Micro-service Data Segmentation for Parallel Processing
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Solution Overview
Problem
Current data processing methods in AI technologies, such as deep learning and computer vision, face challenges in efficiently handling large volumes of diverse data types, particularly in scenarios like image and video processing, where data segmentation and parallel processing are not effectively integrated to enhance accuracy and efficiency.
Innovation Solution
The method involves using a first micro-service for data segmentation to obtain sub-data types and multiple second micro-services for processing these sub-data types, each corresponding to specific algorithmic tasks, and then combining the results to achieve a comprehensive data processing outcome, thereby improving adaptability and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing methods are used for large volumes of diverse data types, then processing capability is limited, but system complexity increases when attempting to handle multiple data types
Solution Approach 1:
The patent segments the data processing system into multiple independent micro-services, where each micro-service is responsible for processing a specific type of sub-data. This segmentation allows the system to handle diverse data types efficiently without increasing overall system complexity, as each micro-service can be developed, deployed, and scaled independently.
Solution Approach 2:
The patent creates a universal data processing framework that can handle multiple types of data through a common architecture. The first micro-service universally segments various data types (images, videos, audio, text) into standardized sub-data types, which are then processed by specialized second micro-services, providing both universality in data intake and specialization in processing.
2Measurement precision
If data segmentation and parallel processing are integrated, then processing accuracy and efficiency improve, but implementation complexity increases
Solution Approach 1:
The patent implements segmentation by dividing input data into different sub-data types (image data, video data, audio data, text data) through the first micro-service. Each segmented sub-data type is then processed in parallel by dedicated second micro-services, improving processing accuracy through specialized handling while maintaining manageable implementation complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary data structure and communication mechanism between the first micro-service and multiple second micro-services. This intermediary layer standardizes data flow and coordination, enabling parallel processing without requiring complex direct integration between all components, thus improving accuracy while controlling implementation complexity.
3Adaptability or versatility
If multiple micro-services are used for processing different sub-data types, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent designs a universal first micro-service that can segment various input data types into standardized sub-data types, providing adaptability to handle diverse data formats. This universal component works in conjunction with specialized second micro-services, achieving both adaptability in data intake and specialized processing capability without proportionally increasing system complexity.
Solution Approach 2:
The patent implements a dynamic system where the first micro-service can adaptively segment different types of input data based on the specific data received, and the system can dynamically route different sub-data types to appropriate second micro-services. This dynamic adaptability allows the system to handle diverse data types efficiently without requiring a fixed, complex structure for every possible data type.
Data Source
AI summary
A data processing method, an electronic equipment, and a storage medium. The method includes: acquiring target data; performing a data segmentation on the target data by using a first micro-service to obtain a plurality of types of sub-data; using a plurality of second micro-services to process the plurality of types of sub-data to obtain a plurality of data processing results respectively corresponding to the plurality of second micro-services, in which each second micro-service corresponds to one type of sub-data; combining the plurality of data processing results to obtain a target data processing result.