AI camera system for actively predicting home safety

Abstract

The invention discloses an AI camera system for actively predicting home safety, and relates to the technical field of home safety prediction. The system comprises a multi-sensor data acquisition module which is used for acquiring comprehensive data of a family environment; the AI analysis module is used for analyzing the data acquired by the data acquisition module and identifying abnormal behaviors; the behavior prediction and early warning module is used for predicting possible security threats based on the analysis result of the AI analysis module in combination with real-time data and historical behavior modes; early warning is given out before the threat occurs; the automatic alarm and protection module is used for giving an alarm when potential threats are detected, sending warning information to intelligent equipment of a user and executing protection measures; and the continuous learning and feedback module optimizes algorithms of the AI analysis module and the behavior prediction and early warning module by collecting user feedback and environmental changes so as to improve prediction and protection accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of home safety early warning, and relates to an AI camera system for actively predicting home safety. Background Art

[0002] In modern society, with the rapid development of science and technology and the acceleration of digitalization, smart home devices have become increasingly popular, and home security issues have become a growing focus of attention. Traditional home security systems mainly rely on surveillance cameras and alarm devices, which usually record or alarm after dangerous events occur, and play the role of post-event evidence collection or alarm. However, this passive monitoring method has obvious limitations and cannot provide timely warnings before threats occur, resulting in the continuation and expansion of security risks.

[0003] The main function of traditional surveillance cameras is to record video images and sound an alarm when motion or abnormal conditions are detected. Although this method has improved home security to a certain extent, it still has many shortcomings. First, traditional cameras cannot identify the nature and severity of threats, and are prone to false alarms that lead to user neglect or incorrect reactions. Second, traditional systems are usually unable to interact with other smart devices, making it difficult to achieve comprehensive security monitoring and prevention in complex home environments. In addition, with the increasing diversity and intelligence of security threats, such as intrusions, fires, gas leaks, etc., it is difficult to meet users' all-round needs for home security by relying solely on traditional monitoring equipment.

[0004] In summary, the existing technology has obvious shortcomings and cannot meet the high standards of users for home security. Based on this, it is necessary to develop an AI camera system that can actively predict home security threats to achieve more intelligent and efficient home security management and protect the safety of family members and property. Summary of the invention

[0005] In order to make up for the shortcomings of the prior art, the present invention provides an AI camera system for actively predicting home safety. The system combines advanced artificial intelligence algorithms and multi-sensor technology, and can monitor various activities and environmental changes in the home environment in real time, identify potential security threats, and issue warnings or take necessary protective measures before danger occurs. The design of this system overcomes the limitations of traditional monitoring equipment and provides a more intelligent and comprehensive home security solution. The present invention is implemented through the following technical solutions: The present invention provides an AI camera system for actively predicting home safety, including, Multi-sensor data acquisition module: used to collect comprehensive data of the home environment, including images, sounds, ambient temperature, humidity, etc. The multi-sensor data acquisition module includes a video camera, infrared sensor, temperature sensor, humidity sensor and microphone, which are used to comprehensively monitor the home environment; AI analysis module: used to analyze the data collected by the data collection module and identify abnormal behaviors; the AI ​​analysis module uses an AI algorithm based on deep learning to analyze the collected data. The AI ​​analysis module has a self-learning function and can gradually improve the accuracy of abnormal behavior identification by processing new data and user feedback; Behavior prediction and warning module: used to predict possible security threats based on the analysis results of the AI ​​analysis module, combined with real-time data and historical behavior patterns; and issue warnings before the threats occur; Automated alarm and protection module: used to sound an alarm and take protective measures when a potential threat is detected; the automated alarm and protection module can automatically trigger an audible and visual alarm, send a warning message to the user's smart device, and take protective measures when a potential threat is detected; The continuous learning and feedback module optimizes the algorithms of the AI ​​analysis module and the behavior prediction and early warning module by collecting user feedback and environmental changes to improve the accuracy of prediction and protection.

[0006] The beneficial effects of the present invention are: (1) Early warning to improve safety protection level Traditional surveillance cameras mainly record events passively, usually only after danger occurs. The AI ​​camera system of the present invention can actively predict potential security threats, such as illegal intrusion, fire risks, etc., through multi-sensor data collection and intelligent analysis modules. This early warning capability can significantly improve the level of home security protection and help users take effective measures before the threat actually occurs, thereby reducing or avoiding losses.

