Family safety real-time monitoring and early warning method, system and terminal

Abstract

The invention discloses a home security real-time monitoring and early warning method, system and terminal, and the method comprises the steps: determining the demands and targets of a smart home security system according to the actual demands of a home user; deploying smart home devices and sensors according to the requirements and targets of the smart home security system, and connecting and controlling all the smart home devices and sensors through a preset operating system; when data transmission and communication are carried out in the smart home security system, data are encrypted and transmitted through a security protocol; and acquiring sensor data acquired by each sensor, analyzing and processing the sensor data by adopting a machine learning algorithm, outputting a data analysis result, and triggering an early warning mechanism if the data analysis result shows that monitoring abnormity exists. According to the invention, the security is improved, the user experience is enhanced, and the system integration is promoted.

Description

Technical Field

[0001] The present invention relates to the field of smart home security technology, and in particular to a real-time monitoring and early warning method, system, terminal and computer-readable storage medium for home security. Background Art

[0002] Smart home security system refers to a system that uses advanced computer system technology, network communication technology, Internet of Things technology, etc. to provide security protection for the home. It mainly includes: video surveillance, intrusion alarm, access control system, fire alarm, gas detection, emergency assistance, smart locks, environmental monitoring, etc.

[0003] At present, there are some problems with smart home security systems, such as low security, poor user experience, and difficulty in system integration. The reasons for the low security are: hardware vulnerabilities, software vulnerabilities, network insecurity, unencrypted data transmission, etc.; the reasons for the poor user experience are: complex operation, difficult system integration, slow response speed, high false alarm rate, and poor after-sales service; the reasons for the difficulty in system integration are: inconsistent protocols, poor hardware compatibility, high degree of customization, and difficulty in updating and maintenance.

[0004] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0005] The main purpose of the present invention is to provide a real-time monitoring and early warning method, system, terminal and computer-readable storage medium for home security, aiming to solve the problems of low security, poor user experience and difficult system integration of smart home security systems in the prior art.

[0006] To achieve the above object, the present invention provides a real-time monitoring and early warning method for home safety, the real-time monitoring and early warning method for home safety comprising the following steps:

[0007] Determine the requirements and goals of the smart home security system based on the actual needs of home users;

[0008] Deploy smart home devices and sensors according to the requirements and goals of the smart home security system, and connect and control all smart home devices and sensors through a preset operating system;

[0009] When data transmission and communication are performed in the smart home security system, data is encrypted and transmitted through a security protocol;

[0010] The sensor data collected by each sensor is obtained, the sensor data is analyzed and processed using a machine learning algorithm, and a data analysis result is output. If the data analysis result indicates that there is a monitoring anomaly, an early warning mechanism is triggered.

[0011] Optionally, the real-time monitoring and early warning method for home safety, wherein the deployment of smart home devices and sensors further comprises:

[0012] The preset operating system is pre-installed in all the smart home devices, and all the smart home devices are interconnected through the preset operating system.

[0013] Optionally, in the real-time monitoring and early warning method for home safety, the implementation steps of the safety protocol include:

[0014] Configure security protocols in the default operating system;

[0015] Assign certificates to each smart home device;

[0016] Generate, distribute and store cryptographic keys;

[0017] Establish an encrypted channel between smart home devices and servers, and all data transmission is carried out through the encrypted channel;

[0018] Regularly update security protocols and encryption algorithms, audit system logs, and monitor any suspicious network activities.

[0019] Optionally, in the real-time monitoring and early warning method for home safety, the sensor data includes: motion sensor data, door and window sensor data, smoke sensor data, flame sensor data, gas leak sensor data, temperature and humidity sensor data, sound sensor data, light sensor data and vibration sensor data;

[0020] The motion sensor data is used to detect whether there is intrusion;

[0021] The door and window sensor data is used to monitor the opening and closing status of doors and windows;

[0022] The smoke sensor data is used to detect smoke concentration and issue a fire warning based on the smoke concentration;

[0023] The flame sensor data is used to detect whether a flame exists;

[0024] The gas leakage sensor data is used to detect the concentration of gas or other harmful gases;

[0025] The temperature and humidity sensor data is used to monitor indoor temperature and humidity;

[0026] The sound sensor data is used to capture environmental sounds and detect abnormal sounds;

[0027] The light sensor data is used to monitor the ambient light intensity and automatically adjust the lighting;

[0028] The vibration sensor data is used to detect whether abnormal vibration occurs.

