校园守护场景下宿舍开门异常监测预警数据

本数据在推进校园宿舍管理的数字化转型进程中将发挥至关重要的作用。本数据通过集成校园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异常时间段频繁开门等潜在安全威胁，形成校园场景异常时间段开门数据，并即时向宿舍运营管理人员发出预警通知，有助于运营管理人员迅速响应潜在的安全威胁，有效预防和处理紧急情况，提升学生居住的安全性。
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，为校园宿舍运营管理人员提供精准风控评估与策略优化建议。基于数据分析结果，校园宿舍运营管理人员能够灵活调整安全管理措施，如实现特定区域精准巡查等，提升校园居住安全管理的针对性和有效性。
3.优化资源配置提升学生居住满意度：通过开门数据的深入分析，可洞察学生的日常出行规律与生活习惯，为宿舍运营管理人员提供精细化服务支持，如优化宿舍资源配置和应急响应机制等，从而增强学生对宿舍管理服务的满意度。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校园宿舍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：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。（2）对开门异常行为进行监测预警：根据智能门锁在特定周期内正常居住模式下的开门特征，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 campus dormitory management. It integrates door opening records from campus smart locks and big data analysis platforms to build an efficient and intelligent security monitoring and early warning system, with specific application scenarios as follows:
1. Intelligent Identification and Real-time Early Warning: By 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 frequent door openings during abnormal time periods are automatically identified. The system generates data on door opening events occurring during abnormal time periods in campus scenarios, and sends real-time early warning notifications to dormitory operation managers. This enables managers to rapidly respond to potential security threats, effectively prevent and address emergencies, and enhance the safety of student accommodation.
2. Data-driven Security Management Decision-making: Through big data analysis of abnormal door opening data over a continuous period, the spatio-temporal distribution characteristics of violations are revealed, providing accurate risk control assessment and strategy optimization suggestions for campus dormitory operation managers. Based on the analysis results, managers can flexibly adjust security management measures, such as conducting precise inspections in specific areas, thereby improving the targeting and effectiveness of campus accommodation security management.
3. Optimizing Resource Allocation to Improve Student Accommodation Satisfaction: Through in-depth analysis of door opening data, we can gain insights into the daily travel patterns and living habits of students, providing refined service support for dormitory operation managers, such as optimizing dormitory resource allocation and emergency response mechanisms, thereby enhancing students' satisfaction with dormitory management services.

I. Data Extraction and Preprocessing
(1) Data Extraction: Extract door opening information of campus dormitory 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 using 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 opening times during the statistical period, today's number of door openings, and specific time points corresponding to today's door opening times.
(2) Data Preprocessing: Clean the extracted data to remove duplicate, erroneous or irrelevant information.

II. Data Processing and Analysis
(1) Identifying Door Opening Characteristics under Normal Living Patterns: Based on historical data, machine learning algorithms are used to learn the door opening characteristic performance of each smart lock under normal living patterns in a specific period, including average daily entries and exits, average daily door openings, and 90% of door opening time periods.
(2) Monitoring and Early Warning of Abnormal Door Opening Behavior: According to the door opening characteristics of smart locks under normal living patterns in a specific period, set door opening early warning rules and thresholds, and conduct real-time monitoring of daily door opening times and corresponding door opening times. When the thresholds are exceeded, it is determined as an abnormality and an early warning is issued.

搜集汇总

数据集介绍

特点

该数据集记录了校园宿舍智能门锁的开门信息，用于监测和预警异常开门行为，包含每日开门次数、时间点、异常状态等数据。通过大数据分析平台，数据集支持智能识别、安全管理决策和资源配置优化，提升校园宿舍的安全性和学生满意度。