[0007] (2) Intelligent behavior recognition and self-learning The AI ​​analysis module in the present invention uses a deep learning algorithm, which can not only identify abnormal behavior in real time, but also continuously optimize the recognition model through self-learning. The system can learn the daily behavior habits of users, gradually improve the accuracy of identifying abnormal behavior, and reduce false positives and false negatives. This intelligent behavior recognition capability enables the system to more accurately judge potential threats in the current environment and provide personalized security protection solutions.

[0008] (3) Multimodal data fusion to achieve all-round monitoring The multi-sensor data acquisition module of the present invention integrates multiple sensors such as video, infrared, temperature, humidity and sound, and can monitor changes in the home environment from multiple angles. The fusion of these multi-modal data enables the system to more comprehensively perceive abnormal conditions in the environment, such as abnormal opening of doors and windows, abnormal changes in ambient temperature, suspicious sounds, etc., thereby providing more accurate and reliable security monitoring.

[0009] (4) Automated protection measures to quickly respond to threats After detecting a potential threat, the automatic alarm and protection module of the present invention can immediately take corresponding measures, including triggering an audible and visual alarm, sending a warning message to the user's smart device, automatically locking doors and windows, or activating an alarm system, etc. Such automatic protection measures can respond quickly in an emergency, effectively prevent the further development of the threat, and protect the safety of family members and property.

[0010] (5) Continuous learning and system optimization to adapt to the needs of different families The continuous learning and feedback module of the present invention enables the system to continuously optimize the AI ​​algorithm based on user feedback and environmental changes. The system automatically adjusts threat identification and protection strategies to gradually adapt to the personalized needs of different families. This continuous learning and optimization capability enables the system to not only cope with current security challenges, but also maintain efficient identification and protection of emerging threat types and behavior patterns over time.

[0011] (6) Reduce false positives and improve user experience By combining multiple sensor data and deep learning algorithms, the AI ​​camera system of the present invention can more accurately identify abnormal behavior and reduce user interference caused by false alarms. At the same time, the system continuously optimizes the algorithm based on user feedback, further improving the accuracy of threat prediction and identification. This efficient and accurate threat identification mechanism significantly improves the user experience.

[0012] The rapid development of artificial intelligence (AI) technology has provided new possibilities for upgrading home security systems. By introducing AI technology, cameras can go beyond passive recording and can actively perform data analysis, behavior prediction, and real-time warning of security threats. AI technology can accurately identify abnormal behaviors in the environment through deep learning algorithms, combined with big data analysis and multi-sensor fusion technology, thereby improving the intelligent level of security monitoring. This active prediction and prevention capability has greatly enhanced the effectiveness and reliability of home security systems.

[0013] In addition, AI cameras can continuously learn and optimize their analysis and prediction capabilities to adapt to the unique needs of different families. This adaptive learning ability enables the camera system to maintain efficient operation in a constantly changing environment, further improving the overall protection level of home security. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings; Figure 1 It is the overall system architecture diagram of the present invention; Figure 2It is a schematic diagram of the working process of the multi-sensor data acquisition module; Figure 3 This is the deep learning algorithm structure diagram of the AI ​​analysis module; Figure 4 This is the flowchart of the behavior prediction and early warning module; Figure 5 This is the workflow diagram for the automated alert and protection module. DETAILED DESCRIPTION

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0016] The accompanying figure is a specific embodiment of an AI camera system for proactively predicting home safety of the present invention. The embodiment includes a multi-sensor data acquisition module: used to collect comprehensive data of the home environment, including images, sounds, ambient temperature, humidity, etc.; the multi-sensor data acquisition module includes a video camera, an infrared sensor, a temperature sensor, a humidity sensor and a microphone, which are used to comprehensively monitor the home environment.

[0017] AI analysis module: used to analyze the data collected by the data collection module and identify abnormal behaviors; the AI ​​analysis module uses an AI algorithm based on deep learning to analyze the collected data and identify abnormal behaviors including abnormal opening of doors and windows, abnormal temperature changes in the house, suspicious sounds or abnormal human movements. The AI ​​analysis module has a self-learning function and can gradually improve the accuracy of abnormal behavior identification by processing new data and user feedback.

