Intelligent dormitory management system based on Internet of Things

Abstract

The invention discloses a smart dormitory management system based on Internet of Things. A central data processing module is configured to receive, store and process data from each sensor and module; the environment monitoring and control module is configured to monitor the temperature, humidity and air quality in a dormitory in real time; the safety management module is configured to utilize face recognition and fingerprint recognition technologies to ensure that only authorized personnel can enter and exit a dormitory; the emotion perception module is configured to judge the emotion fluctuation state of the student through face recognition, voice analysis and behavior pattern analysis; the psychological health monitoring module is configured to monitor psychological health conditions of students in real time and provide customized psychological health suggestions or online consultation services; the personalized environment adaptation module is configured to automatically adjust the dormitory environment through the living habits and preferences of the students. According to the technical scheme, a more comfortable, safer and more efficient living environment is provided, and intelligence and humanization of dormitory management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dormitory management, and particularly relates to an intelligent dormitory management system based on the Internet of Things. Background Art

[0002] With the rapid development of cutting-edge technologies such as the Internet of Things (IoT) and artificial intelligence (AI), the management methods of university dormitories are also constantly innovating. Traditional dormitory management systems usually rely on manual monitoring and control methods with fixed rules, which are not only inefficient but also have many problems that cannot be flexibly addressed. For example, the environmental control means are single and difficult to meet the personalized needs of students; safety management mainly relies on physical access control and video monitoring, and real-time early warning and intelligent response cannot be achieved; equipment management relies on manual operation, and problems such as lagging fault information or untimely maintenance are likely to occur. Summary of the Invention

[0003] The main object of the present invention is to provide an intelligent dormitory management system based on the Internet of Things, aiming to provide a more comfortable, safe and efficient living environment and improve the intelligence and humanization of dormitory management.

[0004] To achieve the above object, an intelligent dormitory management system based on the Internet of Things proposed by the present invention includes a central data processing module and an environment monitoring and control module, a security management module, an emotion perception module, a mental health monitoring module, and a personalized environment adaptation module that are signal-connected to the central data processing module; The central data processing module is configured to receive, store and process data from various sensors and modules; The environment monitoring and control module is configured to monitor the temperature, humidity and air quality in the dormitory in real time; The security management module is configured to use face recognition and fingerprint recognition technologies to ensure that only authorized personnel can enter and exit the dormitory; The emotion perception module is configured to judge the emotional ups and downs of students by using face recognition, voice analysis and behavior pattern analysis; The mental health monitoring module is configured to monitor the mental health status of students in real time and provide customized mental health advice or online consultation services; The personalized environment adaptation module is configured to automatically adjust the dormitory environment according to the living habits and preferences of students.

[0005] In a possible implementation manner, the system further includes: An energy consumption management module, configured to monitor the power consumption of each dormitory, analyze the power consumption pattern, and provide energy-saving suggestions; An equipment management module, configured to centrally control the air conditioners and water heaters in the dormitory through the Internet of Things platform to achieve remote control and timing operation; An information management and interaction module, configured to publish dormitory management - related notices, announcements or emergency information through a display screen in the dormitory or a mobile phone APP; An intelligent learning module, configured to automatically adjust the light and sound effects in the dormitory according to students' learning tasks and habits, monitor students' learning behaviors and time, analyze learning efficiency and provide suggestions.

[0006] In a possible implementation manner, the emotion perception module includes: An emotion recognition unit, configured to judge students' emotional states in real - time through face recognition, voice analysis, and behavior pattern analysis technologies; An emotion data feedback unit, configured to upload emotion data to the central data processing module for analysis and decision - making.

[0007] In a possible implementation manner, the mental health monitoring module includes: A mental state monitoring unit, configured to monitor students' mental health status in real - time by collecting data from the emotion perception module; A personalized suggestion unit, configured to provide customized mental health suggestions for students according to the analysis results.

[0008] In a possible implementation manner, the personalized environment adaptation module includes: An automatic environment adjustment unit, configured to automatically adjust the temperature, humidity, and light of the dormitory based on students' living habits and preferences; A user habit learning unit, configured to adapt to and predict students' needs by learning and recording students' preferences.

[0009] In a possible implementation manner, the energy consumption management module includes: A power consumption monitoring unit, configured to monitor the power usage of each dormitory in real - time, record and analyze the electricity consumption of each electrical device; An electricity consumption pattern analysis unit, configured to identify peak electricity consumption periods and unnecessary energy consumption through historical data analysis, and discover energy - saving potential; An energy - saving suggestion unit, configured to provide personalized energy - saving suggestions to students and dormitory management staff according to the analysis results.

