Dynamic Data Conversion Plugin Generator for Sender Resource Reduction
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Solution Overview
Problem
Existing data processing systems face challenges in efficiently converting raw data from various sources with different data structures into a target format, especially when the sender lacks the computational resources to adapt the data.
Innovation Solution
A data processing device is designed with an input interface to extract raw data and data structure information, a generator to select source code generation plugins for specific data types, and a converter to execute the generated conversion plugins, thereby converting the raw data to a target format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data are recorded and transmitted as raw data without conversion at the sender, then the computational resources required at the sender are reduced, but the receiver must interpret complex data structures which increases the receiver's processing burden
Solution Approach 1:
Instead of converting data at the sender before transmission, the invention inverts the approach by transmitting raw data without conversion and performing the conversion at the receiver side. The receiver dynamically generates conversion routines based on received data structure information, eliminating the need for complex preprocessing at the sender while maintaining data integrity.
Solution Approach 2:
The receiver serves itself by automatically generating conversion routines from received data structure information without requiring manual programming or pre-configured conversion logic. The system extracts data structure information from the raw data, uses it to generate appropriate conversion code, and executes the conversion automatically, making the receiver self-sufficient in handling various data formats.
2Manufacturing precision
If manual conversion routines are programmed for each data structure, then data conversion accuracy is improved, but the effort and time required to maintain and update conversion routines increases
Solution Approach 1:
The system performs preliminary extraction of data structure information from the raw data before conversion. By analyzing the data structure information in advance and using it to generate conversion routines dynamically, the system ensures accurate conversion without requiring manual programming for each data structure variant.
Solution Approach 2:
The invention changes the parameter of conversion routine creation from static manual programming to dynamic generation based on data structure parameters. The receiver extracts data structure information and uses it to generate appropriate conversion routines automatically, allowing the system to adapt to different data structures without manual intervention while maintaining conversion accuracy.
3Reliability
If conversion routines are created for each specific data structure, then conversion reliability is improved, but the adaptability to rapidly changing data structures deteriorates
Solution Approach 1:
The system transitions from static conversion routines to dynamic generation of conversion routines. The receiver extracts data structure information from incoming data and dynamically generates conversion routines based on the actual data structure encountered, allowing the system to adapt reliably to changing data structures without requiring pre-programmed routines for each variant.
Solution Approach 2:
The invention introduces data structure information as an intermediary between the raw data and the conversion process. This intermediary contains the necessary structural information that enables the receiver to generate appropriate conversion routines dynamically, ensuring both reliability through structured information and adaptability to changing data formats.
4Adaptability or versatility
If source code generation plugins are provided for all data types, then conversion capability for all data types is improved, but the complexity and resource requirements of the generator increase
Solution Approach 1:
The generator is designed with universal source code generation plugins that can handle multiple data types. Instead of creating separate conversion routines for each data type, the plugins provide multi-functional capabilities to generate conversion code for various data types using common patterns and templates, reducing generator complexity while maintaining broad conversion capability.
Solution Approach 2:
The conversion system is segmented into modular source code generation plugins, each responsible for specific data type conversions. These modular plugins can be selectively applied based on the data type encountered, allowing the generator to handle all data types without requiring a single complex conversion routine, thus reducing overall system complexity.
Data Source
AI summary
A data processing device. The data processing device includes: an input interface configured to receive input data and extract raw data and information about a data structure according to which the raw data are structured; a generator configured to, for at least one data type used by the data structure, select a source code generation plugin, assigned to the data type, generate, using the selected source code generation plugin, conversion source code for converting the data type to a target format, combine the conversion source codes for converting the data types to the target format to form conversion source code for converting the data structure to the target format, and generate a conversion plugin for converting the data structure to the target format; and a converter configured to execute the conversion plugin and thus to convert the raw data to the target format.