0001] This invention belongs to the field of meteorological big data processing and scientific computing technology, specifically involving a method for extracting and parsing meteorological NC structured data based on feature description files. Background Technology

[0002] In the current meteorological field, structured data files such as NC (NetCDF) are widely used to store meteorological elements (such as temperature, humidity, air pressure, etc.). Their data structure contains multidimensional variables, attribute information and coordinate system, and is characterized by strong professionalism, fixed format but complex variable hierarchy.

[0003] Existing technologies have the following problems when extracting and parsing NC files: 1. Heavy reliance on hard coding: Traditional parsing methods require writing code directly in a programming language (such as Python's netCDF4 library) to specify variable names, dimension indices, etc. If the variables or data types to be extracted change, the source code needs to be modified and recompiled, which is inflexible and especially unfriendly to non-developers (such as meteorological business personnel).

[0004] 2. Low reusability: For different NC files (such as meteorological data from different regions and times) or different extraction requirements (such as adding variables or modifying data types), the parsing logic needs to be developed repeatedly, resulting in code redundancy and high maintenance costs.

[0005] 3. Errors in data type matching: The data types of variables in the NC file (such as float32, int64) need to be manually declared in the code. If they do not match the types in the actual file, it can easily lead to parsing failure or data distortion, especially in multi-source meteorological data fusion scenarios.

[0006] 4. Delayed response to sudden demands: In the event of sudden weather events such as extreme weather, meteorological staff (non-programmers) cannot quickly adjust the data extraction rules and need to wait for developers to intervene and modify the code, resulting in untimely data support.

[0007] Existing technologies tightly couple "extraction rules" (variables, data types, etc. to be extracted) with "parsing logic" (code implementation), failing to separate configuration from logic. Furthermore, they do not design specific solutions that take into account the characteristics of meteorological data and business scenarios, resulting in insufficient adaptability, scalability, and weak response capabilities to sudden demands. Summary of the Invention

[0008] A brief overview of embodiments of the invention is provided below to provide a basic understanding of certain aspects of the invention. It should be understood that this overview is not an exhaustive summary of the invention. It is not intended to identify key or essential parts of the invention, nor is it intended to limit the scope of the invention. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0009] According to one aspect of this application, a method for extracting and parsing structured meteorological NC data is provided, comprising the following steps: A) Construct a feature description file, which is a structured file containing meteorological semantic configuration items, spatiotemporal filtering conditions, and multi-source data compatibility configuration; B) Semantic parsing: The parsing engine reads the feature description file, converts business terms into technical parameters according to the built-in meteorological semantic dictionary, and identifies application scenarios; C) Spatiotemporal index calculation: Based on the spatiotemporal filtering conditions in the feature description file, the coordinate system information of the meteorological data is automatically associated to calculate the slice index of the target data; D) Data extraction and transformation: The underlying parsing library is called and connected to the target NC file. The target variable is located by combining the calculated slice index, and data type validation, unit conversion and dimension alignment are performed. E) Output results: Output the extracted results in the specified format.

[0010] Furthermore, the meteorological semantic configuration item in step A) includes a meteorological_element (meteorological element label) field, which is used to associate variable names with business meaning labels, and a data_scenario (application scenario label) field, which is used to identify application scenarios, including extreme weather or routine forecasts.

[0011] Furthermore, the multi-source data compatibility configuration in step A) includes a `data_source` (data source label) field and a `compatibility_adapter` (multi-source adaptation rule) field. The parsing engine calls the corresponding data type mapping rule based on the `data_source` field and performs dimension alignment based on the preset adaptation rule associated with the `compatibility_adapter` field. The multi-source adaptation rule field is a string identifier used to associate with preset dimension alignment, unit conversion, and resampling rule tables.

[0012] Furthermore, the spatiotemporal filtering conditions in step C) include semantic parameters in the time dimension and / or administrative region matching rules in the spatial dimension. The parsing engine automatically retrieves a preset latitude and longitude boundary library based on the administrative region matching rules to generate a grid index, thereby enabling precise slicing extraction in the spatial dimension without the need for manual input of coordinates.

[0013] Furthermore, the data type verification and conversion process in step D) includes the following steps: The corresponding data type mapping rule is invoked based on the multi-source data compatibility configuration (data_source field); Perform dimension alignment operations for heterogeneous data sources; Perform unit conversion operations to unify the units of physical quantities to standard units (e.g., unify the temperature unit to degrees Celsius (°C) and the air pressure unit to hectopascals (hPa)).