[0029] Optionally, in the real-time monitoring and early warning method for home safety, the machine learning algorithm includes: a supervised learning algorithm, an unsupervised learning algorithm, and a deep learning algorithm;

[0030] The adopting of a machine learning algorithm to analyze and process the sensor data specifically includes:

[0031] Performing data preprocessing on the sensor data, wherein the data preprocessing includes cleaning the data and standardizing or normalizing the data;

[0032] Extract target features from sensor data after data preprocessing;

[0033] Use the labeled historical data set to train the anomaly recognition model, use the validation set and test set to evaluate the performance of the anomaly recognition model, and adjust the model parameters until the anomaly recognition model achieves satisfactory accuracy and recall;

[0034] Deploy the trained anomaly recognition model into the smart home security system to analyze target features in sensor data in real time;

[0035] When an abnormal pattern is detected, an early warning mechanism is triggered.

[0036] Optionally, the real-time monitoring and early warning method for home safety, wherein the real-time monitoring and early warning method for home safety further comprises:

[0037] Develop and integrate various smart home security applications using the open interfaces and application development platform of the preset operating system.

[0038] Optionally, the real-time monitoring and early warning method for home security, wherein the use of an open interface and application development platform of a preset operating system to develop and integrate various smart home security applications, specifically includes:

[0039] Determine user needs, where the user needs are functions or extended services that the user wants to customize;

[0040] Select appropriate smart home devices and components based on the user's needs;

[0041] Configure using the application development platform or user interface of the preset operating system;

[0042] According to the specific content of the customization, use the software development kit provided by the preset operating system to write scripts or use the application programming interface provided by the application development platform of the preset operating system to develop applications;

[0043] After configuration or development is complete, the pre-configured operating system is tested to ensure that the newly added features work as expected and to make necessary optimizations.

[0044] In addition, to achieve the above-mentioned purpose, the present invention also provides a real-time monitoring and early warning system for home safety, wherein the real-time monitoring and early warning system for home safety comprises:

[0045] A demand determination module is used to determine the needs and goals of the smart home security system based on the actual needs of home users;

[0046] A device deployment module, used to deploy smart home devices and sensors according to the requirements and goals of the smart home security system, and connect and control all smart home devices and sensors through a preset operating system;

[0047] A data transmission module, used to encrypt and transmit data through a security protocol when performing data transmission and communication in the smart home security system;

[0048] The abnormal warning module is used to obtain the sensor data collected by each sensor, analyze and process the sensor data using a machine learning algorithm, and output the data analysis results. If the data analysis results indicate that there is a monitoring abnormality, the early warning mechanism is triggered.

[0049] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a real-time monitoring and early warning program for home safety stored on the memory and executable on the processor, and when the real-time monitoring and early warning program for home safety is executed by the processor, the steps of the real-time monitoring and early warning method for home safety as described above are implemented.

[0050] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a real-time monitoring and early warning program for home safety, and when the real-time monitoring and early warning program for home safety is executed by a processor, the steps of the real-time monitoring and early warning method for home safety as described above are implemented.

[0051] In the present invention, the needs and goals of the smart home security system are determined according to the actual needs of home users; smart home devices and sensors are deployed according to the needs and goals of the smart home security system, and all smart home devices and sensors are connected and controlled through a preset operating system; when data transmission and communication are performed in the smart home security system, data is encrypted and transmitted through a security protocol; sensor data collected by each sensor is obtained, and the sensor data is analyzed and processed using a machine learning algorithm, and a data analysis result is output. If the data analysis result indicates that there is a monitoring anomaly, an early warning mechanism is triggered. The present invention combines a preset operating system with smart home devices and sensors, and realizes comprehensive monitoring and control of home security through security protocols and machine learning algorithms, thereby improving security, enhancing user experience, and promoting system integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a preferred embodiment of the real-time monitoring and early warning method for family safety of the present invention;

[0053] Figure 2 It is a structural diagram of a preferred embodiment of the real-time monitoring and early warning system for home safety of the present invention;

[0054] Figure 3 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] The real-time monitoring and early warning method for home safety described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the real-time monitoring and early warning method for home safety includes the following steps:

[0057] Step S10: Determine the requirements and goals of the smart home security system based on the actual needs of the home users.

