Inspection robot infrared vision AI analysis system

Abstract

The invention discloses an inspection robot infrared vision AI analysis system, and relates to the technical field of AI inspection. The inspection robot infrared vision AI analysis system comprises an infrared imaging module used for capturing temperature data of a target device and a surrounding environment thereof in real time and generating an infrared image; the AI analysis module is connected with the infrared imaging module, and is used for processing the image data by adopting algorithms such as a convolutional neural network (CNN) and identifying temperature abnormity or equipment faults; the sensor fusion module comprises an infrared sensor, a laser radar and an ultrasonic sensor; the automatic navigation module can automatically plan an inspection path according to the real-time environment information and the inspection task, and has an obstacle avoidance function; the cloud platform data management module is used for receiving the inspection data and generating an equipment health report; the infrared vision technology, AI analysis and fusion of various sensors are combined, so that more accurate fault prediction, equipment monitoring, automatic inspection and partial self-repairing functions can be realized.

Description

Technical Field

[0001] The present invention relates to the field of AI inspection technology, and in particular to an infrared vision AI analysis system based on an inspection robot. Background Art

[0002] With the advancement of industrial automation and intelligence, traditional manual inspection methods have obvious deficiencies in efficiency, accuracy, reliability, etc., especially for equipment inspections in high-risk environments (such as high temperature, high pressure or harmful environments). Manual inspections have great safety risks and limitations. Most existing inspection systems rely on a single sensor or device, and fail to achieve multi-dimensional data fusion and intelligent analysis, resulting in the inability to comprehensively and accurately assess the health status of equipment and timely warn of faults. Summary of the invention

[0003] The purpose of this invention is to provide an inspection robot system based on infrared vision and AI analysis. The system can obtain equipment status data in real time in complex environments, analyze the health status of equipment operation, and issue early warnings before failures occur through multi-sensor fusion (including infrared imaging, lidar, ultrasonic sensors, etc.), combined with artificial intelligence image processing and autonomous navigation technology.

[0004] The system of the present invention not only improves the accuracy and efficiency of equipment inspection, but also effectively reduces the risk of manual inspection, optimizes the operation and maintenance management of equipment, and further promotes the development of intelligent industry. Summary of the invention

[0006] The purpose of the present invention is to solve at least one of the technical problems existing in the prior art. The present invention combines infrared imaging technology, artificial intelligence image processing, sensor fusion and automatic navigation technology to design an inspection system that can efficiently, accurately and intelligently detect and warn equipment faults. The system is widely used in equipment inspections of large facilities such as power, petroleum, chemical, mining, and data centers.

[0007] To achieve the above objectives, the present invention provides the following technical solutions: The present invention proposes an infrared vision AI analysis system based on an inspection robot, which can perform efficient and accurate inspections in a variety of environments. The system includes the following key technologies:

[0008] Infrared imaging module: Using a high-resolution infrared camera, it can capture the temperature distribution of the target object in real time and identify possible safety hazards such as temperature anomalies and heat source leakage.

[0009] AI analysis module: Based on deep learning and image recognition technology, it can quickly and accurately analyze infrared images, automatically identify equipment failure, temperature anomalies, thermal leakage and other problems, and process and warn data in real time. It is connected to the infrared imaging module and uses algorithms such as convolutional neural networks (CNN) to process image data and identify temperature anomalies or equipment failures.

[0010] Multi-sensor fusion technology: Combining data from infrared, ultrasonic, lidar and other sensors, the accuracy and reliability of the system can be improved through data fusion technology.

[0011] Dynamic path planning and automatic navigation: The automatic navigation module can automatically plan the inspection path according to the real-time environmental information and inspection tasks, and has the obstacle avoidance function; the inspection robot has the ability of autonomous navigation, and can intelligently plan the best inspection route and perform tasks according to the inspection tasks and real-time environmental conditions; the sensor fusion module fuses the infrared imaging data with the lidar and ultrasonic sensor data to generate accurate environmental perception data to optimize the robot's navigation path; the automatic navigation module constructs a three-dimensional environmental model through lidar and infrared sensors, and combines machine learning algorithms to optimize path planning and ensure safe inspections in complex environments.