[0010] The technical solution of the present invention, by adopting a central data processing module and an environment monitoring and control module, a security management module, an emotion perception module, a mental health monitoring module, and a personalized environment adaptation module that are signal - connected to the central data processing module, through the coordinated cooperation of these modules, the intelligent dormitory management system can not only effectively improve energy utilization efficiency, enhance safety and comfort, but also improve students' quality of life and learning efficiency. Description of the Drawings

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0012] Figure 1 It is a schematic structural diagram of an embodiment of the intelligent dormitory management system based on the Internet of Things of the present invention.

[0013] The realization of the object of the present invention, functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0014] In order to make the purpose, technical solutions and advantages of the present application more clear, the following will further describe the present application in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0015] In response to the problems in the background technology, with reference to Figure 1 As shown, an intelligent dormitory management system based on the Internet of Things proposed by the present invention includes a central data processing module and an environment monitoring and control module, a security management module, an emotion perception module, a mental health monitoring module, and a personalized environment adaptation module that are signal-connected to the central data processing module; The central data processing module is configured to receive, store and process data from various sensors and modules; The environment monitoring and control module is configured to monitor the temperature, humidity and air quality in the dormitory in real time; The security management module is configured to use face recognition and fingerprint recognition technologies to ensure that only authorized personnel can enter and leave the dormitory; The emotion perception module is configured to judge the emotional ups and downs of students by using face recognition, speech analysis and behavior pattern analysis; The mental health monitoring module is configured to monitor the mental health status of students in real time and provide customized mental health advice or online consultation services; The personalized environment adaptation module is configured to automatically adjust the dormitory environment according to the living habits and preferences of students.

[0016] It is understandable that the central data processing module is the core of the entire system and is configured to receive, store, and process data from various sensors and modules. This module communicates with each sub-module through the Internet of Things platform, uniformly manages and analyzes data, and provides corresponding control instructions according to the requirements of different modules. The central data processing module adopts cloud computing and big data analysis technologies, and can conduct in-depth analysis and predictive processing on environmental data, security data, emotional data, and mental health data in the dormitory.

[0017] The environmental monitoring and control module realizes real-time monitoring of the dormitory environment through a variety of sensors deployed in the dormitory (such as temperature sensors, humidity sensors, air quality sensors). Specifically, this module is configured to monitor the temperature, humidity, and air quality in the dormitory and send the monitored data to the central data processing module in real time. After analyzing the data, the central data processing module can automatically adjust devices such as air conditioners, humidifiers, and air purifiers according to preset rules or the personalized needs of students to ensure that the dormitory environment is always in a comfortable state.

[0018] The security management module is configured to use advanced biometric technologies (such as face recognition, fingerprint recognition) to ensure that only authorized personnel can enter and leave the dormitory. This module monitors the dormitory entrance in real time through cameras and fingerprint recognition devices. When someone tries to enter the dormitory, the system will automatically identify their identity information and match it with the authorized information in the database. If the match is successful, the system will allow entry; if the match fails, an alarm will be triggered and the management staff will be notified. The security management module is signal-connected to the central data processing module, and all identification and verification information is uniformly stored and managed by the central data processing module.

[0019] The emotion perception module is configured to judge the emotional state of students through face recognition, voice analysis, and behavior pattern analysis technologies. This module uses cameras and microphones in the dormitory to capture the facial expressions and voice characteristics of students in real time, and combines behavior patterns (such as activity frequency, study duration) for comprehensive analysis to judge the emotional fluctuations of students. The analysis results are transmitted to the central data processing module, and the system can adjust the dormitory environment (such as adjusting the lights, playing music) or send emotional care prompts according to the emotional state of students to help students adjust their emotions.

[0020] The mental health monitoring module is connected to the smart devices (such as smart watches and smart bands) worn by students to monitor the mental health status of students in real time. This module is configured to collect students' physiological data (such as heart rate and sleep quality), and combine the students' emotional perception data and behavior patterns to evaluate the mental health status of students. If it is found that there are abnormalities in the mental state of students, the module will automatically generate mental health suggestions or notify relevant mental health service personnel to provide online consultation services. All data is uploaded to the central data processing module for subsequent analysis and tracking.

