Property facility remote monitoring system based on Internet of Things

Abstract

The invention discloses a property facility remote monitoring system based on Internet of Things, which is characterized in that property facility operation data are continuously acquired through a sensor and are transmitted to a data platform in real time, and a manager can perform remote operation through a PC terminal or a mobile terminal; machine learning is adopted to analyze historical and real-time data to predict equipment faults, early warning is automatically given out, and slight faults are repaired; the AES256 encryption technology guarantees data security, multi-factor authentication ensures that only authorized personnel operate, and all operation logs are recorded and audited; based on real-time data and AI analysis, equipment operation is automatically adjusted, an energy consumption report is generated, and optimization suggestions are provided; an intuitive and customizable management interface is adopted, so that a manager can check various facility states and energy efficiency report notifications through a mobile terminal; according to the property facility remote monitoring system based on the Internet of Things, AI is adopted to optimize energy consumption scheduling, and on-demand energy supply is realized and energy waste is reduced through real-time data analysis and automatic adjustment of an equipment operation mode.

Description

Technical Field

[0001] The present invention belongs to the field of remote monitoring of property facilities based on the Internet of Things, and specifically relates to a remote monitoring system for property facilities based on the Internet of Things. Background Art

[0002] A remote monitoring system for property facilities based on the Internet of Things (IoT) is a system that uses IoT technology to monitor and manage various facilities in a property in real time. Its main features are data collection, remote control, and status monitoring of property facilities (such as elevators, air conditioners, lighting systems, security facilities, etc.) through sensors, devices, and Internet connections. Sensors are used to detect data such as the operating status, temperature and humidity, pressure, and energy consumption of the facilities; to ensure the transmission of data from sensors to the control center, common communication methods include Wi-Fi, Zigbee, NB-IoT, etc.; to collect, analyze, and display data, usually a cloud platform or a local server, which can provide real-time monitoring, alarm, and reporting functions; and managers can view and operate the system through devices such as computers or mobile phones to achieve functions such as remote monitoring, control, and fault diagnosis.

[0003] However, existing remote monitoring systems for property facilities based on the Internet of Things, although having significant advantages in improving management efficiency and saving costs, also have some defects and challenges. Some IoT devices (especially sensors and monitoring devices that need to operate stably for a long time) may consume more power, especially when there is no reasonable design of an energy-saving mechanism, which will increase the overall energy consumption of the property. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a remote monitoring system for property facilities based on the Internet of Things, which adopts AI to optimize energy consumption scheduling, automatically adjusts the operating mode of devices through real-time data monitoring and analysis, realizes energy supply on demand, and reduces energy waste.

[0005] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0006] A remote monitoring system for property facilities based on the Internet of Things includes:

[0007] A real-time monitoring and remote control module, which is used to continuously collect the operating status data of property facilities through various sensors and transmit the collected data to the data processing platform in real time, and managers can remotely operate the facilities through the PC side or the mobile side;

[0008] A fault warning and intelligent diagnosis module, which is used to analyze historical data and real-time data by using machine learning algorithms to predict equipment failures, automatically send warning signals when equipment anomalies are predicted, and at the same time, automatically repair or adjust minor faults;

[0009] The data security and privacy protection module is used to ensure that data is not leaked or tampered with during transmission and storage by using the AES 256-bit encryption technology, adopt a multi-factor authentication mechanism to ensure that only authorized personnel can perform operations, and record all operation logs and conduct regular audits;

[0010] The energy efficiency management and optimization module is used to automatically adjust the operating modes of air conditioners, lighting, and elevator equipment according to real-time data and AI analysis, generate energy consumption reports through the system's data analysis, provide property management personnel with the changes in high-energy-consuming equipment and give optimization suggestions;

[0011] The user experience and operation module is used to generate an intuitive and easy-to-operate management interface. The interface customizes the layout according to actual needs. Through the mobile App, property management personnel can view the operating status of property facilities at any time and perform remote control and adjustment, and receive intelligent alarm, maintenance suggestion, and energy efficiency report notifications through the mobile end.

[0012] Preferably, the operating status data of property facilities is continuously collected through various sensors, and the collected data is transmitted to the data processing platform in real time. The remote operations of the facilities by management personnel through the PC or mobile end are as follows:

[0013] Monitor the operating status of facilities through temperature and humidity sensors, pressure sensors, flow sensors, and access control sensors, collect data, and send the collected real-time data to the data collection device or gateway through the IoT gateway and communication protocol;

[0014] After the data is transmitted to the cloud or the local data processing platform, it is stored and processed through the data storage system. During the processing, data cleaning, preprocessing, and data analysis are used to process the transmitted data;

[0015] Management personnel connect to the data processing platform through the PC or mobile App to view real-time data and perform remote control of facilities. The real-time status of property facilities is displayed through the dashboard;

[0016] When management personnel issue control commands to turn on / off the air conditioner and adjust the operating mode of the elevator at the PC or mobile end, the operation commands are transmitted to the target facility equipment for execution through the API interface or Modbus and BACnet control protocols.

