Portable gas leakage detection device based on refrigeration type superlattice infrared detector

Abstract

The invention discloses a portable gas leakage detection device based on a refrigeration type superlattice infrared detector, which comprises a detector module, an optical lens group and a data processing unit, and is characterized in that the detector module comprises a refrigeration type detector and a refrigerator, and the refrigeration type detector is based on an infrared radiation absorption effect; the optical lens group comprises a medium-wave infrared lens and a visible light lens; and the data processing unit obtains data including an infrared image and a visible light image, and judges the gas leakage condition after the data is processed by a detection algorithm. According to the portable gas leakage detection device based on the refrigeration type superlattice infrared detector, long-distance and non-contact rapid detection and accurate leakage positioning of organic compounds and other industrial hazardous gases are achieved, and industrial production safety and environment safety are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas leakage detection of environmental monitoring and industrial safety equipment, and in particular relates to a portable gas leakage detection device based on a refrigeration-type superlattice infrared detector. Background Art

[0002] In industrial production and daily life, the leakage of organic compounds (VOCs) and other industrial hazardous gases can cause serious accidents such as fires, explosions, and poisoning, while also polluting the environment. Currently, common gas leak detection methods include contact detection and non-contact detection. Contact detection requires the detection probe to be placed directly in the gas environment, which has problems such as slow detection speed, susceptibility to environmental interference, and inability to achieve long-distance detection. In addition, the detection personnel face great safety risks. Traditional infrared detection equipment in non-contact detection has difficulty in capturing and accurately locating weak gas leakage signals due to insufficient detector sensitivity and unreasonable optical system design. Especially in long-distance detection scenarios, the detection effect is not ideal.

[0003] Therefore, further improvements are made to the above problems. Summary of the Invention

[0004] The main purpose of the present invention is to provide a portable gas leak detection device based on a refrigerated superlattice infrared detector, which can realize long-distance, non-contact rapid detection and precise leak location of organic compounds (VOCs) and other industrial hazardous gases, thereby ensuring industrial production safety and environmental safety.

[0005] To achieve the above objectives, the present invention provides a portable gas leak detection device based on a refrigerated superlattice infrared detector, comprising a detector module, an optical lens assembly, and a data processing unit, wherein: The detector module includes a refrigerated detector and a refrigerator. The refrigerated detector captures the infrared radiation energy distribution of the gas in the target area based on the infrared radiation absorption effect and converts it into an electrical signal. The refrigerator is used to cool the refrigerated detector to a preset temperature to reduce the thermal noise of the refrigerated detector itself. The optical lens group includes a medium-wave infrared lens and a visible light lens. The medium-wave infrared lens is used to efficiently collect medium-wave infrared radiation from the target area. When gas leaks, gas molecules near the leak source absorb infrared radiation emitted by background objects, forming an abnormal area with a different radiation intensity from the background. The medium-wave infrared lens focuses the infrared radiation from the area onto a refrigerated detector, converts it into an electrical signal, and forms an infrared image. The grayscale value of the gas leak area in the image is different from that of the background, thereby achieving visual positioning of the leak. The visible light lens synchronously collects a visible light image of the target area and fuses it with the infrared image. The visible light image provides clear scene details, and the infrared image provides location information of the gas leak. By combining the two, the specific location of the leak source in the actual scene can be accurately located. The data processing unit obtains data including infrared images and visible light images, and determines the gas leakage situation after processing through a detection algorithm.

[0006] As a further preferred technical solution of the above technical solution, the detection algorithm is specifically implemented as follows: Step S1: Data input, input the infrared image sequence of the original gas, and synchronously obtain the visible light image, GPS positioning and laser ranging data; Step S2: Preprocessing, aligning the spatiotemporal references of the infrared and visible light images, and using a motion compensation algorithm to eliminate image offsets caused by device jitter or environmental vibrations; Step S3: Temporal noise suppression, performing multi-frame sliding average and temporal difference enhancement respectively; Step S4: spatial domain filtering, performing adaptive wavelet denoising and anisotropic diffusion filtering respectively; Step S5: edge enhancement, combining adaptive dual thresholds to enhance the continuity of the gas leakage edge and using morphological closing operations to fill edge breaks; Step S6: Post-processing and output, including concentration inversion and mapping, real-time alarm and visualization.

[0007] As a further preferred technical solution of the above technical solution, step S3 is specifically implemented as follows: Step S3.1: For multi-frame sliding average, random noise is suppressed by multi-frame weighted averaging, and a stable gas signal is retained. The weighted average of N consecutive frames of images is performed to suppress random noise. The formula is: ; in, The pixel point of the t-th frame image Gray value at ; is the weight coefficient of the t-th frame; is the number of frames involved in averaging; is the averaged output image pixel value; Step S3.2: For time domain difference enhancement, the absolute difference between adjacent frames is calculated to highlight the dynamic changes of the gas leakage area and suppress static background interference. The difference image between adjacent frames is calculated to highlight the dynamic leakage area. The formula is: , is the pixel grayscale value of two adjacent frames, is the pixel value of the difference image, indicating the intensity of dynamic changes.

