INTELLIGENT INDICATIVE MONITORING OF THE DISPATCHING SYSTEM FOR MAIN HYDROCARBON TRANSPORT OBJECTS

The article presents the author's intellectual system for monitoring and managing the efficiency of dispatch control systems for the technological processes of hydrocarbon transportation enterprises. The article proposes intellectual algorithms, models, and methods for assessing the state of elements of the dispatch control system (DCS), which are aimed at solving three main tasks of monitoring and management: controlling the efficiency of the dispatch control system, assessing the state of the object, and conducting a systematic analysis of indicators to formulate a strategy and tactics for management and decision-making. The article demonstrates how intellectual data analysis can be used to develop a well-founded strategy for improving the efficiency of the DCS. The relevance of the work is confirmed by the need to ensure the quality of process management when the operating conditions, technical condition of the object, abnormal situations, and operation of the system in conditions of diversification of transport flows change. The scientific novelty of the work lies in the creation of a new methodological framework for monitoring and managing technological and organizational processes at hydrocarbon transportation enterprises, which includes a system of algorithms, intelligent methods, and models for evaluating the effectiveness of the dispatch management and control system. The paper provides a detailed description of the methodology for using neural network technologies for both operational management of production situations and identifying opportunities for optimizing existing dispatch management systems, as well as for solving the overall task of improving the efficiency of the enterprise. The paper presents the results of implementing an algorithm for comprehensive analysis and management of the dispatch management system's effectiveness using criteria-based, indicator-based, and proposed approaches. The solutions proposed by the authors allow for the prompt resolution of current issues related to the regulation and control of an enterprise's activities in real time. The validity and reliability of the author's developments have been confirmed by research results using neural network technologies and machine learning. The obtained results allow us to conclude about the possibilities of expanding operational management tools, as well as the potential for increasing the reliability, safety, and efficiency of the SDU using the developed intelligent system in the current conditions of the need to implement import substitution programs and technological sovereignty of the Russian Federation.