Recommended by cloud maintenance experts: In the era of Industry 4.0, how do big models reshape the "car manufacturing" model?

1. About the large model

A large model is a deep neural network model trained with massive amounts of data, characterized by a large parameter size and complex computational structure. By continuously training on large datasets, large models can learn rich patterns and features, thus having strong generalization capabilities and making accurate predictions on unknown data. These models are designed to solve a variety of complex tasks, such as natural language processing, computer vision, speech recognition, and recommendation systems.

Compared with previous AI, the most essential advantage of the big model is that "it can continuously optimize the model's internal reasoning logic based on real-time data." The essence of this optimization process is a highly simulated simulation of the evolution of human cognitive level. From this point of view, in specific fields or application scenarios, it is a high probability event for the big model to surpass 80% of humans in the field at the level of causal reasoning. Because the life span of human individuals is limited, the amount of data they can access throughout their lives is also quite limited, and intelligence cannot be efficiently inherited; as a machine, the life span and amount of data can be said to be infinite, and the essence of infinity is the sustainable improvement and complete inheritance of intelligence.

2. Main application scenarios of large models in the field of automobile manufacturing

First, automobile design and R&D: shorten the automobile development cycle: assist designers in generating ideas, conducting design verification and simulation; conduct vehicle dynamics simulation, collision testing, fuel efficiency optimization, etc.

Second, automobile manufacturing: reducing inventory and optimizing production processes: improving production efficiency and quality through production data forecasting. By simulating and optimizing supply chain management, automobile manufacturers can reduce costs, reduce waste, and better respond to market demand fluctuations.

3. The impact of large models on automobile manufacturing mode

01Accelerate innovation and product iteration

Big models can analyze a large amount of vehicle operation data and simulation test results to help engineers optimize vehicle structure and material selection, thereby improving product performance, safety and reliability. For example, through simulation testing and data analysis, manufacturers can identify defects and failure modes of components, make improvements and optimizations early, and improve product quality and life.

In the research and development of new energy vehicles, it can also help manufacturers optimize battery management systems and charging strategies by analyzing a large amount of vehicle operation data and charging data to improve battery performance and life. In addition, large models can also help manufacturers design more efficient electric drive systems and energy recovery systems, thereby improving the range and energy efficiency of new energy vehicles.

02Improve production efficiency and quality

By using large models to optimize production processes and supply chain management, automakers can achieve more efficient production. First, large models can analyze large amounts of production data and supply chain information to help manufacturers optimize production line layout, process flow, and material distribution, thereby improving production efficiency and reducing costs. By simulating and optimizing supply chain management, manufacturers can achieve timely supply of raw materials and reasonable allocation of production plans, reduce inventory backlogs and production stagnation, and improve production line utilization and capacity.

Secondly, big models can help manufacturers achieve quality control. By analyzing vehicle operation data and production process data, manufacturers can promptly detect equipment failures and quality problems on the production line, and take targeted maintenance and improvement measures to avoid production stagnation and product quality problems caused by equipment failures.

03Provide personalized after-sales service

Large models can also help manufacturers provide personalized after-sales services and user experience. By analyzing vehicle operation data and user feedback, it is possible to promptly discover and solve user problems and needs, and provide customized maintenance and service solutions. For example, using large models to analyze vehicle failure data and maintenance records can achieve predictive maintenance and remote diagnosis, promptly discover and solve potential problems, and improve vehicle reliability and user satisfaction.

04Data-driven decision making

The application of big models has improved the mining and utilization of data value in the automobile manufacturing process. Traditional automobile design and engineering decisions often rely on expert experience and physical testing. Big models, with their powerful learning ability and advantages in processing massive amounts of complex data, integrate and analyze the huge data resources accumulated during the long development process, including but not limited to past research results, detailed and rich vehicle parameter data, diverse and comprehensive user behavior and preference data, in-depth market research data, and detailed demand analysis results. With the big model's ability to interpret large-scale survey data, the development team can more accurately predict potential market space and competitive trends, thereby making more forward-looking and competitive product development decisions, and comprehensively improving the overall quality and market competitiveness of automobile products.

The rise of big models marks the official start of the fourth industrial revolution, which has promoted a new wave of the entire artificial intelligence industry. Now all walks of life are exploring the integration with big models. Not only the automotive industry, but also more production companies or equipment manufacturers will continue to move towards an era of deep integration of artificial intelligence, Internet of Things and big data under the wave of Industry 4.0, and realize the next revolutionary digital transformation.