[0008] As a further preferred technical solution of the above technical solution, step S4 is specifically implemented as follows: Step S4.1: For adaptive wavelet denoising, wavelet transform is used to decompose the high-frequency and low-frequency components of the image, retaining the gas edge features and filtering out high-frequency noise; and the noise coefficient is suppressed by the threshold shrinkage method. The formula is: , is the jth coefficient after wavelet decomposition, is the preset threshold, is the coefficient after threshold processing; Step S4.2: For anisotropic diffusion filtering, based on partial differential equations, the image is smoothed along the gas concentration gradient direction to preserve edge details. The formula is: , is the image gradient, is the diffusion coefficient function, controlling the smoothing intensity, is the divergence operator.

[0009] As a further preferred technical solution of the above technical solution, step S6 is specifically implemented as follows: Step S6.1: For concentration inversion and mapping, convert the filtered grayscale value into leakage concentration according to the gas absorption rate model. The formula is: , is the background infrared light intensity when there is no gas leakage, is the infrared light intensity after passing through the leaked gas, is the absorption coefficient of the gas, which is related to the gas type and wavelength. is the optical path, is the gas concentration; Step S6.2: For real-time alarm and visualization, a heat map with the leakage area superimposed is output, and an audible and visual alarm is triggered when the concentration exceeds a preset threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of the detection algorithm of the present invention.

[0011] Figure 2 It is a schematic diagram of the principle of the present invention. DETAILED DESCRIPTION

[0012] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0013] In the preferred embodiment of the present invention, those skilled in the art should note that the touch screen and the like involved in the present invention may be regarded as prior art.

[0014] Preferred embodiment.

[0015] like Figure 1-2 As shown, the present invention discloses a portable gas leak detection device based on a refrigerated superlattice infrared detector, comprising a detector module, an optical lens group and a data processing unit, wherein: The detector module includes a (Type II superlattice (T2SL)) cooled detector and a (Stirling) refrigerator. The cooled detector is based on the infrared radiation absorption effect (organic compounds (VOCs) and industrial hazardous gases (such as benzene, formaldehyde, ammonia, etc.) have characteristic absorption spectra in the mid-wave infrared band (usually 3-5μm). When gas molecules absorb infrared radiation of a specific wavelength, the molecular vibration or rotational energy level transitions, resulting in attenuation of the radiation intensity of that wavelength). It captures the infrared radiation energy distribution of the gas in the target area and converts it into an electrical signal (because different gases have different characteristic absorption wavelengths, the detector can identify the gas type and concentration distribution by analyzing the change in radiation intensity). The refrigerator is used to cool the cooled detector to a preset temperature and reduce the thermal noise of the cooled detector itself (increasing its sensitivity to weak infrared signals. In a low-temperature environment, the signal-to-noise ratio of the detector is significantly improved, enabling the capture of weak infrared signals of gas leaks at long distances (e.g., tens to hundreds of meters), meeting the needs of long-distance detection). The optical lens assembly includes a medium-wave infrared lens and a visible light lens. The medium-wave infrared lens (focal length 23mm, F-number 1.2) features a large aperture (the smaller the F-number, the greater the amount of light passing through) and is designed to efficiently collect medium-wave infrared radiation from the target area. When a gas leak occurs, gas molecules near the leak source absorb infrared radiation emitted by background objects (such as equipment and walls), forming an abnormal area (infrared shadow or area with abnormal radiation intensity) with a different intensity from the background radiation. The medium-wave infrared lens focuses the infrared radiation from the area onto a cooled detector, converting it into an electrical signal to form an infrared image. The grayscale value of the gas leak area in the image differs from that of the background, thereby enabling visual positioning of the leak. The visible light lens (1 / 2.7-inch CMOS) simultaneously captures a visible light image of the target area and fuses it with the infrared image. The visible light image provides clear scene details (such as equipment location and pipeline layout), while the infrared image provides location information for the gas leak. By combining the visible light image and the infrared image, the specific location of the leak source in the actual scene (such as a pipeline interface or valve gap) can be accurately located. The data processing unit (equipped with an FPGA chip) obtains data including infrared images and visible light images, and determines the gas leakage situation after processing by a detection algorithm.

