Intelligent security system based on artificial intelligence and Internet of Things

Abstract

The invention provides an intelligent security system based on artificial intelligence and Internet of Things. The intelligent security system comprises a sensing layer, a data processing and analysis unit, an intelligent decision and control layer, a cloud platform service unit and a user interaction layer, the sensing layer is provided with a plurality of Internet of Things sensors and biological recognition equipment in a target area, the target area is monitored and people are recognized through the Internet of Things sensors and the biological recognition equipment, and collected data are sent to the data processing and analyzing unit in real time; the data processing and analyzing unit is used for mining and analyzing collected data in the sensing layer through a machine learning or deep learning algorithm, constructing a security threat model according to the collected data, setting an abnormal behavior database in the security threat model, performing automatic identification and early-warning tracking on abnormal behaviors in the collected data, and performing early-warning tracking on the abnormal behaviors in the security threat model. The processing information is fed back to the intelligent decision-making and control layer; and the intelligent decision and control layer sets a processing plan according to a preset security rule, and processes the information uploaded by the analysis unit according to the data.

Description

Technical Field

[0001] The present invention belongs to the field of smart security, and in particular relates to a smart security system based on artificial intelligence and the Internet of Things. Background Art

[0002] With the rapid development of social economy and the continuous advancement of science and technology, the construction of more advanced and reliable security systems has become technically possible, and the demand is also showing a trend of further growth. In order to better serve the society and the public, we have built a new generation of intelligent security system in combination with industry needs and the development of new technologies.

[0003] Traditional security management systems mainly rely on manual patrols, video surveillance, and access control management, which have problems such as small scope, slow response, many blind spots, high false alarm rate, and insufficient data analysis capabilities. Especially in complex and changing environments with sudden security threats, traditional security systems are often difficult to respond effectively and can only deal with them after the fact, which leads to frequent security accidents, posing a serious threat to the lives and property of the general public.

[0004] In recent years, the rapid development of high-tech technologies such as artificial intelligence, big data and the Internet of Things has provided a major opportunity for the intelligent transformation of the security industry. We have focused on applying these advanced technologies to the security field, first of all, to achieve pre-warning, in-process handling and post-event analysis. We have further realized multi-target, all-round, three-dimensional, and blind-angle intelligent joint monitoring of security incidents. Through data intercommunication and data processing between intelligent devices, single-dimensional monitoring has been transformed into multi-dimensional perception.

[0005] While sensing in multiple dimensions, we analyze the development trend of the incident based on the current situation and algorithm, and recommend emergency response plans based on the requirements of emergency response methods and laws and regulations for security incidents.

[0006] The system can be combined with settings to achieve automated intervention in events. Command personnel can combine system plans to achieve remote control of equipment, achieve rapid response and processing of security incidents, and thus improve the efficiency and effectiveness of security protection.

[0007] At present, most of the smart security systems on the market are still in the early stages, with many problems such as single service functions, low intelligence, and poor system integration, which make it difficult to meet users' needs for efficient, intelligent and comprehensive security services. Therefore, it is particularly important to develop a smart security system that integrates multiple advanced technologies, can overcome the shortcomings of existing security systems, and achieve efficient and intelligent security protection. Summary of the invention

[0008] The present invention proposes a smart security system based on artificial intelligence and the Internet of Things. Aiming at the system problems existing in existing security services, the present invention proposes a smart security service solution based on the combination of artificial intelligence and high-tech technologies such as the Internet of Things. The system integrates artificial intelligence algorithms, Internet of Things sensors, big data analysis platforms, cloud computing services, and biometrics to achieve multi-target, all-round, three-dimensional, all-weather, and intelligent monitoring and management capabilities for security threats.

[0009] The technical solution of the present invention is implemented as follows: a smart security system based on artificial intelligence and the Internet of Things includes a perception layer, a data processing and analysis unit, an intelligent decision-making and control layer, a cloud platform service unit, and a user interaction layer; the perception layer is equipped with several types of Internet of Things sensors and biometric devices in the target area, and the target area is monitored and people are identified through the Internet of Things sensors and biometric devices, and the collected data is sent to the data processing and analysis unit in real time;

[0010] The data processing and analysis unit mines and analyzes the data collected in the perception layer through machine learning or deep learning algorithms, builds a security threat model based on the collected data, sets an abnormal behavior database in the security threat model, automatically identifies and tracks abnormal behaviors in the collected data, and feeds back the processed information to the intelligent decision-making and control layer;

