A smart inspection and detection system for grain conveyor belts at port terminals

Abstract

This invention discloses an intelligent inspection and detection system for grain conveyor belts at port terminals, comprising a main control module, an operational inspection module, a full lifecycle module, an alarm center module, and a monitoring center module. It adopts a three-layer technical architecture: front-end, back-end, and deployment. The front-end uses a WebGL 3D rendering engine that supports plugin-free operation in mainstream browsers. The back-end consists of an IoT data platform, an AI video analysis engine, and a distributed alarm system, supporting cloud and local deployment and adapting to industrial network environments. The main control module, operational inspection module, full lifecycle module, alarm center, and monitoring center collaborate to achieve intelligent monitoring and management of the grain conveyor belt's operating status, operational processes, and safety risks throughout its entire lifecycle. It achieves full-domain transparency and visibility: through digital twins and intelligent video surveillance, it enables real-time monitoring of the grain conveyor belt's operating status around the clock and from all directions, leaving no hidden dangers and solving the problem of traditional inspections being "invisible."

Description

Technical Field

[0001] This invention relates to the field of industrial intelligent monitoring technology, specifically to an intelligent inspection and detection system for grain conveyor belts at port terminals. Background Technology

[0002] The grain storage and logistics system is a crucial guarantee for national food security. As the core hub of grain circulation, the stability and reliability of the grain conveyor belt system directly impact the efficiency and safety of grain distribution. However, the traditional grain conveyor belt management model has many drawbacks: Hidden dangers are "invisible": Problems such as belt misalignment, material spillage, and equipment wear in long-distance and complex environments (high-altitude, underground, silos) rely on manual inspection, which is high-risk, inefficient, easy to miss, and results in delayed fault detection. Maintenance is “unpredictable”: It adopts a fixed cycle or emergency repair mode, lacks the ability to predict equipment health, has poor spare parts management, high maintenance costs and frequent unplanned downtime; Losses and energy consumption are “uncontrollable”: losses from grain spillage are difficult to quantify and pinpoint, and equipment idling and inefficient operation result in significant energy waste; Data is "unconnected": Data throughout the entire lifecycle, including design, installation, operation, and maintenance, is scattered and fragmented, forming information barriers that cannot support scientific decision-making; Safety risks are “unavoidable”: manual inspections face high risks such as dust explosions and falls from heights, and there is a lack of real-time early warning for abnormal equipment conditions, resulting in great pressure on safety management; Operational efficiency is "not improved": slow response to emergencies, reliance on experience for fault diagnosis, and long handling time affect the overall efficiency of grain circulation.

[0003] Meanwhile, grain conveyor belt systems are large-scale, widely distributed, and operate in harsh environments (high dust, high humidity, corrosion), and are also affected by seasonal high-intensity operating pressures. These problems become even more pronounced in large-scale operations. With the mature integration of IoT, AI, big data, cloud computing, and digital twin technologies, technical support has been provided to address the pain points of traditional grain conveyor belt management. There is an urgent need for an intelligent inspection and monitoring system adapted to the grain conveyor belt scenario, enabling a transformation and upgrade from extensive, passive, and inefficient management to intelligent, visualized, lean, and safe management. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent inspection and detection system for grain conveyor belts at port terminals, which solves the problems of delayed detection of hidden dangers, inaccurate maintenance, uncontrolled energy consumption, data silos, high safety risks, and low operational efficiency in the traditional management of grain conveyor belts. Through the triple drive of digital twin, Internet of Things, and AI technologies, an intelligent monitoring and management system for the entire life cycle of grain conveyor belts is constructed to achieve the goals of zero-latency fault detection, reduced operation and maintenance costs, controllable safety risks, and data support for decision-making.

[0005] A smart inspection and detection system for grain conveyor belts at port terminals includes a main control module, an operational inspection module, a full lifecycle module, an alarm center module, and a monitoring center module. It adopts a three-layer technical architecture: front-end, back-end, and deployment. The front-end uses a WebGL 3D rendering engine that supports plugin-free operation in mainstream browsers. The back-end consists of an IoT data platform, an AI video analysis engine, and a distributed alarm system, supporting cloud and local deployment and adapting to industrial network environments. The main control module, operational inspection module, full lifecycle module, alarm center, and monitoring center work together to achieve intelligent monitoring and management of the grain conveyor belt equipment's operating status, operational processes, and safety risks throughout its entire lifecycle.