[0021] The personalized environment adaptation module automatically adjusts the dormitory environment by learning the living habits and preferences of students. This module is configured to record the daily behavior patterns and environmental preferences of students (such as temperature, light, volume, etc.), and through data interaction with the central data processing module, automatically adjust the environmental parameters when students enter the dormitory or are about to rest to achieve the best comfort level. The personalized environment adaptation module can also automatically adjust devices such as air conditioners, lights, and curtains according to the students' work and rest schedules to provide a more personalized living experience.

[0022] It should be noted that each monitoring module (such as the environmental monitoring and control module and the emotional perception module) collects data in real time and transmits the data to the central data processing module through wireless communication technology. The central data processing module integrates and analyzes the received data, and sends control instructions to relevant control modules according to the analysis results. Each control module (such as the personalized environment adaptation module and the security management module) automatically adjusts relevant devices according to the instructions of the central data processing module to complete tasks such as environmental regulation, security management, and emotional care. The storage and analysis of data are carried out through a cloud platform, and big data technology provides data support for the optimization of the system and the personalized needs of students.

[0023] In summary, through the collaborative work of the above modules, the intelligent dormitory management system of the present invention can significantly improve the intelligent level of dormitory management and the living experience of students. The system realizes the goals of energy conservation, environmental protection, and comfortable living through real-time monitoring and automatic adjustment of the dormitory environment; through emotional perception and mental health monitoring, the system can timely discover and respond to students' mental health problems and provide personalized mental care; through the personalized environment adaptation module, the system can automatically optimize the living environment according to the personal needs of students and provide more considerate services.

[0024] In a possible implementation manner, the system further includes: An energy consumption management module, configured to monitor the power consumption of each dormitory, analyze the electricity usage pattern, and provide energy-saving suggestions; A device management module, configured to centrally control air conditioners and water heaters in the dormitory through an Internet of Things platform to achieve remote control and timed operation; An information management and interaction module, configured to publish dormitory management-related notices, announcements or emergency information through a display screen in the dormitory or a mobile phone APP; An intelligent learning module, configured to automatically adjust the light and sound effects in the dormitory according to the learning tasks and habits of students, monitor the learning behaviors and time of students, analyze the learning efficiency and provide suggestions.

[0025] Specifically, the energy consumption management module is configured to monitor the power consumption of each dormitory, analyze the electricity usage pattern, and provide energy-saving suggestions. This module uses smart electricity meters and power sensors to monitor the electricity usage of the dormitory in real time and transmits the data to the central data processing module. The central data processing module analyzes the power consumption data, identifies peak electricity usage and potential waste sources, generates an energy-saving report, and provides energy-saving suggestions to students or dormitory management staff. The energy consumption management module can also cooperate with the personalized environment adaptation module to automatically adjust the operating time of devices based on the students' daily schedules to optimize the power usage efficiency. For example, when the system detects that there is no one in the dormitory, the energy consumption management module can automatically turn off the air conditioner and lights to reduce unnecessary energy consumption.

[0026] The device management module centrally controls various devices in the dormitory through the Internet of Things platform, including air conditioners, water heaters, lighting devices, etc., to achieve remote control and timed operation. The devices in the dormitory are all connected to the Internet of Things platform through smart sockets or controllers. The device management module can remotely control the devices according to the needs of students or the preset rules of the system. Students can operate these devices through the mobile phone APP or the Web interface, such as remotely turning on the air conditioner or setting the working time of the water heater. The device management module also supports the timed operation function, which can automatically control the start and stop of the devices according to the students' daily schedules to ensure that the devices are in the best state when students need them. For example, the system can turn on the water heater in advance before the students get up to ensure the supply of hot water.

[0027] The information management and interaction module is configured to publish dormitory management-related notices, announcements or emergency information through a display screen in the dormitory or a mobile phone APP. This module is connected to the central data processing module, and management staff can publish various information to the designated dormitories or student groups through the background system. The information can include daily notices (such as water and electricity maintenance notices), emergency announcements (such as fire alarms), activity information (such as club activity notices), etc. Students receive and view this information through the display screen in the dormitory, the mobile phone APP or the web interface to ensure timely access to all kinds of important information related to dormitory life. The information management and interaction module also supports two-way interaction between students and dormitory management staff. Students can submit feedback, repair requests or other opinions through the system.

