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In 2024, the application of industrial AI in China is gradually penetrating into all aspects of manufacturing, logistics, maintenance, and quality control. Through machine learning and data analysis, AI technology plays an important role in real-time monitoring of production processes, equipment failure prediction, and production process optimization. For example, through sensor data analysis, AI is able to identify potential problems with equipment in advance and perform preventive maintenance, significantly reducing downtime and improving production efficiency.
In logistics and supply chain management, the application of AI has also shown great potential. Intelligent algorithms are able to optimize logistics routes and predict market demand, thereby improving efficiency and responsiveness throughout the supply chain. The application of AI in quality control and inspection, through image recognition and machine vision technology, has greatly improved the accuracy and speed of inspection.
However, issues such as security and reliability, data security and privacy protection, and changes in the structure of labor demand need to be solved urgently. Especially in industrial scenarios, enterprises need to focus on improving the applicability of AI algorithms, building data governance structures, and planning technical strategies and paths.
In terms of technical implementation, data collection strategies, algorithm and model selection, data cleaning, and feature extraction have become key issues in industrial AI applications. According to the survey results, more than 48% of respondents believe that data collection strategy and content definition are prominent issues in AI applications. The selection of suitable algorithms and models was also a key focus of respondents.
Talent training is also the key to the development of industrial AI. The ability to disassemble and architect complex system problems, understand the boundaries of AI tools and algorithms, and standardize software engineering are all key capabilities that AI talents need to pay attention to.
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