[0011] The intelligent decision-making and control layer sets up processing plans according to preset security rules, and authorizes any processing plan according to the information uploaded by the data processing and analysis unit, and automatically makes decisions and triggers corresponding security response measures after the plan is authorized;

[0012] The cloud platform service unit supports the machine learning or deep learning algorithm of the data processing and analysis unit through the cloud server to perform real-time calculations, and schedules and manages the monitoring data and analysis results, and simultaneously uploads the real-time monitoring screen, alarm information and system parameters to the user interaction layer, and makes adjustments based on the feedback from the user interaction layer;

[0013] The user interaction layer sets security rules and sends the set security rules to the intelligent decision-making and control layer, receives alarm information fed back by the cloud platform service unit, records and stores the received alarm information and security rules, and controls the Internet of Things sensors and biometric devices in the perception layer for fixed-point monitoring.

[0014] Compared with existing technologies, this intelligent security system based on artificial intelligence and the Internet of Things has significant differences and innovations, mainly in terms of system architecture, data processing capabilities, intelligent decision-making, and user interaction. The multi-level architecture design of the system enables more efficient functional separation and collaboration. The perception layer can monitor the target area in real time by installing a variety of IoT sensors and biometric devices, and centrally manage and analyze data through data processing and analysis units. Compared with traditional security systems that rely on manual patrols or single monitoring equipment, this data collection method significantly improves the comprehensiveness and real-time nature of monitoring.

[0015] In terms of data processing capabilities, the system mines and analyzes the collected data through machine learning and deep learning algorithms to build a security threat model. This process can not only automatically identify abnormal behaviors, but also conduct early warning tracking, greatly enhancing the system's ability to respond to potential security threats. Traditional security systems often rely on static rules and manual judgment, making it difficult to respond to complex security threats in a timely and accurate manner, which can easily lead to security blind spots.

[0016] The introduction of the intelligent decision-making and control layer is another highlight of the system. This layer can formulate processing plans based on preset security rules, and automatically make decisions and execute corresponding security response measures when triggered by the information uploaded by the data processing and analysis unit. This automated decision-making process effectively reduces the need for manual intervention and improves response speed and processing efficiency. Traditional security systems usually require manual review and processing, with a long response time and the possibility of missing the best time for disposal.

[0017] In addition, the design of the cloud platform service unit enables the system to have powerful data processing and management capabilities. The cloud server supports real-time computing, and can schedule monitoring data and analysis results to achieve centralized management and sharing of data. Traditional systems often use local storage and processing, which makes it difficult to achieve real-time analysis and remote access of large-scale data. The introduction of the cloud platform provides the system with greater flexibility and scalability.

[0018] In terms of the user interaction layer, the system supports users to set security rules and exchange information with the intelligent decision-making and control layer. Users can receive alarm information in real time and make adjustments based on feedback. This two-way interactive design not only improves user participation, but also enhances the adaptability of the system. Traditional security systems often lack effective interaction with users. Users can only passively receive information and it is difficult to make flexible adjustments based on actual needs.

[0019] This smart security system based on artificial intelligence and the Internet of Things has demonstrated obvious advantages in architectural design, data processing capabilities, intelligent decision-making, user interaction, etc. Compared with traditional security technologies, it can provide more comprehensive, efficient and intelligent security solutions to meet the higher requirements of modern society for security.

[0020] As a preferred embodiment, the IoT sensors in the perception layer include infrared sensors, radar sensors, smart cameras, vibration sensors and smoke sensors, and the biometric devices include face recognition access control and fingerprint recognition locks. Data is collected in real time through the IoT sensors and biometric devices and sent to the data processing and analysis unit.

[0021] As a preferred embodiment, a network layer is also provided, which includes a WIFI module, a Zigbee module, a LoRa module, a Bluetooth module, a 5G module and Ethernet. The IoT sensor and the biometric device send real-time collected data to the data processing and analysis unit through the network layer.

[0022] As a preferred implementation, the intelligent decision and control layer simultaneously tracks the target of several analysis results uploaded by the data processing and analysis unit, and simultaneously links the Internet of Things sensors and biometric devices in the perception layer to perform synchronous data collection while tracking the target.

[0023] As a preferred implementation, after triggering the corresponding security response measures, the intelligent decision-making and control layer simultaneously performs sound and light alarms, access control system blocking and fire system preparation, and sends the corresponding security response measures to the cloud platform service unit for recording and storage.