[0012] Cloud data analysis platform: The system uploads inspection data to the cloud, combines it with big data analysis for in-depth mining, and implements intelligent decision support, historical data backtracking, and fault prediction. The cloud platform data management module can receive real-time inspection data, support storage, analysis, and generate data reports, while providing remote monitoring and alarm functions for equipment managers. The cloud platform has a prediction function based on deep learning, which can predict the probability of equipment failure through historical data and provide early warning, thereby reducing the risk of sudden failures.

[0013] Deep integration of infrared and AI: Compared with traditional infrared imaging technology, this invention can automatically identify and classify abnormal temperature events by combining AI analysis, thereby improving the accuracy of fault diagnosis.

[0014] The inspection robot is equipped with a variety of data transmission methods, including Wi-Fi, 5G communication and satellite communication, to ensure real-time data transmission capabilities in different environments; the infrared imaging module is equipped with a high-resolution infrared camera, which can perform clear temperature monitoring at night or in low-light environments to improve the all-weather reliability of inspections; the AI ​​analysis module has automatic fault classification and diagnosis functions, and provides targeted fault analysis reports based on different equipment types and fault modes, and recommends maintenance plans; the inspection robot also includes an integrated self-charging module, which can automatically return to the charging station for charging during the inspection process to ensure long-term continuity of operation.

[0015] Intelligent fault prediction: Through long-term accumulation and deep learning of inspection data, this system can provide early warning before equipment failure occurs, reduce the occurrence of sudden failures, and extend the service life of equipment.

[0016] Strong environmental adaptability: In complex environments such as smoke and dust, the infrared imaging system can still work effectively to ensure that data collection is not affected by the environment.

[0017] Multi-scenario application capability: This system is not only suitable for industries such as electricity, petroleum, and chemicals, but can also be widely used for intelligent inspections in complex environments such as data centers, tunnels, and mines.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1) Improve inspection accuracy and efficiency. The present invention combines infrared imaging technology and artificial intelligence image processing technology, which can detect temperature changes of equipment in real time and accurately, identify potential equipment failures or abnormalities, and avoid errors and omissions in manual inspections. In addition, through multi-sensor fusion (including infrared, lidar, and ultrasonic sensors, etc.), the system can more comprehensively perceive the environment and equipment status, thereby improving the accuracy and efficiency of inspections. The automated inspection process enables robots to perform high-frequency inspections in complex environments, far exceeding the time and energy limitations of manual inspections.

[0020] 2) Reduce the risk of manual inspection Traditional manual inspection methods have great safety hazards in dangerous environments such as high temperature, high pressure, and harmful gases, and inspectors may face physical injuries or health risks. The inspection robot of the present invention can replace manual inspection of equipment in dangerous areas, reducing the impact of human factors on safety. It is particularly suitable for high-risk industries (such as electricity, chemical industry, mining, etc.), thereby significantly improving safety.

[0021] 3) All-weather inspection capability The system of the present invention supports 24-hour all-weather equipment monitoring, especially through the infrared imaging module, which can clearly monitor the equipment in low-light environments or under insufficient lighting at night. This makes the inspection work no longer restricted by time or lighting conditions, ensures continuous monitoring of the equipment, timely discovers potential faults, and reduces the risk of damage or downtime of equipment due to failure to discover problems in time.

[0022] 4) Intelligent fault diagnosis and early warning The AI ​​analysis module performs intelligent analysis of the temperature and other environmental data of the equipment based on deep learning algorithms (such as convolutional neural networks (CNN)), which can realize automatic fault diagnosis and prediction. For example, by learning from historical data, the system can predict the failure trend of the equipment, issue early warnings, and help managers take preventive measures to avoid sudden failures or large-scale downtime of the equipment. This intelligent early warning mechanism can greatly improve the operational reliability and efficiency of the equipment.

[0023] 5) Optimize equipment maintenance management The cloud platform data management module of the present invention can centrally store and analyze equipment inspection data, generate detailed equipment health reports, and generate maintenance recommendations based on the equipment operating status. This allows managers to monitor the health of equipment in real time, develop targeted maintenance plans, and reduce equipment downtime and maintenance costs. At the same time, the data analysis function of the cloud platform also provides important decision support for the long-term operation of the equipment.