[0014] Furthermore, when outputting the results in step E), if the application scenario is detected as "extreme weather", a data integrity verification identifier is appended to the header of the output file.

[0015] Furthermore, the feature description file can be quickly generated by non-developers using the "quick_config" template.

[0016] According to a second aspect of this application, a meteorological NC structured data extraction and parsing device is provided, comprising: Feature description file generation module: Configured to receive meteorological business requirement parameters input by the user, the parameters including meteorological semantic configuration items, spatiotemporal filtering conditions, and multi-source data compatibility configuration, and generate a structured feature description file containing the following fields: The meteorological_element field (meteorological element label) is used to associate the business meaning label of a meteorological element with the underlying variable name; The data_scenario (application scenario label) field is used to identify the application scenario, which includes extreme weather or regular forecasts; The `data_source` field (data source label) is used to identify the source of meteorological data. The compatibility_adapter field (multi-source adaptation rule) is used to specify the adaptation rule identifier when fusing multi-source data. Time filtering criteria: include preset semantic time periods (such as latest_24h, preset time period specific_time_range) or forecast lead time (forecast_lead_time). spatial_filter (spatial filter criteria): contains an administrative region name field, which is used to indicate the target region range to be automatically matched according to a preset latitude and longitude boundary library; Meteorological element label fields and application scenario label fields constitute meteorological semantic configuration items; data source label fields and multi-source adaptation rule fields constitute multi-source data compatibility configurations; time filtering conditions and spatial filtering conditions constitute spatiotemporal filtering conditions. Parsing engine processing module: Communicatively connected to the feature description file generation module, comprising: Semantic parsing submodule: used to read the feature description file, parse the data_scenario field to determine the current processing priority, and convert the meteorological_element into technical variable extraction instructions; The spatiotemporal index calculation submodule is used to call a preset geographic boundary database based on the administrative region name in the spatial_filter to calculate the corresponding spatial grid index; and to calculate the time dimension slice index based on the time filtering conditions. The data extraction and transformation submodule is used to call the underlying NetCDF library to load the target file, extract the original data according to the spatiotemporal index, and perform data type validation, unit conversion and dimension alignment operations according to the data_source and compatibility_adapter, and finally output structured data results that conform to the specified format.

[0017] Furthermore, the feature description file generation module includes a simplified configuration interface, allowing users to select meteorological elements and time ranges via checkboxes, thereby triggering the generation module to automatically generate a feature description file in a default format.

[0018] Furthermore, the parsing engine processing module also includes a data integrity verification unit. When the data_scenario is detected as "extreme weather", the data integrity verification unit automatically performs data integrity verification (e.g., MD5 checksum) and appends a verification identifier to the header of the output file.

[0019] According to a third aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the above-described method for extracting and parsing meteorological NC structured data.

[0020] This application constructs an architecture combining meteorological-specific feature description files and an intelligent parsing engine, decoupling extraction rules from parsing logic. By combining meteorological-specific feature description files with an intelligent parsing engine, it achieves the extraction and parsing of meteorological NC structured data, thereby solving the technical problems of traditional meteorological data processing, such as reliance on hard-coded scripts, high spatial pruning thresholds, and difficulties in multi-source data fusion.

[0021] Compared to existing technologies, this application introduces a meteorological semantic feature description file. By transforming business requirements into a structured configuration containing meteorological element labels, spatial filter conditions, and compatibility adapter rules, it achieves a shift from "code logic" to "configuration-driven" approaches. Simultaneously, the parsing engine includes a semantic parsing submodule to identify business terms and map them to underlying variable names; a spatiotemporal index calculation submodule that automatically calls a geographic boundary library to generate a grid index based on administrative region names, thus achieving accurate tiling without manual latitude and longitude calculations; and a data extraction and transformation submodule responsible for performing cross-platform data type validation, unit conversion, and dimension alignment operations. Furthermore, this application specifically addresses extreme weather scenarios by automatically attaching data integrity verification flags to the output results, significantly improving response efficiency and data reliability during sudden weather events. Attached Figure Description

[0022] The present invention can be better understood by referring to the description given below in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to denote the same or similar parts. These drawings, together with the following detailed description, are incorporated in and form part of this specification, and are used to further illustrate preferred embodiments of the invention and explain the principles and advantages of the invention. In the drawings: Figure 1 This is a schematic diagram of the feature description file module structure according to an embodiment of the present invention; Figure 2 This is a flowchart of the parsing engine workflow in an embodiment of the present invention. Detailed Implementation

[0023] Embodiments of the present invention will now be described with reference to the accompanying drawings. Elements and features described in one drawing or embodiment of the invention may be combined with elements and features shown in one or more other drawings or embodiments. It should be noted that, for clarity, representations and descriptions of components and processes unrelated to the present invention and known to those skilled in the art have been omitted from the drawings and description.