[0006] Preferably, the main control module includes eight major functions: alarm statistics, top 10 alarms, equipment inspection, vibration sensor acceleration display, vibration sensor temperature display, operation inspection statistics, operation inspection list, and equipment inspection pagination list. It is also configured with a belt scene page to realize regional equipment monitoring, real-time status display of each area of ​​the belt, and real-time alarm list presentation. The alarm statistics realize the quantification and proportion analysis of normal, abnormal, ordinary, general, and serious alarms through quantity statistics and pie charts. Vibration sensor related data realize the real-time and historical trend display through line charts. Equipment inspection and operation inspection statistics realize the visual comparison of the total number of inspections and the number of anomalies through bar charts.

[0007] Preferably, the operation and inspection module supports full-process management of inspection tasks, including three main functions: operation and inspection pagination list display, inspection task operation, and inspection task addition. Inspection types are divided into comprehensive inspection and custom inspection. Comprehensive inspection can automatically cover all monitoring points of the entire conveyor belt, while custom inspection allows selection of specific cameras to inspect local areas. Inspection task operation includes viewing details and execution. During execution, the camera direction and angle can be controlled by the PTZ, and the inspection can be manually marked as normal or abnormal. In case of abnormal status, the cause of the abnormality must be submitted and closed-loop processing completed. The operation and inspection pagination list displays full-dimensional task information such as conveyor belt name, inspection type, inspection status, and abnormal status.

[0008] Preferably, the full lifecycle module is an equipment health analysis dashboard, which includes four main functions: equipment status display, equipment list query, operation inspection analysis, and alarm analysis list. The equipment status is displayed quantitatively through charts showing the number of motors, temperature and vibration sensors, cameras, stethoscopes, and microphones in operation, shutdown, and fault status. The equipment list supports filtering by region and displays full information about the equipment, such as the belt, equipment number, real-time data, and running time. The operation inspection analysis uses bar charts to realize the number of customized / comprehensive inspections and the number of anomalies, and supports filtering data for a single belt. The alarm analysis list displays the core information of equipment alarms; temperature and vibration sensor alarms can be viewed through data charts, and camera alarms can be viewed through images of the alarm scene.

[0009] Preferably, the alarm center module realizes centralized management and processing of alarms, including three major functions: alarm pagination list, one-click processing, and single alarm processing. The alarm pagination list displays information such as processing status, alarm level, alarm image, and alarm time, and supports filtering and querying by alarm title, level, status, and date. It supports one-click batch processing of all alarms, and can also process a single alarm individually. The processing operation is equipped with a secondary confirmation mechanism to avoid accidental operation.

[0010] Preferably, the monitoring center module realizes full-area monitoring of the grain conveyor belt cameras, including three major functions: camera count statistics, camera list display, and real-time camera video playback; it counts the number of online and offline cameras in real time; the camera list displays the camera name, status, and area information, and supports filtering and adding; online cameras can play live video, realizing a one-screen overview of the grain conveyor belt monitoring screen.

[0011] Preferably, the system is equipped with a 3D visualization digital twin function, which uses high-precision modeling to realistically restore the structure of the grain conveying corridor, belt conveyors, idlers, sensors, cameras and other equipment appearances, and supports interactive operations such as zooming and rotating on the web page; and integrates multi-source sensor data such as current, voltage, temperature, vibration acceleration, sound decibels, etc., to display time domain / frequency domain waveforms and equipment operating parameters in real time.

[0012] Preferably, the system is equipped with intelligent video monitoring and AI early warning functions, which can monitor in real time 24 / 7. Combined with video stream analysis, it can automatically identify risks such as belt tearing, belt deviation, material spillage, personnel intrusion, abnormal temperature rise, and excessive noise. It supports dividing the belt into clickable areas, and the abnormal equipment trigger area is automatically marked to achieve rapid fault location.