[0024] After adopting the above technical scheme, the beneficial effects of the present invention are: Highly intelligent: By integrating advanced artificial intelligence algorithms and Internet of Things technologies, the system can automatically identify, warn and track multiple security threat elements, thereby improving security protection capabilities. Comprehensive monitoring: By deploying a variety of Internet of Things sensors and biometric devices, all-round, three-dimensional, and no-dead-angle monitoring of the target area is achieved to ensure that safety control is in place. Real-time response: The system can achieve real-time monitoring and uninterrupted data analysis. Therefore, once an abnormality is found, the event processing mechanism and alarm will be triggered immediately, and relevant personnel will be notified through multiple channels to achieve rapid response. Efficient management: The cloud platform provides powerful computing power and data storage support. Users can view monitoring images, receive alarm information, adjust system parameters, etc. at any time through APP or web pages, so as to achieve comprehensive control and remote control management of the security system. High system integration: The system adopts unified interface standards and protocol specifications to achieve seamless docking and collaborative work between different devices, and improve the integrated efficiency of the system. Strong security: Biometric technology is used to improve the accuracy and security of identity authentication and prevent illegal intrusion and identity fraud. The system's advancement, competitiveness and reliability are maintained by continuously optimizing system algorithms and models, and regularly updating and upgrading system software and hardware equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 is a system block diagram of the present invention;

[0027] Figure 2 This is a flow chart of the operation of the system of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Example:

[0030] like Figures 1-2As shown, the technical solution of the present invention is implemented as follows: a smart security system based on artificial intelligence and the Internet of Things includes a perception layer, a data processing and analysis unit, an intelligent decision-making and control layer, a cloud platform service unit, and a user interaction layer; the perception layer is equipped with several types of Internet of Things sensors and biometric devices in the target area, and the target area is monitored and people are identified through the Internet of Things sensors and biometric devices, and the collected data is sent to the data processing and analysis unit in real time;

[0031] The data processing and analysis unit mines and analyzes the data collected in the perception layer through machine learning or deep learning algorithms, builds a security threat model based on the collected data, sets an abnormal behavior database in the security threat model, automatically identifies and tracks abnormal behaviors in the collected data, and feeds back the processed information to the intelligent decision-making and control layer;

[0032] The intelligent decision-making and control layer sets up processing plans according to preset security rules, and authorizes any processing plan according to the information uploaded by the data processing and analysis unit, and automatically makes decisions and triggers corresponding security response measures after the plan is authorized;

[0033] The cloud platform service unit supports the machine learning or deep learning algorithm of the data processing and analysis unit through the cloud server to perform real-time calculations, and schedules and manages the monitoring data and analysis results, and simultaneously uploads the real-time monitoring screen, alarm information and system parameters to the user interaction layer, and makes adjustments based on the feedback from the user interaction layer;

[0034] The user interaction layer sets security rules and sends the set security rules to the intelligent decision-making and control layer, receives alarm information fed back by the cloud platform service unit, records and stores the received alarm information and security rules, and controls the Internet of Things sensors and biometric devices in the perception layer for fixed-point monitoring.

[0035] The design of the smart security system based on artificial intelligence and the Internet of Things aims to achieve comprehensive monitoring and security management of the target area through a multi-level architecture. The system is mainly composed of a perception layer, a data processing and analysis unit, an intelligent decision-making and control layer, a cloud platform service unit, and a user interaction layer. Through effective collaboration at each level, real-time monitoring, data analysis, and security response can be achieved.

[0036] In terms of working principle, first of all, in the perception layer, the system comprehensively monitors the target area by deploying a variety of IoT sensors (such as cameras, infrared sensors, door and window sensors, etc.) and biometric devices (such as fingerprint recognition, facial recognition, etc.). These sensors and devices can collect environmental data and personal information in the target area in real time, and transmit these data to the data processing and analysis unit. Through IoT technology, all sensors and devices can be interconnected to form an efficient data collection network.

[0037] In the data processing and analysis unit, the system uses machine learning or deep learning algorithms to conduct in-depth mining and analysis of the data collected by the perception layer. By building a security threat model, the system can identify potential security risks and compare abnormal behaviors with a pre-set abnormal behavior database. When abnormal behavior is detected, the system automatically issues an early warning message and feeds back the processing information to the intelligent decision-making and control layer. This process not only improves the efficiency of identifying abnormal behaviors, but also reduces the pressure of manual monitoring.

