An artificial intelligence-based real-time monitoring system and device
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-18
- Publication Date
- 2026-08-11
AI Technical Summary
传统的监控装置在应对复杂多变的监控环境时，暴露出诸多局限性，现有的监控装置监控范围固定且有限，难以根据实际监控需求进行灵活调整，在一些需要全方位、多角度监控的场景中，往往需要安装多个独立的监控设备，这不仅增加了设备成本和安装复杂度，还导致监控画面分散，给后续的数据管理和分析带来不便，且传统的监控装置采用简单的信号收集传输并通过人工观察的方式进行安全监测，大量视频信息直接传输到同一监控端，数据收集和处理能力相对较弱，大多仅能实现简单的视频录制和回放功能，在面对大量监控数据时，容易出现数据传输延迟、处理效率低下等问题，对于监控画面中的目标检测、识别以及行为分析等高级功能难以胜任，在面对大量监控数据时，传统系统缺乏有效的智能处理手段，需要人工进行大量的查看和分析工作，不仅效率低下，而且容易出现漏检和误判的情况，传统监控装置的安装和调试过程较为复杂，灵活性不足，不同的监控场景对监控设备的安装位置要求，传统装置在调整监控范围和方向时往往不够便捷，难以快速适应各种复杂的监控环境；
本发明提供了一种基于人工智能的实时监控系统与装置，具备以下有益效果:本方案基于人工智能的实时监控装置通过局域式监控网络，由多个结构相同的调节式监控器组成，能够覆盖较大区域，有效消除监控盲区，实现全方位的安全监控，调节式监控器可通过组合式安装结构进行灵活固定，并根据实际需求调整安装位置和角度，进一步增强了监控的全面性，调节式监控器的结构设计使得其安装和调试过程更加便捷。通过驱动器、锥齿轮等部件的配合，可实现监控探头的灵活转动和角度调整，能够快速适应各种复杂的监控环境，满足不同场景下的监控需求。组合式安装结构和安装组件的多样化设计，也为监控装置的安装提供了更多选择，进一步增强了系统的灵活性，该监控系统引入了人工智能技术，人工智能监控端能够对采集到的原始数据进行清洗、去噪、归一化等预处理操作，提高数据质量，利用卷积神经网络、YOLO、Faster R - CNN 等算法，可对图像或视频中的目标进行精准检测和识别，在安防监控中能够准确识别出人员、车辆、异常物体等，同时，借助循环神经网络（RNN）对目标的运动轨迹、行为模式进行分析和预测，为安全防范提供更有价值的信息，系统配备人机交互端，开发了用户界面，方便用户对系统进行配置、监控和管理。用户可以通过 Web 界面或移动客户端应用的形式，实现实时监控画面展示、警报信息推送、历史数据查询、系统参数设置等功能。并且，人工智能监控端与人机交互端连通使用，用户能够实时查看监控画面，并将相关指令传输至人机交互端，实现远程操控与交互，提高了监控的便捷性和灵活性，人机智能监控端可自动分析各区域视频信号的观察监测强度，并根据视频信号的复杂内容对算力进行自动分配。这一功能在保证监控以及分析效果的同时，减少了人工的干预，提高了系统的运行效率和资源利用率。
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Figure CN122554599A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent monitoring equipment technology, specifically to a real-time monitoring system and device based on artificial intelligence. Background Technology
[0002] In today's society, with the acceleration of urbanization and the expansion of various venues, such as large commercial complexes, industrial parks, and transportation hubs, the demand for real-time, efficient, and comprehensive monitoring is increasing. Traditional monitoring devices exhibit numerous limitations when dealing with complex and ever-changing monitoring environments. Existing monitoring devices have fixed and limited monitoring ranges, making it difficult to flexibly adjust them according to actual monitoring needs. In scenarios requiring comprehensive, multi-angle monitoring, multiple independent monitoring devices are often needed. This not only increases equipment costs and installation complexity but also results in fragmented monitoring images, causing inconvenience for subsequent data management and analysis. Furthermore, traditional monitoring devices rely on simple signal collection and transmission, and security monitoring is conducted through manual observation. A large amount of video information is directly transmitted to the same monitoring terminal, resulting in relatively weak data collection and processing capabilities, mostly only capable of basic video monitoring. Recording and playback functions are prone to problems such as data transmission delays and low processing efficiency when faced with large amounts of monitoring data. They are also inadequate for advanced functions such as target detection, recognition, and behavior analysis in the monitoring screen. Traditional systems lack effective intelligent processing methods when dealing with large amounts of monitoring data, requiring a lot of manual viewing and analysis work, which is not only inefficient but also prone to missed detections and misjudgments. The installation and debugging process of traditional monitoring devices is relatively complex and lacks flexibility. Different monitoring scenarios have different requirements for the installation location of monitoring equipment, and traditional devices are often not convenient enough to adjust the monitoring range and direction, making it difficult to quickly adapt to various complex monitoring environments. With the rapid development of artificial intelligence (AI) technology, significant achievements have been made in areas such as image recognition, target detection, and behavior analysis. Applying AI technology to real-time monitoring devices and systems can effectively solve the aforementioned problems existing in traditional monitoring systems, improve monitoring efficiency and accuracy, and bring new development opportunities to the field of security monitoring. Therefore, developing an AI-based real-time monitoring device and system has significant practical implications. Summary of the Invention
