Safety Warning Method for Waste Heat Recovery System Based on Infrared Thermal Image

Abstract

The present invention relates to the technical field of image data processing, and particularly relates to a safety warning method for a waste heat recovery system based on an infrared thermal map, including: obtaining each historical image and current image of different regions of the waste heat recovery system; calculating the danger threshold of each region; evenly dividing any one of the current images into multiple target images, and obtaining the hot spots in the any one of the images, where the hot spot is the pixel point with the largest sum of absolute values of the temperature difference between any pixel point in any target image and the remaining pixel points; calculating the complexity of each hot spot, where the complexity characterizes the abnormal degree of the hot spot; if the complexity of the hot spot is greater than or equal to the danger threshold of the region where it is located, it is determined that the region is abnormal and a warning is given. The present invention solves the problem of low accuracy of safety warning for waste heat recovery systems.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing. More specifically, the present invention relates to a safety warning method for waste heat recovery systems based on infrared thermal maps. Background Art

[0002] With the continuous development of industrial automation and energy conservation and emission reduction technologies, waste heat recovery systems are composed of heat exchangers, heat exchange media, steam generators, hot air blowers, etc. Waste heat recovery systems have been applied in multiple industries, especially in high-temperature environments such as metallurgy, chemical industry, and steel. These systems recover waste heat, reduce energy waste, and improve energy utilization efficiency. However, during long-term operation of waste heat recovery systems, due to changes in environmental factors such as temperature and humidity, equipment abnormalities may occur, and even safety accidents such as fires may be triggered.

[0003] With the development of infrared thermal imaging technology, monitoring methods based on infrared thermal maps have gradually become a new type of safety warning means. Infrared thermal maps can accurately reflect the surface temperature distribution of objects, monitor the temperature changes of equipment in real time, and provide early warnings when the temperature is abnormal. Especially in complex industrial environments, using infrared thermal maps to monitor waste heat recovery systems can not only achieve real-time detection of the operating status of equipment, but also quickly identify potential risk points when the temperature is abnormal. However, when analyzing infrared thermal maps in the prior art, it mainly relies on the detection of a single temperature change rate and hot spot areas, and fails to fully consider the dynamic changes of the system and the impact of various environmental factors on safety warnings.

[0004] The patent application document with the application publication number CN119090869A discloses a safety warning method for ink tanks based on infrared thermal maps. This patent application document calculates the probability of a local hot spot area causing a fire based on the temperature abnormality degree of the local hot spot area and the temperature change speed of the solvent aggregation area adjacent to the local hot spot area, and then obtains the fire risk degree of the ink tank for safety warning.

[0005] However, although the above technical solution can, to a certain extent, achieve the early identification and response to potential fire risks, it only makes warning judgments based on the temperature change speed, fails to comprehensively consider the applicability and variability in complex environments, and a single temperature change speed index is not sufficient to fully adapt to various environmental conditions, resulting in the problem of low warning accuracy. Summary of the Invention

[0006] To solve the problems of low detection result accuracy and insufficient reliability proposed in the above background art, the present invention provides the following solutions.

[0007] The present invention provides a safety warning method for a waste heat recovery system based on an infrared thermal map, including: obtaining historical images and current images of different regions of the waste heat recovery system; calculating the failure probability of the th region , , being the entropy value of the infrared thermal map of the th image in the historical images of the th region, being the mean value of the entropy values of the infrared thermal maps of all images in the historical images of the th region, being the total number of historical images of the th region; calculating the danger threshold of the th region , , where is a preset danger threshold, is an exponential function with the natural constant as the base; dividing any one image in the current images into multiple target images, obtaining the hot spots in the any one image, where the hot spot is the pixel point with the largest sum of absolute values of the temperature differences between any pixel point and the remaining pixel points in any target image; calculating the complexity of the th hot spot, where the complexity characterizes the abnormality degree of the hot spot; if the complexity of the hot spot is greater than or equal to the danger threshold of the region where it is located, it is determined that the region where it is located is abnormal and a warning is issued.

[0008] The above technical solution provides a high-precision and reliable safety warning method, which can not only timely detect local abnormalities, but also flexibly cope with changes under different working conditions, greatly improving the accuracy and response speed of fault detection, reducing the risk of system shutdown and safety accidents, and enhancing the overall safety and reliability.