[0018] Behavior prediction and warning module: used to predict possible security threats based on the analysis results of the AI ​​analysis module, combined with real-time data and historical behavior patterns; the behavior prediction and warning module can predict possible security incidents based on historical behavior patterns and real-time data, and issue warnings before the threat occurs.

[0019] Automated Alarm and Protection Module: used to sound an alarm and take protective measures when potential threats are detected; when a potential threat is detected, the automated alarm and protection module can automatically trigger an audible and visual alarm, send a warning message to the user's smart device, and take protective measures.

[0020] The continuous learning and feedback module optimizes the algorithms of the AI ​​analysis module and the behavior prediction and early warning module by collecting user feedback and environmental changes to improve the accuracy of prediction and protection.

[0021] The multi-sensor data acquisition module is the front-end sensing part of the system, responsible for comprehensively collecting various data in the home environment; this module includes the following sensors, Video camera: used to capture real-time video images of the home environment and monitor human activities and environmental changes; Infrared sensor: used to detect infrared signals at night or in low-light environments and identify activities in such situations; Temperature sensor: monitors temperature changes in the home environment in real time and detects temperature fluctuations that may cause fire or other abnormal conditions; Humidity sensor: monitors ambient humidity and helps detect abnormal humidity conditions, including water leaks or sudden changes in humidity; Microphone: used to collect environmental sounds and identify suspicious sounds or abnormal noises, including the sound of breaking doors or high-decibel screams.

[0022] The AI ​​analysis module includes the following functional components: Deep learning algorithm: responsible for analyzing and processing multi-sensor data to identify abnormal behaviors in the environment, including abnormal opening of doors and windows and abnormal temperature changes; the algorithm can continuously self-learn and optimize to improve the accuracy and robustness of abnormal identification; Abnormal behavior recognition engine: Based on the analysis results of deep learning, it detects and identifies various potential abnormal activities in real time, including identifying illegal intrusions and fire risks; Self-learning module: This module can continuously optimize the deep learning algorithm based on historical data and user feedback, and gradually improve the system's ability to identify abnormal behaviors.

[0023] The behavior prediction and warning module predicts possible security incidents based on real-time data and the user's historical behavior patterns, and issues an alarm when necessary; this module includes the following parts: Behavior pattern database: stores the user's historical behavior data and environmental patterns for comparison and analysis of the abnormality of current behavior; Threat prediction engine: predicts potential security threats, such as illegal intrusion and fire risks, by analyzing the differences between real-time data and historical behavior patterns; Early warning system: issues an alarm to notify users or related devices when a potential threat is predicted.

[0024] The automated alarm and protection modules include: Sound and light alarm device: After detecting a threat, the system automatically triggers sound and light alarms, such as buzzers and flashing lights, to attract the attention of family members; Smart device communication interface: Through this interface, the system can send warning information to the user's smart device, such as a smartphone, in real time, and indicate the specific threat type and location; Automatic protection mechanism: When a high-threat event is detected, the system automatically executes protective measures, including locking doors and windows, and activating alarm systems to prevent potential intruders from entering or reduce the spread of disasters.

[0025] The continuous learning and feedback module is responsible for the long-term optimization and adaptive learning of the system, ensuring that the system's threat identification and protection strategies can be continuously improved; the specific contents of this module include: User feedback interface: Through this interface, the system can receive user feedback on alarms and protection measures to help adjust and optimize threat identification strategies; Environmental change adaptation module: This module adjusts system parameters according to environmental changes, such as seasonal changes, changes in the entry and exit patterns of family members, etc., to maintain the efficiency and accuracy of the system; Algorithm optimization module: Through user feedback and continuous input of new data, the algorithm optimization module continuously optimizes the AI ​​algorithm, enabling the system to more accurately predict and prevent future security threats, thereby improving the overall prediction and protection capabilities of the system.

[0026] As attached Figure 2 The workflow of the multi-sensor data acquisition module shown includes: startup and initialization, environmental data acquisition, data preprocessing, real-time data transmission, system feedback and adjustment.

[0027] Startup and initialization: 1) Sensor startup: Start all data collection sensors, including video cameras, infrared sensors, temperature sensors, humidity sensors, and microphones.

[0028] 2) System self-check: The system performs a self-check to ensure that all sensors are working properly and are ready to collect data.