[0017] Preferably, machine learning algorithms are used to analyze historical data and real-time data to predict equipment failures, and when equipment anomalies are predicted, warning signals are automatically sent. At the same time, for minor failures, automatic repair or adjustment is performed as follows:

[0018] Collect the operation data of the device in real time through temperature, humidity, vibration, and current sensors, extract the working cycle, operating temperature, and load characteristics of the device from the collected historical data and real-time data, and use principal component analysis PCA, L1 regularization, and recursive feature elimination RFE algorithms to select predictive features;

[0019] Adopt supervised learning, deep learning, and anomaly detection machine learning algorithms, train the machine learning model through historical data, and use the validation set to evaluate the accuracy and robustness of the model;

[0020] Deploy the trained model to the actual operating environment, monitor the operating status of the device in real time, and input the real-time collected data into the machine learning model for prediction. The model analyzes the patterns of real-time data and historical data to identify and judge the possibility of device failure;

[0021] When detecting abnormal or failure risks, notify the management personnel through the SMS, email, and APP push alarm system;

[0022] For small faults or device anomalies, adjust through a preset automatic repair algorithm. The adjustment methods are as follows:

[0023] Automatically adjust the air conditioner temperature and adjust the operating parameters of the current or load;

[0024] According to the real-time situation, use a closed-loop control system to automatically repair the device by automatically adjusting the working frequency, temperature, or pressure of the device.

[0025] Preferably, adopt AES256-bit encryption technology to ensure that data is not leaked or tampered with during transmission and storage, adopt a multi-factor authentication mechanism to ensure that only authorized personnel can operate, and record all operation logs and conduct regular audits as follows:

[0026] During data transmission, use AES256 encryption to encrypt the data to ensure that the data is not stolen or tampered with during transmission, and combine the SSL / TLS protocol to protect the security of the data transmission layer;

[0027] During the storage process, save the data after encryption, and only authorized personnel or systems with the correct key can decrypt the data. Adopt a multi-factor authentication mechanism of knowledge factor, possession factor, and biometric factor. After passing the multi-factor authentication, the access permission is granted;

[0028] The system details record the operation behaviors of users and administrators. The operation behaviors include login time, operation content, modified settings, and system access. For each operation, the operation IP address and device source are recorded again;

[0029] Regularly audit the operation logs, and use automated audit tools to analyze a large number of logs to check for any abnormal operation behaviors, unauthorized access, and privilege abuse issues;

[0030] By real-time monitoring the operation logs of the system, after identifying suspicious activities such as multiple failed logins and operations beyond permissions, immediately trigger an alarm to notify the administrator.

[0031] Preferably, automatically adjust the operation modes of air conditioners, lighting, and elevator equipment according to real-time data and AI analysis, and generate an energy consumption report through the data analysis of the system, providing property management personnel with the changes in high-energy-consuming equipment and optimization suggestions as follows:

[0032] Through sensors and intelligent devices, collect the operation data of air conditioners, lighting, and elevator equipment in the building in real time, use AI algorithms to analyze the real-time collected data, identify the energy consumption patterns in the building, and conduct predictive analysis on the equipment operation;

[0033] Based on factors such as time, season, and personnel activities, AI makes predictions and adjusts the operation modes of air conditioners and lighting equipment. Based on AI analysis, the system automatically adjusts the operation modes of air conditioners, lighting, and elevator equipment to optimize energy efficiency. The methods for optimizing energy efficiency are as follows:

[0034] Air conditioner adjustment: Automatically adjust the temperature and operation mode of the air conditioner according to the indoor temperature, humidity, weather changes, and the number of people.

[0035] Lighting adjustment: Automatically adjust the lighting brightness according to the natural light intensity and space usage.

[0036] Elevator optimization: Automatically adjust the elevator dispatching according to the floor usage.

[0037] The system regularly generates a detailed energy consumption report, analyzes the energy consumption situation, equipment efficiency, and usage patterns of each device. AI identifies the energy consumption change trends and potential high-energy-consuming problems through the analysis of historical energy consumption data, generates corresponding reports, and the system generates targeted energy-saving optimization suggestions for property management personnel according to the energy consumption analysis results;

[0038] AI real-time monitors the energy consumption changes of each device and automatically identifies high-energy-consuming devices. For high-energy-consuming devices, AI will generate optimization suggestions based on historical data and real-time data. According to the energy consumption report and suggestions generated by the system, property management personnel set energy-saving goals for them and track their progress.

[0039] Preferably, generate an intuitive and easy-to-operate management interface. The interface customizes the layout according to actual needs. Through the mobile App, management personnel can view the operation status of property facilities at any time and perform remote control and adjustment, and receive intelligent alarms, maintenance suggestions, and energy efficiency report notifications through the mobile terminal as follows:

[0040] The system provides a customizable management interface, allowing users to adjust the layout according to actual needs. Meanwhile, the interface is compatible with PC, tablet, and mobile devices. Through the mobile app, managers can view the operating status of property facilities at any time, including real-time data of equipment and operating health conditions;

[0041] Managers can adjust the air conditioning temperature, turn on / off the lighting, and adjust the elevator dispatching to control the equipment remotely through the app;

[0042] Based on the real-time data and historical trends of the equipment, the system can intelligently identify potential problems and automatically issue alarms. When the energy consumption of the equipment exceeds the set threshold, the system automatically generates an alarm and pushes it to the manager's app;

[0043] According to the usage, energy consumption data, and historical maintenance records of the equipment, the system provides maintenance suggestions through AI analysis. By analyzing the operating data of the equipment, AI predicts potential failures and issues early warnings to managers;

[0044] The system automatically generates monthly or quarterly energy efficiency reports, analyzes the energy consumption of the air conditioning system, lighting equipment, and elevators in the building, and provides optimization suggestions. Managers can receive energy efficiency reports, alarms, and optimization suggestion notifications in real-time through the mobile app.