[0009] Further, the complexity is , where is the complexity of the th hot spot, is the mean value of the color gradient amplitudes of all pixel points in the connected domain where the hot spot is located in the th image of the current image, is the mean value of the color gradient amplitudes of all pixel points in the connected domain where the hot spot is located in the th image of the current image, is the total number of hot spots, is the total number of current images, is the hot spot The standard deviation of the magnitude of color gradients in different images relative to the current image.

[0010] The above technical solution can accurately evaluate the detailed features and spatial complexity of each hotspot by considering color gradients and pixel differences. This method can flexibly respond to hotspot changes in different scenarios, improve the ability to identify abnormal areas, enhance the accuracy and robustness of image processing, and ensure effective capture and response to potential problems in complex environments.

[0011] Furthermore, the complexity is, , where For the The complexity of each hotspot, Hotspot In the current image The mean value of the color gradient amplitude of all pixels in the connected domain in the image. Hotspot In the current image The mean value of the color gradient amplitude of all pixels in the connected domain in the image. is the total number of hotspots, is the total number of current images, Hotspot The standard deviation of the magnitude of color gradients in different images relative to the current image.

[0012] The above technical solution introduces an improved complexity calculation method, so that the analysis of hot spots not only takes into account the mean difference of color gradients, but also integrates the relative standard deviation between hot spots, and weights the complexity through a nonlinear function. This method can more accurately capture the subtle changes of hot spots in different images, especially when processing images with significant spatial or temperature differences, and can more sensitively reflect the complex characteristics of hot spots. Through this comprehensive analysis, the system can more efficiently identify abnormal hot spots, especially in complex backgrounds or with more interference, and improve the accuracy and robustness of hot spot detection. At the same time, nonlinear weighted processing makes the complexity assessment more in line with the needs of practical applications, and improves the reliability and accuracy of fault prediction and alarm.

[0013] Furthermore, the waste heat recovery system is composed of a heat exchanger, a heat exchange medium, a steam generator and a hot air blower.

[0014] Furthermore, the historical images and the current image are acquired using a CMOS camera or a CCD camera.

[0015] Furthermore, it also includes performing denoising processing on the historical images and the current image.

[0016] The above technical solution helps to significantly improve the quality of images and the accuracy of analysis by denoising each historical image and the current image. By removing the noise in the image, the system can more clearly capture the real thermal information in the image, reduce misjudgments caused by noise interference, and ensure the accuracy of subsequent image analysis, feature extraction, and anomaly detection.

[0017] Further, the early warning includes triggering an alarm signal through an audible and visual alarm.

[0018] The above technical solution triggers an alarm signal through an audible and visual alarm, realizing real-time early warning of abnormal situations. Through the combination of audible and visual signals, it can quickly attract people's attention, improve the reaction speed, and timely handle potential safety risks. This early warning mechanism enhances the reliability of the system and the emergency response ability, helps to ensure the effectiveness of the security monitoring system at critical moments, and reduces possible losses.

[0019] Further, the preset danger threshold is 0.6.

[0020] Further, the image is divided into multiple target images, including evenly dividing the image to obtain multiple images of the same size.

[0021] Further, the image is divided into multiple target images, including dividing the image into multiple images of arbitrary sizes.

[0022] The beneficial effects of the present invention are as follows:

[0023] The present invention provides a high-precision and reliable safety early warning method for the waste heat recovery system by combining multi-dimensional analysis of historical images and current images. By calculating the failure probability, danger threshold, and hotspot complexity, it can accurately identify potential abnormal areas in the system, thereby realizing early warning of safety hazards. In addition, denoising processing improves the image quality and ensures the accuracy of image analysis, while triggering an alarm signal through an audible and visual alarm can timely remind the operator at critical moments, effectively avoiding accidents. The present invention has the advantages of high efficiency, flexibility, and adaptability, can continuously monitor and accurately predict failure risks under different working conditions, and greatly improves the safety and stability of the waste heat recovery system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart schematically showing a safety early warning method for a waste heat recovery system based on an infrared thermal image according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] An embodiment of a safety early warning method for a waste heat recovery system based on an infrared thermal image.

[0026] As Figure 1As shown in the figure, the flowchart of the safety warning method for the waste heat recovery system based on the infrared thermal map according to the embodiment of the present invention includes the following steps:

[0027] S1: Obtain the historical images and current images of different regions of the waste heat recovery system.