[0029] Environmental data collection: 1) Video data capture: Video cameras capture real-time images and video streams.

[0030] 2) Infrared data acquisition: Infrared sensors detect infrared signals in the environment and identify activities at night or in low-light environments.

[0031] 3) Temperature and humidity monitoring: Temperature sensors and humidity sensors monitor the temperature and humidity changes of the home environment in real time.

[0032] 4) Sound collection: The microphone collects ambient sound in real time and detects suspicious sounds or abnormal noises.

[0033] Data reception: Receive raw data from various content sources to ensure data integrity and correct format.

[0034] Data preprocessing: preprocess the received raw data, including data cleaning, format conversion, deduplication and preliminary screening. Tools: use advanced data processing tools and algorithms to process data efficiently.

[0035] Data preprocessing steps: 1) Data cleaning: Perform preliminary cleaning and filtering on the collected data to remove noise and redundant information.

[0036] 2) Data synchronization and integration: Time-synchronize and integrate multiple sensor data to form a unified data stream.

[0037] 3) Feature extraction: Extract key features from the integrated data, such as temperature anomalies, infrared motion trajectories, sound characteristics, etc.

[0038] Real-time data transmission: 1) Data upload: The processed data is transmitted to the AI ​​analysis module in real time for further analysis.

[0039] 2) Data storage: Store both raw and processed data locally or in the cloud for easy subsequent analysis and tracing.

[0040] System feedback and adjustments: 1) Sensor status monitoring: Continuously monitor the working status of each sensor to ensure the stability of data collection.

[0041] 2) Data collection strategy adjustment: According to the feedback from the AI ​​analysis module, dynamically adjust the data collection frequency and scope to improve the accuracy and efficiency of monitoring.

[0042] As attached Figure 3 The deep learning algorithm structure of the AI ​​analysis module shown includes input layer, data preprocessing layer, convolutional neural network layer, recurrent neural network layer, fully connected layer, output layer and model optimization and feedback.

[0043] Input layer: data input, receiving integrated data streams from the multi-sensor data acquisition module, including multimodal data such as images, sounds, temperature, and humidity.

[0044] Data preprocessing layer: 1) Data standardization: Normalize the input data to ensure that different types of data are processed at the same scale.

[0045] 2) Feature extraction: Extract key features from the input data, such as edges in images, spectral features in sounds, temperature change trends, etc.

[0046] Convolutional Neural Network (CNN) Layers: 1) Convolution operation: Apply convolution operation to image and video data to extract spatial features such as motion trajectory, object contour, etc.

[0047] 2) Pooling operation: The pooling layer reduces the dimension of the feature map, reduces the amount of calculation and improves the generalization ability of the model.

[0048] 3) Deep feature extraction: By stacking multiple convolutional layers, more abstract and advanced image features are extracted.

[0049] Convolutional Neural Network (RNN) Layer: 1) Time series processing: Use RNN or long short-term memory network (LSTM) to process time series data, such as sound and sensor data, to capture dynamic features that change over time.

[0050] 2) Behavior pattern recognition: The RNN layer analyzes the behavior patterns in continuous time periods and identifies abnormal behaviors, such as abnormal opening of doors and windows or abnormal movement trajectories of people.

[0051] Fully connected layer: 1) Feature fusion: The spatial and temporal features extracted from the convolutional neural network (CNN) and convolutional neural network (RNN) layers are fused to form a global feature vector.

[0052] 2) Pattern classification: The fused feature vector is classified through the fully connected layer to distinguish normal and abnormal behaviors.

[0053] Output layer: 1) Threat prediction: Generate prediction results for the current home environment security status, such as normal, potential threat, immediate threat, etc.

[0054] 2) Abnormal behavior identification: Output identification results, including specific abnormal behavior types, such as illegal intrusion, fire risk, abnormal sound, etc.

[0055] Model optimization and feedback: 1) Error calculation: Calculate the error between the predicted result and the actual situation and generate an error signal.

[0056] 2) Back propagation and weight update: Through the back propagation algorithm, the network weights are adjusted according to the error signal to gradually optimize the model performance.

[0057] 3) Continuous learning: Combining user feedback and new data, the system continuously updates model parameters to improve the accuracy of threat identification and prediction.

[0058] As attached Figure 4The process of the behavior prediction and warning module shown includes input layer, data preprocessing, behavior pattern analysis, threat prediction model, alarm and warning, model feedback and optimization, and continuous learning.

