Synthetic Plant Plan Training Data for Accurate Symbol Recognition
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Solution Overview
Problem
The challenge of achieving high recognition accuracy in digitizing plant plans using supervised machine learning methods is hindered by the lack of a sufficient amount of annotated training data, and existing methods do not guarantee the accuracy of plant plan and symbolic examples.
Innovation Solution
A training data generator that extracts symbols from digital plant diagrams, applies positioning rules, and generates synthetic plant layouts using a random selection of symbols and rules to create a large number of training examples, which are then used to train a trainable image recognition module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning methods are used to digitize plant plans, then recognition accuracy can be improved, but a very large number of annotated training data are required which is often impractical
Solution Approach 1:
The patent creates synthetic copies of plant plan symbols by extracting symbol classes from existing annotated plans and generating new symbolic examples through random selection and positioning rules. This copying approach generates large quantities of training data without requiring manual annotation of each individual plan, thus improving recognition accuracy while avoiding the impractical requirement of collecting vast amounts of real annotated data.
Solution Approach 2:
The patent performs preliminary extraction of symbol classes and positioning rules from existing annotated plant plans before the actual training process. By pre-processing and organizing the training data into structured formats with defined symbol classes and positioning constraints, the system prepares high-quality training materials in advance, reducing the need for extensive manual annotation during the training phase.
2Manufacturing precision
If manual annotation of plant plans is performed to create training data, then training data accuracy can be improved, but time consumption and manual effort increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting symbol classes and positioning rules from existing annotated plans, then generating new training examples without requiring continuous manual intervention. The automated generation process maintains data accuracy through structured rules while eliminating the time-consuming aspect of manual annotation for each training example.
Solution Approach 2:
Instead of manually creating each training example, the system copies and transforms existing symbol classes and positioning patterns into new synthetic training data. This copying mechanism preserves the accuracy characteristics of the original annotated data while rapidly generating additional training examples without proportional increases in manual effort.
3Adaptability or versatility
If diverse symbolic examples are collected from different sources to improve training coverage, then model versatility can be improved, but data consistency and quality control become difficult to guarantee
Solution Approach 1:
The patent applies local quality by defining specific symbol classes and positioning rules for different types of technical objects, devices, and functions in plant plans. Each symbol type has its own class definition and positioning constraints, ensuring that diversity in symbol representation is maintained while quality and consistency are controlled through localized rules for each symbol category.
Solution Approach 2:
The system uses parameter changes by varying the positioning parameters of symbols according to defined rules while maintaining consistent symbol class definitions. This allows diverse training examples to be generated through different positional arrangements and configurations, improving versatility while maintaining data quality consistency through controlled parameter variations.
Data Source
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AI summary
The invention relates to a training data generator comprising an interface designed to read symbols extracted from provided digital system plans for technical systems, wherein each of the system plans represents the design and/or the functionality of a technical system by means of symbols, and the system plans are identical. The training data generator also comprises a storage module which is designed to store the extracted symbols, a selection module which is designed to randomly select a symbol sub-quantity of the stored symbols using a random generator, a generator which is designed to generate at least one synthetic system plan on the basis of the selected symbol sub-quantity, and an output module which is designed to output the at least one synthetic system plan as training data in order to train a trainable image detection module, wherein the trainable image detection module is designed to generate a digital system plan using an analog system plan of a technical system.