[0058] Specifically, determine the needs and goals of the smart home security system, including the objects that need to be monitored and controlled (the various entities and devices involved in the system: such as home entrances, windows, the elderly, pets, cameras, etc.), security requirements, etc. For example, basic security monitoring, real-time monitoring of home entrances, windows, important rooms and other areas, the goal is to prevent illegal intrusions and ensure the personal and property safety of family members.

[0059] Step S20: deploy smart home devices and sensors according to the requirements and goals of the smart home security system, and connect and control all smart home devices and sensors through a preset operating system.

[0060] Specifically, smart home devices and sensors are deployed, and the devices are connected and controlled through a preset operating system (for example, the kaihongOS operating system. The KaihongOS operating system is the core of the home security system. KaihongOS plays a core role in the entire smart home security system. It is not only the center of data processing, but also the center of device management and user interaction. It is a bridge connecting all smart home devices and users. Through kaihongOS, users can manage and control their smart home devices more conveniently and enjoy a safer, smarter and more convenient life experience). The preset operating system is pre-installed in all the smart home devices, and all the smart home devices are interconnected through the preset operating system.

[0061] Step S30: When data transmission and communication are performed in the smart home security system, the data is encrypted and transmitted through a security protocol.

[0062] Specifically, common security protocols include:

[0063] (1) SSL / TLS (Secure Sockets Layer / Transport Layer Security): used to establish an encrypted link between the client and the server to ensure the security of data during transmission.

[0064] (2) IPsec (Internet Protocol Security): used to protect network communications at the IP layer and can provide encryption and authentication for data packets.

[0065] (3) WPA3 (Wi-Fi Protected Access 3): used to protect the security of wireless networks and prevent unauthorized access and data leakage.

[0066] (4) ZigBee Security: If the smart home device uses the ZigBee protocol, the protocol provides encryption and authentication mechanisms.

[0067] In the present invention, the process of protecting data transmission and communication:

[0068] (1) Device authentication: When a device joins the network, the system will perform device authentication to ensure that only authenticated devices can join the network. Device identity authentication is performed using certificates or pre-shared keys (PSK).

[0069] (2) Data encryption: For the transmitted data, encryption algorithms (such as AES-256) are used to encrypt it to ensure that the data cannot be deciphered even if it is intercepted during transmission. The encryption process usually involves key exchange, such as using the Diffie-Hellman key exchange protocol to securely exchange keys.

[0070] (3) Data integrity: Use hash functions (such as SHA-256) and message authentication codes (MAC) to ensure data integrity and prevent data tampering during transmission. The receiver verifies the MAC of the data packet to ensure that the data has not been modified.

[0071] (4) Security protocol implementation steps:

[0072] Step 1. Configure security protocols: Configure security protocols in kaihongOS, such as setting SSL / TLS certificates and configuring IPsec policies.

[0073] Step 2: Device certificate management: Assign a certificate to each device to ensure the authenticity and uniqueness of the device identity.

[0074] Step 3: Key management: Generate, distribute and store encryption keys to ensure key security.

[0075] Step 4: Establish an encrypted channel: Establish an encrypted channel between the device and the server, and all data transmission is carried out through this encrypted channel.

[0076] Step 5: Regularly update and audit: Regularly update security protocols and encryption algorithms to respond to new security threats. Audit system logs to monitor any suspicious network activities.

[0077] Through the above process, the smart home security system based on kaihongOS can effectively protect the security of data transmission and communication, prevent unauthorized access and data leakage, and ensure the security of the home network environment. After the configuration or development is completed, the user needs to test the system to ensure that the newly added functions work as expected and make necessary optimizations.

[0078] Step S40: acquiring sensor data collected by each sensor, analyzing and processing the sensor data using a machine learning algorithm, and outputting a data analysis result; if the data analysis result indicates that there is a monitoring anomaly, triggering an early warning mechanism.