[0038] In the intelligent decision-making and control layer, the system formulates a processing plan based on the preset security rules, and authorizes any processing plan based on the information uploaded by the data processing and analysis unit. When a plan is authorized, the system will automatically make a corresponding decision and trigger the corresponding security response measures, such as notifying security personnel, activating the alarm system, or taking other protective measures. The automated decision-making capabilities of this layer enable the system to respond quickly to emergencies and ensure the timeliness of security precautions.

[0039] The cloud platform service unit is responsible for supporting the real-time operation of the machine learning or deep learning algorithms of the data processing and analysis unit, and scheduling and managing the monitoring data and analysis results. Through the powerful computing power of the cloud, the system can process massive amounts of data and upload the results to the user interaction layer simultaneously. The cloud platform will also make system adjustments based on the feedback from the user interaction layer to improve the system's intelligence level and user experience.

[0040] The user interaction layer provides a user-friendly interface that allows users to set security rules and send them to the intelligent decision-making and control layer. Users can also receive alarm information from the cloud platform service unit and record and store this information and the set security rules. In addition, users can directly control the IoT sensors and biometric devices in the perception layer to achieve fixed-point monitoring and personalized security needs.

[0041] In terms of the installation process, the target area must first be evaluated to determine the types of sensors and devices that need to be deployed. According to the design plan, IoT sensors and biometric devices are reasonably arranged in the target area to ensure that all key areas are covered. The system network connection and configuration are carried out to enable normal communication between modules at each layer. The software is installed and debugged to ensure that the data processing and analysis unit can effectively receive and process data from the perception layer. The system is tested and optimized to ensure that all functions are operating normally, and necessary adjustments are made based on user feedback.

[0042] In this application document, the perception layer: the system will deploy a variety of IoT sensors (such as infrared sensors, radar sensors, smart cameras, etc.) and biometric devices (such as face recognition access control, fingerprint recognition locks, etc.) to achieve all-round, no-dead-angle monitoring and accurate identification of the target area. These devices transmit the collected data to the data center in real time through the IoT technology, providing basic data support for subsequent intelligent analysis.

[0043] Data processing and analysis: The system will establish a big data analysis platform and use advanced artificial intelligence algorithms (such as deep learning, machine learning, etc.) to conduct in-depth mining and analysis of the collected data. Then, by building a security threat model, it can realize automatic identification, early warning and tracking of abnormal behaviors. At the same time, biometric technology is used to improve the accuracy and security of identity authentication and prevent illegal intrusion and identity fraud.

[0044] Intelligent decision-making and control layer: Based on the operation results of the intelligent analysis layer and combined with security rules, the system will automatically recommend a handling plan for the event. Under authorized conditions, the system can also automatically make decisions and trigger corresponding security response measures. For example, when abnormal behaviors such as intrusion are detected, the system will automatically send an alarm message to the security personnel and automatically adjust the angle and focal length of the surveillance camera to obtain a clearer on-site picture; when a threat to personal safety is confirmed, the system can also automatically activate the emergency plan, such as shutting down the access control system, activating the sound and light alarm, etc.

[0045] Cloud platform service: The system will use cloud computing technology to provide powerful computing power and data storage support. The cloud platform can not only process massive amounts of monitoring data and analysis results, but also realize remote monitoring and scheduling management of the system. Users can view monitoring images, receive alarm information, adjust system parameters, etc. anytime and anywhere through the APP or web page to achieve comprehensive control of the security system.

[0046] System integration and optimization: The present invention also focuses on the integration and optimization of the system. Through unified interface standards and protocol specifications, seamless connection and collaborative work between different devices can be achieved; through continuous optimization of algorithms and models, the accuracy and stability of the system can be improved; through regular updates and upgrades of system software and hardware equipment, the advancement and competitiveness of the system can be maintained.

[0047] The IoT sensors in the perception layer include infrared sensors, radar sensors, smart cameras, vibration sensors and smoke sensors. The biometric devices include face recognition access control and fingerprint recognition locks. Data is collected in real time through the IoT sensors and biometric devices and sent to the data processing and analysis unit.

[0048] The perception layer of this smart security system introduces a variety of advanced IoT sensors and biometric devices, including infrared sensors, radar sensors, smart cameras, vibration sensors and smoke sensors, as well as face recognition access control and fingerprint recognition locks, which have significant differences and advantages compared with existing technologies. First, existing security systems usually use a single or a small number of monitoring devices, which cannot achieve comprehensive environmental perception and event detection. The system can fully cover the target area and monitor different types of security events in real time through the collaborative work of multiple sensors. For example, infrared sensors can monitor changes in body temperature, radar sensors can detect moving objects, smart cameras provide real-time video monitoring, vibration sensors can detect abnormal vibrations, and smoke sensors are used for fire alarms. This diversified perception method not only improves the comprehensiveness of monitoring, but also enhances the ability to respond to complex security threats.