[0028] The intelligent learning module automatically adjusts the light and sound effects in the dormitory according to the learning tasks and habits of students, monitors the learning behaviors and time of students, analyzes the learning efficiency and provides suggestions. This module is equipped with light sensors, sound sensors and motion sensors to monitor the environmental parameters in the dormitory and the learning behaviors of students in real time. When a student starts learning, the intelligent learning module can automatically adjust the brightness and color temperature of the lights to create a suitable learning environment; when needed, the system can also play background music suitable for learning, block external noises, and improve learning efficiency. The intelligent learning module also monitors the learning time and behavior patterns of students and uploads the data to the central data processing module for analysis. The system evaluates the learning efficiency of students through the analysis of learning data and provides personalized learning suggestions. For example, the system can suggest that students adjust their learning time, increase rest intervals, or change the learning environment to improve learning effects.

[0029] Each module collects data in real time through the Internet of Things platform and transmits the data to the central data processing module for analysis and processing. The central data processing module generates energy-saving suggestions, learning suggestions or device control instructions based on the analysis results and sends the relevant information or instructions to the corresponding module or student terminal. The energy consumption management module automatically optimizes energy use according to the analysis of the central data processing module; the device management module performs remote control and timing operations; the information management and interaction module publishes and manages dormitory-related notifications and interactive information; the intelligent learning module dynamically adjusts the learning environment according to the needs of students and provides learning suggestions. The system interacts with students and dormitory managers through mobile applications, web interfaces or displays in the dormitory to ensure that the functions of the system can flexibly and accurately meet the user needs.

[0030] Through the collaborative work of the above modules, the intelligent dormitory management system of the present invention can significantly improve the management efficiency of the dormitory, optimize energy use, enhance the learning experience of students, and provide personalized services. The system effectively controls energy consumption and reduces waste through the energy consumption management module; realizes intelligent operation of devices through the device management module, improving the convenience of life; realizes efficient communication of dormitory management information and student interaction through the information management and interaction module; provides the most suitable learning environment for students through the intelligent learning module to help improve learning efficiency.

[0031] In a possible implementation manner, the emotion perception module includes: An emotion recognition unit configured to judge the emotional state of students in real time through face recognition, speech analysis, and behavior pattern analysis technologies; An emotion data feedback unit configured to upload emotion data to the central data processing module for analysis and decision-making.

[0032] Specifically, the emotion recognition unit is configured to continuously determine the emotional state of students in real time through facial recognition, speech analysis, and behavior pattern analysis technologies. This unit utilizes cameras, microphones, and other sensors within the dormitory to capture students' facial expressions, speech characteristics, and behavior patterns. Through advanced artificial intelligence algorithms, the emotion recognition unit can analyze this data to identify the current emotional state of students, such as happy, sad, stressed, tired, etc. The emotion recognition unit can continuously and seamlessly monitor emotions without disturbing students, ensuring the accuracy and real-time nature of the emotion data.

[0033] The emotion recognition unit captures students' facial expressions through cameras and uses facial recognition technology to analyze features such as micro-expressions and facial muscle movements to determine the emotional state of students. For example, frequent frowning may indicate stress or confusion, while smiling may indicate pleasure or relaxation.

[0034] The emotion recognition unit captures students' speech data through microphones and uses speech analysis technology to detect features such as pitch, speech rate, and pauses in the speech, thereby inferring the emotions of students. For example, an increased speech rate and raised pitch may indicate anxiety or excitement, while a slowed speech rate and lowered pitch may indicate tiredness or frustration.

[0035] The emotion recognition unit monitors students' behavior patterns through sensors, such as activity frequency, sitting posture changes, and device usage frequency, and comprehensively determines the emotional state of students. For example, prolonged sitting still may indicate a low mood or concentration, while frequent walking may indicate anxiety or restlessness.

[0036] The emotion data feedback unit is configured to upload the emotion data captured by the emotion recognition unit to the central data processing module for further analysis and decision-making. This unit transmits the emotion data to the central data processing module in real time through the Internet of Things platform, ensuring the timeliness and accuracy of the data.

[0037] The emotion data feedback unit is responsible for uploading the raw data of the emotion recognition unit (such as facial expressions, speech characteristics, behavior patterns) to the central data processing module. The uploaded data can include information such as timestamps, emotion categories, and intensities for the central data processing module to integrate and analyze.

