DeepSeek, as a general large model independently developed by my country, is reshaping the field of industrial automation with a strong influence, especially on the work mode, skill requirements and career development path of PLC (programmable logic controller) engineers. Combining the development trend of technology with the actual application of the industry, its impact is mainly reflected in the following key aspects:
1. Significant improvement in efficiency and complete innovation in working methods：
Programming efficiency has achieved a leap-forward growth: DeepSeek uses natural language processing and deep learning algorithms to quickly generate PLC code based on the parameter requirements entered by engineers. For example, it used to take several hours to write complex robot arm control function blocks (FBs). Now, with the help of DeepSeek, optimized code can be generated in just 1 minute, and some details are even better than manual writing. This change frees engineers from the tedious code debugging work, so that they can devote more energy to system design and process optimization.
Give the system powerful dynamic decision-making capabilities: Traditional PLCs can only execute pre-set programs, while DeepSeek gives it the ability to adapt in real time. In the mixed-line production scenario of automobiles, the PLC equipped with DeepSeek can autonomously optimize the robot's motion path, shorten the changeover time by 40%, and increase production capacity by 30%. The role of engineers has also changed from a simple "logic executor" to a "strategy designer", with more emphasis on algorithm tuning and scenario adaptation.
Intelligent predictive maintenance: DeepSeek can predict faults in advance and generate corresponding maintenance plans by analyzing the vibration, temperature and other data of the equipment. After a petrochemical enterprise applied it, the accuracy of pump failure prediction was as high as 92%, and unplanned downtime was reduced by 70%. This requires engineers to master data analysis tools and transform from passive maintenance to active health management.
2. Structural upgrade of skill requirements：
Compound capabilities become core competitiveness: basic programming requirements have decreased, while understanding of AI algorithms, data science, and multimodal fusion has become crucial. Foxconn requires engineers to master deep reinforcement learning (DRL) algorithms to optimize robot arm scheduling. Traditional electrical engineers need to transform into compound talents of "AI+industry" and have the ability to integrate cross-domain knowledge.
Strengthen natural language interaction and system design capabilities: DeepSeek supports natural language code generation. For example, the ABB platform can directly convert instructions into ST code, shortening the development cycle by 45%. Engineers need to describe requirements more accurately and lead the system architecture design. At the same time, they also need to be familiar with new technologies such as digital twins and edge computing to build a globally coordinated production system.
Quickly learn and adapt to new tool chains: DeepSeek's open source models and protocol adaptation middleware (such as Profibus and EtherCAT) reduce the difficulty of cross-brand equipment integration, but engineers need to master the configuration and optimization of tool chains. For example, after the Siemens S7-1500 PLC integrates the lightweight model, it needs to reduce the localized debugging inference delay to less than 500 microseconds.
3. Chain reaction in the industry ecology:
Redistribution of industrial chain value: PLC manufacturers and AI companies have launched in-depth cooperation, such as Siemens and DeepSeek, and system integrators have transformed into intelligent solution providers, promoting the birth of new unicorn companies in the industrial software track.
Changes in the education and training system: Colleges and training institutions have accelerated the opening of "AI + Industrial Automation" courses, emphasizing mathematical foundations and interdisciplinary practice. A professor at Tsinghua University pointed out that "mastering deep learning algorithms and hardware collaborative optimization is a compulsory course for future engineers."
Facing data security and ethical challenges: The application of DeepSeek has exacerbated the risk of industrial data flow. Engineers need to be familiar with encryption technology and permission management mechanisms to ensure the security of production data during transmission and storage.
4. Future Outlook and Response Strategies:
Actively embrace technology iteration: Engineers need to continue to learn AI tool chains, such as DeepSeek's model distillation technology, participate in open source community contributions, and improve their own technical sensitivity. Demonstrate optimization algorithms through GitHub or participate in international top conference papers to enhance professional competitiveness.
Focus on high-value links: Focus on areas with strong irreplaceability such as demand analysis, system architecture design, and AI model tuning. Lead the construction of digital twin libraries or participate in the development of industry-specific models, such as high-precision visual inspection models for the electronics manufacturing industry.
Build a cross-border collaborative network: Form a collaborative team with data scientists and algorithm engineers to jointly solve complex industrial problems. In dynamic control optimization, engineers provide process knowledge, and AI experts design reinforcement learning algorithms to achieve a 18% reduction in slab crack rate.