机器地图是为提升无人平台复杂环境认知与理解能力而提出的一类新型地图。本文以机器地图的概念模型为基础，从认知科学视角提出一种以记忆结构与过程为参考、符合认知逻辑的机器地图建图理论模型。通过分析记忆的结构与过程、心象地图的内容与组织，认知架构和机器人系统中的环境认知等问题，梳理出机器地图的认知逻辑及其对机器地图建图模型的支撑。在此基础上，分析了机器地图的任务目标和内容分类，从信息组织、逻辑结构与生成过程三方面提出机器地图建图模型的设计原则。基于该原则，设计了机器地图建图的逻辑结构和过程模型。在逻辑结构方面，从空间、视觉、情境、图式、规则等方面细化了感知地图、工作地图和长时地图中的内容与结构关系；在过程模型方面，按照认知活动抽象出包括理解、注意、推理、学习、行动的机器地图建图基本活动，并提出内隐和外显两类建图过程。机器地图的认知逻辑与建图模型，本质上是对机器地图认知计算机制的阐释，从抽象层次上为研究人员提供协同研究的基础框架，也为相关技术、数据的集成、评估、运用提供参考。本文的研究还为数字孪生或虚拟地理环境的构建提出新的目标与要求。

Unmanned platforms are rapidly being used in many different fields. However, improving their cognition and understanding of complex environments remains a challenging research problem. Machine maps are a new class of maps proposed to address this problem. Based on the conceptual model of the machine map, this paper adopts the research perspective of cognitive science and further proposes a theoretical model of the machine map"s map construction, which is cognitive-plausible and consistent with the cognitive structure. This paper first discusses the theoretical roots of machine maps in cognitive science in terms of the origin, formation, and development of machine maps. Second, we briefly review the research on the structure and generation of memory models, mental image maps, cognitive architectures, and environmental cognition issues of robotic systems. Furthermore, we discuss the cognitive structure of the machine map and its supporting role in the map construction model of machine maps. Third, we propose the design principles of the machine map"s map construction model, which includes the organization of environment information using distributed representations, structural design of the machine map using a multi-store memory system, and modeling of the generation of the machine map with a reference of brain cognitive activities. Furthermore, the task objectives, content classification, detailed logical structure, and map generation process of the machine map"s map construction are presented. In particular, the perceptual map conducts preliminary processing of information acquired by sensors to obtain information about the features, location, geometry, and semantics of entities in the surrounding environment; the working map is functionally similar to working memory in human brains, which contains visual information, spatial information, situational information, and specialized maps constructed to accomplish specific tasks; the long-time map uses perceptual map and working map as information sources, and the fragmented information in the perceptual map and working map is associated, managed, and processed more extensively to form an environment model with global reference. Finally, machine map generation"s primary activities (e.g., understanding, attention, inference, learning, and action) and processes（e.g., implicit map generation and explicit map generation）are discussed based on the logical structure. The implicit map generation refers to the process in which the content and the knowledge in the long-term map are continuously enriched and accumulated through the continuous evolution and support of the perceptual map and the working map during the operation of the unmanned platform. This process contains three activities: shallow understanding, deep understanding, and implicit learning. Explicit map generation refers to the process in which the working map will form a specialized map for a given task to meet the specific task requirements and support the generation of spatial behavior under the support of itself, perceptual map, and long-term map. The process consists of six activities: superficial understanding, inference, attention, deep understanding, episodic learning, and action. The cognitive structure and map construction model, which is an interpretation of the machine map cognitive computing system, can serve as a basic framework for researchers that are interested in the machine map, allowing them to carry out collaborative research at a more abstract level and also provide references for the integration, evaluation, and application of related technologies and data. This research also illuminates new requirements and goals for constructing digital twin or virtual geographic environments.