[0024] This application proposes a method for extracting and parsing structured meteorological NC data using a "feature description file." The core of this method is to decouple the "extraction rules" from the "parsing logic," design a dedicated feature description file specification based on the spatiotemporal characteristics of meteorological data, optimize the parsing engine functionality, and adapt to the operational needs of non-developer users. The specific solution is as follows: I. Technical Architecture The system comprises two core components: a feature description file module and a parsing engine module. The feature description file module incorporates designs specific to the meteorological field, while the parsing engine module is adaptively improved to suit the characteristics of meteorological data. 1. Feature Description File Module: Serving as the dedicated carrier for meteorological data extraction rules, this module is customized based on general configuration files and meteorological operations. It adopts a structured text format (supporting readable formats such as JSON and YAML) and, in addition to basic information, includes the following key meteorological-specific designs: (1) Meteorological semantic configuration items: Built-in exclusive semantic tags for meteorological elements, such as associating variables with business meanings through the "meteorological_element" field (e.g., "temperature" corresponds to the tag "temperature" and "relative_humidity" corresponds to the tag "relative humidity"), supporting the retrieval of variables by business terms; the newly added "data_scenario" field identifies the application scenario (e.g., "extreme_weather" corresponds to extreme weather and "routine_forecast" corresponds to regular forecast), which facilitates the reuse of scenario-based rules.

[0025] (2) Spatiotemporal Dimension Adaptation Design: In view of the strong spatiotemporal characteristics of meteorological data, a standardized spatiotemporal filtering configuration is designed. The time dimension supports semantic parameters such as “latest_24h” (last 24 hours), “specific_time_range” (specific time period), and “forecast_lead_time” (forecast lead time). The spatial dimension supports automatic matching of latitude and longitude range by “administrative_region” (administrative region, such as “Guizhou Province”), without the need to manually input coordinates. At the same time, it is compatible with multiple positioning methods such as “latitude_longitude_range” (latitude and longitude range) and “grid_id” (grid number) to adapt to meteorological data from different sources.

[0026] (3) Multi-source data compatibility configuration: The "data_source" field is added to identify the data source (such as "ECMWF", "CMA" and "GFS"). The type mapping table of mainstream meteorological data formats is built in. The "type_mapping_strategy" field can be used to select automatic matching or manually specify type conversion rules to solve the problem of differences in data types from multiple sources. For heterogeneous data, the "compatibility_adapter" field can be used to associate preset adaptation rules (such as dimension alignment rules for satellite remote sensing and ground observation data).

[0027] (4) Minimalist configuration mode: Provides a simplified template "quick_config". Meteorological personnel can quickly generate feature description files by selecting business elements (such as "temperature and air pressure"), selecting time range (such as "future 12 hours"), and specifying output format (such as "CSV"). No configuration syntax is required, which can adapt to the rapid response of sudden meteorological needs.

[0028] 2. Parsing Engine Module: Receives the feature description file and the NC file to be parsed, makes corresponding improvements to the meteorological-specific design of the feature description file, and performs the following steps: (1) Semantic parsing and rule verification: After reading the feature description file, the built-in meteorological semantic dictionary is used to parse exclusive fields such as “meteorological_element” and “data_scenario” and convert them into technical extraction parameters. At the same time, the rationality of the configuration is verified. For example, the parsing priority is automatically increased for the “extreme_weather” scenario, and the spatiotemporal range is checked to match the target NC file to avoid invalid configuration.

[0029] (2) Automatic calculation of spatiotemporal index: Based on the spatiotemporal filtering conditions in the feature description file, the coordinate system information of the meteorological data is automatically associated to calculate the slice index of the target data. The time dimension quickly locates the timestamp range based on semantic parameters such as “latest_24h”, without the need for full traversal. The spatial dimension automatically generates a grid index by matching the preset latitude and longitude boundary library through the “administrative_region” field, thereby achieving accurate slice extraction and improving parsing efficiency.