[0045] Preferably, the operation formula is:

[0046] Real-time monitoring and remote control are achieved through sensor data collection and communication protocols for real-time data transmission and control. The optimized data transmission formula is:

[0047]

[0048] The performance evaluation of the communication protocol is carried out through the throughput formula, and the throughput formula is:

[0049]

[0050] Machine learning algorithms play a key role in the module. They mainly predict failures through time series analysis and prediction algorithms. When predicting failures based on historical data, a regression model or neural network model is used for prediction. The regression model formula is:

[0051]

[0052] Among them, Y is the predicted equipment status, are different characteristics of the equipment, are regression coefficients;

[0053] The neural network formula is:

[0054] h t = σ(W h · x t + U h · h t＝1 + b h )

[0055] where h t is the output of the hidden layer, σ is the activation function, W h and U h are weight matrices, x t is the current input, h t＝1 is the hidden state at the previous moment, and b h is the bias;

[0056] When the AES256 encryption algorithm is adopted, the formula involves operations of symmetric encryption. The AES encryption formula is:

[0057] C = E k (P)

[0058] where C is the ciphertext after encryption, P is the plaintext, and E k is the encryption operation using the key k;

[0059] In the multi-factor authentication process, the reliability of authentication is calculated through the following authentication formula: Authentication success rate = P(authentication passed | valid certificate ∩ correct password ∩ correct dynamic password)

[0060] where P is the probability of successful authentication;

[0061] Energy efficiency management adjusts the device operation mode through real-time data and optimizes the operation formula of the device by minimizing the energy consumption objective function:

[0062]

[0063] where P i is the power of the th device, t i is the operation time of the device, and the goal is to minimize the total energy consumption;

[0064] For the intelligent optimization algorithm, the cloud uses the following objective function formula:

[0065]

[0066] where x is the optimization decision variable;

[0067] The user experience and operation module involves user interface design and interaction models, and involves collaborative filtering algorithms or matrix factorization techniques. The user interface optimization analyzes user behavior data, and the formula is:

[0068]

[0069] Among them, for the predicted score of the user project N(u) is the set of users similar to the user r ui is the score given by user υ to the project .

[0070] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the remote monitoring system for property facilities based on the Internet of Things as described in any one of the above is implemented.

[0071] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the remote monitoring system for property facilities based on the Internet of Things is implemented.

[0072] The beneficial effects of the present invention are as follows:

[0073] Through the real-time monitoring and remote control module, managers can view and operate property facilities anytime and anywhere, whether on the PC side or the mobile side, saving on-site operation time and labor costs and improving management efficiency; the fault warning and intelligent diagnosis module can predict equipment failures through machine learning algorithms and automatically issue warnings or repair minor faults, reducing losses caused by equipment failures and improving the operational stability of the equipment; the data security and privacy protection module uses AES256-bit encryption technology and multi-factor authentication to ensure that data is not leaked or tampered with during transmission and storage, and at the same time, through operation logs and regular audit mechanisms, the security and transparency of the system are strengthened; the energy efficiency management and optimization module automatically adjusts the operation mode of the equipment through real-time data analysis and AI optimization, reduces energy waste, provides energy consumption reports and proposes optimization suggestions to help property managers achieve energy-saving goals and reduce operating costs; the user experience and operation module makes the operation more intuitive and flexible by customizing the management interface layout. Managers can receive alarms, maintenance suggestions, and energy efficiency reports at any time through the mobile App to ensure that the facilities are operating in the best state and improve user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a schematic flow diagram of the remote monitoring system for property facilities based on the Internet of Things of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not intended to limit the scope of the present invention. The present invention will be described more specifically by way of example in the following paragraphs. The advantages and features of the present invention will be more apparent from the following description and the claims.

[0076] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of the present invention herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0077] Embodiment

[0078] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0079] A remote monitoring system for property facilities based on the Internet of Things, comprising:

[0080] A real-time monitoring and remote control module, which is used to continuously collect the operation status data of property facilities through various sensors, and transmit the collected data to the data processing platform in real time. Managers can remotely operate the facilities through the PC terminal or the mobile terminal;

[0081] A fault warning and intelligent diagnosis module, which is used to analyze historical data and real-time data by using machine learning algorithms, predict equipment failures, automatically send warning signals when equipment anomalies are predicted, and automatically repair or adjust minor faults;

[0082] A data security and privacy protection module, which is used to ensure that data is not leaked or tampered with during transmission and storage by using AES256-bit encryption technology, adopt a multi-factor authentication mechanism to ensure that only authorized personnel can operate, and record all operation logs and conduct regular audits;

[0083] An energy efficiency management and optimization module, which is used to automatically adjust the operation modes of air conditioners, lighting, and elevator equipment according to real-time data and AI analysis, generate an energy consumption report through the data analysis of the system, provide property managers with the changes of high-energy-consuming equipment, and give optimization suggestions;

[0084] A user experience and operation module, which is used to generate an intuitive and easy-to-operate management interface. The interface customizes the layout according to actual needs. Through the mobile App, managers can view the operation status of property facilities at any time, remotely control and adjust them, and receive intelligent alarms, maintenance suggestions, and energy efficiency report notifications through the mobile terminal.