[0024] 6) Reduce equipment downtime Through real-time monitoring and early fault warning, the present invention can help equipment operators discover potential problems in advance and take maintenance measures, thereby significantly reducing the probability and duration of equipment downtime. For large-scale production facilities, equipment downtime directly affects production efficiency and economic benefits, and intelligent inspection and early warning mechanisms help reduce unnecessary downtime and maintenance and improve the overall operating efficiency of equipment.

[0025] 7) Automation and self-charging function The inspection robot of the present invention is equipped with automatic navigation, obstacle avoidance and self-charging modules, and can automatically return to the charging station for charging after completing the inspection task, and continue to work after charging. This automation function ensures the continuous operation of the system, avoids inspection interruptions caused by battery exhaustion, and significantly improves the applicability and continuous working ability of the robot in large-scale facilities.

[0026] 8) Flexible adaptation to different environments The inspection system of the present invention is not only suitable for inspection of fixed equipment (such as power substations), but also for inspection in dynamic environments (such as oil and gas pipelines, mines, etc.). The robot can flexibly adjust its working mode according to environmental changes. Combined with high-precision laser radar and infrared sensors, the system can adapt to a variety of complex environments, including narrow spaces, areas with dense obstacles, extreme temperature conditions, etc., to ensure the comprehensiveness and applicability of inspections.

[0027] 9) Save manpower and reduce operating costs. The automated inspection system can reduce the need for manual inspection personnel, saving labor costs and time costs during the inspection process. In addition, the system automatically generates equipment health reports and early warning information, reducing the reliance on manual analysis and report generation, further reducing the workload and cost of operation management.

[0028] 10) Easy to expand and integrate The inspection system of the present invention has good scalability and modular design, and can be easily integrated with existing equipment management systems or other industrial automation systems. With the advancement of technology, the system can flexibly access more sensors and devices to achieve continuous expansion and optimization of functions to meet the needs of different industrial scenarios.

[0029] To sum up, the infrared vision AI analysis system based on the inspection robot of the present invention combines automation and intelligent technology, has significant advantages, can improve inspection accuracy and efficiency, reduce risks, achieve all-weather monitoring, intelligent diagnosis and early warning, optimize equipment management, reduce operating costs and other beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention is further described below in conjunction with the accompanying drawings and embodiments:

[0031] Figure 1 It is a logic block diagram of the present invention. DETAILED DESCRIPTION

[0032] This section will describe in detail the specific embodiments of the present invention. The preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the accompanying drawings is to supplement the description of the text part of the specification with graphics, so that people can intuitively and vividly understand each technical feature and the overall technical solution of the present invention, but it cannot be understood as a limitation on the scope of protection of the present invention.

[0033] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0034] In the description of the present invention, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0035] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0036] Example 1: Power equipment inspection system combining infrared vision and AI analysis

[0037] Background and Objectives:

[0038] This embodiment proposes an inspection method based on infrared vision and artificial intelligence analysis technology for equipment in power substations. It can efficiently detect abnormal equipment temperature and timely diagnose possible electrical faults to ensure the safe and stable operation of power facilities.

[0039] Implementation method:

[0040] Infrared vision sensor:

[0041] An infrared thermal imaging sensor is installed on the inspection robot to monitor the temperature distribution of various electrical equipment in the substation (such as switches, transformers, distribution boards, etc.) in real time. When the temperature of the equipment rises abnormally, the infrared sensor can accurately capture the temperature hotspot and generate a thermal map to ensure that any potential overheating problems can be discovered as early as possible.

[0042] AI Analysis Module:

[0043] The robot has a built-in AI computing module that uses deep learning algorithms (such as convolutional neural networks (CNN)) to intelligently analyze infrared images acquired in real time. The AI ​​algorithm can automatically determine whether the device is in a state of precursor to a fault, such as overheating, short circuit, or poor electrical contact, through learning and pattern matching based on the device's historical data and the device's normal temperature range.

