智慧城市商业区位选择的智能方法

Khrystyna Lipianina-Honcharenko
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引用次数: 0

摘要

该主题的相关性在于在智慧城市中选择创业地点的复杂性,因为它需要分析大量数据并考虑各种因素,如人口,竞争,基础设施和其他参数。使用基于机器学习的智能方法,可以收集、处理和分析大量数据,以进行准确的位置评估,并为企业家提供建议。这增强了决策过程,确保了更明智的选择,并增加了智慧城市中商业成功的机会。问题陈述涉及需要加快在智能城市中为企业选址选择最佳位置的过程。这项任务具有挑战性和长期性,需要分析大量数据,并考虑影响业务成功的各种因素,如地理位置、竞争、潜在客户群和其他相关方面。同样重要的是,为企业家提供快速获取信息和精确建议的途径,以便他们就其业务地点做出明智的决定。解决这一问题将促进资源的高效利用,确保智慧城市的商业成功。本研究的目的是开发一种在智慧城市中选择创业地点的智能方法。这种方法的目的是利用从各种来源收集的大量数据来确定开办新企业的最优地点。该方法基于现有的机器学习技术,如图像识别、数据预处理、分类和数值数据聚类。结果和主要结论。已经开发了一种方法,该方法的实施将允许在智能城市中为企业推荐最佳地点。这将有助于提高客户满意度,提高生活质量,增加企业家的利润。智能方法是解决智慧城市创业选址问题的有力工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intellectual method for business location selection in smart cities
The relevance of the topic lies in the complexity of selecting a location for starting a business in smart cities, as it requires analyzing a large amount of data and considering vari-ous factors such as population, competition, infrastructure, and other parameters. The use of an intelligent method based on machine learning enables the collection, processing, and analysis of large volumes of data for accurate location assessment and providing recommen-dations to entrepreneurs. This enhances the decision-making process, ensures more informed choices, and increases the chances of business success in a smart city. The problem statement involves the need to expedite the process of selecting an optimal location for business placement in a smart city. This task is challenging and long-term, re-quiring the analysis of extensive data and consideration of various factors that impact busi-ness success, such as geographical position, competition, potential customer base, and other relevant aspects. It is also crucial to provide entrepreneurs with fast access to information and precise recommendations to make informed decisions regarding their business location. Solving this problem will facilitate efficient resource utilization and ensure business success in a smart city. The purpose of the study is to develop an intelligent method for choosing a location for starting a business in a smart city. This method aims to use large amounts of data collected from various sources to determine the most optimal locations for starting a new business. The method is based on existing machine learning techniques such as image recognition, data preprocessing, classification, and clustering of numerical data. Results and key conclusions. A method has been developed, the implementation of which will allow recommending optimal locations for business in smart cities. This will help to increase customer satisfaction, improve the quality of life and increase the profit of entre-preneurs. The intelligent method is a powerful tool for solving the problems of choosing a lo-cation for starting a business in smart cities.
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