Spatiotemporal Vector AI for Engineering Graphics and Thematic Documents
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Solution Overview
Problem
Current artificial intelligence technologies primarily focus on textual and raster data formats, failing to effectively process and generate vector graphics and thematic graphics-text documents in human engineering applications, which require spatiotemporal data processing and analysis.
Innovation Solution
A mutually generative artificial intelligence system utilizing a multi-dimensional spatiotemporal large model that integrates text, speech, images, and vector graphics, embedded with engineering knowledge, to automatically process and generate multi-dimensional vector graphics and thematic graphics-text documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current AI technology processes text and raster data formats, then semantic understanding and reasoning capacities are improved, but the ability to process and generate vector graphics and thematic graphics-text documents deteriorates
Solution Approach 1:
The patent applies universality by designing a multi-modal large model that can process multiple data types including text, raster images, and vector graphics within a single system. The model integrates different processing capabilities to handle diverse engineering data formats, enabling one system to perform multiple functions rather than requiring separate specialized systems for each data type.
Solution Approach 2:
The patent addresses the limitation by introducing a new dimension of processing - vector graphics processing capability - to the existing AI system. This involves adding spatial coordinate processing and geometric transformation capabilities that operate in a different dimensional space compared to traditional text or raster image processing, enabling the model to understand and generate vector-based engineering drawings and diagrams.
2Productivity
If GIS achieves partial automatic processing through topological spatial relationship, then processing efficiency is improved, but the ability to achieve intelligent processing deteriorates due to lack of spatiotemporal relationship description
Solution Approach 1:
The patent applies feedback by creating a bidirectional generation system where vector graphics can be automatically processed to generate thematic documents, and conversely, thematic documents can be processed to generate or update vector graphics. This closed-loop feedback mechanism enables the system to learn from the relationship between graphical representations and their textual descriptions, progressively improving intelligent processing capabilities while maintaining high efficiency.
3Manufacturing precision
If drawing of vector graphics and processing of thematic graphics-text documents is done manually, then processing accuracy is improved, but labor intensity and time consumption deteriorate
Solution Approach 1:
The patent applies preliminary action by pre-training the multi-modal large model on extensive datasets containing vector graphics, thematic documents, and their corresponding relationships. The model is pre-equipped with knowledge of engineering drawing standards, spatial relationships, and document structures, enabling it to perform accurate automatic generation and processing without requiring manual intervention during actual operation, thus maintaining high accuracy while dramatically reducing time consumption.
Data Source
AI summary
Disclosed is a mutually generative artificial intelligence system based on multi-dimensional spatiotemporal information vector graphics, relating to the field of artificial intelligence and engineering applications. Based on a geographic information system (GIS) or a computer-aided design (CAD) platform and a data source, a multi-dimensional vector spatiotemporal large model terminal, a multi-dimensional spatiotemporal information processing agent terminal, and an intelligent information system application terminal are constructed. For multi-dimensional spatiotemporal data such as two-dimensional and three-dimensional vectors and temporal states, a multimodal spatiotemporal large model having an understanding capacity for an engineering professional knowledge system, a data processing flow, multi-dimensional vector graphics, and thematic graphics-text documents is pre-trained to achieve the mutual expression and generation of engineering multi-dimensional vector graphics and thematic graphics-text documents and to form an intelligent engineering graphic data processing application.