[0013] Preferably, the system's web-based software is compatible with Chinese versions of mainstream operating systems such as Windows, Linux, and Mac, with IE10 or later as the primary browser and a recommended system resolution of 1920×1080. The hardware configuration requirements are: CPU 2.5GHz 6 cores 12 threads, 32GB of RAM, a dedicated graphics card with WDDM1.0 driver and DirectX 9 or higher support, a hard drive of 512GB or more, and a 24-inch or larger monitor with a 1920×1080 resolution.

[0014] Compared with existing technologies, the beneficial effects of this invention are: achieving full-area transparency and visibility: through digital twins and intelligent video monitoring, the real-time monitoring of the grain conveyor belt's operation status is achieved around the clock and in all directions, leaving no room for various hidden dangers to hide, and solving the problem of traditional inspections being "invisible"; Predictive and accurate maintenance: Based on IoT data and AI analysis, accurately assess the health status of equipment, predict failure trends, and achieve "predictive repair" of equipment, significantly reducing downtime losses and maintenance costs, and solving the problem of "inaccurate calculations" in traditional maintenance; Achieve dual control over losses and energy consumption: By using AI to accurately identify and quantify grain spillage losses, and by optimizing operating strategies based on equipment operation data, significantly reduce the cost per ton of grain and energy consumption, thus solving the problem of uncontrollable losses and energy consumption; Building a full lifecycle data platform: Connecting data from the entire lifecycle of grain conveyor belt design, manufacturing, operation, and maintenance, breaking down information silos, providing data support for scientific decision-making by enterprises, and solving the problem of data "inability"; Strengthen the inherent safety defense line: By intelligently identifying high-risk behaviors and working conditions such as personnel intrusion and equipment overheating, real-time early warning can be achieved, reducing the risks of manual inspection, improving safety early warning and emergency response capabilities, and solving the problem of safety risks being "unpreventable"; Improve lean operation efficiency: achieve knowable equipment status, controllable operation process, and optimized operating efficiency, improve grain circulation efficiency and system resilience, and solve the problem of "not being able to improve" operational efficiency; Significantly reduce operation and maintenance costs: Intelligent inspection replaces manual inspection, improving efficiency by more than 80%, reducing reliance on on-site manpower, extending equipment lifespan, optimizing spare parts procurement plans, and further reducing operation and maintenance costs; Highly adaptable and scalable: Supports cloud / local deployment, adaptable to various large-scale material transportation scenarios such as ports, grain warehouses, and thermal power plants. The system framework is scalable and can adapt to the continuous development of business needs. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] Example 1 System deployment and hardware / software configuration In this embodiment, the system is deployed in a grain logistics park at a port terminal, adopting a localized deployment method to adapt to the industrial network environment of the park and ensure the stability and security of data transmission.

[0017] Software configuration: The server uses a Linux operating system, the client uses a Windows 10 operating system, the browser uses IE11, and the system resolution is uniformly set to 1920. 1080; Hardware configuration: The core server is configured with a 2.5GHz CPU with 6 cores and 12 threads, 32GB of memory, a dedicated graphics card supporting DirectX 9, a 1TB solid-state drive, and a 27-inch high-definition monitor; the on-site configuration includes 6 explosion-proof dual-spectrum tube cameras, 8 explosion-proof zoom tube cameras, 8 explosion-proof sound stethoscope sensors, 2 microphones, 68 explosion-proof temperature and vibration composite detection modules, and 1 sound stethoscope sensor host, realizing full-area data acquisition and video monitoring of the grain conveyor belt.

[0018] Example 2 System core function application In this embodiment, the system is applied to the intelligent inspection and monitoring of two grain conveyor belts (belt 1 and belt 2) at a port terminal. The operating results of each functional module are as follows: The main module provides real-time statistics on alarm data from belts 1 and 2, displaying the percentages of severe alarms (5%), general alarms (15%), ordinary alarms (20%), abnormal states (30%), and normal states (30%) through a pie chart; it also displays the camera inspection anomaly rate (10%), microphone inspection anomaly rate (8%), and motor inspection anomaly rate (12%) through a bar chart; and it provides a real-time trend chart of the temperature and vibration sensor acceleration (0-9.79 m / s²) and temperature (0-32℃) to enable one-click access to the overall situation.