[0085] Through the real-time monitoring and remote control module, managers can view and operate property facilities anytime and anywhere without on-site inspections, improving management efficiency and reducing labor costs; the fault warning and intelligent diagnosis module uses machine learning algorithms to analyze data in real time, predict equipment failures and automatically send warning signals, reducing downtime and maintenance costs; the data security and privacy protection module ensures the security of data during transmission and storage through AES256 encryption and multi-factor authentication, avoiding data leakage or tampering and enhancing system security; the energy efficiency management and optimization module can automatically adjust the operating modes of equipment such as air conditioners, lighting, and elevators according to real-time data and AI analysis, thereby achieving energy supply on demand and reducing energy waste; the user experience and operation module provides an intuitive and easy-to-operate management interface, supporting custom layout according to actual needs.

[0086] By continuously collecting the operation status data of property facilities through various sensors and transmitting the collected data to the data processing platform in real time, the remote operations of facilities by managers through the PC or mobile terminal are as follows:

[0087] Monitor the operation status of facilities through temperature and humidity sensors, pressure sensors, flow sensors, and access control sensors, collect data, and send the collected real-time data to the data acquisition device or gateway through the IoT gateway and communication protocol;

[0088] After the data is transmitted to the cloud or local data processing platform, it is stored and processed through the data storage system. During the processing, data cleaning, preprocessing, and data analysis are used to process the transmitted data;

[0089] Managers connect to the data processing platform through the PC or mobile terminal App to view real-time data and remotely control facilities, and the real-time status of property facilities is displayed through the dashboard;

[0090] When managers issue control commands to turn on / off the air conditioner and adjust the operation mode of the elevator on the PC or mobile terminal, the operation commands are transmitted to the target facility equipment through the API interface or Modbus and BACnet control protocols for execution.

[0091] Through a variety of sensors such as temperature and humidity sensors, pressure sensors, flow sensors, and access control sensors, the system can comprehensively monitor the operating status of property facilities to ensure the accuracy and comprehensiveness of data collection; managers can remotely control the facilities through the PC or mobile app, such as turning on / off the air conditioner, adjusting the elevator operation mode, etc.; after the data is transmitted to the cloud or local data processing platform, through data cleaning, preprocessing, and analysis, the accuracy and effectiveness of the data are ensured; through the real-time data displayed on the dashboard, managers can quickly understand the operating status of the facilities and take timely measures when abnormalities occur; through data monitoring and analysis, managers can discover the energy consumption trends of the facilities and make timely adjustments, such as adjusting the air conditioner temperature, elevator scheduling, etc., so as to reduce energy consumption, optimize the equipment usage efficiency, and reduce operating costs.

[0092] Using machine learning algorithms to analyze historical data and real-time data, predict equipment failures, and automatically send warning signals when equipment anomalies are predicted. At the same time, for minor failures, automatic repair or adjustment is carried out as follows:

[0093] Real-time collect the equipment operation data through temperature, humidity, vibration, and current sensors, extract the working cycle, operating temperature, and load characteristics of the equipment from the collected historical data and real-time data, and use the principal component analysis PCA, L1 regularization, and recursive feature elimination RFE algorithms to select predictive features;

[0094] Adopt supervised learning, deep learning, and anomaly detection machine learning algorithms to train machine learning models through historical data, and use the validation set to evaluate the accuracy and robustness of the models;

[0095] Deploy the trained model to the actual operating environment, monitor the operating status of the equipment in real time, and input the real-time collected data into the machine learning model for prediction. Based on the pattern analysis of real-time data and historical data, the model identifies and judges the possibility of equipment failure;

[0096] When detecting anomalies or failure risks, notify managers through the SMS, email, and APP push alarm systems;

[0097] For small failures or equipment anomalies, adjust through a preset automatic repair algorithm, and the adjustment method is as follows:

[0098] Automatically adjust the air conditioner temperature and adjust the operating parameters of the current or load;

[0099] According to the real-time situation, adopt a closed-loop control system to automatically adjust the working frequency, temperature, or pressure of the equipment for automatic repair.

[0100] By analyzing historical and real-time data through machine learning algorithms, it is possible to predict equipment failures in advance and reduce the probability of sudden failures. For minor faults or equipment anomalies, the system adjusts the operating parameters of the equipment (such as adjusting air conditioner temperature, current or load) through an automatic repair algorithm, avoiding manual intervention and improving the stability and continuity of equipment operation. By monitoring and adjusting the equipment status in real time, it ensures that the equipment is always in the best operating state, optimizes energy efficiency, reduces energy consumption, and extends the service life of the equipment. Prediction and optimization can effectively avoid unnecessary energy waste. The combination of real-time monitoring and intelligent early warning can quickly respond to equipment anomalies and automatically push alarm information to management personnel via text messages, emails or apps, improving the response speed and efficiency of property management. The machine learning model provides accurate predictions and trend analyses through the analysis of historical and real-time data, helping management personnel make scientific decisions.

