随着社会经济及智能硬件技术的发展，人们与各类移动终端、物联网设备的交互时间显著增长，传统的信息获取、商业交易与社交沟通需求逐步向各类智能终端迁移，社会大众对共享充电宝的需求逐渐增强。但共享充电宝行业内长期存在设备过度铺设，电池分布不均，设备利用率低等问题，严重影响企业的盈利能力。要进一步推动共享充电宝行业发展，合理地评估充电宝投放点位及数量，提高共享充电机柜和充电宝的盈利能力是各共享充电运营企业亟需解决的问题。优化共享充电宝投放及运营策略为提升企业盈利能力的关键举措，本研究焦点在于探索共享充电宝需求影响要素，并建立共享充电宝需求预测模型，从而进一步结合企业运营实例，提供盈利能力优化建议与方案，为共享充电设备投放及运营提供理论支持。本文首先介绍了共享充电宝的概念及特点，指出了共享充电业务模式与社会信息化进程的相关性及我国共享充电宝市场的潜在需求，进一步将技术接受度理论应用于共享充电宝需求影响因素探索，结合专家及消费者调研，最终选取6个一级影响因素，23个二级影响因素，并采用灰色关联度法，对处于行业领先地位的某共享充电公司订单数据进行实证分析，评估了订单需求与影响因素的相关性水平。为进一步建立面向具体商圈的共享充电宝需求预测能力，本文基于灰色关联度筛选需求影响因素，构建了前馈神经网络，解决共享充电宝需求探测中变量数量众多且不具备线性条件的问题。最后结合共享充电宝运营企业实例，提出基于需求的收益优化方案，针对企业具体的问题提出相应的策略并进行综合优化，为共享充电宝行业企业推动收益优化变革提供了良好样板。目前，共享充电宝行业在资源的使用上存在一定的浪费，同时对充电宝需求影响因素的分析和盈利能力优化策略的研究也相对较少。现有研究成果大多停留在理论层面，缺乏实际共享充电运营企业数据的支持。本文基于实际数据，将需求影响因素转化为理论模型，建立预测模型，并结合企业实例提出资源投放与运营优化建议，具有实际应用价值。

Interacting with various mobile terminals and IoT devices is becoming increasingly common as a result of the development of the social economy and electronic information technology. The traditional demand for information acquisition, business transactions and social communication has gradually shifted to various intelligent terminals. As the infrastructure of mobile terminals, batteries have not achieved a breakthrough in capacity in recent years, resulting in the sharing of charging treasure becoming a rigid product for many users in outdoor scenes. However, there are long-term problems in the industry of shared power bank, such as over-laying of equipment, uneven distribution of batteries and low utilization of equipment, which seriously affect the profitability of enterprises. The operating companies of shared power banks need to address several issues to further promote the development of the industry, including reasonably evaluating the location and quantity of power banks and improving the level of asset income.Determining the factors that affect the demand for shared power banks is the core of this study, which aims to optimize the layout and operation strategies of shared power banks as an important means to improve the profitability of enterprises, and to establish a demand prediction model for shared power bank, so as to further combine with enterprise operation examples, provide recommendations and plans for optimization of profitability, and provide theoretical support for the layout and operation of shared power bank.This thesis first introduces the concept and characteristics of shared power bank, and then introduces the correlation between the shared charging service model and the process of social informatization and the potential demand of our shared power bank market, and further applying the theory of technology acceptance to explore the influencing factors of the demand for shared power bank. Combined with the investigation of experts and consumers, this thesis finally selected 6 first-class influencing factors and 23 second-class influencing factors. This thesis utilizes the gray correlation method to analyze the order data from a leading company in the shared power bank industry. The aim of the analysis is to evaluate the degree of correlation between order demand and influencing factors.In order to further establish the demand prediction ability of shared power banks for specific business districts, this thesis establishes a BP neural network based on the gray correlation degree to screen demand factors, and solves the multivariate nonlinear problem in demand forecasting shared charging stations.Finally, combined with the examples of shared power bank operating companies, a demand-based revenue optimization plan is proposed, and corresponding strategies are proposed for specific problems of the company and comprehensively optimized, providing a good model for companies in the shared power bank industry to promote revenue optimization reforms.At present, the resource investment of the shared power bank industry is rather extensive, and There are few studies analyzing the factors that affect the demand for power banks and researching profitability optimization strategies. the existing research results are biased and theoretical, in this thesis, Based on the actual data of the shared power bank operating companies, map the demand influencing factors to the theoretical level, and establish a prediction model, and then combined with the enterprise example, the resource allocation and operation optimization suggestions are put forward, it has certain practical value.