[0059] Input Layer: 1) Real-time data acquisition: Receive real-time monitoring data from the multi-sensor data acquisition module, including images, sounds, temperature, humidity, etc.

[0060] 2) Historical data access: retrieve the user's historical behavior data and environmental change records from the system database.

[0061] Data preprocessing: 1) Data cleaning: Clean real-time data and historical data to remove noise and invalid data to ensure data quality.

[0062] 2) Data integration: Integrate multimodal sensor data and historical data to form a unified input data set.

[0063] Behavioral pattern analysis: 1) Daily behavior model call: call the user's daily behavior pattern model to identify whether the current behavior conforms to the normal pattern.

[0064] 2) Abnormal behavior detection: Identify abnormal behaviors that are inconsistent with daily behaviors and mark them as potential threats.

[0065] Threat prediction model: 1) Risk assessment: Based on the abnormal behavior detection results, risk assessment is performed on potential threats to determine the threat level (such as low, medium, or high).

[0066] 2) Scenario simulation: Use threat prediction models to conduct scenario simulations to predict the development trends and possible consequences of potential threats.

[0067] Alerts and warnings: 1) Risk alert generation: Generate corresponding risk alerts based on the prediction results, such as approach of suspicious persons, increased fire risk, etc.

[0068] 2) Sending early warning information: Send the alarm information to the user's smart device in real time, and notify the system's automated protection module at the same time.

[0069] Model Feedback and Optimization: 1) User feedback collection: Collect user feedback on the alarm, such as whether it is accurate or contains false alarms.

[0070] 2) Model adjustment and optimization: Based on user feedback and new data, the threat prediction model is adjusted and optimized to improve prediction accuracy.

[0071] Continuous Learning: 1) Adaptive learning: The system continuously updates the behavior prediction model based on new data and feedback to adapt to changes in the home environment and user behavior.

[0072] 2) Model upgrade: Regularly upgrade the prediction model to ensure that the system can cope with the latest threat types and behavior patterns.

[0073] As attached Figure 5 The workflow of the automated alert and protection module shown includes threat detection triggering, alert generation, automated protection measures, user interaction and control, threat removal and resetting, and continuous optimization and learning.

[0074] Threat detection triggers: 1) Receiving threat signals: The automated alarm and protection module receives signals of potential threats (such as illegal intrusion, fire risk, etc.) from the behavior prediction and early warning module.

[0075] 2) Threat level assessment: Based on the threat signals received, the threat level is assessed (low, medium, high) and the intensity of protective measures to be taken is determined.

[0076] Alert generation: 1) Sound and light alarm triggering: According to the threat level, the system automatically triggers the corresponding sound and light alarm (such as buzzer sounding, light flashing).

[0077] 2) User notification: The system immediately sends a warning message to the user’s smart device (such as a mobile phone or tablet), including the threat type, location, and time.

[0078] Automated protection measures: 1) Automatic locking of doors and windows: When illegal intrusion or other high-threat events are detected, the system automatically locks doors and windows to prevent intruders from entering.

[0079] 2) Start the alarm system: The system activates the existing security alarm system in the home and sends a distress signal to the security company or relevant department.

[0080] 3) Emergency lighting control: Activate preset emergency lighting modes to increase visibility or confuse potential threats.

[0081] User Interaction and Control: 1) Remote control: Users can remotely view on-site conditions through smart devices and manually control protective measures, such as unlocking doors and windows, canceling alarms, etc.

[0082] 2) Feedback and confirmation: The user confirms whether the threat is real, and the system adjusts subsequent protection strategies based on user feedback.

[0083] Threat elimination and restoration: 1) Confirmation of threat elimination: The system confirms that the threat has been eliminated (through user feedback or automatic detection) and automatically lifts the alarm and protective measures.

[0084] 2) System reset: Restore normal monitoring mode and record the entire process of the event for future reference and model optimization.

[0085] Continuous optimization and learning: 1) Event recording and analysis: Detailed records of all threat detections, alarm triggering and protective measures are saved to the system database.

[0086] 2) Feedback adjustment: Automatically adjust the system’s threat identification and protection strategies based on event records and user feedback to improve future response efficiency.