[0101] The AES 256-bit encryption technology is adopted to ensure that data is not leaked or tampered with during transmission and storage. A multi-factor authentication mechanism is adopted to ensure that only authorized personnel can perform operations, and all operation logs are recorded and regularly audited as follows:

[0102] During data transmission, AES 256 encryption is used to encrypt the data to ensure that the data is not stolen or tampered with during transmission, and the SSL / TLS protocol is combined to protect the security of the data transmission layer;

[0103] During the storage process, the data is encrypted and saved, and only authorized personnel or systems holding the correct key can decrypt the data. A multi-factor authentication mechanism of knowledge factor, possession factor, and biometric factor is adopted, and after passing the multi-factor authentication, the access permission is granted;

[0104] The system details the operation behaviors of users and administrators. The operation behaviors include login time, operation content, modified settings, and system access. For each operation, the operation IP address and device source are recorded again;

[0105] The operation logs are audited regularly. An automated audit tool is used to analyze a large number of logs to analyze whether there are any abnormal operation behaviors, unauthorized access, or privilege abuse problems;

[0106] By monitoring the operation logs of the system in real time, after identifying suspicious activities such as multiple failed logins and super-privilege operations, an alarm is immediately triggered to notify the administrator.

[0107] The AES 256-bit encryption technology is adopted to ensure that data is not stolen or tampered with during transmission and storage; multi-factor authentication, such as knowledge factor (password), possession factor (mobile phone, hardware token), and biometric factor (fingerprint, face recognition), enhances the security of the system; the system records all operation behaviors of users and administrators in detail, including login time, operation content, setting modification, system access, etc.; by monitoring the operation logs of the system in real time, security threats such as multiple failed logins, unauthorized operations, and suspicious activities can be quickly identified. Once an abnormal situation is detected, the system will immediately trigger an alarm and notify the administrator to ensure timely handling of potential security risks and reduce losses.

[0108] Automatically adjust the operation modes of air conditioners, lighting, and elevator equipment according to real-time data and AI analysis, and generate an energy consumption report through the data analysis of the system, providing property managers with the change situations of high-energy-consuming equipment and optimization suggestions as follows:

[0109] Through sensors and intelligent devices, collect the operation data of air conditioners, lighting, and elevator equipment in the building in real time, analyze the real-time collected data using AI algorithms, identify the energy consumption patterns in the building, and conduct predictive analysis on the equipment operation;

[0110] Based on factors such as time, season, and personnel activities, AI makes predictions and adjusts the operation modes of air conditioners and lighting equipment. Based on AI analysis, the system automatically adjusts the operation modes of air conditioners, lighting, and elevator equipment to optimize energy efficiency. The methods for optimizing energy efficiency are as follows:

[0111] Air conditioner adjustment: Automatically adjust the temperature and operation mode of the air conditioner according to the indoor temperature, humidity, weather changes, and the number of people.

[0112] Lighting adjustment: Automatically adjust the lighting brightness according to the natural light intensity and space usage.

[0113] Elevator optimization: Automatically adjust the elevator dispatching according to the floor usage.

[0114] The system regularly generates detailed energy consumption reports, analyzes the energy consumption situation, equipment efficiency, and usage patterns of each device. AI identifies the energy consumption change trends and potential high-energy-consuming problems through the analysis of historical energy consumption data, generates corresponding reports, and the system generates targeted energy-saving optimization suggestions for property managers according to the energy consumption analysis results;

[0115] AI monitors the energy consumption changes of each device in real time and automatically identifies high-energy-consuming devices. For high-energy-consuming devices, AI will generate optimization suggestions based on historical data and real-time data. According to the energy consumption reports and suggestions generated by the system, property managers set energy-saving goals for them and track their progress.

[0116] By collecting the operation data of equipment such as air conditioners, lighting, and elevators in a building in real time and automatically adjusting their operation modes in combination with AI algorithms, it is possible to significantly improve the operation efficiency of the equipment and avoid energy waste; AI can automatically optimize the operation of air conditioners, lighting, and elevators according to different usage patterns and environmental conditions, avoiding over - operation or inefficient work of the equipment; based on multi - dimensional factors such as time, season, and personnel activities, the system can predict the energy consumption pattern in the building and make adaptive adjustments in advance; the system can monitor the energy consumption of equipment in real time. Through the analysis of historical data and real - time data, AI can identify the trends, potential problems, and high - energy - consuming equipment of energy consumption, and issue alarms in a timely manner to help property managers quickly discover problems and take corresponding energy - saving measures; property managers can set energy - saving goals according to the reports and optimization suggestions generated by the system and track the energy - saving effects of each equipment through the system; by optimizing the energy use in the building and reducing energy waste, it not only reduces the operating costs but also helps the enterprise achieve sustainable development goals.

[0117] Generate an intuitive and easy - to - operate management interface. The interface customizes the layout according to actual needs. Through the mobile App, managers can view the operation status of property facilities at any time, conduct remote control and adjustment, and receive intelligent alarms, maintenance suggestions, and energy efficiency report notifications through the mobile end.

