Research on collaborative optimization of big data analysis and artificial intelligence based on deep learning

Drawing upon deep learning techniques, this study investigates big data analysis methods across various scenarios to enhance data value. The proposed intelligent collection device is capable of harvesting both unstructured data, such as videos and images, and structured data, including geographical coordinates, operational metrics, and electronic device identifiers. Building on this, the paper introduces an architecture for a big data analysis system underpinned by deep learning, elaborating on its critical technologies. Furthermore, it underscores how community and end-to-end power transactions bolster the adaptability and maximize the value of power generation entities. Although Multi-Agent Deep Reinforcement Learning offers a novel approach to managing energy among multiple prosumers, challenges such as environmental volatility, the safeguarding of prosumer privacy, and computational demands persist. This research aims to explore a multi-agent reinforcement learning algorithm that employs parameter sharing and deep deterministic policy gradients to enhance learning efficiency and mitigate training complexities through shared strategies and experiences among agents. Additionally, by leveraging a reputable third party for disseminating comprehensive community market data to prosumers, this approach not only promises to secure prosumer privacy effectively but also to minimize environmental uncertainty and augment the algorithm's scalability.