[0038] The central data processing module conducts in-depth analysis on the received emotion data and makes intelligent decisions by combining data from other modules (such as the environmental monitoring and control module, personalized environment adaptation module). For example, when the system detects that a student is in a low mood, the central data processing module can instruct the personalized environment adaptation module to adjust the lighting and music in the dormitory to improve the student's mood; when detecting that a student is anxious, the system can suggest appropriate rest or provide a soothing environment adjustment.

[0039] Based on the analysis results of the central data processing module, the system can send emotional care reminders to students through channels such as mobile phone apps, display screens in the dormitory, or voice assistants, reminding students to pay attention to emotion management or providing psychological support options such as online counseling services or mental health advice.

[0040] Through the collaborative work of the emotion recognition unit and the emotion data feedback unit, the intelligent dormitory management system of the present invention can achieve real-time monitoring and intelligent management of students' emotional states. The system can respond in a timely manner to students' emotional changes and help students maintain a positive emotional state, improving the comfort and efficiency of life and study through means such as environmental adjustment and emotional care.

[0041] In a possible implementation manner, the mental health monitoring module includes: A mental state monitoring unit configured to collect data from the emotion perception module to monitor the mental health status of students in real time; A personalized advice unit configured to provide customized mental health advice for students based on the analysis results.

[0042] Specifically, the mental state monitoring unit is configured to collect data from the emotion perception module to monitor the mental health status of students in real time. The emotion data collected by the emotion perception module includes students' facial expressions, voice characteristics, and behavior patterns, and these data are transmitted to the mental state monitoring unit for analysis and processing. The mental state monitoring unit uses artificial intelligence algorithms and mental health models to analyze students' emotional trends and behavior changes to determine whether their mental state is within the healthy range.

[0043] The mental state monitoring unit receives and analyzes the emotion data from the emotion perception module in real time, comprehensively considering characteristics such as students' emotional fluctuations and behavioral abnormalities to judge their mental health status. For example, frequent mood swings and long periods of loneliness may indicate increased psychological stress or the risk of depression.

[0044] Through continuous emotion data analysis, the mental state monitoring unit can dynamically monitor students' mental health. When the system detects potential mental health problems, it will record the relevant data and conduct further analysis to generate an accurate health report.

[0045] The personalized advice unit is configured to provide customized mental health advice for students based on the analysis results of the mental state monitoring unit. This unit combines the historical behavior data and personalized preferences of students through the central data processing module to generate mental health management advice for individual students.

[0046] After receiving the analysis results from the psychological state monitoring unit, the personalized recommendation unit generates customized mental health recommendations by combining the student's personalized data (such as past emotional data, living habits, learning pressure, etc.). For example, for students with large emotional fluctuations, the system may recommend relaxation training or psychological counseling; for students with high learning pressure, the system may recommend adjusting the learning rhythm or taking time management training.

[0047] The personalized recommendation unit feeds back the generated recommendations to the students through channels such as the mobile phone APP, the display screen in the dormitory, or the voice assistant. Students can adjust their living habits according to the recommendations to improve their mental state; the system can also automatically adjust the dormitory environment (such as lighting, music) to help students relax.

[0048] The personalized recommendation unit continuously optimizes and adjusts the recommendation content based on the students' feedback and the continuous changes in emotional data to ensure the effectiveness and pertinence of the recommendations. The system can provide continuous mental health management support to help students maintain a good mental state in their daily lives.

[0049] Through the collaborative work of the psychological state monitoring unit and the personalized recommendation unit, the intelligent dormitory management system of the present invention can achieve real-time monitoring of the students' mental health status and provide personalized mental health recommendations. The system can promptly identify potential mental health problems and take effective intervention measures to help students maintain mental health and improve the quality of life and learning.

[0050] In a possible implementation manner, the personalized environment adaptation module includes: An automatic environment adjustment unit configured to automatically adjust the temperature, humidity, and lighting of the dormitory based on the students' living habits and preferences; A user habit learning unit configured to adapt to and predict the students' needs by learning and recording the students' preferences.

[0051] Specifically, the automatic environment adjustment unit is configured to automatically adjust the temperature, humidity, and lighting of the dormitory based on the students' living habits and preferences. This unit is interconnected with the environment monitoring and control module and the personalized environment adaptation module, and real-time monitors the environmental parameters in the dormitory through Internet of Things sensors, and automatically adjusts the environmental conditions according to the students' preferences and current activity status to ensure a comfortable living and learning environment.