[0003] To achieve the above objectives, the present invention provides the following technical solution: a real-time monitoring device based on artificial intelligence, comprising: a regional data collector and a local monitoring network, wherein the local monitoring network consists of multiple adjustable monitors with identical structures, the adjustable monitors are connected to the regional data collector via optical fiber, and the adjustable monitors are fixed by a modular installation structure. The area data collector includes a device base and a data collection host. The data collection host is installed on the device base and is connected to the regulating monitor. The adjustable monitor includes: a mounting base, a fixing ring, a device mounting cavity, a rotating bracket, a transmission bevel gear ring, a driver, a first bevel gear, several mounting rods, several second bevel gears, a monitoring probe, and a video signal receiver. The fixing ring is installed above the mounting base. Both the fixing ring and the mounting base are hollow structures. The mounting base is installed on the combined mounting structure via the fixing ring. The equipment mounting cavity is opened inside the mounting base. The rotating bracket is installed on the inner wall of the equipment mounting cavity. The transmission bevel gear ring is movably embedded in the rotating bracket. The driver is installed on the outer wall of the mounting base, and its output end extends through into the equipment mounting cavity. The first bevel gear is installed on the output end of the driver and meshes with the lower end of the transmission bevel gear ring. Several mounting rods are laterally embedded through the mounting base and extend into the equipment mounting cavity. Several second bevel gears are fitted on several mounting rods and mesh with the upper end of the transmission bevel gear ring. Each of the mounting rods is equipped with a monitoring probe. The video signal receiver is installed at the top of the combined mounting structure and is wiredly connected to the monitoring probe.
[0004] Preferably, the monitoring probe is equipped with an audio collector, and the monitoring probe is connected to a video signal receiver via a plug-in connection cable.
[0005] Preferably, several of the mounting rods are fitted inside the device mounting cavity via rotary bearings.
[0006] Preferably, the combined installation structure includes: an adjusting fixing rod, several locking holes, a combined screw, a combined assembly base, a screw-type combined frame, several screw-on clips, and installation components; The adjusting and fixing rod is embedded in the middle of the mounting base and the fixing ring. The video signal receiver is installed at the top of the adjusting and fixing rod. The combined screw is installed at the bottom of the adjusting and fixing rod. The combined assembly base is opened on the tightening combined frame. The tightening combined frame is combined and installed at the lower end of the adjusting and fixing rod and located outside the combined screw and combined and fixed thereto. A number of tightening clips are evenly arranged in a ring on the lower wall surface of the tightening combined frame.
[0007] Preferably, the fixing ring is screwed into the locking hole by a fixing bolt to install the mounting base onto the adjusting fixing rod.
[0008] Preferably, the mounting assembly includes: a combination card holder, a plurality of insert-type tightening slots, and a mounting bracket; Several insertable tightening slots are formed on the upper wall of the combined card holder, which is mounted on the mounting bracket.
