Размерно-качественные характеристики круглых сортиментов, заготавливаемых с применением многооперационных лесных машин

Abstract

It is necessary to carry out a statistical assessment of the harvested wood parameters in order to increase the yield of round timber and the overall productivity of logging operations while operating multi-operational forest machines. Modern forestry harvesters are equipped with a variety of CAN control sensors to monitor operation and have onboard software that can collect and store input data on various file types to ensure continuous correct operation. One of the main file types is STM (trunk files). These files are presented in two forms: some contain information for each individual tree trunk in different files, and others store all the data for all trunks in one. The second type of STM files is the most relevant when analyzing the operations carried out at the logging site. However, it is inconvenient to use STM files every time to get information, so there is a need to convert the initial data into a table for easy transfer and decision-making at risk and uncertainty. The article proposes a method for transferring such data to a CSV table using Python pandas, numpy, seaborn, matplotlib programming language libraries, which help to process large data arrays quickly and efficiently and display them graphically. The data obtained by two operators using a medium-class Ponsse Ergo 8W machine in the typical natural and production conditions of the Mondi Syktyvkar JSC (Komi Republic, middle taiga zone) were used for the transfer. The efficiency of the operators’ work was assessed. We obtained functions for determining the volume of a tree trunk on the basis of the reported data from the forest machines. The analysis of structured data on the operation of multi-operational forest machines helps to improve decision-making during subsequent felling of trees with the selection of species, which provides the largest volume of round timber output. Moreover, it is possible to adjust assortment tables (APT-matrices) for shortand medium-term planning of assortment harvesting volumes.