[0087] Multi-sensor data acquisition module: One of the cores of the system is the multi-sensor data acquisition module. In addition to the image capture function of the traditional camera, the camera of the present invention also integrates multiple sensors such as infrared sensors, temperature sensors, humidity sensors and microphones. These sensors can comprehensively monitor various physical parameters in the home environment, such as ambient temperature, humidity, sound and infrared signals. Through the combination of multi-source data, the system can understand the environmental conditions more comprehensively and accurately, and provide rich basic data for subsequent intelligent analysis.

[0088] AI Analysis Module: The AI ​​Analysis Module is the core of the system’s analysis. It uses advanced deep learning algorithms to analyze data collected by multiple sensors in real time. The module can identify various abnormal behaviors and potential threats in the environment. For example, the system can detect abnormal opening of doors and windows, abnormal temperature changes in the house, suspicious sounds, or abnormal human movements. Through continuous learning, the AI ​​Analysis Module can gradually improve its recognition accuracy, reduce false alarms, and improve the accuracy of threat detection.

[0089] Behavior prediction and warning module: Based on the AI ​​analysis module, the behavior prediction and warning module further processes the analysis results. This module predicts activities in the home environment by combining real-time data with historical behavior patterns. For example, if the system identifies an attempted illegal intrusion or detects a potential fire risk, it can issue a warning signal before these risks actually occur. The prediction function of this module can significantly enhance the system's preventive capabilities and help users take countermeasures before the threat occurs, thereby avoiding potential dangers.

[0090] Automated Alarm and Protection Module: The automated alarm and protection module is responsible for automatically taking appropriate security measures when the system detects potential threats. When the system issues an early warning, the module can immediately trigger a variety of response actions. For example, it can issue a high-decibel alarm through an audible and visual alarm, send a warning message to the user's smartphone, or directly link with the home's smart door locks, windows and other devices to automatically lock doors and windows or activate the alarm system. This automated protection mechanism can respond to threats in the shortest time and provide immediate security.

[0091] Continuous Learning and Feedback Module: The Continuous Learning and Feedback Module is a key part of the system's self-optimization. This module is able to continuously adjust and optimize the AI ​​algorithm based on user feedback and changes in the environment. For example, the system can adjust the sensitivity of the alarm based on the user's habits, or update the algorithm's parameter settings based on long-term monitored changes in the home environment. In this way, the system can maintain efficient operation in a constantly changing environment and provide personalized security solutions for different families.

Claims

1. An AI camera system for proactively predicting home safety, characterized in that: include Multi-sensor data acquisition module: used to collect comprehensive data of the home environment, including images, sounds, ambient temperature, and humidity; the multi-sensor data acquisition module includes a video camera, infrared sensor, temperature sensor, humidity sensor, and microphone, which are used to comprehensively monitor the home environment; AI analysis module: used to analyze the data collected by the data collection module and identify abnormal behaviors; the AI ​​analysis module uses an AI algorithm based on deep learning to analyze the collected data. The AI ​​analysis module has a self-learning function and can gradually improve the accuracy of abnormal behavior identification by processing new data and user feedback; Behavior prediction and early warning module: used to predict possible security threats based on the analysis results of the AI ​​analysis module, combined with real-time data and historical behavior patterns; and issue early warnings before threats occur; Automated Alert and Protection Module: used to issue alerts when potential threats are detected, send warning messages to users’ smart devices, and execute protective measures; The continuous learning and feedback module optimizes the algorithms of the AI ​​analysis module and the behavior prediction and early warning module by collecting user feedback and environmental changes to improve the accuracy of prediction and protection.

2. The AI ​​camera system for proactively predicting home security according to claim 1, characterized in that: The multi-sensor data acquisition module is the front-end sensing part of the system, responsible for comprehensively collecting various data in the home environment; this module includes the following sensors, Video camera: used to capture real-time video images of the home environment and monitor human activities and environmental changes; Infrared sensor: used to detect infrared signals at night or in low-light environments and identify activities in such situations; Temperature sensor: monitors temperature changes in the home environment in real time and detects temperature fluctuations that may cause fire or other abnormal conditions; Humidity sensor: monitors ambient humidity and helps detect abnormal humidity conditions, including water leaks or sudden changes in humidity; Microphone: used to collect environmental sounds and identify suspicious sounds or abnormal noises, including the sound of breaking doors or high-decibel screams.

