Intelligent patrol system based on Internet of Things

Abstract

The invention provides an intelligent patrol system based on the Internet of Things, which adopts various detection means (multi-optical-spectrum camera, infrared camera, laser radar, composite gas detection, Fourier infrared gas telemetering and the like), and carries out omnibearing and all-weather detection and detection on a fixed area in a fixed and movable (unmanned aerial vehicle and unmanned vehicle) mode. And the data is displayed to managers such as enterprises through data transmission processing. Through organic integration of a multi-source sensing detection module, a fixed and mobile coordinated three-dimensional monitoring network, Internet of Things communication and an edge computing technology, all-weather and omnibearing intelligent monitoring of a hazardous chemical production area is realized, and the problems of missing detection, delay, potential safety hazards and the like existing in traditional manual inspection are effectively solved; according to the system, the accuracy and the early warning capability of anomaly detection are remarkably improved through a visual Internet of Things platform, and meanwhile, the operation cost and the safety risk of an enterprise are reduced by means of digital inspection records and an emergency linkage mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to an intelligent patrol and detection system based on the Internet of Things. Background Art

[0002] In the prior art, the daily patrol inspections of production enterprises, especially hazardous chemical production enterprises such as petroleum and chemical industries, mainly rely on the manual fixed-point and fixed-time patrol inspection method. This method has many limitations: patrol inspectors may miss inspections due to fatigue or negligence; manual patrol inspections cannot achieve round-the-clock and uninterrupted monitoring; there are safety hazards for patrol inspections in dangerous areas or high-altitude areas; it is difficult to detect early small leaks or abnormalities through manual patrol inspections; the inspection results rely on personal experience judgments and lack objective data support.

[0003] Especially when production safety problems such as gas leakage occur, the traditional manual patrol inspection method often has difficulty in detecting abnormal situations in a timely manner, resulting in the inability to take treatment measures in a timely manner, which may trigger major safety accidents and cause casualties and property losses. In addition, the efficiency of manual patrol inspections is low, the data records are incomplete, and it is difficult to form an effective early warning mechanism and historical data analysis. Summary of the Invention

[0004] In view of this, in order to solve the problems existing in the technical background, the present invention proposes an intelligent patrol and detection system based on the Internet of Things. The present invention adopts a variety of detection means (multi-spectral cameras, infrared cameras, lidar, compound gas detection, Fourier transform infrared gas remote sensing, etc.) and conducts full-range and round-the-clock detection and detection of fixed areas through fixed and mobile (unmanned aerial vehicles, unmanned vehicles) methods, and displays the data through transmission and processing to managers such as enterprises. The specific technical solutions are as follows: An intelligent patrol and detection system based on the Internet of Things includes a multi-spectral camera, an infrared thermal imaging camera, a lidar scanner, a compound gas detection sensor, and a Fourier transform infrared gas remote sensor, and also includes mobile patrol units such as an unmanned aerial vehicle patrol system and an unmanned vehicle patrol system. The multi-spectral camera can identify the spectral characteristics of visible light and specific wavelengths and is used to detect the spectral characteristics of leaked substances. The infrared thermal imaging camera is used to monitor abnormal equipment temperatures and hot spots. The lidar is used for three-dimensional space modeling and micro-displacement monitoring; the compound gas detection sensor can simultaneously detect the concentrations of multiple hazardous gases, and the Fourier transform infrared gas remote sensor can achieve long-distance gas composition analysis; Fixed monitoring terminals are set at key positions in the production area to form a full-coverage monitoring network. Each fixed node includes a combination of the above-mentioned multiple sensors and is connected to the Internet of Things platform through the Internet of Things communication network; The described UAV inspection system uses UAVs equipped with lightweight sensor pods, which can perform aerial inspection tasks and are suitable for monitoring high-altitude equipment, pipelines, and tank farms; unmanned vehicles are equipped with multi-functional detection platforms, which can autonomously navigate and inspect on the ground in the production area, and have obstacle recognition and avoidance functions. The mobile inspection unit and the fixed monitoring network work together to form a three-dimensional monitoring system.

[0005] Furthermore, the IoT communication network adopts industrial-grade wireless communication technologies, including hybrid networking methods such as LoRa, NB-IoT, 5G, etc., to ensure the reliability and real-time performance of data transmission. The system designs redundant communication links, and can automatically switch to the backup channel when the main communication fails.