[0044] Real-time feedback and early warning:

[0045] When the AI ​​module determines that a certain device has an abnormal risk, the system transmits the alarm information to the operator's monitoring platform in real time through wireless communication. The monitoring personnel can accurately locate and arrange maintenance based on the detailed data provided by the system (such as temperature change curve, equipment location, etc.), thereby reducing the downtime caused by equipment failure.

[0046] Specific innovations:

[0047] This embodiment innovatively combines infrared imaging and AI analysis to achieve remote diagnosis and early warning of equipment. The robot can not only conduct inspections, but also predict faults through intelligent analysis, avoiding the lag and blind spots of manual inspections.

[0048] Applicable scenarios:

[0049] It is suitable for inspection of power substations and high-voltage electrical equipment. It can effectively prevent sudden failures of equipment due to overheating and ensure the stable operation of the power system.

[0050] Example 2: Application of intelligent adaptive inspection robots in oil and gas pipelines

[0051] Background and Objectives:

[0052] Oil and gas pipelines are often in a vast and complex environment, with risks such as equipment wear and leakage. This embodiment proposes an inspection robot that can adapt to complex environments, equipped with infrared vision and sensor technology, automatically performs comprehensive inspection and monitoring of pipelines, and detects abnormalities in a timely manner.

[0053] Implementation method:

[0054] Adaptive Navigation System:

[0055] The robot uses LiDAR and visual sensors to obtain real-time information about the surrounding environment, build a three-dimensional map, and automatically plan the optimal inspection path based on the map. The robot can flexibly cope with different terrains and environments, such as rugged mountains, slippery ground, or obstacles around pipelines.

[0056] Infrared sensor and gas detection module:

[0057] The robot is equipped with a high-resolution infrared imaging sensor to monitor temperature changes on the surface of oil and gas pipelines, paying special attention to leak-prone areas such as joints and pipe elbows. At the same time, the robot is also equipped with a gas sensor to detect whether there is a risk of combustible gas leakage around the pipeline. By combining the data from these two sensors, the robot can accurately identify temperature anomalies and potential risks of gas leakage.

[0058] Data fusion and AI diagnosis:

[0059] The system uses sensor data fusion technology to integrate infrared imaging, lidar data, gas detection results, etc., and the AI ​​module performs intelligent analysis and diagnosis. For example, the robot automatically predicts the potential failure points of the pipeline based on the changing trends of the pipeline surface temperature and gas concentration, sends early warning information to the remote operation platform in a timely manner, and recommends further inspection or maintenance.

[0060] Specific innovations:

[0061] This embodiment innovatively combines infrared imaging and gas detection technology, integrates multiple sensor data through AI analysis algorithms, and achieves accurate pipeline fault prediction. Through adaptive navigation and automatic planning of inspection paths, the robot can work efficiently in complex environments, minimize human errors and improve inspection efficiency.

[0062] Applicable scenarios:

[0063] It is suitable for regular inspection of oil and gas pipelines, especially for unmanned monitoring in harsh environments or inaccessible areas (such as remote mountainous areas, submarine pipelines, etc.).

[0064] Example 3: Intelligent power equipment fault warning and self-repair system

[0065] Background and Objectives:

[0066] This embodiment proposes an intelligent early warning system that combines infrared vision and AI diagnosis for the maintenance of power equipment (such as high-voltage motors, substation equipment, etc.), which can realize remote fault detection, early warning and self-repair functions.

[0067] Implementation method:

[0068] Real-time equipment status monitoring:

[0069] The robot uses infrared imaging technology to monitor the operating status of power equipment in real time, paying particular attention to temperature changes in the equipment. Combined with sensor data such as temperature, vibration and sound, the robot can fully perceive the health of the equipment and detect equipment anomalies in a timely manner.

[0070] Fault prediction and intelligent diagnosis:

[0071] Based on the collected equipment data (such as temperature, pressure, operating hours, etc.), the AI ​​diagnostic system can learn the historical behavior of the equipment and predict potential failures (such as motor overheating, insulation damage, etc.). Through big data analysis, the system can establish equipment failure models and feed back the analysis results to maintenance personnel or the dispatch system.

