Memory Weaver: Smart Factory Anomaly Detector

Inspired by the meticulous visual cataloging in fashion, the narrative of 'I, Robot's' positronic brains struggling with logical anomalies, and Christopher Nolan's 'Memento' with its focus on reconstructing events from fragmented information, 'Memory Weaver' is designed as an individual-friendly, niche, and low-cost smart factory solution.

The core concept is to treat the ideal manufacturing process of a specific product (e.g., a custom-designed widget, a specific textile pattern) as a 'fashion catalog' of perfect states. We'll achieve this by scraping and analyzing high-resolution images or video feeds of the product at various stages of production, creating a detailed visual 'memory' of what constitutes 'correct.' This 'memory' is not just a single image, but a sequence of expected visual characteristics and transformations.

Leveraging the 'I, Robot' theme of understanding complex systems through observation and logic, the system uses simple yet effective image processing techniques. For instance, when a new batch of products is produced, their visual data is captured. This data is then compared against the stored 'memory' from the fashion catalog. The 'Memento' influence comes into play through the focus on identifying discrepancies and deviations – the 'clues' that something has gone wrong. A small scratch, an incorrect color shade, a misplaced component, or a deviation in texture will be treated as an anomaly that breaks the expected sequence.

How it works:
1. Data Scrapping & Cataloging (Fashion Catalog Inspiration): Initially, high-quality images or short video clips of the ideal product at each critical manufacturing step are collected. This forms the 'golden standard' visual database. This can be done by photographing the first perfect batch or by using design blueprints and rendering them visually.
2. Anomaly Detection Engine (I, Robot & Memento Inspiration): Image processing algorithms (e.g., OpenCV) are used to compare the incoming product images against the cataloged 'memory.' Techniques like template matching, color histogram comparison, and edge detection can be employed to identify deviations.
3. Alerting System: When a significant anomaly is detected, the system generates an immediate alert, highlighting the specific deviation and its location on the product. This allows factory floor personnel to quickly identify and rectify issues before they impact a large batch.

Low-Cost Implementation: This project can be implemented using readily available hardware like a Raspberry Pi with a camera, and open-source image processing libraries. The cost is significantly lower than complex industrial vision systems.

Niche Focus: The system can be tailored to very specific product lines or manufacturing processes, making it highly valuable for small to medium-sized businesses with specialized production.

High Earning Potential: By reducing defects, minimizing waste, and improving quality control, 'Memory Weaver' offers substantial cost savings and revenue increases for manufacturers, making it a highly marketable solution.