[0052] The automatic environment adjustment unit real-time monitors the environmental parameters in the dormitory through temperature sensors, humidity sensors, and lighting sensors. The data is continuously transmitted to the central data processing module for analysis and processing.

[0053] Based on the students' living habits and preset preference parameters, the automatic environment adjustment unit can automatically adjust the operating states of the air conditioner, humidifier, and lighting system. For example, when the student is sleeping, the system can automatically lower the light intensity and indoor temperature; when the student is studying, the system will increase the light intensity and adjust the temperature to improve comfort and concentration.

[0054] The automatic environment adjustment unit dynamically adjusts the environment settings according to the real-time monitored data and the students' immediate feedback. The system can automatically adapt to different needs according to environmental changes (such as seasonal changes, weather changes) and the students' activity states.

[0055] The user habit learning unit is configured to adapt to and predict the students' needs by learning and recording the students' preferences. This unit conducts data interaction with the central data processing module, and gradually learns the students' living habits and preferences by analyzing the students' historical behavior data and environment adjustment records, so as to optimize the environment settings and provide more personalized services.

[0056] The user habit learning unit gradually establishes a personalized user preference model by continuously recording and analyzing the students' daily behaviors, environment adjustment history, feedback information, etc. For example, the system can learn that a certain student prefers a lower temperature and soft lighting in the evening, or likes to keep the room at a lower humidity on weekend mornings.

[0057] Based on the learned students' habits and preferences, the user habit learning unit can predict the students' needs. For example, the system can automatically adjust the temperature and light before the student arrives at the dormitory to ensure that the environment is in the most suitable state when the student arrives; or when it detects that the student is about to enter the sleep mode, it adjusts the light and temperature in advance.

[0058] Through learning and prediction, the user habit learning unit can continuously optimize the environment adjustment strategy, making the system's response more personalized and intelligent. The changes and feedback of the students' needs will also be continuously integrated into the learning model to ensure that the system can continuously evolve and improve the service quality over time.

[0059] Through the collaborative work of the automatic environment adjustment unit and the user habit learning unit, the intelligent dormitory management system of the present invention can achieve highly intelligent and personalized management of the dormitory environment. The system can automatically adjust the environment parameters according to the students' living habits, improving the students' comfort and quality of life; at the same time, through the continuous learning and optimization of the user habit learning unit, the system can continuously adapt to and predict the students' needs, realizing more considerate and intelligent dormitory management.

[0060] In a possible implementation manner, the energy consumption management module includes: The power consumption monitoring unit is configured to monitor the power usage of each dormitory in real time, record and analyze the electricity consumption of each electrical device; The electricity consumption pattern analysis unit is configured to identify peak electricity consumption periods and unnecessary energy consumption through historical data analysis, and discover energy-saving potential; The energy-saving advice unit is configured to provide personalized energy-saving advice to students and dormitory management staff based on the analysis results.

[0061] Specifically, the power consumption monitoring unit is configured to monitor the power usage of each dormitory in real time, record and analyze the electricity consumption of each electrical device. This unit obtains the electricity consumption data of all electrical devices in the dormitory in real time through smart meters and power sensors, and transmits this data to the central data processing module for further processing.

[0062] The power consumption monitoring unit measures and records the power consumption of each electrical device in real time through smart meters and sensors installed in the dormitory. The data includes the actual electricity consumption of each device, usage time, etc.

[0063] All collected power usage data is recorded in the database and regularly uploaded to the central data processing module. The system can track power consumption over a long period, providing basic data for subsequent analysis.

[0064] The power consumption monitoring unit can monitor each electrical device individually, providing detailed electricity usage information, such as the power consumption of devices like air conditioners, lights, water heaters, etc.

[0065] The electricity consumption pattern analysis unit is configured to identify peak electricity consumption periods and unnecessary energy consumption through historical data analysis, and discover energy-saving potential. This unit uses the historical data collected by the power consumption monitoring unit, through data mining and analysis techniques, to identify key features and problem areas in the electricity consumption pattern.

[0066] The electricity consumption pattern analysis unit conducts in-depth analysis of historical electricity consumption data, including peak electricity consumption periods, the duration of peak electricity consumption, the energy consumption characteristics of each device, etc. The system can identify the main sources of electricity consumption and unnecessary energy consumption.

[0067] The system can discover energy-saving potential, such as frequent peak electricity consumption periods, devices with long standby times, and energy-wasting behavior patterns. Based on these findings, the system can generate detailed energy-saving reports and analysis results.