[0028] In one embodiment, high-performance CMOS or CCD cameras can be used to accurately capture the historical images and current images. Compared with traditional image acquisition methods, these cameras can provide higher resolution and better dynamic range, thus ensuring that the acquired image quality is clearer and the details are richer. To further improve the image quality, advanced denoising processing techniques are used to optimize each frame of the image, removing the interference information introduced by sensor noise, light changes or environmental interference.

[0029] Specifically, the denoising processing step helps to significantly reduce the random noise in the image, improve the signal-to-noise ratio of the image, and thus enhance the accuracy and robustness of subsequent image analysis algorithms. For example, using denoising methods such as median filtering or Gaussian filtering can smooth the image details, optimizing the accuracy of subsequent steps such as edge detection and texture analysis. This denoising processing can not only effectively reduce the influence of noise, but also retain the key feature information in the image, avoid excessive blurring of image details, and ensure the efficiency and reliability of the analysis results.

[0030] Using CMOS or CCD cameras to obtain high-quality images and combining advanced denoising techniques not only effectively improves the quality of the images themselves, but also provides a more reliable basis for subsequent image processing and analysis steps.

[0031] S2: Calculate the failure probability and danger threshold of each region based on the historical images.

[0032] In one embodiment, calculate the failure probability of the th region , , where is the entropy value of the infrared thermal map of the th image in the historical images of the th region, is the mean value of the entropy values of the infrared thermal maps of all images in the historical images of the th region, The total number of historical images of a region; by calculating the failure probability of each region, the entropy value change of the infrared thermal map in the historical images is used to quantify the abnormality of the region. Through the standardized calculation of the entropy value of the historical images, regions with large deviations from the normal situation can be effectively identified, and then the potential failure probability can be deduced. This method can comprehensively evaluate the stability and consistency of each region based on multiple historical image data, thereby improving the accuracy and reliability of fault detection. In addition, through this entropy-based analysis, small changes or abnormalities can be sensitively captured, and potential faults can be warned in a timely manner.

[0033] Calculate the hazard threshold of the , , where is the preset hazard threshold, is the exponential function with the natural constant as the base. By combining the failure probability to calculate the hazard threshold of each region, the exponential function is used to model the non-linear relationship of the failure probability, thereby dynamically adjusting the hazard threshold. In this way, the warning level can be adaptively adjusted according to the failure probability of the region, maintaining a lower hazard threshold when the failure probability is low to ensure that alarms are not overly triggered during normal operation, while increasing the threshold when the failure probability is high to warn of potential hazards in advance. Through this dynamic adjustment mechanism, it can respond flexibly under different working conditions, effectively improving the accuracy of fault detection and warning, and at the same time avoiding false alarms and missed alarms.

[0034] S3: Obtain the hot spots of the current image and calculate the complexity of each hot spot.

[0035] In one embodiment, any one image in the current image is divided into multiple target images, and the hot spots in the any one image are obtained. The hot spot is the pixel point with the largest sum of the absolute values of the temperature differences between any pixel point and the remaining pixel points in any target image; dividing the image into multiple target images includes evenly dividing the image to obtain multiple images of the same size.

[0036] Calculate the complexity of the th hot spot, and the complexity is where is the complexity of the th hot spot, is the average value of the color gradient magnitudes of all pixel points in the connected domain where the hot spot is located in the th image of the current image, is the average value of the color gradient magnitudes of all pixel points in the connected domain where the hot spot is located in the is the total number of hotspots, is the total number of current images, Hotspot The standard deviation of the magnitude of color gradients in different images relative to the current image.

[0037] By refining the image processing steps and combining hotspot detection and complexity calculation, the hotspots in the image can be accurately analyzed. By dividing the image into multiple target areas and identifying hotspots with significant temperature differences, the most representative abnormal areas in the image can be effectively highlighted. The complexity of calculating the hotspots further enhances the multi-dimensional analysis capability of the hotspots. By considering color gradients and pixel differences, the detailed features and spatial complexity of each hotspot can be accurately evaluated. This method can flexibly respond to hotspot changes in different scenarios, improve the ability to identify abnormal areas, enhance the accuracy and robustness of image processing, ensure the effective capture and response to potential problems in complex environments, and thus improve the accuracy of fault warning and decision support.

[0038] In another embodiment, the image is divided into multiple images of any size. The complexity is, , where For the The complexity of each hotspot, Hotspot In the current image The mean value of the color gradient amplitude of all pixels in the connected domain in the image. Hotspot In the current image The mean value of the color gradient amplitude of all pixels in the connected domain in the image. is the total number of hotspots, is the total number of current images, Hotspot The standard deviation of the magnitude of color gradients in different images relative to the current image.