[0030] (3) Multi-source data compatibility processing: The corresponding data type mapping rule is called according to the “data_source” field, and the parsing engine automatically completes the data type verification and conversion; for the adaptation rule associated with “compatibility_adapter”, operations such as dimension alignment and unit conversion (such as converting “K” to “℃”) are performed to ensure the consistency of multi-source data fusion.

[0031] (4) Efficient data extraction and output: The target file is loaded by calling the NC file parsing library (such as netCDF4), and the target variable is located by combining the calculated slice index and data extraction is performed. Finally, the data is output in the format specified by the feature description file. At the same time, a data integrity verification mark is automatically added for the "extreme_weather" scenario to ensure the reliability of data in sudden scenarios.

[0032] II. Core Principles By separating "what to extract (meteorological business requirements)" from "how to extract (technical implementation)" through meteorological-specific feature description files, the flexibility of decoupling configuration and logic is preserved, while improvements such as meteorological semantic design and spatiotemporal adaptation differentiate it from general configuration files. The parsing engine achieves efficient parsing of meteorological data through dedicated functions such as semantic conversion and automatic index calculation. Users (including non-developers) can change extraction requirements simply by configuring or editing feature description files without modifying the parsing engine code, achieving "develop once, reuse in multiple scenarios," especially suitable for rapid response to sudden weather demands.

[0033] Example 1: Rapid Extraction of Surface Temperature Data in Guizhou Region under Extreme Rainstorm Scenarios Application scenario: When extreme rainstorms occur, meteorological personnel (non-programmers) need to extract the ground temperature data of the Guizhou region in the past 24 hours within 10 minutes and output it in CSV format for emergency analysis.

[0034] Building the feature description file: Business personnel use the "Quick Configuration for Extreme Weather" template provided by the system, select the element "Temperature", choose the region "Guizhou Province", the time range "Last 24 Hours", and the output format "CSV". The system automatically generates a feature description file, the content of which is as follows: { "data_scenario": "extreme_weather", / / Extreme weather scenario "meteorological_element": [{"name": "temperature", "label": "temperature"}], / / Meteorological elements and operational labels "data_source": "CMA", / / Data source is China Meteorological Administration "time_filter": {"type": "latest_24h"}, / / Time filter: Last 24 hours "spatial_filter": {"type": "administrative_region", "value": "Guizhou Province"}, / / Spatial filter: Guizhou Province "target_type": "float32", / / Target data type "output_format": "CSV", / / Output format "compatibility_adapter": "CMA_NC_v4" / / Adapts to China Meteorological Administration NC file v4 version } The execution flow of the parsing engine: (1) Semantic parsing: After the parsing engine reads the file, it identifies the “extreme_weather” scenario through the meteorological semantic dictionary and automatically increases the processing priority; it associates the “temperature” label with the variable name “temperature”, and “Guizhou Province” matches the preset latitude and longitude range (24.3°N-29.3°N, 103.3°E-109.3°E).

[0035] (2) Spatiotemporal index calculation: The time dimension quickly locates the timestamp range in the NC file based on "latest_24h" and generates a time index; the spatial dimension calculates the corresponding grid index based on the latitude and longitude range to avoid full reading.

[0036] (3) Data extraction and conversion: Call the netCDF4 library to load the target file (such as "guizhou_cma_202509.nc"), locate the "temperature" variable according to the index, verify that the original data type is int16, automatically convert it to float32 according to the rules, and perform unit conversion (from K to ℃).

[0037] (4) Output and verification: The extracted data is output in CSV format, and the file name is automatically appended with the identifier "extreme_weather". At the same time, the data integrity verification result is added to the header of the file so that business personnel can quickly confirm that the data is usable.

[0038] Example 2: Joint Extraction of Temperature and Humidity in Multi-Source Data Fusion Scenarios Application scenario: Meteorological researchers need to integrate NC data from both ECMWF and GFS sources to extract temperature and humidity data for the next 12 hours in North China, ensuring dimensional alignment and unit consistency.