[0009] Preferably, both the combination card holder and the screw-on combination frame are provided with combination fixing holes and are connected and fixed by locking pins.
[0010] Preferably, the mounting bracket has two structures: a ground mounting bracket with a long pole and a suspended mounting bracket with a short pole.
[0011] An artificial intelligence-based real-time monitoring system includes: a storage cloud, an artificial intelligence monitoring terminal, a human-computer interaction terminal, a regional data collector, and a local area monitoring network; The cloud storage is used to collect surveillance video data and retain the collected data for later retrieval and viewing. It adopts a database management system to store and manage the collected data and algorithm processing results. The database has high concurrency processing capabilities, data security and scalability, and can meet the storage and query needs of large amounts of data. The AI monitoring terminal is connected and used in conjunction with the human-computer interaction terminal. The AI monitoring terminal allows for real-time viewing of surveillance footage and transmission of relevant commands to the human-computer interaction terminal, enabling remote control and interaction. It also utilizes AI to perform preprocessing operations such as cleaning, denoising, and normalization on the collected raw data, improving data quality. Furthermore, the system employs AI deep learning algorithms such as convolutional neural networks, YOLO, and Faster R-CNN to detect and identify targets in images or videos. In security monitoring, it can identify personnel, vehicles, and abnormal objects, and uses recurrent neural networks (RNNs) to analyze and predict the movement trajectory and behavior patterns of these targets. The human-computer interaction interface is developed to facilitate user configuration, monitoring, and management of the system. The user interface can take the form of a web interface or a mobile client application, providing functions such as real-time monitoring screen display, alarm information push, historical data query, and system parameter settings. Regional data collectors are responsible for collecting overall monitoring data within a specific area, ensuring the integrity and accuracy of the data; local monitoring networks connect various monitoring points, enabling comprehensive security monitoring within a region and facilitating rapid information transmission and sharing. The human-machine intelligent monitoring terminal can automatically analyze the observation and monitoring intensity of video signals in each area, and automatically allocate computing power according to the complexity of the video signals, reducing human intervention while ensuring monitoring and analysis effects.
[0012] Beneficial effects This invention provides an artificial intelligence-based real-time monitoring system and device, which has the following advantages: This AI-based real-time monitoring device, through a local area monitoring network, consists of multiple identical adjustable monitors, capable of covering a large area, effectively eliminating monitoring blind spots, and achieving comprehensive security monitoring. The adjustable monitors can be flexibly fixed through a modular installation structure, and their installation position and angle can be adjusted according to actual needs, further enhancing the comprehensiveness of monitoring. The structural design of the adjustable monitors makes their installation and debugging process more convenient. Through the cooperation of components such as drivers and bevel gears, the monitoring probes can be flexibly rotated and their angles adjusted, enabling rapid adaptation to various complex monitoring environments and meeting the monitoring needs of different scenarios. The modular installation structure and diverse design of installation components provide more options for the installation of monitoring devices, further enhancing the system's flexibility. This monitoring system incorporates artificial intelligence technology. The AI monitoring terminal can perform preprocessing operations such as cleaning, noise reduction, and normalization on the collected raw data to improve data quality. Utilizing algorithms such as convolutional neural networks, YOLO, and Faster R-CNN, it can accurately detect and identify targets in images or videos. In security monitoring, it can accurately identify personnel, vehicles, and abnormal objects. Simultaneously, it uses recurrent neural networks (RNNs) to analyze and predict the movement trajectory and behavior patterns of targets, providing more valuable information for security prevention. The system is equipped with a human-computer interaction terminal, featuring a user interface for convenient configuration, monitoring, and management. Users can access functions such as real-time monitoring display, alarm information push, historical data query, and system parameter settings through a web interface or mobile client application. Furthermore, the AI monitoring terminal is connected to the human-machine interface, allowing users to view monitoring footage in real time and transmit relevant commands to the interface for remote control and interaction. This enhances the convenience and flexibility of monitoring. The AI-powered monitoring terminal can automatically analyze the observation and monitoring intensity of video signals in different areas and automatically allocate computing power based on the complexity of the video signals. This function ensures effective monitoring and analysis while reducing human intervention, thus improving system efficiency and resource utilization. Attached Figure Description
[0013] Figure 1 This is a first three-dimensional structural diagram of a real-time monitoring device based on artificial intelligence according to the present invention.