[0006] Furthermore, both the fixed monitoring terminal and the mobile inspection unit have edge computing capabilities to achieve data preprocessing and preliminary analysis, reduce the network transmission burden. The edge nodes can perform simple anomaly recognition and alarm judgment, and achieve fast local response to obvious abnormal situations.

[0007] Furthermore, the IoT platform receives and integrates data streams from fixed and mobile inspection units, and uses big data analysis technology for comprehensive research and judgment. The platform includes a machine learning module, which establishes a normal operating condition model through training with historical data, and compares the detection data in real time to achieve anomaly early warning. The platform supports multi-dimensional data analysis, including spatio-temporal correlation analysis, trend prediction, and fault diagnosis. According to the severity of the anomaly, different response processes are triggered, and it can be docked with control systems such as the enterprise's DCS and SIS to achieve automatic emergency linkage, and supports multiple alarm methods such as SMS, email, sound, and light to ensure that relevant personnel can learn about the alarm situation in a timely manner.

[0008] Furthermore, the IoT platform is located on a visual human-machine interface. The visual human-machine interface provides multi-level management interfaces for the Web end and the mobile end, and supports real-time monitoring data display, historical data query, alarm management, and report generation. The GIS technology is used to realize the spatial visualization of monitoring data, and the VR / AR method is supported to view the equipment status and anomaly point positioning.

[0009] Furthermore, the fixed node adopts an explosion-proof design, adapts to dangerous environments such as chemical industries, and has self-checking and calibration functions.

[0010] Adopting the above technical solutions, the following beneficial effects are obtained: Through the organic integration of the multi-source sensing detection module, the three-dimensional monitoring network with fixed and mobile collaboration, IoT communication, and edge computing technologies, the present invention realizes all-weather and all-round intelligent monitoring of the hazardous chemical production area, and effectively solves problems such as missed inspections, delays, and safety hazards existing in traditional manual inspections; The system significantly improves the accuracy of anomaly detection and early warning capabilities through a visual Internet of Things platform. At the same time, with the help of digital inspection records and emergency linkage mechanisms, it not only reduces the operating costs and safety risks of enterprises, but also provides data support for accident traceability and preventive maintenance. Ultimately, it realizes the upgrade of safety management from passive response to proactive prevention, comprehensively improving the intrinsic safety level and emergency response efficiency of hazardous chemical production enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 FIG. is a schematic diagram of the module structure of an intelligent patrol and detection system based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0012] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0013] See Figure 1 An intelligent patrol and detection system based on the Internet of Things as shown includes a multispectral camera, an infrared thermal imaging camera, a lidar scanner, a composite gas detection sensor, and a Fourier transform infrared gas remote sensor. It also includes mobile patrol units such as an unmanned aerial vehicle patrol system and an unmanned vehicle patrol system. The multispectral camera can identify visible light and spectral characteristics of specific wavelengths for detecting the spectral characteristics of leaked substances. The infrared thermal imaging camera is used to monitor abnormal equipment temperatures and hot spots. The lidar is used for three-dimensional space modeling and micro displacement monitoring. The composite gas detection sensor can simultaneously detect the concentrations of multiple hazardous gases, and the Fourier transform infrared gas remote sensor can achieve long-distance gas composition analysis. Fixed monitoring terminals are set at key positions in the production area to form a full-coverage monitoring network. Each fixed node includes a combination of the above-mentioned multiple sensors and is connected to the Internet of Things platform through the Internet of Things communication network. The described unmanned aerial vehicle (UAV) inspection system uses UAVs equipped with lightweight sensor pods, which can perform aerial inspection tasks and are suitable for monitoring high-altitude equipment, pipelines, and tank farms. The unmanned vehicle is equipped with a multi-functional detection platform and can autonomously navigate and inspect on the ground in the production area, with the functions of obstacle recognition and avoidance. The mobile inspection unit works in coordination with the fixed monitoring network to form a three-dimensional monitoring system. The Internet of Things (IoT) communication network uses industrial-grade wireless communication technologies, including hybrid networking methods such as LoRa, NB-IoT, and 5G, to ensure the reliability and real-time nature of data transmission. The system is designed with redundant communication links, which can automatically switch to the backup channel when the primary communication fails. Both the fixed monitoring terminal and the mobile inspection unit have edge computing capabilities to perform data preprocessing and preliminary analysis, reducing the network transmission burden. The edge nodes can perform simple anomaly recognition and alarm judgment to achieve a fast local response to obvious abnormal situations. The IoT platform receives and integrates the data streams from the fixed and mobile inspection units, and uses big data analysis technology for comprehensive research and judgment. The platform includes a machine learning module, which establishes a normal operating condition model through training with historical data and compares the detection data in real time to achieve anomaly warning. The platform supports multi-dimensional data analysis, including spatio-temporal correlation analysis, trend prediction, and fault diagnosis. Different response processes are triggered according to the severity of the anomaly, and it can be docked with control systems such as the enterprise's DCS and SIS to achieve automatic emergency linkage. It supports multiple alarm methods such as SMS, email, sound, and light to ensure that relevant personnel can be informed of the alarm in a timely manner. The IoT platform is located on a visual human-machine interface, which provides multi-level management interfaces for the Web end and the mobile end, supporting real-time monitoring data display, historical data query, alarm management, and report generation. GIS technology is used to achieve spatial visualization of the monitoring data, and VR / AR methods are supported to view the equipment status and abnormal point positioning. The fixed nodes adopt an explosion-proof design, are suitable for dangerous environments such as chemical industries, and have self-checking and calibration functions.

