A. Operational process of AIoT Alarm System
- Data collection
Various types of sensors (temperature, humidity, millimeter wave radar, human infrared, smoke, gas concentration sensors, etc.) and other devices distributed in various scenarios (such as homes, enterprises, public places) continuously collect environmental and status information.
For example, human infrared sensors detect whether someone has entered a restricted area, and smoke sensors monitor the smoke concentration in the air.
- Data transmission
Through IoT communication technology (Wi-Fi, Bluetooth, ZigBee, LoRa, 4G/5G, etc.), the collected data is transmitted from the sensor to the edge computing device or directly uploaded to the cloud server.
For example, sensors in the home use Wi-Fi to send data to the home gateway, and then the gateway uploads it to the cloud platform through the broadband network.
- Data processing and analysis
At the edge or cloud, artificial intelligence algorithms are used to analyze data in real time. Through the AI agent model, normal behavior patterns and data threshold models are established to determine whether the current data is within the normal range. For example, analysis monitoring, with the help of recognition algorithms, determines whether abnormal behavior (such as fighting, falling) occurs.
- Alarm triggering
Once the data is judged to be abnormal, the system immediately triggers the alarm mechanism. If the smoke concentration is detected to exceed the set threshold, or the video analysis identifies abnormal behavior, the alarm process will be initiated.
- Alarm notification
The alarm information is sent to relevant personnel in a variety of ways, such as push notifications to the user’s mobile phone, dialing a preset phone number, sending text messages, and can also be notified to medical institutions, security centers, or home care, community care and other management offices.
Advantages of our AIoT health care alarm system
- Intelligent and accurate alarm
Artificial intelligence analysis can accurately identify real abnormal situations and reduce false alarms. For example, through the deep learning analysis of human behavior in human body monitoring by millimeter wave radar, normal activities and abnormal behaviors can be accurately distinguished, avoiding false alarms caused by interference or non-abnormal actions.
- Real-time monitoring response
AIoT realizes 24-hour uninterrupted monitoring, and once there is an abnormality, the alarm is triggered immediately to respond quickly to security threats. For example, if the smoke sensor detects an abnormality at the beginning of a fire, it can alarm immediately to buy precious time for rescue.
- Applicable in multiple scenarios
It can be widely used in smart home, smart security, industrial monitoring and other fields to meet the security needs of different scenarios. It can be applied to both home security protection and community security and protection management.
- Data integration and analysis
It can integrate multiple types of sensor data for comprehensive analysis and mine more potential information. For example, by combining temperature, smoke and gas concentration data, it can more accurately determine whether a fire has occurred and the degree of fire danger.
Challenges
- Network security risks
There are many IoT devices and complex communication networks, which are vulnerable to network security threats such as hacker attacks and data leaks. For example, hackers may invade the system to tamper with the alarm threshold, causing the alarm to fail.
- Equipment compatibility issues
There may be compatibility issues between sensors and alarm devices of different brands and models, affecting the overall performance of the system and data interaction.
- Data processing pressure
A large number of sensors generate massive amounts of data, which places high demands on data transmission bandwidth and data processing capabilities on the cloud or edge. If the processing is not timely, it will affect the timeliness of the alarm.
- False alarm problem
Although artificial intelligence can reduce false alarms, it is still difficult to completely avoid them in complex environments, and multiple false alarms will reduce users’ trust in the system.
How to reflect the advantages of the alarm system in an end-to-end platform?
- Visual display
Display alarm information in an intuitive way on the platform interface, including alarm location, type, time, etc., so that users can quickly understand the situation. For example, mark the specific location where the alarm occurred through an electronic map.
- Linkage control
Link with other devices, such as automatically turning on the relevant area to the alarm point when the alarm is triggered, to facilitate subsequent processing.
- Historical data backtracking
Provide historical alarm data query and analysis functions to help users summarize patterns, optimize alarm system settings, and reduce false alarm rates.