[0014] Figure 2 This is a second three-dimensional structural diagram of a real-time monitoring device based on artificial intelligence as described in this invention.
[0015] Figure 3 This is a schematic diagram of the three-dimensional assembly structure of the combined installation structure of the real-time monitoring device based on artificial intelligence described in this invention.
[0016] Figure 4 This is a schematic diagram of the local monitoring network layout structure of an artificial intelligence-based real-time monitoring device according to the present invention.
[0017] Figure 5 This is a three-dimensional structural diagram of the area data collector of the real-time monitoring device based on artificial intelligence described in this invention.
[0018] Figure 6 This is a three-dimensional structural diagram of an adjustable monitor for a real-time monitoring device based on artificial intelligence, as described in this invention.
[0019] Figure 7 This is a schematic diagram of the main structure of the adjustable monitor of the real-time monitoring device based on artificial intelligence described in this invention.
[0020] Figure 8 This is a schematic diagram of the first three-dimensional structure of the mounting support for the artificial intelligence-based real-time monitoring device described in this invention.
[0021] Figure 9 This is a schematic diagram of the second three-dimensional structure of the mounting support for the real-time monitoring device based on artificial intelligence as described in this invention.
[0022] Figure 10 This is a schematic diagram of the main sectional view of the mounting base of the real-time monitoring device based on artificial intelligence according to the present invention.
[0023] Figure 11 This is a three-dimensional structural diagram of the transmission bevel gear ring of the real-time monitoring device based on artificial intelligence described in this invention.
[0024] Figure 12 This is an exploded view of the combined installation structure of the real-time monitoring device based on artificial intelligence described in this invention.
[0025] Figure 13 This is a bottom-view three-dimensional structural diagram of the screw-on assembly frame of the real-time monitoring device based on artificial intelligence described in this invention.
[0026] Figure 14 This is a flowchart illustrating the real-time monitoring system based on artificial intelligence as described in this invention.
[0027] In the diagram: 1-Area data collector; 101-Equipment base; 102-Data collection host; 2-Local area monitoring network; 3-Adjustable monitor; 301-Mounting base; 302-Fixing ring; 303-Equipment mounting cavity; 304-Rotating bracket; 305-Transmission bevel gear ring; 306-Driver; 307-First bevel gear; 308-Mounting rod; 309-Second bevel gear; 310-Monitoring probe; 311-Video signal receiver; 4-Fiber optic cable; 5-Audio collector; 6-Connecting cable; 7-Rotary bearing; 8-Adjusting fixing rod; 9-Locking hole; 10-Combination screw; 11-Combination assembly base; 12-Tightening combination frame; 13-Tightening clip; 14-Fixing bolt; 15-Combination clip holder; 16-Insertion-type tightening groove; 17-Mounting support; 18-Combination fixing hole; 19-Locking pin. Detailed Implementation
[0028] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0029] Example: Please refer to Figure 1-14 The monitoring center selects a suitable location to install the area data collector 1, places the equipment base 101 stably on the prepared ground, and uses expansion bolts to firmly fix the equipment base 101. Then, the data collection host 102 is installed on the equipment base 101, ensuring that the connection is tight and there is no looseness. The data collection host 102 is wired to the regulating monitor 3 in the subsequent local area monitoring network 2 through the fiber optic cable 4. Select appropriate installation components for different areas. For example, in areas with high ceilings and open spaces, use a long-pole ground mounting bracket as the mounting support 17. In relatively confined spaces, use a short-pole suspended mounting support 17. Install the combination bracket 15 on the mounting support 17, ensuring that the combination bracket 15 is placed horizontally and securely connected. Install the adjusting fixing rod 8, embedding it between the mounting base 301 and the fixing ring 302. Use the adjusting fixing rod 8 to support the monitoring equipment. Use the fixing bolts 14 to screw into the locking holes 9 to firmly install the mounting base 301 onto the adjusting fixing rod 8. Install the combination screw at the bottom of the adjusting fixing rod 8. The rod 10 is then installed, and the screw-tightening assembly 12 is assembled and installed at the lower end of the adjusting and fixing rod 8 and located outside the assembly screw 10. The screw-tightening assembly 12 is tightly assembled and fixed with the assembly screw 10 using a nut. Several screw-tightening clips 13 are evenly arranged in a ring on the lower wall of the screw-tightening assembly 12. The screw-tightening clips 13 at the lower end of the screw-tightening assembly 12 are inserted into the insertion screw-tightening grooves 16 opened on the assembly clip 15 and rotated to lock them. The screw-tightening assembly 12 is aligned with the assembly fixing holes 18 opened on the assembly clip 15. The locking pin 19 is used to lock the connection and fix it, ensuring that the entire assembly installation structure is stable and reliable.