[0118] The system provides a customizable management interface. Users can adjust the layout according to actual needs. At the same time, the interface is compatible with PC, tablet, and mobile devices. Through the mobile App, managers can view the operation status of property facilities at any time, including the real - time data and operation health status of the equipment.

[0119] Managers remotely control and adjust the air - conditioner temperature, turn on / off the lighting, and adjust the elevator dispatching through the App to regulate the equipment.

[0120] Based on the real - time data and historical trends of the equipment, the system can intelligently identify potential problems and automatically issue alarms. When the energy consumed by the equipment exceeds the set threshold, the system automatically generates an alarm and pushes it to the manager's App.

[0121] According to the usage situation, energy consumption data, and historical maintenance records of the equipment, the system provides maintenance suggestions through AI analysis. AI analyzes the operation data of the equipment, predicts potential failures, and issues early warnings to managers in advance.

[0122] The system automatically generates monthly or quarterly energy efficiency reports, analyzes the energy consumption of the air - conditioning system, lighting equipment, and elevators in the building, and provides optimization suggestions. Managers receive energy efficiency reports, alarms, and optimization suggestion notifications in real time through the mobile App.

[0123] Managers can view the operating status of property facilities at any time through the mobile App, PC or tablet device, including the real-time data and health status of the equipment; the system provides a customizable management interface according to requirements, and users can adjust the layout of the interface according to actual business needs to ensure that the most important facilities and data can be displayed most intuitively and conveniently; the system intelligently analyzes the operation of the equipment through real-time data and historical trends, and can identify potential faults or anomalies in a timely manner; based on the usage of the equipment, energy consumption data and historical maintenance records, the AI system can analyze the operation status of the equipment, predict potential faults, and send early warnings to managers; the system regularly generates detailed energy efficiency reports, analyzes the energy consumption of air conditioning, lighting and elevator equipment, and provides optimization suggestions based on historical data and actual usage; managers can easily view the status of all equipment, alarm information and maintenance suggestions through the mobile App, and track the energy efficiency report in real time, without the need for cumbersome manual data collection and analysis; the system supports PC, tablet and mobile devices, enabling managers to conveniently manage property facilities whether in the office, at home or out.

[0124] The operation formula is:

[0125] Real-time monitoring and remote control achieve real-time data transmission and control through sensor data collection and communication protocols. The optimized data transmission formula is:

[0126]

[0127] The performance evaluation of the communication protocol is carried out through the throughput formula. The throughput formula is:

[0128]

[0129] Machine learning algorithms play a key role in the module. They mainly predict faults through time series analysis and prediction algorithms. When predicting faults based on historical data, a regression model or neural network model is used for prediction. The regression model formula is:

[0130]

[0131] Among them, Y is the predicted equipment status, are different characteristics of the equipment, are regression coefficients;

[0132] The neural network formula is:

[0133] h t =σ(W h ·x t +U h ·h t＝1 +b h )

[0134] Among them, h t is the output of the hidden layer, σ is the activation function, W h and U h are weight matrices, x t is the current input, h t＝1 is the hidden state at the previous moment, b h is the bias;

[0135] When using the AES256 encryption algorithm, the formula involves the operation of symmetric encryption. The AES encryption formula is:

[0136] C = E k (P)

[0137] Among them, C is the ciphertext after encryption, P is the plaintext, and E k is the encryption operation using the key k;

[0138] In the multi-factor authentication process, the reliability of authentication is calculated through the following authentication formula: Authentication success rate = P(authentication passed | valid certificate ∩ correct password ∩ correct dynamic password)

[0139] Among them, P is the probability of successful authentication;

[0140] Energy efficiency management adjusts the device operation mode through real-time data and optimizes the operation formula of the device by minimizing the energy consumption objective function:

[0141]

[0142] Among them, P i is the power of the th device, t i is the operation time of the device, and the goal is to minimize the total energy consumption;

[0143] For the intelligent optimization algorithm, the cloud uses the following objective function formula:

[0144]

[0145] Among them, x is the optimization decision variable;

[0146] The user experience and operation module involves user interface design and interaction models, and involves collaborative filtering algorithms or matrix factorization techniques. The user interface optimization analyzes user behavior data, and the formula is:

[0147]

[0148] Among them, is the predicted score for the user project and N(u) is related to the user Set of similar users, r ui The rating of item by user υ.

[0149] Through sensor data acquisition and communication protocol optimization, real-time monitoring and remote control become more efficient. The optimized data transmission formula can maximize the throughput of the communication protocol, reduce latency and packet loss. The application of machine learning algorithms, especially regression models and neural networks, can accurately predict faults based on historical data, which means that the device will be predicted and alarmed in advance before a fault occurs. The application of the AES256 encryption algorithm ensures the security during data transmission. The use of multi-factor authentication and the calculation of the reliability of authentication through the authentication formula can ensure that only authorized users can access the system. The device operation mode is adjusted through real-time data, and the device operation is optimized through the objective function of minimizing energy consumption. The intelligent optimization algorithm used on the cloud platform optimizes the decision variables through the objective function, helping property managers automate the decision-making process. Through collaborative filtering algorithms or matrix factorization techniques, the system can make personalized recommendations based on users' behavior data, enhancing the user experience.

[0150] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the Internet of Things-based remote monitoring system for property facilities as described above.