[0039] Construction of feature description files: Add bivariate configuration and multi-source adaptation rules to the basic template. The core content is as follows: { "data_scenario": "routine_forecast", "meteorological_element": [{"name": "temperature", "label": "temperature"},{"name": "rh", "label": "relative humidity"}], "data_source": ["ECMWF", "GFS"], "time_filter": {"type": "forecast_lead_time", "value": 12}, "spatial_filter": {"type": "administrative_region", "value": "North China region"}, "compatibility_adapter": "multi_source_alignment", / / Multi-source data alignment adaptation rules "output_format": "JSON" } When the parsing engine is executed, it automatically calls the "multi_source_alignment" rule to handle the time dimension alignment (unified to the hour level), spatial grid matching (resampled according to the North China regional grid) and unit conversion of ECMWF and GFS data respectively, and finally outputs the fused temperature and humidity data, without requiring R&D personnel to manually handle format differences.

[0040] Existing technologies typically employ a hard-coded dimensional indexing process followed by distributed extraction and output, with configuration files storing only technical parameters such as "variable name and start / end index." This application proposes a two-layer architecture of "meteorological semantics-technology decoupling." Upper layer: Purely operational semantics visible to meteorological personnel, such as "latest_24h, Guizhou Province, extreme weather"; The lower layer: The parsing engine has a built-in meteorological dictionary and scenario strategy (extreme weather priority + verification mark), which automatically generates slice indexes, type mappings, unit conversions, and dimension alignments.

[0041] Furthermore, existing technologies only record "variable names and dimension start and end indexes," still requiring technicians to manually enter the numbers. In contrast, the meteorological semantic feature description file of this application can achieve automatic business-technology translation, for example: meteorological_element: Business tag "temperature" → automatically mapped to temperature; data_scenario: Extreme weather → Automatically increases parsing priority and adds integrity checks; administrative_region: Guizhou Province → Automatically query the latitude and longitude boundary database to generate a grid index.

[0042] Furthermore, the disclosed NetCDF parsing schemes still rely on technicians to specify hard-coded start and end values ​​of dimensions. This application can realize automatic calculation of spatiotemporal index: by using the semantic tag "latest_24h", the time coordinate variable in the NC file is automatically located, and the slice index is calculated in reverse to avoid full reading, which can significantly improve efficiency.

[0043] Furthermore, while existing technologies also mention multi-source data, they do not mention multi-source fusion. This application, however, uses the "type_mapping_strategy + compatibility_adapter" field in the parsing phase of the multi-source adaptation rule field to drive automatic type conversion, dimension alignment, and unit unification, thereby significantly improving the success rate of multi-source data consistency processing.

[0044] In summary, this application upgrades the extraction rules in traditional hard-coded systems to a structured configuration that includes meteorological semantics and spatiotemporal adaptation by constructing a "meteorological-specific feature description file + intelligent parsing engine" architecture. The parsing engine then implements improved functions such as semantic conversion and index calculation. This technical architecture overcomes the limitations of traditional general-purpose configuration schemes while simultaneously addressing the operational barriers for non-developers and the challenges of responding to sudden demands. Compared with existing technologies, it has the following advantages: (1) Significantly improved flexibility and business adaptability: Meteorological business personnel can quickly generate feature description files with a simple template without modifying the source code. Especially in sudden scenarios such as extreme weather, the data extraction rule configuration can be completed within 5 minutes, greatly improving the response speed. Semantic configuration items reduce the operation threshold and achieve seamless connection between business needs and technical implementation.

[0045] (2) Improved parsing efficiency and reliability: The spatiotemporal index is used to automatically calculate and extract accurate slices, avoiding full reading and greatly improving parsing efficiency; the verification and adaptation rules for meteorological data characteristics reduce the risk of data distortion and greatly improve the parsing success rate in multi-source data fusion scenarios.

[0046] (3) Excellent reusability and scalability: The parsing engine is compatible with meteorological NC files from different sources and in different scenarios. The scenario-based templates of the feature description files can be reused, reducing redundant development. When adding meteorological elements or data formats, it is only necessary to extend the semantic tags of the feature description files and the mapping rules of the parsing engine, without reconstructing the core logic.

[0047] (4) Enhanced multi-source data fusion capability: Through dedicated compatibility configuration and automatic conversion function, the problem of differences in data types and dimensions of multi-source meteorological data is solved, providing efficient support for meteorological data fusion analysis.

[0048] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0049] Although the invention has been disclosed above through the description of specific embodiments, it should be understood that all the embodiments and examples described above are exemplary and not restrictive. Those skilled in the art can design various modifications, improvements, or equivalents to the invention within the spirit and scope of the appended claims. These modifications, improvements, or equivalents should also be considered to be included within the protection scope of the invention.