[0030] A fixing ring 302 is installed above the mounting base 301. Both the fixing ring 302 and the mounting base 301 are hollow structures, facilitating installation on the adjusting fixing rod 8. An equipment mounting cavity 303 is formed inside the mounting base 301. A rotating bracket 304 is installed on the inner wall of the equipment mounting cavity 303. A transmission bevel gear ring 305 is movably fitted onto the rotating bracket 304, ensuring that the transmission bevel gear ring 305 can rotate flexibly. A driver 306 is installed on the outer wall of the mounting base 301, with its output end extending through into the equipment mounting cavity 303. A first bevel gear 307 is installed on the output end of the driver 306, ensuring accurate meshing between the first bevel gear 307 and the lower end of the transmission bevel gear ring 305. Several mounting rods 308 are laterally inserted into the mounting base 301 and extend into the equipment mounting cavity 303 via a rotary bearing 7, ensuring safety. The mounting rods 308 can rotate freely. Several second bevel gears 309 are mounted on several mounting rods 308, and the second bevel gears 309 mesh with the upper end of the transmission bevel gear ring 305. Monitoring probes 310 are installed on several mounting rods 308. The initial angle of the monitoring probes 310 is adjusted so that they can cover the required monitoring area. An audio collector 5 is set on the monitoring probe 310 to collect sound information in the monitoring area. A video signal receiver 311 is installed at the top of the adjusting and fixing rod 8. The monitoring probes 310 and the video signal receiver 311 are wired together using a plug-in connecting cable 6 to ensure stable signal transmission. These adjustable monitors 3 are wired together with the area data collector 1 through optical fiber 4 to form a local area monitoring network 2, realizing the rapid transmission and sharing of information between various monitoring points.
[0031] An artificial intelligence-based real-time monitoring system includes: a storage cloud, an artificial intelligence monitoring terminal, a human-computer interaction terminal, a regional data collector, and a local area monitoring network; The cloud storage is used to collect surveillance video data and retain the collected data for later retrieval and viewing. It adopts a database management system to store and manage the collected data and algorithm processing results. The database has high concurrency processing capabilities, data security and scalability, and can meet the storage and query needs of large amounts of data. The AI monitoring terminal is connected and used in conjunction with the human-computer interaction terminal. The AI monitoring terminal allows for real-time viewing of surveillance footage and transmission of relevant commands to the human-computer interaction terminal, enabling remote control and interaction. It also utilizes AI to perform preprocessing operations such as cleaning, denoising, and normalization on the collected raw data, improving data quality. Furthermore, the system employs AI deep learning algorithms such as convolutional neural networks, YOLO, and Faster R-CNN to detect and identify targets in images or videos. In security monitoring, it can identify personnel, vehicles, and abnormal objects, and uses recurrent neural networks (RNNs) to analyze and predict the movement trajectory and behavior patterns of these targets. The human-computer interaction terminal develops a user interface to facilitate users in configuring, monitoring and managing the system. The user interface can take the form of a web interface or a mobile client application, providing functions such as real-time monitoring screen display, alarm information push, historical data query, and system parameter setting. Regional data collectors are responsible for collecting overall monitoring data within a specific area, ensuring the integrity and accuracy of the data; local monitoring networks connect various monitoring points, enabling comprehensive security monitoring within a region and facilitating rapid information transmission and sharing. The human-machine intelligent monitoring terminal can automatically analyze the observation and monitoring intensity of video signals in each area, and automatically allocate computing power according to the complexity of the video signals, reducing human intervention while ensuring monitoring and analysis effects.
