居家场景公共租赁住房开门异常监测预警数据

本数据在推进公共租赁住房管理的数字化转型进程中将发挥至关重要的作用。本数据通过集成智能门锁的开门记录及大数据分析平台，构建了一套高效、智能的安全监测与预警体系，具体应用场景如下：
1.智能识别与即时预警：
利用智能门锁和系统内置智能算法，全天候记录开门事件并自动识别居住者身份特征和非授权访问、异常时间段频繁开门等潜在安全威胁，形成特殊人群异常时间段开门数据，并即时向公租房管理部门发出预警通知，有助于管理部门迅速响应潜在的安全威胁，有效预防和处理紧急情况，提升住房的安全性。
2.数据驱动的安全管理决策：
通过对持续一段周期的开门异常数据经大数据分析，揭示出违规行为的时空分布特征，为公租房管理层提供精准风控评估与策略优化建议。基于数据分析结果，公租房监管部门能够灵活调整安全管理措施，如实现特定区域精准巡查等，提升住房安全管理的针对性和有效性。
3.优化资源配置提升居民服务满意度：
通过开门数据的深入分析，可洞察居民的日常出行规律与生活习惯，为公租房监管部门提供精细化服务支持，如优化公租房资源配置和应急响应机制等，从而增强居民对公租房管理服务的满意度。一、数据抽取与预处理
（1）数据抽取：在公司XX平台上抽取每把智能门锁的在不同时间周期内的开门信息，包括智能门锁编号、对应的房间编号（为保护隐私，采用重新编号的方法对真实的房间门牌号进行脱敏处理）、时间周期、周期内开门总次数、周期内所有开门次数对应的具体时间点、本日开门次数、本日开门次数对应的具体时间点等。（2）数据预处理：对抽取的数据进行清洗，去除重复、错误或无关的信息，以便后续的加工和分析。
二、数据加工和分析
（1）识别正常居住模式下的开门特征以及居住者身份：运用机器学习算法，学习每把智能门锁在特定周期内正常居住模式下的开门特征表现，并根据这些开门特征进一步识别居住者的身份特征，例如在非节假日期间平均开门次数是X次，开门时间90%分布在5:30-9:00、16:30-20:50，那么居住者有较大概率为老年人。
（2）对开门异常行为进行监测预警：按居住者身份特征识别结果，对智能门锁设定开门预警规则和阈值，对每日的开门次数及对应的开门时间进行实时监测，当超出阈值时发出预警，例如针对老年人，在非节假日期间连续3天低于平均开门次数60%，开门时间70%不在正常时间段，则发出预警。

This dataset plays a vital role in advancing the digital transformation of public rental housing (PRH) management. By integrating door opening records of smart locks and a big data analysis platform, this dataset constructs an efficient and intelligent security monitoring and early warning system. The specific application scenarios are as follows:
1. Intelligent Identification and Real-time Early Warning
Utilizing smart locks and the system's built-in intelligent algorithms, door opening events are recorded around the clock, and potential security threats such as unauthorized access, frequent door opening during abnormal time periods, as well as resident identity characteristics are automatically identified. Data on door opening by special groups during abnormal time periods is generated, and early warning notifications are immediately sent to PRH management departments. This helps management departments respond quickly to potential security threats, effectively prevent and handle emergencies, and improve housing safety.
2. Data-driven Security Management Decision-making
Through big data analysis of door opening anomaly data over a continuous period, the spatiotemporal distribution characteristics of violations are revealed, providing precise risk control assessment and strategy optimization suggestions for PRH management teams. Based on the analysis results, PRH regulatory authorities can flexibly adjust security management measures, such as conducting precise patrols in specific areas, to enhance the targeting and effectiveness of housing security management.
3. Optimizing Resource Allocation to Improve Resident Service Satisfaction
Through in-depth analysis of door opening data, insights can be gained into residents' daily travel patterns and living habits, providing refined service support for PRH regulatory authorities, such as optimizing PRH resource allocation and emergency response mechanisms, thereby enhancing residents' satisfaction with PRH management services.

I. Data Extraction and Preprocessing
(1) Data Extraction
Door opening information of each smart lock within different time periods is extracted from the company's XX platform, including smart lock serial number, corresponding room number (the real room numbers are desensitized via re-numbering to protect privacy), time period, total number of door openings within the period, specific timestamps of all door openings within the period, daily door opening times, and specific timestamps corresponding to daily door opening times, etc.
(2) Data Preprocessing
The extracted data is cleaned to remove duplicate, erroneous or irrelevant information for subsequent processing and analysis.

II. Data Processing and Analysis
(1) Identifying Door Opening Characteristics and Resident Identity in Normal Living Patterns
Machine learning algorithms are employed to learn the door opening characteristics of each smart lock under normal living patterns within a specific period, and further identify resident identity features based on these door opening characteristics. For example, if the average number of door openings during non-holidays is X times, and 90% of door opening times fall between 5:30-9:00 and 16:30-20:50, the resident is highly likely to be an elderly person.
(2) Monitoring and Early Warning of Abnormal Door Opening Behavior
Based on the identified resident identity features, door opening early warning rules and thresholds are set for smart locks. Real-time monitoring of daily door opening times and corresponding door opening timestamps is conducted, and early warnings are issued when thresholds are exceeded. For example, for elderly residents, if the number of door openings is 60% lower than the average for 3 consecutive days during non-holidays, and 70% of door opening times fall outside the normal time window, an early warning will be issued.