Сравнение и отбор ситуаций в системах вывода решений на прецедентах для «умной» фермы

Abstract

The trend of development of smart farms is aimed at their becoming fully autonomous, robotic enterprises. The prospects for the intellectualization of agricultural production and smart farms, in particular, today are associated with the development of technology systems used to detect, recognize complex production situations and search for effective solutions in these situations. The article presents the concept of such a decision support system on smart farms using the method of decision support based on case-based reasoning - CBR system. Its implementation requires a number of non-trivial tasks, which include, first of all, the tasks of formalizing the presentation of situations and creating methods for comparing and retrieving situations from the KB on this basis. In this study, a smart farm is presented as a complex technological object consisting of interrelated components, which are the technological subsystems of a smart farm, the products produced, the objects of the operational environment, as well as the relationships between them. To implement algorithms for situational decision-making based on precedents, a formalized representation of the situation in the form of a multivector is proposed. This allowed us to develop a number of models of the trained similarity function between situations. The conducted experiments have shown the operability of the proposed models, on the basis of which ensemble architecture of a neural network has been developed for comparing situations and selecting them from the knowledge base in decision-making processes. Of practical interest is monitoring the condition of plants by their video and photo images, which allows detecting undesirable plant conditions (diseases), which can serve as a signal to activate the process of searching for solutions in the knowledge base.