亲邻科技住宅小区用户出入画像分析数据

该数据集合为深圳市亲邻科技有限公司的用户出入画像数据，包括用户id、开门方式、回家时间、出门时间、月均大门开门次数等数据字段。数据采用多源数据采集机制，通过智能门禁终端日志、APP交互接口、物联网传感器实时采集原始数据，并经过去重、格式标准化及异常值修正。该数据集主要解决的问题是通过分析用户通行时空规律，构建用户居家画像，并通过日均大门开门次数进行用户开门频率的划分，可有效解决两大核心问题：一是优化高峰期人员通行效率，基于时段标签合理配置安保人力；二是识别异常出入行为（如非活跃时段高频通行），强化社区安全管理。This dataset is user access profile data from Shenzhen Qinlin Technology Co., Ltd., which includes data fields such as user ID, door opening method, home arrival time, departure time, and average monthly main door opening times. Raw data is collected in real time through a multi-source data acquisition mechanism that uses smart access control terminal logs, APP interaction interfaces, and IoT sensors, and then undergoes post-processing steps including deduplication, format standardization, and outlier correction. The core problems addressed by this dataset include constructing user home profiles by analyzing the spatio-temporal patterns of user access, and classifying user door opening frequencies based on average daily main door opening times. It can effectively solve two core issues: first, optimizing the efficiency of personnel passage during peak hours and reasonably allocating security manpower based on time slot tags; second, identifying abnormal access behaviors such as high-frequency access during inactive periods to strengthen community safety management.

搜集汇总

背景与挑战

背景概述

该数据集为深圳市亲邻科技有限公司提供的住宅小区用户出入画像数据，包含用户ID、开门方式、回家时间等字段，用于优化通行效率和强化社区安全管理。数据经过多源采集和标准化处理，适用于智能楼宇和社区管理场景。