[0072] Self-repair and maintenance suggestions:

[0073] When the robot finds a minor fault in the equipment, such as abnormal temperature but the fault is not serious enough to affect operation, the robot can provide remote maintenance guidance or perform small-scale self-repair operations (such as automatic cleaning, cooling, adjusting equipment settings, etc.) according to the type of fault. If the problem is serious, the robot will send an emergency maintenance request and provide the fault location and analysis report.

[0074] Specific innovations:

[0075] The innovation of this embodiment lies in the combination of infrared vision, AI diagnosis and self-repair technology, which realizes the comprehensive functions of intelligent early warning, fault diagnosis and partial self-repair. The system can not only foresee and warn equipment failures, but also perform intelligent maintenance or repair according to specific circumstances, greatly reducing equipment downtime and manual maintenance costs.

[0076] Applicable scenarios:

[0077] It is suitable for daily inspections of high-value equipment in power companies and industrial enterprises, especially when the equipment is concentrated and deployed on a large scale, and can effectively improve equipment operation and maintenance efficiency and ensure safety.

[0078] Summarize

[0079] These three embodiments provide specific application examples in power equipment inspection, oil and gas pipeline monitoring, and power equipment fault warning and self-repair, respectively, fully reflecting the innovation of the present invention: combining infrared vision technology, AI analysis and multiple sensor fusion, it can achieve more accurate fault prediction, equipment monitoring, automated inspection and partial self-repair functions.

[0080] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in the technical field without departing from the purpose of the present invention.

Claims

1. Inspection robot infrared vision AI analysis system, characterized by: The system comprises: Infrared imaging module, used to capture temperature data of the target device and its surrounding environment in real time and generate infrared images; An AI analysis module, connected to the infrared imaging module, processes image data using algorithms such as a convolutional neural network (CNN) to identify temperature anomalies or equipment failures; Sensor fusion module, including infrared sensors, lidar, and ultrasonic sensors, improves the device's environmental perception capabilities through data fusion; Automatic navigation module, which can automatically plan inspection routes based on real-time environmental information and inspection tasks, and has obstacle avoidance function; The cloud platform data management module receives inspection data and generates equipment health reports, supporting data analysis and report presentation.

2. The inspection robot infrared vision AI analysis system according to claim 1 is characterized by: The AI ​​analysis module performs image recognition through a convolutional neural network (CNN), identifies features such as temperature anomalies, overheating, and equipment failure in the image, and generates warning information.

3. The inspection robot infrared vision AI analysis system according to claim 2 is characterized by: The sensor fusion module fuses infrared imaging data with lidar and ultrasonic sensor data to generate accurate environmental perception data to optimize the robot's navigation path.

4. The inspection robot infrared vision AI analysis system according to claim 3 is characterized by: The automatic navigation module builds a three-dimensional environmental model through lidar and infrared sensors, combines machine learning algorithms to optimize path planning, and ensures safe inspections in complex environments.

5. The inspection robot infrared vision AI analysis system according to claim 4 is characterized by: The cloud platform data management module can receive real-time inspection data, support storage, analysis and generation of data reports, and provide remote monitoring and alarm functions for equipment management personnel.

6. The inspection robot infrared vision AI analysis system according to claim 5 is characterized by: The cloud platform has a prediction function based on deep learning, which can predict the probability of equipment failure through historical data and provide early warning, thereby reducing the risk of sudden failure.

7. The inspection robot infrared vision AI analysis system according to claim 6 is characterized by: The inspection robot is equipped with multiple data transmission modes, including Wi-Fi, 5G communication and satellite communication, to ensure real-time data transmission capabilities in different environments.

8. The inspection robot infrared vision AI analysis system according to claim 7 is characterized by: The infrared imaging module is equipped with a high-resolution infrared camera, which can perform clear temperature monitoring at night or in low-light environments to improve the all-weather reliability of inspections.

9. The inspection robot infrared vision AI analysis system according to claim 8, characterized in that: The AI ​​analysis module has the function of automatic fault classification and diagnosis, provides targeted fault analysis reports according to different equipment types and fault modes, and recommends maintenance solutions.

10. The inspection robot infrared vision AI analysis system according to claim 9, characterized in that: The inspection robot also includes an integrated self-charging module, which can automatically return to the charging station for charging during the inspection process to ensure long-term operation continuity.

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