Software Implementation of Statistical Data Processing Systems for the Production Sector of the Penitentiary System

The article considers the results of the development of an information system for working with statistical data of the industrial penitentiary sector. The relevance of the work is due to both the regulatory and legal documentation and the scale of the penitentiary system production organized in the territory of the Russian Federation: according to official current data, the total volume of penitentiary production amounted to 44.3 billion rubles. The analysis of the reporting statistical forms of the penitentiary system was carried out. The results of the set-theoretical and information-theoretical analysis of statistical data of the industrial penitentiary sector are presented. It was established that for the industrial sector of the penitentiary system, the study of labor resources is one of the most important components (in turn, it can include parameters that reflect the results of educational work, medical and sanitary support of the special contingent). Based on the results obtained, solutions were developed for the formation of database architectures for statistical analysis of the activities of the industrial sector. The methods, approaches and technical solutions that were applied in the development are presented: principles of the object-oriented approach, the functional approach, information system design patterns, SOLID principles. The features of using such libraries as Pandas, SciKit-Learn, TensorFlow, Keras, CatBoost and Plotly in data processing are considered. Approaches to the development of user interfaces are given. Approaches to the deployment of an information system using web technologies are proposed. The development of an access control system for the user of the information system is considered. The practical significance of the work lies in the implementation of the system in the divisions of the Federal Penitentiary Service of Russia, where it can be used to analyze production processes, predict risks and support decision-making. In the future, it is planned to continue research in the field of system scaling, ensuring information security and maintaining software life cycles.