[0079] Specifically, the sensor data includes: motion sensor data, door and window sensor data, smoke sensor data, flame sensor data, gas leak sensor data, temperature and humidity sensor data, sound sensor data, light sensor data and vibration sensor data; the motion sensor data is used to detect whether there is an intrusion (i.e., detect whether there is any movement, usually used for intrusion detection); the door and window sensor data is used to monitor the opening and closing status of doors and windows; the smoke sensor data is used to detect the smoke concentration and issue a fire warning based on the smoke concentration; the flame sensor data is used to detect whether there is a flame; the gas leak sensor data is used to detect the concentration of gas or other harmful gases; the temperature and humidity sensor data is used to monitor indoor temperature and humidity; the sound sensor data is used to capture ambient sounds and abnormal sound detection; the light sensor data is used to monitor the ambient light intensity and automatically adjust the lighting; the vibration sensor data is used to detect whether there is an abnormal vibration (i.e., detect whether there is a vibration, which may be used for earthquake warning or intrusion detection).

[0080] Among them, the machine learning algorithms include: supervised learning algorithms, unsupervised learning algorithms and deep learning algorithms; supervised learning algorithms: decision trees: used for classification and regression tasks, can identify patterns in sensor data; support vector machines (SVM): used for classification problems, can distinguish between normal and abnormal behaviors. Unsupervised learning algorithms: clustering algorithms (such as K-means): used to find natural groupings in data, can be used for anomaly detection; principal component analysis (PCA): used for data dimensionality reduction, can extract the main features of sensor data. Deep learning algorithms: convolutional neural networks (CNN): used for image and video analysis, can process data from cameras; recurrent neural networks (RNN): used for time series data analysis, suitable for processing continuous sensor data.

[0081] The process of analyzing and processing sensor data through machine learning algorithms:

[0082] (1) Data preprocessing: clean the data, remove noise and irrelevant information; standardize or normalize the data to make it suitable for machine learning algorithm processing.

[0083] (2) Feature extraction: Extract useful features from the raw data, such as using PCA to perform feature dimensionality reduction.

[0084] (3) Model training: When using a supervised learning algorithm, the labeled data set is used to train the anomaly recognition model; when using an unsupervised learning algorithm, the algorithm is allowed to discover patterns in the data on its own.

[0085] (4) Model validation and testing: Use validation sets and test sets to evaluate the performance of the anomaly recognition model; adjust model parameters until satisfactory accuracy and recall rates are achieved.

[0086] (5) Real-time monitoring and early warning: Deploy the trained model into the system to analyze sensor data in real time; when abnormal patterns are detected, trigger the early warning mechanism.

[0087] For example, scenario: using machine learning algorithms to detect abnormal intrusion behavior. Sensor data: motion sensor and door and window sensor data. Algorithm: decision tree. The process is as follows:

[0088] (1) Data preprocessing: Collect data from motion and door and window sensors to remove false alarms caused by pets or daily activities.

[0089] (2) Feature extraction: extract features such as time (such as night), door and window status (open or closed), and movement activity (continuous or instantaneous).

[0090] (3) Model training: Use the decision tree algorithm to train the model based on historical data (normal and intrusion events).

[0091] (4) Model validation and testing: Use cross-validation method to evaluate model performance and adjust decision tree parameters.

[0092] (5) Real-time monitoring and early warning: Deploy models to analyze sensor data in real time. If the model predicts an intrusion, the system will send an alert to the user and may trigger other security measures, such as automatic alarm or notification to a security company.

[0093] Through the above process, the smart home security system can more intelligently identify abnormal situations in the home environment and respond in a timely manner.

[0094] Furthermore, various smart home security applications, such as intrusion detection, fire alarm, etc., are developed and integrated using the open interfaces and application development platform of the preset operating system. The details are as follows:

[0095] (1) Determine user needs, which are functions or extended services that users want to customize. Users first need to clarify the functions or extended services that they want to customize. For example, users may want to add a pet monitoring function, or want to expand an automatic watering system on the existing system.

[0096] (2) Select appropriate smart home devices and components based on the user's needs; Based on the needs, the user needs to select appropriate smart home devices and components. For example, for pet monitoring, the user may need to purchase a movable camera and a motion sensor. For an automatic watering system, the user may need to purchase a soil moisture sensor and an automatic watering controller.

[0097] (3) Use the application development platform or user interface of the preset operating system to configure; users can use the application development platform or user interface of kaihongOS to perform the following configurations:

[0098] Pet monitoring configuration:

[0099] 1) Add a new camera device in the kaihongOS application;

[0100] 2) Set up a motion sensor to trigger camera recording when pet activity is detected;

[0101] 3) Configure the notification system to send notifications to the user's mobile phone when the camera captures pet activities.