[0014] Specifically, it includes multi-source sensors, intelligent mobile inspection units, and an edge computing architecture to build a full-sensing monitoring network for hazardous chemical production areas. The core hardware of the system consists of a multi-spectral optical module, an infrared thermal imager, a 3D lidar, and a composite gas sensor cluster. Among them, the multi-spectral camera uses an IMEC CMOS sensor combined with a tunable filter to achieve 16-channel spectral analysis in the 400-1000nm band, and a hydrocarbon leakage spectral feature database is established in cooperation with deep learning algorithms. The infrared thermal imager uses a 640×512 pixel vanadium oxide detector with a temperature resolution of 0.03°C, and in cooperation with a self-developed temperature field anomaly detection algorithm, it can identify abnormal temperature rises of more than 3°C on the surface of the equipment.

[0015] The fixed monitoring nodes adopt a stainless steel explosion-proof enclosure with an IP68 protection standard, internally integrated with a quad-core processor, equipped with 256GB industrial-grade SSD storage, and support wide-temperature operation from -40°C to 70°C.

[0016] In terms of the mobile inspection unit, the drone system selects the M300 RTK flight platform, equipped with a customized sensor pod, integrating a micro Fourier infrared spectrometer and a MEMS lidar. A three-dimensional scene map is constructed through digital twin technology to achieve millimeter-level modeling of high-altitude pipe racks (>30m) with autonomous flight path planning. The unmanned vehicle uses a four-wheel independent drive chassis, equipped with a 32-line lidar and a binocular vision system, with a positioning accuracy of ±2cm, and can autonomously complete grid inspections of a 500m×500m area, with a battery life of more than 8 hours.

[0017] The communication network adopts a heterogeneous redundant design. A 5G NR-U private network is deployed in open areas, LoRaWAN Class B gateways are used in underground pipe galleries, and NB-IoT dual-mode communication is adopted in the control room area. Each fixed node is configured with a multi-mode communication module, and the main and backup link switching time <300ms, ensuring 99.99% data transmission reliability. Edge computing nodes deploy the TensorRT-optimized YOLOv5s model to achieve local real-time video analysis (frame rate >25fps), only transmitting structured data, reducing the network load by 82%.

[0018] The Internet of Things platform adopts a distributed stream processing architecture. The Kafka cluster processes 500,000 monitoring data per second, and the Flink engine executes a real-time rule engine (rule library >1000). The machine learning module trains a device health model based on the LightGBM algorithm. The input features include 28-dimensional parameters such as vibration spectrum and temperature trend. The leakage prediction accuracy is measured to be 92.3%. The platform is connected to the enterprise DCS system through the Modbus TCP protocol, and can automatically trigger the SIS interlock under abnormal conditions, with a response time <1.5 seconds.

[0019] In terms of visualization, the GIS engine adopts WebGL rendering technology, supporting real-time rendering of more than 200,000 monitoring points. The AR interface integrates the Microsoft HoloLens2 to achieve three-dimensional visualization of equipment and spatial positioning of leakage points, with a positioning error <0.5 meters. Application cases in a petrochemical enterprise show that after the system is launched, the frequency of manual inspections in dangerous areas is reduced by 75%, the discovery time of minor leaks is shortened from an average of 42 minutes to 8 seconds, the annual preventive maintenance cost is reduced by 38%, and 2 major leakage accidents are successfully avoided.