[0049] The introduction of biometric devices, especially face recognition access control and fingerprint recognition locks, has significantly improved the accuracy and security of personnel identification. Traditional security systems often rely on simple passwords or keys for access control, which is vulnerable to theft or cracking and has low security. Face recognition and fingerprint recognition technologies use unique biometrics for identity authentication, which can effectively prevent unauthorized entry and improve the security level of the security system. At the same time, these biometric devices can also record the information of people entering and leaving in real time, providing data support for subsequent security tracing.

[0050] In terms of data collection and transmission, existing technologies often have problems with untimely data updates or information islands, while this system collects data in real time through IoT sensors and biometric devices, and sends the data to the data processing and analysis unit for centralized processing, ensuring the timeliness and integrity of the information. This real-time data transmission capability enables the system to quickly respond to various security incidents, improving the real-time and effectiveness of security.

[0051] The comprehensive application of the system enables linkage between different types of sensors and devices to achieve multi-level security protection. For example, when a smoke sensor detects an abnormality, it can automatically trigger the surveillance camera to record in real time and send an alarm message to the user. This linkage mechanism is often difficult to achieve in traditional security systems, resulting in slow response and low processing efficiency.

[0052] A network layer is also provided, which includes a WIFI module, a Zigbee module, a LoRa module, a Bluetooth module, a 5G module and an Ethernet. The IoT sensor and the biometric device send real-time collected data to the data processing and analysis unit through the network layer.

[0053] Existing security systems usually rely on a single network connection method, such as WIFI or Ethernet, which limits the adaptability and coverage of the system. By integrating WIFI modules, Zigbee modules, LoRa modules, Bluetooth modules, 5G modules and Ethernet, this system not only supports multiple network protocols, but also can flexibly select the optimal transmission method according to specific application scenarios. For example, for scenarios requiring high bandwidth and low latency, 5G modules can provide an ideal solution, while Zigbee or Bluetooth modules are more suitable in the case of low power consumption and short-distance communication. This diverse network selection enables the system to maintain stable connections and efficient data transmission under different environmental conditions.

[0054] The introduction of the LoRa module enables the system to have the ability of remote communication. With its long-distance and low-power characteristics, LoRa technology is suitable for large-scale monitoring and data collection, especially in scenarios such as urban security or rural monitoring, and can effectively improve signal coverage. Traditional systems often find it difficult to achieve such coverage, especially in remote areas, which easily leads to the creation of monitoring blind spots.

[0055] The design of the network layer also enhances the reliability and redundancy of the system. In existing technologies, if the network connection fails, the entire security system may lose its effectiveness. By combining multiple network modules, the system can automatically switch to other available networks when a certain network connection fails, ensuring the continuity and stability of data transmission. This flexible network architecture greatly improves the system's anti-interference ability and availability.

[0056] The diversity of the network layer also supports the scalability of the equipment. As the demand for smart security continues to grow, new sensors or devices may need to be added in the future. The network layer design of the system allows new devices to be easily connected through different network protocols, avoiding the high cost and complexity of replacing the entire system. This modular design concept makes the system highly adaptable to the future.

[0057] The ability of IoT sensors and biometric devices to send real-time data to data processing and analysis units through the network layer improves the real-time and accuracy of data. Traditional security systems often require manual intervention to collect and summarize data, resulting in delayed response and information lag. The system's automated data transmission mechanism ensures timely updates of information, providing key support for subsequent data analysis and decision-making.

[0058] The intelligent decision-making and control layer simultaneously tracks the target of the multiple analysis results uploaded by the data processing and analysis unit, and simultaneously links the Internet of Things sensors and biometric devices in the perception layer to perform synchronous data collection while tracking the target.

[0059] Traditional security systems often rely on a single data source for target tracking, lacking comprehensiveness and real-time performance, resulting in slow response when dealing with complex security incidents. This system integrates multiple analysis results for comprehensive judgment, achieves multi-dimensional tracking of targets, enhances the ability to identify potential security threats, and thus improves the overall response speed and accuracy of the system.

[0060] The linkage mechanism between the target tracking function and the perception layer of the system makes the data collection process more efficient and dynamic. Traditional systems usually collect data only after the target is identified, and cannot achieve real-time environmental perception. While tracking the target, this system links the IoT sensors and biometric devices in real time to continuously obtain information about changes in the target's surrounding environment. This real-time data collection mechanism not only improves the ability to monitor the target's behavior, but also allows timely adjustment of response measures when the target's status changes to prevent the escalation of potential risks.

