Edge AI Rule Filtering for Inspection Data Collection
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Solution Overview
Problem
Existing AI pipelines face challenges in efficiently collecting and uploading inspection data from edge devices to the cloud, due to non-systematic data collection methods and the need for large, complex training datasets that consume significant computational resources.
Innovation Solution
The method involves applying AI rules on edge devices to evaluate and refine the inspection data set, ensuring only data that meets predefined criteria is included, thereby reducing data size and computational requirements for upload and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data augmentation and synthetic data generation techniques are used to enhance the training data set, then the training data size is increased to improve model performance, but the model complexity and computational resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by filtering and selecting data samples before they are used for training. The system pre-evaluates collected data against predefined criteria (such as image quality, relevance to inspection objectives, and completeness) and selects only the most suitable samples for training, avoiding the need to process and store all collected data for training purposes
2Loss of information
If all collected inspection data is uploaded to the cloud for analysis, then comprehensive data availability is maintained, but data transmission time and processing resources are consumed excessively
Solution Approach 1:
The patent extracts only the essential and high-quality data samples from the complete collected data set before upload. The system identifies and selects data samples that meet specific criteria (such as containing relevant inspection information, having sufficient quality, and representing the inspection objectives) and transmits only these extracted samples to the cloud, rather than uploading all collected data
3Adaptability or versatility
If edge devices collect data in a non-systematic way with varying positions and characteristics, then flexibility in data collection is maintained, but data analysis difficulty increases due to inconsistent image characteristics
Solution Approach 1:
The patent applies local quality by evaluating and selecting data samples based on their specific local characteristics and quality attributes. The system assesses individual data samples for properties such as image quality, relevance to inspection objectives, and completeness, and selects only those samples that meet the required local quality standards, thereby maintaining the flexibility of non-systematic collection while ensuring analysis readiness
Data Source
AI summary
A computer-implemented method, according to one approach, includes obtaining, on a first edge device, a plurality of artificial intelligence (AI) rules. The method further includes applying, on the first edge device, the AI rules to a plurality of evaluated data samples for determining whether to include the data samples in an inspection collected data set. In response to a determination that a first of the data samples satisfies each of the AI rules, the first data sample is caused to be included in the inspection collected data set. The inspection collected data set is caused to be uploaded to a cloud site. A computer program product, according to another approach, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a first edge device to cause the first edge device to perform any combination of features of the foregoing methodology.