居家守护场景下网约房开门异常监测预警数据

本数据在推进网约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，并即时向网约房运营管理单位发出预警通知，有助于运营管理单位迅速响应潜在的安全威胁，有效预防和处理紧急情况，提升居住的安全性。
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，为网约房运营管理单位提供精准风控评估与策略优化建议。基于数据分析结果，运营管理单位能够灵活调整安全管理措施，如实现特定区域精准巡查等，提升居住安全管理的针对性和有效性。
3.优化资源配置提升顾客居住满意度：通过开门数据的深入分析，可洞察顾客的日常出行规律与生活习惯，为网约房运营管理单位提供精细化服务支持，如优化网约房资源配置和应急响应机制等，从而增强顾客对网约房管理服务的满意度。一、数据抽取与预处理
（1）数据抽取：在公司智能门锁运营管理平台上抽取网约房智能门锁在不同时间周期内的开门信息，包括本日日期、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）、统计开始时间、统计结束时间、统计周期内出入总次数、统计周期内开门总次数、统计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。（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。
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：基于历史数据，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（包括日均出入次数、日均开门次数、90%开门时间段），并根据这些开门特征进一步识别居住者的身份特征（包括疑似老年人、青少年和成年人）。
（2）对开门异常行为进行监测预警：根据正常居住模式下的开门特征和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，当超出阈值时判定为异常，并发出预警。

This dataset will play a crucial role in advancing the digital transformation of online short-term rental housing management. It integrates smart lock door opening records and a big data analysis platform to build 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: Using smart locks and the system's built-in intelligent algorithms, door opening events are recorded around the clock, and occupant identity characteristics as well as potential security threats such as unauthorized access and frequent door opening during abnormal time periods are automatically identified. The system generates data on door openings by special groups during abnormal time periods, and sends real-time early warning notifications to online short-term rental housing operation and management units. This helps the operation and management units quickly respond to potential security threats, effectively prevent and handle emergencies, and improve residential 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 accurate risk control assessment and strategy optimization suggestions for online short-term rental housing operation and management units. Based on the analysis results, the operation and management units can flexibly adjust security management measures, such as targeted inspections in specific areas, to improve the pertinence and effectiveness of residential security management.
3. Optimizing Resource Allocation to Improve Customer Residential Satisfaction: In-depth analysis of door opening data can reveal customers' daily travel patterns and living habits, providing refined service support for online short-term rental housing operation and management units, such as optimizing the allocation of online short-term rental housing resources and emergency response mechanisms, thereby enhancing customer satisfaction with the rental housing management services.

I. Data Extraction and Preprocessing
(1) Data Extraction: Extract door opening information of online short-term rental housing smart locks over different time periods from the company's smart lock operation and management platform, including today's date, smart lock serial number, corresponding room number (the real room numbers are desensitized via re-numbering to protect privacy), statistical start time, statistical end time, total number of entries and exits during the statistical period, total number of door openings during the statistical period, specific time points corresponding to all door openings during the statistical period, number of door openings today, and specific time points corresponding to today's door openings.
(2) Data Preprocessing: Clean the extracted data to remove duplicate, erroneous or irrelevant information.

II. Data Processing and Analysis
(1) Identifying Door Opening Characteristics and Occupant Identity under Normal Living Patterns: Based on historical data, machine learning algorithms are used to learn the door opening characteristic performances of each smart lock under normal living patterns within a specific period (including average daily entries and exits, average daily door openings, 90% of door opening time periods), and further identify the identity characteristics of occupants (including suspected elderly, adolescents and adults) based on these door opening characteristics.
(2) Monitoring and Early Warning of Abnormal Door Opening Behaviors: Based on the identification results of door opening characteristics and occupant identity characteristics under normal living patterns, set door opening early warning rules and thresholds for smart locks, monitor the daily number of door openings and corresponding door opening times in real time, determine it as abnormal when the threshold is exceeded, and issue an early warning.

搜集汇总

数据集介绍

特点

该数据集记录了网约房智能门锁的开门异常监测预警数据，包含详细的开门记录和居住者身份特征，用于构建安全监测与预警体系，提升网约房的安全管理效率和服务质量。