Claims

1. A method for extracting and parsing structured meteorological NC data, characterized in that, Includes the following steps: A) Construct the feature description file: The feature description file is a structured file containing meteorological semantic configuration items, spatiotemporal filtering conditions, and multi-source data compatibility configuration. B) Semantic parsing: The parsing engine reads the feature description file, converts business terms into technical parameters according to the built-in meteorological semantic dictionary, and identifies application scenarios; C) Spatiotemporal index calculation: Based on the spatiotemporal filtering conditions in the feature description file, the coordinate system information of the meteorological data is automatically associated to calculate the slice index of the target data; D) Data extraction and transformation: The underlying parsing library is called to load the target NC file, the target variable is located by combining the calculated slice index, and data type validation, unit conversion and dimension alignment operations are performed; E) Output results: Output the extracted results in the specified format.

2. The method according to claim 1, characterized in that, The meteorological semantic configuration item in step A) includes a meteorological element label field, which is used to associate the business meaning label of meteorological elements with the underlying variable name; and an application scenario label field, which is used to identify the application scenario, wherein the application scenario includes extreme weather or regular forecast. The multi-source data compatibility configuration includes a data source tag field and a multi-source adaptation rule field; the parsing engine calls the corresponding data type mapping rule according to the data source tag field, and performs dimension alignment according to the preset adaptation rule associated with the multi-source adaptation rule field.

3. The method according to claim 1, characterized in that, The spatiotemporal filtering conditions in step C) include administrative region matching rules, and the parsing engine automatically retrieves a preset latitude and longitude boundary library according to the administrative region matching rules to generate a grid index.

4. The method according to claim 1, characterized in that, The data type verification and conversion process in step D) includes the following steps: The corresponding data type mapping rules are invoked based on the multi-source data compatibility configuration. Perform dimension alignment operations for heterogeneous data sources; Perform a unit conversion operation to unify the units of physical quantities to standard units.

5. The method according to claim 1, characterized in that, When outputting the results in step E), if the application scenario is detected as "extreme weather", a data integrity verification identifier is appended to the header of the output file.

6. A device for extracting and parsing meteorological NC structured data, characterized in that, include: Feature description file generation module: Configured to receive meteorological business requirement parameters input by the user, the parameters including meteorological semantic configuration items, spatiotemporal filtering conditions, and multi-source data compatibility configuration, and generate a structured feature description file containing the following fields: Meteorological Element Label Field: Used to associate the business meaning label of meteorological elements with the underlying variable name; The application scenario label field is used to identify the application scenario, which includes extreme weather or regular forecasts; Data source label: Used to identify the source of meteorological data; Multi-source adaptation rule field: Used to specify the adaptation rule identifier when fusing multi-source data; Time filtering criteria: including preset semantic time periods (such as latest_24h, specific_time_range) or forecast lead time (forecast_lead_time). Spatial filtering criteria: Includes an administrative region name field, which indicates the target region range to be automatically matched based on a preset latitude and longitude boundary database; Parsing engine processing module: Communicatively connected to the feature description file generation module, comprising: Semantic parsing submodule: used to read the feature description file, parse the application scenario label field to determine the current processing priority, and convert the meteorological element labels into technical variable extraction instructions; The spatiotemporal index calculation submodule is used to call a preset geographic boundary database based on the administrative region name in the spatial filtering conditions to calculate the corresponding spatial grid index; and to calculate the time dimension slice index based on the time filtering conditions. The data extraction and transformation submodule is used to call the underlying NetCDF library to load the target file, extract the original data according to the spatiotemporal index, and perform data type validation, unit conversion and dimension alignment operations according to the data source tags and multi-source adaptation rules, and finally output structured data results that conform to the specified format.

7. The apparatus according to claim 6, characterized in that, The feature description file generation module further includes a simplified configuration interface, which allows users to select meteorological elements and time ranges through checkboxes, thereby triggering the generation module to automatically generate a feature description file in a default format.

8. The apparatus according to claim 6, characterized in that, The parsing engine processing module also includes a data integrity verification unit. When the application scenario tag "extreme weather" is detected, the data integrity verification unit automatically performs data integrity verification and appends a verification identifier to the header of the output file.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 5.

Images