3. The AI ​​camera system for proactively predicting home security according to claim 1, characterized in that: The AI ​​analysis module includes the following functional components: Deep learning algorithm: responsible for analyzing and processing multi-sensor data to identify abnormal behaviors in the environment; the algorithm can continuously self-learn and optimize to improve the accuracy and robustness of abnormal identification; Abnormal behavior recognition engine: Based on the analysis results of deep learning, it detects and identifies various potential abnormal activities in real time; Self-learning module: This module can continuously optimize the deep learning algorithm based on historical data and user feedback, and gradually improve the system's ability to identify abnormal behaviors.

4. The AI ​​camera system for proactively predicting home security according to claim 1, characterized in that: The behavior prediction and warning module predicts possible security incidents based on real-time data and users' historical behavior patterns, and issues alerts when necessary; this module includes the following parts: Behavior pattern database: stores the user's historical behavior data and environmental patterns for comparison and analysis of the abnormality of current behavior; Threat prediction engine: predicts potential security threats by analyzing the differences between real-time data and historical behavior patterns; Early warning system: issues an alarm to notify users or related devices when a potential threat is predicted.

5. The AI ​​camera system for proactively predicting home security according to claim 1, characterized in that: The automated alarm and protection modules include: Sound and light alarm device: after detecting a threat, the system automatically triggers the sound and light alarm; Smart device communication interface: Through this interface, the system can send warning information to the user's smart device in real time, and prompt the specific threat type and location; Automatic protection mechanism: When a high-threat event is detected, the system automatically executes protective measures, including locking doors and windows, and activating alarm systems to prevent potential intruders from entering or reduce the spread of disasters.

6. The AI ​​camera system for proactively predicting home security according to claim 1, characterized in that: The continuous learning and feedback module is responsible for the long-term optimization and adaptive learning of the system, ensuring that the threat identification and protection strategies of the system can be continuously improved; the specific contents of this module include: User feedback interface: Through this interface, the system can receive user feedback on alarms and protection measures to help adjust and optimize threat identification strategies; Environmental change adaptation module: This module adjusts system parameters according to environmental changes to maintain the efficiency and accuracy of the system; Algorithm optimization module: Through user feedback and continuous input of new data, the algorithm optimization module continuously optimizes the AI ​​algorithm, enabling the system to more accurately predict and prevent future security threats.

7. The AI ​​camera system for proactively predicting home security according to claim 1, characterized in that: The deep learning algorithm structure of the AI ​​analysis module includes input layer, data preprocessing layer, convolutional neural network layer, recurrent neural network layer, fully connected layer, output layer, and model optimization and feedback; Input layer: data input, receiving integrated data streams from multi-sensor data acquisition modules, including multi-modal data of images, sounds, temperature, and humidity; The data preprocessing layer includes data normalization and feature extraction; Data standardization: normalize the input data to ensure that different types of data are processed at the same scale; Feature extraction: extract key features from input data; The steps of a convolutional neural network layer include, Convolution operation: Apply convolution operation to image and video data to extract spatial features; Pooling operation: The pooling layer reduces the dimension of the feature map, reduces the amount of calculation and improves the generalization ability of the model; Deep feature extraction: By stacking multiple convolutional layers, more abstract and advanced image features are extracted. The steps of a convolutional neural network layer include, Time series processing: Use convolutional neural networks or long short-term memory networks to process time series data, such as sound and sensor data, to capture dynamic features that change over time; Behavior pattern recognition: Analyze behavior patterns over continuous time periods through convolutional neural network layers to identify abnormal behaviors; The steps of the fully connected layer include: Feature fusion: The spatial and temporal features extracted from the convolutional neural network (CNN) and the convolutional neural network (RNN) layers are fused to form a global feature vector. Pattern classification: Classify the fused feature vectors through the fully connected layer to distinguish normal and abnormal behaviors; The output layer consists of: Threat prediction: Generates prediction results on the current security status of the home environment; Abnormal behavior identification: output identification results; Model optimization and feedback include: Error calculation: Calculate the error between the predicted result and the actual situation and generate an error signal; Back propagation and weight update: Through the back propagation algorithm, the network weights are adjusted according to the error signal to gradually optimize the model performance; Continuous learning: Combining user feedback and new data, the system continuously updates model parameters to improve the accuracy of threat identification and prediction.

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