[0061] This intelligent decision-making and control layer design enhances the intelligence level of the system. Through comprehensive processing of multiple analysis results, the system can automatically identify and judge the optimal response strategy in different scenarios and make autonomous decisions. This process often requires manual intervention in traditional security systems, which takes a long time to respond and easily misses the best time to deal with it. However, through intelligent target tracking and data linkage, this system can respond on an almost real-time basis, improving the efficiency and effectiveness of security work.

[0062] The system's design also reflects its ability to adapt to complex situations. When faced with multiple targets or complex events, the system can track multiple targets at the same time and collect and analyze the corresponding data. Traditional security systems usually have difficulty handling the dynamic changes of multiple targets, which can easily lead to information omissions or misjudgments. This multi-target tracking capability makes the system more flexible and efficient in handling actual security scenarios.

[0063] By linking target tracking with real-time data collection, the system provides a richer and more comprehensive information basis for subsequent data analysis and decision-making. This advantage is particularly important when dealing with emergencies, and can provide strong support for the rapid response and handling of security incidents, ensuring the efficient operation of the security system.

[0064] As a preferred implementation, after triggering the corresponding security response measures, the intelligent decision-making and control layer simultaneously performs sound and light alarms, access control system blocking and fire system preparation, and sends the corresponding security response measures to the cloud platform service unit for recording and storage.

[0065] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The intelligent security system based on artificial intelligence and the Internet of Things is characterized by: It includes a perception layer, a data processing and analysis unit, an intelligent decision-making and control layer, a cloud platform service unit and a user interaction layer; the perception layer is equipped with several IoT sensors and biometric devices in the target area, and the target area is monitored and people are identified through the IoT sensors and biometric devices, and the collected data is sent to the data processing and analysis unit in real time; The data processing and analysis unit mines and analyzes the data collected in the perception layer through machine learning or deep learning algorithms, builds a security threat model based on the collected data, sets an abnormal behavior database in the security threat model, automatically identifies and tracks abnormal behaviors in the collected data, and feeds back the processed information to the intelligent decision-making and control layer; The intelligent decision-making and control layer sets up processing plans according to preset security rules, and authorizes any processing plan according to the information uploaded by the data processing and analysis unit, and automatically makes decisions and triggers corresponding security response measures after the plan is authorized; The cloud platform service unit supports the machine learning or deep learning algorithm of the data processing and analysis unit through the cloud server to perform real-time calculations, and schedules and manages the monitoring data and analysis results, and simultaneously uploads the real-time monitoring screen, alarm information and system parameters to the user interaction layer, and makes adjustments based on the feedback from the user interaction layer; The user interaction layer sets security rules and sends the set security rules to the intelligent decision-making and control layer, receives alarm information fed back by the cloud platform service unit, records and stores the received alarm information and security rules, and controls the Internet of Things sensors and biometric devices in the perception layer for fixed-point monitoring.

2. The intelligent security system based on artificial intelligence and the Internet of Things as claimed in claim 1, characterized in that: The IoT sensors in the perception layer include infrared sensors, radar sensors, smart cameras, vibration sensors and smoke sensors. The biometric devices include face recognition access control and fingerprint recognition locks. Data is collected in real time through the IoT sensors and biometric devices and sent to the data processing and analysis unit.

3. The intelligent security system based on artificial intelligence and the Internet of Things as claimed in claim 2, characterized in that: A network layer is also provided, which includes a WIFI module, a Zigbee module, a LoRa module, a Bluetooth module, a 5G module and an Ethernet. The IoT sensor and the biometric device send real-time collected data to the data processing and analysis unit through the network layer.

4. The intelligent security system based on artificial intelligence and the Internet of Things as claimed in claim 1, characterized in that: The intelligent decision-making and control layer simultaneously tracks the target of the multiple analysis results uploaded by the data processing and analysis unit, and simultaneously links the Internet of Things sensors and biometric devices in the perception layer to perform synchronous data collection while tracking the target.

5. The intelligent security system based on artificial intelligence and the Internet of Things as claimed in claim 1, characterized in that: After triggering the corresponding security response measures, the intelligent decision-making and control layer will simultaneously perform sound and light alarms, lock the access control system and prepare the fire protection system, and send the corresponding security response measures to the cloud platform service unit for recording and storage.

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