[0068] The electricity consumption pattern analysis unit evaluates possible energy-saving measures based on the analysis results and quantifies the potential energy-saving effects. The system can provide targeted energy-saving advice and strategies.

[0069] The energy-saving advice unit is configured to provide personalized energy-saving advice to students and dormitory administrators according to the analysis results of the electricity consumption pattern analysis unit. By transforming the analysis results into specific action suggestions, this unit helps users optimize their electricity consumption behaviors and reduce unnecessary energy consumption.

[0070] The energy-saving advice unit generates personalized energy-saving advice based on the analysis results provided by the electricity consumption pattern analysis unit. These suggestions may include adjusting the electricity usage time, turning off standby devices, optimizing device usage, etc. For example, the system can suggest that students reduce the use of high-power devices during peak hours, or remind dormitory administrators to regularly check and maintain devices.

[0071] The energy-saving advice unit timely feedbacks the energy-saving advice to students and dormitory administrators through channels such as mobile phone APPs, display screens in the dormitory, or emails. The advice content includes energy-saving tips, optimization suggestions, and operation guides.

[0072] The energy-saving advice unit can track the implementation of the advice and further optimize the advice according to the actual effects. If students or dormitory administrators take energy-saving measures, the system can record and evaluate the effects of these measures and provide further optimization suggestions.

[0073] The above are only the preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. An Internet of Things-based intelligent dormitory management system, characterized in that, It includes a central data processing module and an environmental monitoring and control module, a security management module, an emotion perception module, a mental health monitoring module, and a personalized environment adaptation module that are signal-connected to the central data processing module; The central data processing module is configured to receive, store, and process data from various sensors and modules; The environmental monitoring and control module is configured to monitor the temperature, humidity, and air quality in the dormitory in real time; The security management module is configured to use face recognition and fingerprint recognition technologies to ensure that only authorized personnel can enter and exit the dormitory; The emotion perception module is configured to judge the emotional ups and downs of students by using face recognition, voice analysis, and behavior pattern analysis; The mental health monitoring module is configured to monitor the mental health status of students in real time and provide customized mental health advice or online consultation services; The personalized environment adaptation module is configured to automatically adjust the dormitory environment according to the living habits and preferences of students.

2. The intelligent dormitory management system based on the Internet of Things according to claim 1, wherein It also includes: An energy consumption management module, configured to monitor the power consumption of each dormitory, analyze the electricity usage pattern, and provide energy-saving suggestions; A device management module, configured to centrally control the air conditioners and water heaters in the dormitory through the Internet of Things platform to achieve remote control and timing operations; An information management and interaction module, configured to publish dormitory management-related notices, announcements, or emergency information through the display screen or mobile APP in the dormitory; An intelligent learning module, configured to automatically adjust the light and sound effects in the dormitory according to the learning tasks and habits of students, monitor the learning behavior and time of students, analyze the learning efficiency, and provide suggestions.

3. The intelligent dormitory management system based on the Internet of Things according to claim 1, characterized in that, The emotion perception module includes: An emotion recognition unit, configured to judge the emotional state of students in real time by using face recognition, voice analysis, and behavior pattern analysis technologies; An emotion data feedback unit, configured to upload emotion data to the central data processing module for analysis and decision-making.

4. The intelligent dormitory management system based on the Internet of Things according to claim 1, characterized in that The mental health monitoring module includes: A mental state monitoring unit, configured to monitor the mental health status of students in real time by collecting data from the emotion perception module; A personalized advice unit, configured to provide customized mental health advice for students according to the analysis results.

5. The intelligent dormitory management system based on the Internet of Things according to claim 1, wherein The personalized environment adaptation module includes: An automatic environment adjustment unit, configured to automatically adjust the temperature, humidity, and light of the dormitory based on the living habits and preferences of students; A user habit learning unit, configured to adapt to and predict the needs of students by learning and recording their preferences.

6. The intelligent dormitory management system based on the Internet of Things according to claim 2, wherein The energy consumption management module includes: A power consumption monitoring unit, configured to monitor the power usage of each dormitory in real time, record and analyze the electricity consumption of each electrical device; An electricity usage pattern analysis unit, configured to identify peak electricity consumption periods and unnecessary energy consumption through historical data analysis, and discover energy-saving potential; An energy-saving advice unit, configured to provide personalized energy-saving suggestions to students and dormitory management personnel according to the analysis results.

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