[0032] The monitoring center is equipped with high-performance servers as cloud storage, and a professional database management system is used to configure the database in a reasonable way to enable it to have high concurrency processing capabilities, data security and scalability. Sufficient storage space is set up to meet the storage needs of a large amount of monitoring video data. At the same time, a data backup strategy is formulated to regularly back up the collected monitoring video data to prevent data loss. Artificial intelligence monitoring software is deployed on the server of the monitoring center. This software integrates algorithms such as convolutional neural networks, YOLO, and Faster R-CNN to detect and identify targets in the acquired images or videos. At the same time, it integrates recurrent neural network (RNN) algorithms to analyze and predict the motion trajectory and behavior patterns of targets. The artificial intelligence monitoring terminal is initialized, including algorithm parameter adjustment and model training, to ensure that it can accurately and efficiently process monitoring data. Develop a human-computer interaction platform based on a web interface and a mobile client application. The web interface is deployed on the server in the monitoring center and is available to internal administrators via the local area network. The mobile client application is developed for Android and iOS systems, allowing administrators to monitor and manage anytime, anywhere via mobile phones or tablets. The user interface features real-time monitoring screen display, showing the monitoring images collected by various controllable monitors in real time. An alarm push function is also included, promptly sending alarm information to administrators when the AI monitoring terminal detects anomalies. A historical data query function is provided, allowing administrators to query historical monitoring data based on time, region, and other criteria. Additionally, a system parameter setting function is developed, allowing administrators to adjust various parameters of the monitoring system, such as the resolution and frame rate of the monitoring screen, and the sensitivity of the AI algorithm.
[0033] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A real-time monitoring device based on artificial intelligence, characterized in that, include: The system includes a regional data collector (1) and a local monitoring network (2), which consists of multiple identical adjustable monitors (3). The adjustable monitors (3) are wired to the regional data collector (1) via optical fiber (4), and the adjustable monitors (3) are fixed by a modular installation structure. The area data collector (1) includes a device base (101) and a data collection host (102). The data collection host (102) is installed on the device base (101) and is connected to the regulating monitor (3). The adjustable monitor (3) includes: a mounting base (301), a fixing ring (302), an equipment mounting cavity (303), a rotating bracket (304), a transmission bevel gear ring (305), a driver (306), a first bevel gear (307), several mounting rods (308), several second bevel gears (309), a monitoring probe (310), and a video signal receiver (311). The fixing ring (302) is installed above the mounting base (301). The fixing ring (302) and the mounting base (301) are hollow structures. The mounting base (301) is installed on the combined mounting structure through the fixing ring (302). The equipment mounting cavity (303) is opened inside the mounting base (301). The rotating bracket (304) is installed on the inner wall of the equipment mounting cavity (303). The transmission bevel gear ring (305) is movably embedded in the rotating bracket (304). The driver (306) is installed on the outer wall of the mounting base (301) and its output end extends through into the equipment mounting cavity (301). 3) Inside, the first bevel gear (307) is mounted on the output end of the driver (306) and meshes with the lower end of the transmission bevel gear ring (305). A plurality of mounting rods (308) are horizontally inserted into the mounting base (301) and extend into the device mounting cavity (303). A plurality of second bevel gears (309) are fitted on the plurality of mounting rods (308) and mesh with the upper end of the transmission bevel gear ring (305). Each of the plurality of mounting rods (308) is provided with a monitoring probe (310). The video signal receiver (311) is mounted on the top of the combined mounting structure and is wired to the monitoring probe (310).
