This article presents the constraining factors that limit the increase in the efficiency of logging production by modern multi-operation machines operating on the Scandinavian cut-to-length technology in the felling phase, namely the selection and registration of wood species. The factors for creating a complete architecture of a fully connected neural network (NN) are given. The dependence of the prediction accuracy of a fully connected NN on a test sample on the size of the training dataset, and an image of the dependence of the prediction accuracy on the number of trees in the random forest method for image classification is shown. For a fully connected NN, a sufficient number of images and a test sample size were established for training, using tree-trunk breed-class labels as target values. A selected list of trees was given, with the size of the training sample of images presenting a problem for the classification of tree trunks using the random forest method. The aim was the discovery of the optimal number of trees necessary to achieve prediction accuracy.
Harvesting of forest raw materials, namely felling and bucking of log on forest areas, is the first and main stage in the logging chain. One of the problems in this industry is the shortage of highly qualified specialists-operators of forest machines, including feller-delimbing-bucking machines (harvesters). Operators who have just come to the industry or have insufficient experience (have worked for less than a year) cannot correctly configure harvesters, as a result of which the processes in the logging chain are disrupted. Thus, it becomes necessary to apply additional models of working with operators and forest machines to reduce the resulting costs for the company. Understanding how much wood raw material will be obtained from the logging site allows predicting not only the amount of equipment required, but also planning actions for the next stages of logging.
The article presents an overview of modern domestic solutions for the transport development of hard-to-reach cutting areas. Recommendations are given on the possibility of using such solutions in typical natural and industrial conditions in waterlogged swampy cutting areas. A software package is proposed to improve the efficiency of such solutions.
Проблема полного учета контроля размерно-качественных характеристик древесины, получаемой в процессе лесозаготовки, является одним из основных аспектов в условиях рыночных отношений для лесозаготовительных предприятий и фактором регулирования издержек производства. Информацию по размерным параметрам заготовленной древесины система контроля-измерения современных многооперационных машин хранит в файлах с различными расширениями. В настоящее время отечественного программного обеспечения, которое позволяло бы динамически проанализировать объем отдельного ствола дерева, не существует, имеются только программные комплексы, позволяющие оценить заготовленную продукцию в целом. Целью работы является создание программного обеспечения для графического представления и динамического расчета объема заготовленной многооперационными лесными машинами продукции, в том числе для оперативного учета размерных параметров древесины, заготовленной из любой части ствола дерева. Разработанное программное обеспечение написано на языке программирования Python в среде программирования PyCharm Community. Разработанное программное обеспечение позволяет осуществлять динамический расчет объема заготовленной многооперационными лесными машинами продукции, в том числе для оперативного учета размерных параметров древесины, заготовленной из любой части ствола дерева, а также графически представить полученные результаты в удобной для анализа форме. Использование разработанной программы позволит оперативно анализировать размерно-качественные характеристики заготовленной древесины с выработкой корректирующих технологических решений в процессах лесозаготовки, что способствует снижению эксплуатационных затрат и себестоимости заготовки древесины, повышению производительности и прибыли для лесопромышленных компаний от производственно-хозяйственной деятельности. The problem of fully accounting for the control of the size and quality characteristics of wood obtained in the process of logging is one of the main aspects in the conditions of market relations for logging enterprises and is a factor in regulating production costs. The control and measurement system of modern multioperation machines stores information on the size parameters of harvested wood in files with various extensions. Currently, there is no domestic software that would allow you to dynamically analyze the volume of a single tree trunk, there are only software packages that allow you to evaluate the harvested products as a whole. The purpose of the work is to create software for graphical representation and dynamic calculation of the volume of harvested wood by multi-operation forest machines of products, including for operational accounting of the dimensional parameters of wood harvested from any part of the tree trunk. The developed software is written in the Python programming language in PyCharm Community programming environment. The developed software allows you to perform dynamic calculation of the volume of harvested wood by multi-operation forest machines of products, including for operational accounting of the dimensional parameters of wood harvested from any part of the tree trunk, as well as graphically present the results in a convenient form for analysis. Using the developed program will allow you to quickly analyze the size and quality characteristics of harvested wood with the development of corrective technological solutions in the logging processes, which helps to reduce operating costs and the cost of harvesting wood, increase productivity and profit for timber companies from production and economic activities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.