[0102] Automatic watering system expansion:

[0103] 1) Integrate a soil moisture sensor in kaihongOS to monitor soil moisture levels;

[0104] 2) Set up an automatic watering controller to automatically start watering when the soil moisture is lower than the set threshold;

[0105] 3) Set up scheduled tasks through kaihongOS, such as checking soil moisture and watering at 4 pm every day.

[0106] (4) According to the specific content of customization, use the software development kit provided by the preset operating system to write scripts or use the application programming interface provided by the application development platform of the preset operating system to develop applications; Write scripts: Users can use the SDK (software development kit) provided by kaihongOS to write scripts, such as using Python scripts to analyze sensor data and control smart devices based on the results. Develop applications: For more complex functions, users may need to develop a complete application. The application development platform of kaihongOS usually provides an API (application programming interface) to allow developers to create custom applications.

[0107] (5) After configuration or development is completed, test the preset operating system to ensure that the newly added functions work as expected and make necessary optimizations.

[0108] Furthermore, users can remotely monitor (remote monitoring is an application installed on a mobile phone or tablet that functions like a remote control to control and view the home security system) and control the smart home security system through terminal devices such as mobile phones and tablets.

[0109] The present invention combines the kaihongOS operating system with smart home devices and sensors, and realizes comprehensive monitoring and control of home security through security protocols and machine learning algorithms. Through the open interface and application development platform of kaihongOS, users can easily customize and expand system functions, improve security, enhance user experience, and promote system integration.

[0110] Furthermore, if Figure 2 As shown, based on the above-mentioned real-time monitoring and early warning method for home safety, the present invention also provides a real-time monitoring and early warning system for home safety, wherein the real-time monitoring and early warning system for home safety comprises:

[0111] The demand determination module 51 is used to determine the demand and goal of the smart home security system according to the actual demand of the home user;

[0112] A device deployment module 52, for deploying smart home devices and sensors according to the requirements and objectives of the smart home security system, and connecting and controlling all smart home devices and sensors through a preset operating system;

[0113] A data transmission module 53, used to encrypt and transmit data through a security protocol when performing data transmission and communication in the smart home security system;

[0114] The abnormal warning module 54 is used to obtain the sensor data collected by each sensor, analyze and process the sensor data using a machine learning algorithm, and output a data analysis result. If the data analysis result indicates that there is a monitoring abnormality, a warning mechanism is triggered.

[0115] Furthermore, if Figure 3 As shown, based on the above-mentioned real-time monitoring and early warning method and system for home safety, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 3 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0116] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a real-time monitoring and early warning program 40 for home safety is stored on the memory 20, and the real-time monitoring and early warning program 40 for home safety can be executed by the processor 10, thereby realizing the real-time monitoring and early warning method for home safety in the present application.

[0117] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the real-time monitoring and early warning method for home security.

[0118] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.

[0119] In one embodiment, when the processor 10 executes the home security real-time monitoring and warning program 40 in the memory 20, the following steps are implemented:

[0120] Determine the requirements and goals of the smart home security system based on the actual needs of home users;

[0121] Deploy smart home devices and sensors according to the requirements and goals of the smart home security system, and connect and control all smart home devices and sensors through a preset operating system;

[0122] When data transmission and communication are performed in the smart home security system, data is encrypted and transmitted through a security protocol;

[0123] The sensor data collected by each sensor is obtained, the sensor data is analyzed and processed using a machine learning algorithm, and a data analysis result is output. If the data analysis result indicates that there is a monitoring anomaly, an early warning mechanism is triggered.

[0124] The deployment of smart home devices and sensors further includes:

[0125] The preset operating system is pre-installed in all the smart home devices, and all the smart home devices are interconnected through the preset operating system.

[0126] The implementation steps of the security protocol include:

[0127] Configure security protocols in the default operating system;

[0128] Assign certificates to each smart home device;

[0129] Generate, distribute and store cryptographic keys;

[0130] Establish an encrypted channel between smart home devices and servers, and all data transmission is carried out through the encrypted channel;

[0131] Regularly update security protocols and encryption algorithms, audit system logs, and monitor any suspicious network activities.

