This article introduces the concept of expert systems based on the bottlenecks in laboratory automation applications and describes the operational situation of laboratories combining expert systems with automation, indicating that expert systems are an important path for current laboratory development.
The full text is approximately 1200 words, and reading it takes about 3 minutes.
Bottlenecks in Laboratory Automation Applications
From the early 20th century to the present, industrial automation has evolved from single-machine automation to production line automation and now to intelligent manufacturing. The popularization of technology and the reduction of costs have laid the foundation for automation in the laboratory field. However, most laboratories are still at the level of single-machine automation, primarily due to the complex experimental processes and diverse experimental types involved in laboratory work, where existing industrial automation products cannot satisfy the requirements for flexibility and complexity. To address these issues, laboratories need more specialized solutions.
Overview of Expert Systems
An expert system is a computer intelligent program system that can solve various complex problems by simulating the analysis process of specific experts in a particular field through reasoning techniques. Different expert systems have varying functions and structures, but generally include six components: human-machine interface, inference engine, rule base and management system, database and management system, knowledge acquisition mechanism, and explanation mechanism.
For laboratories, expert systems primarily handle relevant regulations, standards, and norms, especially in laboratory environments dealing with sensitive data, hazardous substances, or high operational risks. Expert systems use rules, data, and reasoning to ensure that system design and operation meet compliance, safety, and health requirements. Compared to industrial automation systems like DCS (Distributed Control System), which only control equipment processes, expert systems can make automation processes and data analysis decisions tailored to unique industry needs, better addressing specialized laboratory issues.
Expert systems fundamentally change laboratory operations.
Through the integration of expert systems and automation technology, laboratories will operate as follows:
AM8:00
Researchers arrive at the laboratory. They open the interface of the expert system to check today’s experimental plans and tasks. The expert system automatically processes logistics paths and delivery plans based on the laboratory’s real-time operational status, ensuring that the materials required for experiments arrive on time.
Starting the Experiment
Researchers begin the execution of the experiment based on the experimental plan automatically designed by the expert system. During the experiment, the expert system monitors data and parameters in real-time and automatically adjusts according to preset rules, ensuring the stability and accuracy of the experimental process.
PM2:00
Researchers finish the experiment and upload the experimental data to the expert system. The expert system automatically analyzes the experimental data and provides conclusions and suggestions based on domain knowledge. Researchers adjust and optimize the experiment based on these conclusions and suggestions.
PM4:00
The expert system automatically generates the experimental report and organizes the knowledge generated during the experiment. Researchers can easily access and utilize this knowledge as a reference for subsequent experiments.
PM6:00
Researchers conclude their day’s work. Before leaving the laboratory, the expert system automatically adjusts the experimental parameters based on the experimental results and feedback information, achieving adaptive optimization of the experimental process. This way, tomorrow’s experiments will be more efficient and accurate.
Through the new management model of the expert system, researchers can focus more on experimental design and execution while the system handles various issues during the experimental process and optimizes the experimental plan. Not only has work efficiency and result quality improved, but it has also brought new opportunities and challenges to scientific research.
In the AI era, data and knowledge will become the core elements driving laboratory development. Expert systems that can fully utilize knowledge are the main trend in the current intelligent development of laboratories. In the foreseeable future, the management model of expert systems + automation will innovate the basic framework of laboratory operations, becoming the operating system for new knowledge elements. Let us witness this together!
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