[0019] Operation Inspection Module: Operators create a comprehensive inspection task for belt 1 and a custom inspection task for belt 2 (selecting cameras 1, 3, 5, and 7). During task execution, the camera angle is adjusted by controlling the pan-tilt unit to view the scene in real time. After discovering abnormal material spillage in the diversion area of ​​belt 2, the abnormal status is marked and the cause of the abnormality, "belt misalignment caused material spillage", is submitted, completing the closed-loop processing of the inspection task. The entire inspection process takes 85% less time than manual inspection.

[0020] Full lifecycle module: The equipment status chart shows that out of 68 temperature and vibration sensors on site, 65 are running, 2 are down, and 1 is faulty; the operation inspection analysis bar chart shows that there were 10 full inspections and 2 abnormalities this week, and 15 custom inspections and 1 abnormality; by clicking on the alarm analysis list of the faulty temperature and vibration sensor, you can view its vibration acceleration time domain waveform and quickly locate the cause of the fault as bearing wear.

[0021] Alarm Center Module: The system receives alarm information in real time for personnel intrusion in the head area of ​​belt 1 and abnormal temperature and vibration in the tail area of ​​belt 2. Operators can filter alarms by alarm title and click to handle a single alarm. For the three ordinary material spill alarms, the one-click handling function is executed to complete the batch handling. All handling records are automatically saved for easy traceability.

[0022] The monitoring center module provides real-time statistics on 8 online and 6 offline cameras out of 14 cameras on site. The list displays offline cameras, including cameras 11, 13, and 14 in the belt 1 diversion area. Clicking on online cameras 3 (head area) and 6 (area 1) will play the real-time monitoring footage, enabling a "one-screen overview" of the entire belt monitoring status.

[0023] Example 3 System Technical Highlights and Applications 3D Visualized Digital Twin: Through high-precision modeling, the actual appearance of equipment such as the grain conveying corridor, two grain conveying belts, idlers, sensors, and cameras in the park is restored. Operators can view the internal structure of the belt conveyor by zooming and rotating on the web interface. It integrates data such as motor current, temperature of temperature and vibration sensor, and decibel of sound stethoscope, and displays frequency domain waveforms in real time, so as to intuitively grasp the equipment operating parameters. Intelligent video monitoring and AI early warning: The system monitors in real time 24 / 7. AI video analysis automatically identifies abnormal temperature rise of the idler roller in area 3 of belt 1, immediately triggers a red marker for the area, and pushes alarm information to the alarm center. Operators can quickly locate the fault point by the marked location, reducing the handling time by 70% compared to the traditional mode. Remote monitoring: Park management personnel can log in to the system's web interface via their office computers to remotely view the operating status, inspection records, and alarm information of the two grain conveyor belts, completing the monitoring work without going to the site, reducing on-site manpower input by more than 60%.

[0024] The main cabin page can be divided into 8 parts, as shown in the table below: The main cabin – conveyor belt scenario page is divided into 3 parts, as shown in the table below: The main functions of the operation inspection module are shown in the table below: This module primarily showcases the main functions of the equipment health analysis dashboard, as shown in the table below: This module has three main functions, as shown in the table below: This module has three main functions, as shown in the table below: The specific system application value is shown in the table below. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart inspection and detection system for grain conveyor belts at port terminals, comprising a main storage module, an operational inspection module, a full lifecycle module, an alarm center module, and a monitoring center module, characterized in that, The system adopts a three-tier technical architecture consisting of front-end, back-end, and deployment. The front-end is a WebGL 3D rendering engine that supports plugin-free operation in mainstream browsers. The back-end consists of an IoT data platform, an AI video analysis engine, and a distributed alarm system, supporting cloud and local deployment and adapting to industrial network environments. The system collaborates with the main control room, operation inspection, full lifecycle, alarm center, and monitoring center to achieve intelligent monitoring and management of the grain conveyor belt equipment's operating status, operation process, and safety risks throughout its entire lifecycle.

2. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The main module includes eight major functions: alarm statistics, top 10 alarms, equipment inspection, vibration sensor acceleration display, vibration sensor temperature display, operation inspection statistics, operation inspection list, and equipment inspection pagination list. It also features a belt conveyor scene page to enable regional equipment monitoring, real-time status display of each area of ​​the belt, and real-time alarm list presentation. Alarm statistics use quantity statistics and pie charts to quantify and analyze the proportion of normal, abnormal, ordinary, general, and critical alarms. Vibration sensor data is displayed in real-time and historical trend charts using line charts. Equipment inspection and operation inspection statistics use bar charts to visualize the comparison between the total number of inspections and the number of anomalies.

3. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The operation and inspection module supports full-process management of inspection tasks, including three main functions: operation and inspection pagination list display, inspection task operation, and inspection task addition. Inspection types are divided into full inspection and custom inspection. Full inspection can automatically cover all monitoring points of the entire belt, while custom inspection allows selection of specific cameras to inspect local areas. Inspection task operation includes viewing details and execution. During execution, the camera direction and angle can be controlled by the pan-tilt unit. The inspection can be manually marked as normal or abnormal. In case of abnormal status, the cause of the abnormality must be submitted and closed-loop processing must be completed. The task inspection pagination list displays comprehensive task information, including belt name, inspection type, inspection status, and abnormal status.

4. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The full lifecycle module is an equipment health analysis dashboard, which includes four main functions: equipment status display, equipment list query, operation inspection analysis, and alarm analysis list. The equipment status is displayed in a quantitative chart to show the number of motors, temperature and vibration sensors, cameras, stethoscopes, and microphones in operation, shutdown, and fault status. The equipment list supports filtering by region and displays full information about the equipment, such as the belt, equipment number, real-time data, and running time. The operation inspection analysis uses bar charts to statistically analyze the number of inspections and anomalies for customized / comprehensive inspections, and supports data filtering for individual conveyor belts; the alarm analysis list displays the core alarm information of the equipment, and data charts can be viewed for temperature and vibration sensor alarms, while images of the alarm scene can be viewed for camera alarms.

5. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The alarm center module enables centralized management and processing of alarms, including three main functions: alarm pagination list, one-click processing, and single alarm processing. The alarm pagination list displays information such as processing status, alarm level, alarm image, and alarm time, and supports filtering and querying by alarm title, level, status, and date. It supports one-click batch processing of all alarms, as well as individual processing of single alarms. The processing operation is equipped with a secondary confirmation mechanism to avoid accidental operation.

6. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The monitoring center module enables full-area monitoring of the grain conveyor belt cameras, including three main functions: camera count statistics, camera list display, and real-time camera video playback; it provides real-time statistics on the number of online and offline cameras; the camera list displays camera names, statuses, and their respective regions, and supports filtering and adding cameras. The online camera can play live video feeds, providing a unified view of the grain conveyor belt monitoring screen.

7. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The system is equipped with a 3D visualization digital twin function, which uses high-precision modeling to realistically restore the structure of the grain conveying corridor, belt conveyors, idlers, sensors, cameras and other equipment appearances. It supports interactive operations such as zooming and rotating on the web page. It also integrates multi-source sensor data such as current, voltage, temperature, vibration acceleration and sound decibels to display time-domain / frequency-domain waveforms and equipment operating parameters in real time.

8. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The system is equipped with intelligent video monitoring and AI early warning functions, which can monitor in real time 24 / 7. It can automatically identify risks such as belt tearing, belt deviation, material spillage, personnel intrusion, abnormal temperature rise, and excessive noise by combining video stream analysis. It supports dividing the belt into clickable areas, and the areas triggered by abnormal equipment are automatically marked to achieve rapid fault location.

9. The intelligent inspection and detection system for grain conveyor belts at port terminals according to claim 1, characterized in that, The system's web-based software is compatible with mainstream operating systems such as Windows, Linux, and Mac in Chinese, primarily using IE10 or later browsers. A system resolution of 1920×1080 is recommended. Hardware requirements include a 2.5GHz CPU with 6 cores and 12 threads, 32GB of RAM, a dedicated graphics card with WDDM1.0 driver and DirectX 9 or later support, a hard drive with at least 512GB of storage, and a 24-inch or larger monitor with a 1920×1080 resolution.

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