[0020] Through the organic integration of multi-source sensing detection modules, a fixed and mobile collaborative three-dimensional monitoring network, Internet of Things communication, and edge computing technologies, the present invention realizes all-weather and omni-directional intelligent monitoring of hazardous chemical production areas, effectively solving problems such as missed inspections, delays, and safety hazards existing in traditional manual inspections; The system, through a visual Internet of Things platform, has significantly improved the accuracy of anomaly detection and early warning capabilities. At the same time, with the help of digital inspection records and emergency linkage mechanisms, it has not only reduced the enterprise's operating costs and safety risks, but also provided data support for accident traceability and preventive maintenance. Ultimately, it has achieved an upgrade in safety management from passive response to proactive prevention, comprehensively enhancing the intrinsic safety level and emergency response efficiency of hazardous chemical production enterprises.

[0021] The basic principles and main features of the present invention have been described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the invention claimed is defined by the appended claims and their equivalents.

Claims

1. An intelligent patrol system based on the Internet of Things, characterized in that: It includes multi-spectral cameras, infrared thermal imaging cameras, laser radar scanners, composite gas detection sensors and Fourier infrared gas telemeters, as well as mobile inspection units such as drone inspection systems and unmanned vehicle inspection systems. Multi-spectral cameras can identify the spectral characteristics of visible light and specific wavelengths, and are used to detect the spectral characteristics of leaked substances. Infrared thermal imaging cameras are used to monitor equipment temperature anomalies and hot spots. Laser radars are used for three-dimensional space modeling and micro-displacement monitoring. Composite gas detection sensors can detect the concentration of multiple hazardous gases at the same time, and Fourier infrared gas telemeters can realize long-distance gas composition analysis. Fixed monitoring terminals are set up at key locations in the production area to form a fully covered monitoring network. Each fixed node contains a combination of the above sensors and is connected to the IoT platform through the IoT communication network. The drone inspection system uses drones equipped with lightweight sensor pods to perform aerial inspection tasks, and is suitable for monitoring high-altitude equipment, pipelines, and tank farms; The unmanned vehicle is equipped with a multi-functional detection platform and can autonomously navigate and inspect on the ground in the production area. It has obstacle recognition and avoidance functions. The mobile inspection unit works together with the fixed monitoring network to form a three-dimensional monitoring system.

2. According to claim 1, the intelligent patrol system based on the Internet of Things is characterized in that: The IoT communication network adopts industrial-grade wireless communication technology, including hybrid networking methods such as LoRa, NB-IoT, and 5G, to ensure the reliability and real-time performance of data transmission. The system is designed with redundant communication links, which can automatically switch to backup channels when the main communication fails.

3. The intelligent patrol system based on the Internet of Things according to claim 1 is characterized in that: Both the fixed monitoring terminal and the mobile inspection unit have edge computing capabilities to achieve data preprocessing and preliminary analysis, reduce the network transmission burden, and the edge nodes can perform simple anomaly identification and alarm judgment, and achieve rapid local response to obvious abnormal situations.

4. The intelligent patrol system based on the Internet of Things according to claim 1 is characterized in that: The IoT platform receives and integrates data streams from fixed and mobile inspection units, and uses big data analysis technology for comprehensive analysis. The platform includes a machine learning module, which establishes a normal operating condition model through historical data training, and realizes abnormal warning by real-time comparison of detection data. The platform supports multi-dimensional data analysis, including spatiotemporal correlation analysis, trend prediction and fault diagnosis, and triggers different response processes according to the severity of the abnormality. It can be connected with the company's DCS, SIS and other control systems to realize automatic emergency linkage, and supports multiple alarm methods such as SMS, email, sound and light to ensure that relevant personnel are informed of the alarm situation in a timely manner.

5. The intelligent patrol system based on the Internet of Things according to claim 1 is characterized in that: The IoT platform is located on a visual human-machine interface, which provides a multi-level management interface for the Web and mobile terminals, supporting real-time monitoring data display, historical data query, alarm management, and report generation. GIS technology is used to achieve spatial visualization of monitoring data, supporting VR / AR methods to view equipment status and abnormal point location.

6. The intelligent patrol system based on the Internet of Things according to claim 1 is characterized in that: The fixed node adopts an explosion-proof design, is suitable for hazardous environments such as chemical industry, and has self-checking and calibration functions.

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