2. The real-time monitoring device based on artificial intelligence according to claim 1, characterized in that, The monitoring probe (310) is equipped with an audio collector (5), and the monitoring probe (310) is connected to the video signal receiver (311) via a plug-in connection cable (6).
3. The real-time monitoring device based on artificial intelligence according to claim 1, characterized in that, Several of the mounting rods (308) are fitted inside the equipment mounting cavity (303) via a rotary bearing (7).
4. The real-time monitoring device based on artificial intelligence according to claim 1, characterized in that, The combined installation structure includes: an adjusting fixing rod (8), several locking holes (9), a combined screw (10), a combined assembly base (11), a screw-type combined frame (12), several screw-on clips (13), and installation components; The adjusting fixing rod (8) is embedded in the middle position of the mounting base (301) and the fixing ring (302). The video signal receiver (311) is installed at the top of the adjusting fixing rod (8). The combined screw (10) is installed at the bottom of the adjusting fixing rod (8). The combined assembly base (11) is opened on the tightening combined frame (12). The tightening combined frame (12) is combined and installed at the lower end of the adjusting fixing rod (8) and located outside the combined screw (10) and combined and fixed with it. A number of tightening cards (13) are evenly arranged in a ring on the lower wall surface of the tightening combined frame (12).
5. The real-time monitoring device based on artificial intelligence according to claim 4, characterized in that, The fixing ring (302) is screwed into the locking hole (9) by the fixing bolt (14) to install the mounting base (301) onto the adjusting fixing rod (8).
6. The real-time monitoring device based on artificial intelligence according to claim 4, characterized in that, The mounting assembly includes: a combination bracket (15), a plurality of insert screw slots (16), and a mounting bracket (17). Several insert-type tightening slots (16) are formed on the upper wall of the combined card holder (15), which is mounted on the mounting bracket (17).
7. The real-time monitoring device based on artificial intelligence according to claim 6, characterized in that, Both the combined card holder (15) and the screw-on combined frame (12) are provided with combined fixing holes (18) and are connected and fixed by locking pins (19).
8. A real-time monitoring device based on artificial intelligence according to claim 6, characterized in that, The mounting bracket (17) has two structures: a ground mounting bracket with a long pole and a suspended mounting bracket with a short pole.
9. A real-time monitoring system based on artificial intelligence, applied to the real-time monitoring device based on artificial intelligence described in any one of claims 1-4 above. Its features are, This includes: cloud storage, AI monitoring terminals, human-computer interaction terminals, regional data collectors, and local area monitoring networks; The cloud storage is used to collect surveillance video data and retain the collected data for later retrieval and viewing. It adopts a database management system to store and manage the collected data and algorithm processing results. The database has high concurrency processing capabilities, data security and scalability, and can meet the storage and query needs of large amounts of data. The AI monitoring terminal is connected and used in conjunction with the human-computer interaction terminal. The AI monitoring terminal allows for real-time viewing of surveillance footage and transmission of relevant commands to the human-computer interaction terminal, enabling remote control and interaction. It also utilizes AI to perform preprocessing operations such as cleaning, denoising, and normalization on the collected raw data, improving data quality. Furthermore, the system employs AI deep learning algorithms such as convolutional neural networks, YOLO, and Faster R-CNN to detect and identify targets in images or videos. In security monitoring, it can identify personnel, vehicles, and abnormal objects, and uses recurrent neural networks (RNNs) to analyze and predict the movement trajectory and behavior patterns of these targets. The human-computer interaction terminal develops a user interface to facilitate system configuration, monitoring, and management. The user interface can take the form of a web interface or a mobile client application, providing functions such as real-time monitoring screen display, alarm information push, historical data query, and system parameter settings. Regional data collectors are responsible for collecting overall monitoring data within a specific area, ensuring the integrity and accuracy of the data; local monitoring networks connect various monitoring points, enabling comprehensive security monitoring within a region and facilitating rapid information transmission and sharing. The human-machine intelligent monitoring terminal can automatically analyze the observation and monitoring intensity of video signals in each area, and automatically allocate computing power according to the complexity of the video signals, reducing human intervention while ensuring monitoring and analysis effects.