Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as 'No Code' or 'Low Code' alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of 'No Code' is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-code generative model is a viable option for industrial process design.

翻译：工业过程工程与PLC程序开发历来更青睐函数块图（FBD）编程，而非经典的命令式编程范式（如面向对象和函数式编程）。随着现在被归类为“无代码”或“低代码”的理念在采纳与试验中势头渐增，加之统计学习理论（即所谓机器学习）成为主流，我们为数字机器设计可执行程序的方式正被重新定义。“无代码”的核心目标在于直接从一组需求文档或任何定义消费者/客户期望的文件中衍生出可执行程序。本文提出一种从需求文档生成函数块图（FBD）程序的方法，所生成的程序可作为目标系统可执行的中间或最终产物。该方法采用受约束的选择算法，该算法从关联推荐系统的顶层抽取内容。实验结果表明，此类无代码生成模型是工业过程设计的一种可行方案。