[0151] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by the processor, it implements the Internet of Things-based remote monitoring system for property facilities as described above.

[0152] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0154] The above embodiments of the present invention do not limit the protection scope of the present invention. The embodiments of the present invention are not limited thereto. All kinds of modifications, substitutions, or changes made to the above structure of the present invention according to the above content of the present invention, in accordance with the common general knowledge and customary means in the art, without departing from the above basic technical idea of the present invention, shall fall within the protection scope of the present invention.

Claims

1. The remote monitoring system of property facilities based on the Internet of Things is characterized by: Included are: Real-time monitoring and remote control module, which is used to continuously collect the operating status data of property facilities through various sensors, and transmit the collected data to the data processing platform in real time, so that managers can remotely operate the facilities through PC or mobile terminals; Fault warning and intelligent diagnosis module, which uses machine learning algorithms to analyze historical and real-time data, predict equipment failures, and automatically issue warning signals when equipment abnormalities are predicted. It also automatically repairs or adjusts minor faults. Data security and privacy protection module, which uses AES256-bit encryption technology to ensure that data is not leaked or tampered with during transmission and storage, uses a multi-factor authentication mechanism to ensure that only authorized personnel can perform operations, and records all operation logs and conducts regular audits; Energy efficiency management and optimization module, which is used to automatically adjust the operating mode of air conditioning, lighting, and elevator equipment based on real-time data and AI analysis, and generate energy consumption reports through system data analysis, providing property management personnel with changes in high-energy-consuming equipment and optimization suggestions; The user experience and operation module is used to generate an intuitive and easy-to-operate management interface. The interface layout is customized according to actual needs. Through the mobile app, managers can check the operating status of property facilities at any time and perform remote control and adjustment, and receive smart alarms, maintenance suggestions and energy efficiency report notifications through the mobile terminal.

2. The property facility remote monitoring system based on the Internet of Things according to claim 1 is characterized in that: The operating status data of property facilities is continuously collected through various sensors, and the collected data is transmitted to the data processing platform in real time. The management personnel can remotely operate the facilities through the PC or mobile terminal as follows: Monitor the operating status of facilities through temperature and humidity sensors, pressure sensors, flow sensors, and access control sensors, collect data, and send the collected real-time data to data collection devices or gateways through IoT gateways and communication protocols; After the data is transmitted to the cloud or local data processing platform, it is stored and processed through the data storage system. During the processing, data cleaning, preprocessing and data analysis are used to process the transmitted data; Managers can connect to the data processing platform through PC or mobile app to view real-time data and remotely control facilities. The real-time status of property facilities is displayed on the dashboard. When the manager issues a control command to turn on / off the air conditioner or adjust the operating mode of the elevator on the PC or mobile terminal, the operation command is transmitted to the target facility equipment through the API interface or Modbus or BACnet control protocol for execution.

3. The property facility remote monitoring system based on the Internet of Things according to claim 2 is characterized in that: Machine learning algorithms are used to analyze historical and real-time data to predict equipment failures and automatically send out warning signals when equipment abnormalities are predicted. At the same time, minor failures are automatically repaired or adjusted as follows: The device operation data is collected in real time through temperature, humidity, vibration, and current sensors. The working cycle, operating temperature, and load characteristics of the device are extracted from the collected historical data and real-time data. The principal component analysis (PCA), L1 regularization, and recursive feature elimination (RFE) algorithm are used to select predictive features. Use supervised learning, deep learning, and anomaly detection machine learning algorithms to train machine learning models using historical data, and use validation sets to evaluate the accuracy and robustness of the models; Deploy the trained model to the actual operating environment, monitor the operating status of the equipment in real time, and input the real-time collected data into the machine learning model for prediction. The model analyzes the patterns of real-time data and historical data to identify the possibility of equipment failure. When an abnormality or failure risk is detected, the alarm system will notify the management personnel via SMS, email, or APP push; For small faults or equipment abnormalities, adjustments are made through the preset automatic repair algorithm. The adjustment method is as follows: Automatically adjust the air-conditioning temperature and the operating parameters of the current or load; According to the real-time situation, a closed-loop control system is used to automatically adjust the operating frequency, temperature or pressure of the equipment for automatic repair.

4. The property facility remote monitoring system based on the Internet of Things according to claim 3 is characterized in that: AES256-bit encryption technology is used to ensure that data is not leaked or tampered with during transmission and storage. A multi-factor authentication mechanism is used to ensure that only authorized personnel can perform operations. All operation logs are recorded and audited regularly: During data transmission, AES256 encryption is used to encrypt data to ensure that the data is not stolen or tampered with during transmission, and the SSL / TLS protocol is combined to protect the security of the data transmission layer; During the storage process, the data is encrypted and saved. Only authorized personnel or systems holding the correct key can decrypt the data. A multi-factor authentication mechanism using knowledge factors, possession factors, and biometric factors is used. Only after passing the multi-factor authentication can the access rights be granted. The system records the operation behaviors of users and administrators in detail, including login time, operation content, modified settings, and system access. For each operation, the operation IP address and device source are recorded again; Audit operation logs regularly and use automated audit tools to analyze large amounts of logs to determine if there are any abnormal operations, unauthorized access, or abuse of authority. By real-time monitoring of the system's operation logs, once multiple failed logins and suspicious activities such as over-authorized operations are identified, an alarm will be triggered immediately to notify the administrator.