[0132] The sensor data includes: motion sensor data, door and window sensor data, smoke sensor data, flame sensor data, gas leak sensor data, temperature and humidity sensor data, sound sensor data, light sensor data and vibration sensor data;

[0133] The motion sensor data is used to detect whether there is intrusion;

[0134] The door and window sensor data is used to monitor the opening and closing status of doors and windows;

[0135] The smoke sensor data is used to detect smoke concentration and issue a fire warning based on the smoke concentration;

[0136] The flame sensor data is used to detect whether a flame exists;

[0137] The gas leakage sensor data is used to detect the concentration of gas or other harmful gases;

[0138] The temperature and humidity sensor data is used to monitor indoor temperature and humidity;

[0139] The sound sensor data is used to capture environmental sounds and detect abnormal sounds;

[0140] The light sensor data is used to monitor the ambient light intensity and automatically adjust the lighting;

[0141] The vibration sensor data is used to detect whether abnormal vibration occurs.

[0142] Wherein, the machine learning algorithm includes: supervised learning algorithm, unsupervised learning algorithm and deep learning algorithm;

[0143] The adopting of a machine learning algorithm to analyze and process the sensor data specifically includes:

[0144] Performing data preprocessing on the sensor data, wherein the data preprocessing includes cleaning the data and standardizing or normalizing the data;

[0145] Extract target features from sensor data after data preprocessing;

[0146] Use the labeled historical data set to train the anomaly recognition model, use the validation set and test set to evaluate the performance of the anomaly recognition model, and adjust the model parameters until the anomaly recognition model achieves satisfactory accuracy and recall;

[0147] Deploy the trained anomaly recognition model into the smart home security system to analyze target features in sensor data in real time;

[0148] When an abnormal pattern is detected, an early warning mechanism is triggered.

[0149] The real-time monitoring and early warning method for home safety also includes:

[0150] Develop and integrate various smart home security applications using the open interfaces and application development platform of the preset operating system.

[0151] The use of the open interface and application development platform of the preset operating system to develop and integrate various smart home security applications specifically includes:

[0152] Determine user needs, where the user needs are functions or extended services that the user wants to customize;

[0153] Select appropriate smart home devices and components based on the user's needs;

[0154] Configure using the application development platform or user interface of the preset operating system;

[0155] According to the specific content of the customization, use the software development kit provided by the preset operating system to write scripts or use the application programming interface provided by the application development platform of the preset operating system to develop applications;

[0156] After configuration or development is complete, the pre-configured operating system is tested to ensure that the newly added features work as expected and to make necessary optimizations.

[0157] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a real-time monitoring and early warning program for home safety, and when the real-time monitoring and early warning program for home safety is executed by a processor, the steps of the real-time monitoring and early warning method for home safety as described above are implemented.

[0158] In summary, the present invention provides a real-time monitoring and early warning method, system, terminal and computer-readable storage medium for home security, the method comprising: determining the needs and goals of the smart home security system according to the actual needs of home users; deploying smart home devices and sensors according to the needs and goals of the smart home security system, and connecting and controlling all smart home devices and sensors through a preset operating system; encrypting and transmitting data through a security protocol when transmitting and communicating data in the smart home security system; obtaining sensor data collected by each sensor, analyzing and processing the sensor data using a machine learning algorithm, and outputting a data analysis result, and triggering an early warning mechanism if the data analysis result indicates that there is a monitoring anomaly. The present invention combines a preset operating system with smart home devices and sensors, and realizes comprehensive monitoring and control of home security through security protocols and machine learning algorithms, thereby improving security, enhancing user experience, and promoting system integration.

[0159] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or terminal including the element.

[0160] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0161] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A real-time monitoring and early warning method for home safety, It is characterized in that The real-time monitoring and early warning method for family safety includes: Determine the requirements and goals of the smart home security system based on the actual needs of home users; Deploy smart home devices and sensors according to the requirements and goals of the smart home security system, and connect and control all smart home devices and sensors through a preset operating system; When data transmission and communication are performed in the smart home security system, data is encrypted and transmitted through a security protocol; The sensor data collected by each sensor is obtained, the sensor data is analyzed and processed using a machine learning algorithm, and a data analysis result is output. If the data analysis result indicates that there is a monitoring anomaly, an early warning mechanism is triggered.

2. The real-time monitoring and early warning method for home safety according to claim 1, It is characterized in that The deployment of smart home devices and sensors may then include: The preset operating system is pre-installed in all the smart home devices, and all the smart home devices are interconnected through the preset operating system.