[0039] By introducing an improved complexity calculation method, the analysis of hot spots not only takes into account the mean difference of color gradients, but also integrates the relative standard deviation between hot spots, and weights the complexity through a nonlinear function. This method can more accurately capture the subtle changes of hot spots in different images, especially when processing images with significant spatial or temperature differences, and can more sensitively reflect the complex characteristics of hot spots. Through this comprehensive analysis, the system can more efficiently identify abnormal hot spots, especially in complex backgrounds or with more interference, and improve the accuracy and robustness of hot spot detection. At the same time, nonlinear weighted processing makes the complexity evaluation more in line with the needs of practical applications, and improves the reliability and accuracy of fault prediction and alarm.

[0040] S4: If the complexity of the hotspot is greater than or equal to the danger threshold of the area where it is located, then it is determined that the area is abnormal and a warning is issued.

[0041] In one embodiment, the preset danger threshold is 0.6. Of course, it can also be determined according to the actual situation. By setting an appropriate danger threshold, the system can evaluate the status of the device or area in real time to ensure timely response in case of abnormalities.

[0042] The warning includes triggering an alarm signal through an audible and visual alarm. The audible and visual alarm can not only effectively attract the attention of the operator to ensure that the warning signal can be quickly recognized in any environment, but also timely remind relevant personnel to take necessary safety measures in case of emergency. And it provides continuous safety guarantee for the operation of the device under different working conditions, significantly improving the timeliness and accuracy of fault prevention and reducing the occurrence of potential risks.

[0043] The solution of the present invention realizes precise safety warning for the waste heat recovery system by combining infrared thermal image analysis, hotspot detection and complexity evaluation. Through multi-level analysis of historical images and current images, it can effectively identify possible fault areas, calculate the fault probability and danger threshold in time, and provide a scientific basis for the warning mechanism. The calculation of the hotspot complexity further enhances the sensitivity to abnormal changes, can detect subtle temperature fluctuations and abnormal trends during the operation of the system, and issue real-time warnings. The denoising process improves the image quality, avoids noise interference and ensures the accuracy of the data. In addition, through the warning method of the audible and visual alarm, it is ensured that the operator's attention can be quickly attracted in case of abnormalities, thereby improving the response speed and reliability of the system, reducing the risk of faults, and significantly enhancing the safety and stability of the waste heat recovery system.

[0044] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0045] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A waste heat recovery system safety early warning method based on infrared thermal images, characterized in that: include: Obtain historical images and current images of different areas of the waste heat recovery system; Calculate the Failure probability in a region , , For the The historical images of the area The entropy value of the infrared heat map of the image, For the The mean entropy value of the infrared thermal map of all images in the historical images of the region, For the The total number of historical images of the region; calculate the Danger threshold for each region , , where To preset the danger threshold, The natural constant An exponential function with base ; Divide any image in the current image into multiple target images, obtain the hot spots in any image, and the hot spots are the pixels with the largest sum of the absolute values ​​of the temperature differences between any pixel point and the remaining pixel points in any target image; calculate the The complexity of a hotspot, wherein the complexity represents the abnormality of the hotspot; If the complexity of the hotspot is greater than or equal to the danger threshold of the area where the hotspot is located, the area where the hotspot is located is determined to be abnormal and an early warning is issued; The complexity is, , where For the The complexity of each hotspot, Hotspot In the current image The mean value of the color gradient amplitude of all pixels in the connected domain in the image. Hotspot In the current image The mean value of the color gradient amplitude of all pixels in the connected domain in the image. is the total number of hotspots, is the total number of current images, Hotspot The standard deviation of the magnitude of color gradients in different images relative to the current image.

2. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: The waste heat recovery system consists of a heat exchanger, a heat exchange medium, a steam generator and a hot air blower.

3. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: The historical images and the current image are acquired by using a CMOS camera or a CCD camera.

4. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: The method also includes performing denoising processing on the historical images and the current image.

5. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: The early warning includes triggering an alarm signal through an audible and visual alarm.

6. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: The preset danger threshold is 0.

6.

7. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: Dividing the image into multiple target images includes equally dividing the image to obtain multiple images of the same size.

8. The waste heat recovery system safety early warning method based on infrared thermogram according to claim 1 is characterized in that: Divide the image into multiple target images, Including dividing the image into multiple images of any size.

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