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：自动化领域的数字化转型对工业过程中的数据采集与处理提出了新要求。采集数据与循环过程序列之间的逻辑关系必须得到正确解释与评估。为解决这一问题，提出了一种基于进化算法的新方法，用于对复杂过程的系统逻辑进行自优化。根据遗传结果，通过对解进行解码，推导出用于系统实现的程序代码。这是通过一个具有上游单元、中间单元和下游单元的灵活系统结构实现的。在中间单元中，定向学习过程与系统副本及评估函数在闭环中相互作用。代码生成策略通过冗余性、优先级、排序及性能推导来表征。所提出的方法在面临多目标优化问题的工业液体站过程中进行了评估。