3. The real-time monitoring and early warning method for home safety according to claim 1, It is characterized in that The implementation steps of the security protocol include: Configure security protocols in the default operating system; Assign certificates to each smart home device; Generate, distribute and store cryptographic keys; Establish an encrypted channel between smart home devices and servers, and all data transmission is carried out through the encrypted channel; Regularly update security protocols and encryption algorithms, audit system logs, and monitor any suspicious network activities.

4. The real-time monitoring and early warning method for home safety according to claim 1, It is characterized in that The sensor data includes: motion sensor data, door and window sensor data, smoke sensor data, flame sensor data, gas leak sensor data, temperature and humidity sensor data, sound sensor data, light sensor data and vibration sensor data; The motion sensor data is used to detect whether there is intrusion; The door and window sensor data is used to monitor the opening and closing status of doors and windows; The smoke sensor data is used to detect smoke concentration and issue a fire warning based on the smoke concentration; The flame sensor data is used to detect whether a flame exists; The gas leakage sensor data is used to detect the concentration of gas or other harmful gases; The temperature and humidity sensor data is used to monitor indoor temperature and humidity; The sound sensor data is used to capture environmental sounds and detect abnormal sounds; The light sensor data is used to monitor the ambient light intensity and automatically adjust the lighting; The vibration sensor data is used to detect whether abnormal vibration occurs.

5. The real-time monitoring and early warning method for home safety according to claim 1, It is characterized in that The machine learning algorithms include: supervised learning algorithms, unsupervised learning algorithms and deep learning algorithms; The adopting of a machine learning algorithm to analyze and process the sensor data specifically includes: Performing data preprocessing on the sensor data, wherein the data preprocessing includes cleaning the data and standardizing or normalizing the data; Extract target features from sensor data after data preprocessing; Use the labeled historical data set to train the anomaly recognition model, use the validation set and test set to evaluate the performance of the anomaly recognition model, and adjust the model parameters until the anomaly recognition model achieves satisfactory accuracy and recall; Deploy the trained anomaly recognition model into the smart home security system to analyze target features in sensor data in real time; When an abnormal pattern is detected, an early warning mechanism is triggered.

6. The real-time monitoring and early warning method for home safety according to claim 1, It is characterized in that The real-time monitoring and early warning method for family safety also includes: Develop and integrate various smart home security applications using the open interfaces and application development platform of the preset operating system.

7. The real-time monitoring and early warning method for home safety according to claim 6, It is characterized in that The use of the open interface and application development platform of the preset operating system to develop and integrate various smart home security applications specifically includes: Determine user needs, where the user needs are functions or extended services that the user wants to customize; Select appropriate smart home devices and components based on the user's needs; Configure using the application development platform or user interface of the preset operating system; According to the specific content of the customization, use the software development kit provided by the preset operating system to write scripts or use the application programming interface provided by the application development platform of the preset operating system to develop applications; After configuration or development is complete, the pre-configured operating system is tested to ensure that the newly added features work as expected and to make necessary optimizations.

8. A real-time monitoring and early warning system for home safety, It is characterized in that The real-time monitoring and early warning system for home safety includes: A demand determination module is used to determine the needs and goals of the smart home security system based on the actual needs of home users; A device deployment module, used to deploy smart home devices and sensors according to the requirements and goals of the smart home security system, and connect and control all smart home devices and sensors through a preset operating system; A data transmission module, used to encrypt and transmit data through a security protocol when performing data transmission and communication in the smart home security system; The abnormal warning module is used to obtain the sensor data collected by each sensor, analyze and process the sensor data using a machine learning algorithm, and output the data analysis results. If the data analysis results indicate that there is a monitoring abnormality, the early warning mechanism is triggered.

9. A terminal, It is characterized in that The terminal includes: a memory, a processor, and a real-time monitoring and early warning program for home safety stored in the memory and executable on the processor. When the real-time monitoring and early warning program for home safety is executed by the processor, the steps of the real-time monitoring and early warning method for home safety as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a real-time monitoring and early warning program for home safety, and when the real-time monitoring and early warning program for home safety is executed by a processor, the steps of the real-time monitoring and early warning method for home safety as described in any one of claims 1-7 are implemented.

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