## 6 Conclusions  
Life support systems are critical components of crewed autonomous transport for deep space habitation and the need for life cycle cost models in the early stages of design cannot be overstated. In this paper, three architectures of the LSS intellectual ecosystem are considered as alternatives: (1) open LSS in which all life support resources (LSRs) are provided from the outside through delivery by means of cargo carriers; (2) closed LSS in which all LSRs are generated on board the transport system and do not require external support; (3) mixed LSS in which part of the LSRs are generated on board an autonomous transport system, and part of them are delivered from the outside by freight transport. This paper proposes a mathematical model of LSS life cycle cost that makes it possible to estimate the costs of creating and operating a LSS in the early stages of design to choose its most effective architecture, depending on the degree of autonomy. This life cycle cost models of life support systems for autonomous transport systems at an early stage of their design are important and can be useful even though their component values are not known exactly for several reasons:  
* The life cycle cost model can be used for budget planning and resource allocation, making it easier to determine project feasibility and prioritize development. * The model can help focus efforts on areas that can have the greatest impact on the overall cost of a system and can be used to compare design options and make trade-offs between cost and performance. * Even if the exact cost of components is not known a priori, the life cycle cost model can be updated as more information becomes available. This can help improve the accuracy of cost estimates over time, making it easier to make informed decisions. This article defines the boundary conditions of economic efficiency for choosing various options for building intelligent architectures of the LSS ecosystem for crewed autonomous transport for deep space habitation. The proposed life cycle cost model for life support systems can be extended to areas not related to space flights, but wherein there are similar problems and requirements. For example, the proposed approach can be adapted to the analysis of life support systems in extreme conditions, such as deep-sea exploration, Arctic stations, remote oil platforms, and others. Of course, in this case, it will be necessary to take into account additional conditions, for example, climatic ones. However, this is not difficult to achieve even within the framework of the proposed models. For example, taking into account the indicated climatic conditions is easily carried out within the framework of the same LSS models:  
* Climate conditions can influence the consumption rates of resources within the life support system. For example, in Arctic environments, colder temperatures may increase energy requirements for heating, while in hot and humid climates, additional resources may be needed for cooling and ventilation. These variations in resource consumption rates can be included in the equations by adjusting the corresponding parameters. * Climate conditions can influence the logistics and resupply planning for the life support systems. Extreme weather events or seasonal variations may affect the availability and feasibility of delivering resources. Considering these climate-related logistics challenges is possible in the proposed equations. They help optimize the architecture of life support systems according to this factor. * Climate conditions may require specific adaptations or modifications to the life support system design. For instance, in extreme cold environments, additional insulation or heating systems may be necessary, while in hot and arid conditions, water conservation and efficient cooling technologies may be prioritized. Design solutions for adaptation to climate can be taken into account in the proposed equations by appropriately changing the value of the initial costs for the development of LSS.