The economics of automation based on synthetic nervous systems

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 socalled "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 longoutdated 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.

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