Zherdin Dmitry A.

Introduction. The digital economy, which the Russian Federation has embarked on, consists of a set of automated control systems, the effectiveness of each of which determines the effectiveness of the entire digital economy. Therefore, one of the main tasks that needs to be solved on the way to its construction is the creation of a fundamentally new, cost-effective architecture for building automated control systems.Purpose. This scientific study pursues the fundamental objective of systematizing and precisely formulating the postulates for constructing automated control systems (ACS) based on synthetic nervous systems (SNS). It represents a logical extension of the authors' publication series dedicated to a comprehensive assessment of the economic efficiency of this paradigm in developing data acquisition automation and process control systems. The proclaimed economic advantages, thoroughly analyzed in prior research, are attainable exclusively through strict and unwavering adherence to these postulates; their violation entirely nullifies or substantially diminishes the positive economic effect of SNS implementation.Methods. The methodological foundation encompasses a systematic and comprehensive analysis of an extensive corpus of technical and scientific literature on ACS development and optimization. Building on this, the authors consolidated the identified principles under the unified designation "SNS construction postulates", providing detailed descriptions of each alongside quantitative evaluations of their economic impacts. The approach leverages simple yet universal engineering practices, which despite their evident nature to specialists have previously lacked clear and consolidated formulation in available publications.Results. As the key result, postulates are formulated, positioned as fundamental directives and imperatives of design. Thorough assimilation and application of the postulates is declared mandatory for every automation system integration engineer, guaranteeing the achievement of economic efficiency in automation systems. The formulated postulates will not surprise qualified specialists due to their obvious logical simplicity and practical validity. Conclusions. Synthetic nervous systems are a new direction in the development of automated control systems. Russia could become a leader in their production and the birthplace of this technology. It is obvious that the economy of the future is not a digital economy or even a knowledge economy, but, above all, an economy of high, knowledge–intensive technologies. One of the ways the Russian Federation can succeed in the world of the economic future is to find new, more effective methods of using the technologies available to it, even if they are foreign. The development of synthetic nervous system technology is one such way.

Introduction. It is obvious that the Russian Federation can achieve technological independence only by developing its own technologies, but not by copying and using foreign developments as a basis. The author's concept of building automated control systems described in this article is unique and is not applied anywhere in the world. The bionic principles underlying it make it possible to achieve high economic efficiency in the construction of automated control systems, and give a good competitive advantage to economic entities when using the described technology. Purpose. To characterize the author's method of building automated control systems (ACS) - the so-called "Synthetic nervous systems" (SNS). Methods. The proposed method was based on concepts laid down by nature in the nervous systems of invertebrate living organisms. The article shows that the proposed approach reduces the time and financial costs of building automated systems, makes system scaling very simple, and maintenance and repair relatively cheap. The principle of the "bush of devices" proposed by the authors, formed by an artificial neuron, significantly reduces the labor, time and financial costs of creating a cable system. The principle of automatic identification of the type of devices connected to an artificial neuron eliminates the financial costs of programmer services and saves significant time during system integration. A reliable Ethernet local area network protocol for combining artificial neurons into a single system (instead of the long-outdated RS-485) allows you to build huge-scale systems with the possibility of reliable interaction of each neuron with any other neuron of the synthetic nervous system. The TCP/IP protocol stack, which forms the basis for data transmission between neurons, makes it possible to easily debug the system and build advanced graphical user interfaces running on all computing devices with a Web browser. Results and conclusions. It is shown that the integration, scaling and maintenance of automated control systems based on artificial CNS neurons is cost-effective compared to classical automated control systems built on a modular principle. The paper describes a method for increasing the economic efficiency of automated systems due to the principle of sufficient minimalism, i.e. by avoiding duplication of technologies in favor of one, but the most effective from a technical point of view. The principle proposed in this paper describes automation based on artificial nervous systems, which differs significantly from the classical principles of automation based on universal controllers.