5. The property facility remote monitoring system based on the Internet of Things according to claim 4 is characterized in that: Automatically adjust the operating modes of air conditioning, lighting, and elevator equipment based on real-time data and AI analysis, and generate energy consumption reports through system data analysis to provide property management personnel with changes in high-energy-consuming equipment and optimization suggestions: Through sensors and smart devices, the operation data of air conditioning, lighting, and elevator equipment in the building are collected in real time. AI algorithms are used to analyze the real-time collected data, identify energy consumption patterns in the building, and conduct predictive analysis of equipment operation. Based on time, season, and personnel activity factors, AI predicts and adjusts the operating mode of air conditioning and lighting equipment. Based on AI analysis, the system automatically adjusts the operating mode of air conditioning, lighting, and elevator equipment to optimize energy efficiency. The methods for optimizing energy efficiency are: Air conditioning adjustment, automatically adjusting the temperature and operation mode of the air conditioner according to indoor temperature, humidity, weather changes and the number of people; Lighting adjustment: automatically adjust lighting brightness according to natural light intensity and space usage; Elevator optimization, automatically adjusting elevator dispatching according to floor usage; The system regularly generates detailed energy consumption reports, analyzing the energy consumption, equipment efficiency, and usage patterns of each device. AI identifies energy consumption trends and potential high energy consumption issues through analysis of historical energy consumption data, and generates corresponding reports. Based on the energy consumption analysis results, the system generates targeted energy-saving optimization suggestions to property management personnel. AI monitors the energy consumption changes of each device in real time and automatically identifies high-energy-consuming devices. For high-energy-consuming devices, AI will generate optimization suggestions based on historical data and real-time data. Based on the energy consumption reports and suggestions generated by the system, property management personnel set energy-saving goals and track their progress.

6. The property facility remote monitoring system based on the Internet of Things according to claim 5 is characterized in that: Generate an intuitive and easy-to-operate management interface with a customized layout based on actual needs. Through the mobile app, managers can view the operating status of property facilities at any time and perform remote control and adjustment, and receive intelligent alarms, maintenance suggestions and energy efficiency report notifications through the mobile terminal: The system provides a customizable management interface, and users can adjust the layout according to actual needs. The interface is compatible with PC, tablet and mobile devices. Through the mobile app, managers can check the operating status of property facilities at any time, including real-time data and operating health status of equipment; Managers can use the App to remotely control the air conditioning temperature, turn on and off lighting, and adjust elevator dispatching and regulating equipment; The system intelligently identifies potential problems and automatically issues alarms based on the real-time data and historical trends of the equipment. When the energy consumed by the equipment exceeds the set threshold, the system automatically generates an alarm and pushes it to the manager's App; Based on the equipment usage, energy consumption data and historical maintenance records, the system provides maintenance suggestions through AI analysis. AI analyzes the equipment's operating data to predict potential failures and issue early warnings to managers. The system automatically generates monthly or quarterly energy efficiency reports, analyzes the energy consumption of the building's air-conditioning systems, lighting equipment, and elevators, and provides optimization suggestions. Managers receive real-time energy efficiency reports, alarms, and optimization suggestion notifications through the mobile app.

7. The property facility remote monitoring system based on the Internet of Things according to claim 6 is characterized in that: The calculation formula is: Real-time monitoring and remote control realize real-time data transmission and control through sensor data acquisition and communication protocol. The formula for optimizing data transmission is: The performance evaluation of the communication protocol is carried out through the throughput formula, which is: The machine learning algorithm plays a key role in the module. It mainly predicts faults through time series analysis and prediction algorithms. When predicting faults based on historical data, a regression model or a neural network model is used for prediction. The regression model formula is: Where Y is the predicted device status, For different characteristics of the equipment, is the regression coefficient; The neural network formula is: Among them, h t is the output of the hidden layer, σ is the activation function, W h and U h is the weight matrix, is the current input, h t＝1 is the hidden state at the previous moment, b h is bias; When using the AES256 encryption algorithm, the formula involves symmetric encryption operations. The AES encryption formula is: C=E k (P) Among them, C is the encrypted ciphertext, P is the plaintext, and E k is the encryption operation performed using key k; In the multi-factor authentication process, the reliability of authentication is calculated by the following authentication formula: Authentication success rate = P (authentication passed | valid certificate | correct password | correct dynamic password) where P is the probability of successful authentication; Energy efficiency management adjusts the equipment operation mode through real-time data and optimizes the equipment operation formula by minimizing the energy consumption objective function: Among them, P i For the The power of each device, t i is the time the equipment is running, with the goal of minimizing total energy consumption; For the intelligent optimization algorithm, the cloud uses the following objective function formula: Among them, x is the optimization decision variable; The user experience and operation module involves user interface design and interaction models, collaborative filtering algorithms or matrix decomposition technology, and user interface optimization analyzes user behavior data. The formula is: in, For users project Predicted rating, N(u) is the Similar user set, r ui For users to 's rating.

8. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a remote monitoring system for property facilities based on the Internet of Things as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a remote monitoring system for property facilities based on the Internet of Things as described in any one of claims 1-7 is implemented.

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