The digital transformation of automation places new demands on data acquisition and processing in industrial processes. Logical relationships between acquired data and cyclic process sequences must be correctly interpreted and evaluated. To solve this problem, a novel approach based on evolutionary algorithms is proposed to self optimise the system logic of complex processes. Based on the genetic results, a programme code for the system implementation is derived by decoding the solution. This is achieved by a flexible system structure with an upstream, intermediate and downstream unit. In the intermediate unit, a directed learning process interacts with a system replica and an evaluation function in a closed loop. The code generation strategy is represented by redundancy and priority, sequencing and performance derivation. The presented approach is evaluated on an industrial liquid station process subject to a multi-objective optimisation problem.

翻译：工业过程的数字化转型对数据采集和处理提出了新的要求。必须正确解释和评估获得的数据与周期性过程的逻辑关系。为解决这个问题，提出了一种基于进化算法的新方法，用于自我优化复杂过程的系统逻辑。基于遗传结果，通过解码解决方案来导出系统实现的程序代码。这通过具有上游、中间和下游单元的灵活系统结构实现。在中间单元中，有一个定向学习过程与系统副本和一个闭环评估函数互动。代码生成策略由冗余和优先级、排序和性能导出表示。所提出的方法基于一个工业液体站点过程，这个过程的多目标优化问题进行了评估。