[0016] Specifically, the detection algorithm is implemented as follows: Step S1: Data input: input the infrared image sequence of the original gas (frame rate 60Hz), and simultaneously obtain the visible light image, GPS positioning and laser ranging data; Step S2: Preprocessing: aligning the spatial and temporal references of the infrared and visible light images (achieved through microsecond synchronization via FPGA hardware), and using a motion compensation algorithm to eliminate image offsets caused by device jitter or environmental vibrations. The specific motion compensation algorithm is: for (int sizey = 0; sizey<256; sizey++) { for (int sizex = 0; sizex<320; sizex++) { m_DataBuffer_mark[sizey][sizex]= inCommingData[sizey][sizex]; } } isUserPointReferce = false; } m_DataBuffer_mark: calibration frame; isUserPointReferce : variable; Step S3: Temporal noise suppression, performing multi-frame sliding average and temporal difference enhancement respectively; Step S4: spatial domain filtering, performing adaptive wavelet denoising and anisotropic diffusion filtering respectively; Step S5: edge enhancement, combining adaptive double threshold (based on local gradient statistics) to enhance the continuity of the gas leakage edge and using morphological closing operation to fill the edge breaks; The specific implementation of edge enhancement is: 1. Edge enhancement (anisotropic diffusion filtering): The formula is: ; in, is the input image, t is the number of micro-iterations, and k is the gradient threshold; function Control diffusion strength: Diffusion is weak at edges (large gradient areas) and strong in smooth areas (small gradients). This smooths noise while preserving edges, improving the continuity of subsequent edge detection.

[0017] 2. Edge detection (Canny operator): The formula is: ; Gaussian filtering Smooth the image, is the convolution operation; Suppress non-maxima to obtain refined edges .

[0018] 3. Morphological closing operation (filling the gap): The formula is: ; is the dilation operation, is the erosion operation, B is the structural element (such as circle or square); Dilation: Expanding marginal areas and connecting broken parts: ; Erosion: Restore the original edge width: ; Closing can fill small holes and narrow gaps while preserving the shape of the main edges.

[0019] Step S6: Post-processing and output, including concentration inversion and mapping, real-time alarm and visualization.

[0020] More specifically, step S3 is implemented as follows: Step S3.1: For multi-frame sliding average, random noise (such as thermal noise and readout noise) is suppressed by multi-frame weighted averaging to retain a stable gas signal. N consecutive frames (such as N = 10) of images (only for infrared images) are weighted averaged to suppress random noise. The formula is: ; in, The pixel point of the t-th frame image Gray value at ; is the weight coefficient of the t-th frame (usually equal weight 1); is the number of frames involved in averaging; is the averaged output image pixel value; Step S3.2: For time domain difference enhancement, the absolute difference between adjacent frames is calculated to highlight the dynamic changes (such as diffusion and turbulence) in the gas leakage area, suppress static background interference, and calculate the difference image between adjacent frames to highlight the dynamic leakage area. The formula is: , is the pixel grayscale value of two adjacent frames, is the pixel value of the difference image, indicating the intensity of dynamic changes.

[0021] Furthermore, step S4 is specifically implemented as follows: Step S4.1: For adaptive wavelet denoising, wavelet transform (Daubechies wavelet basis) is used to decompose the high-frequency and low-frequency components of the image, retaining the gas edge features and filtering out high-frequency noise; and the noise coefficient is suppressed by threshold shrinkage method (such as soft thresholding). The formula is: , is the jth coefficient after wavelet decomposition (high-frequency components usually correspond to noise), is the preset threshold (adaptively adjusted according to the noise level), is the coefficient after threshold processing (its function is to shrink the wavelet coefficients, suppress noise (high-frequency small coefficients are set to zero), and retain the effective signal (low-frequency large coefficients)); Step S4.2: For anisotropic diffusion filtering, based on the partial differential equation (Perona-Malik model), the image is smoothed along the gas concentration gradient, preserving edge details (while preserving the gas contour and eliminating background texture interference). The formula is: , is the image gradient (characterizing edge strength), is a diffusion coefficient function that controls the smoothing strength (weak diffusion at the edges and strong diffusion in flat areas), is the divergence operator (describing the degree of divergence of the vector field), Represents the rate of change of image I over time t, and describes the update direction (enhancement or weakening) of image brightness during the diffusion process, through iterative update Progressively smooth the image.

[0022] Furthermore, step S6 is specifically implemented as follows: Step S6.1: For concentration inversion and mapping, convert the filtered grayscale value to leakage concentration according to the gas absorption rate model (Beer-Lambert law), the formula is: , is the background infrared light intensity when there is no gas leakage, is the infrared light intensity after passing through the leaked gas, is the absorption coefficient of the gas (unit: cm⁻¹·ppm⁻¹), which is related to the gas type and wavelength. is the optical path (gas layer thickness, unit: cm), is the gas concentration (unit: ppm); Step S6.2: For real-time alarm and visualization, a heat map with the leakage area superimposed is output, and an audible and visual alarm is triggered when the concentration exceeds a preset threshold.

[0023] The present invention includes: Detector module: Cooled type II superlattice detector (320×256 pixels, 30μm pixel pitch).

[0024] Stirling refrigerator (operating temperature 120K, power consumption ≤5W).

[0025] Optical lens group: Medium-wave infrared lens (focal length 23mm, F number 1.2) and visible light lens (1 / 2.7-inch CMOS).

[0026] Data processing unit: The FPGA chip implements real-time image processing (frame rate 60Hz) and supports H.264 video encoding and Bluetooth / WiFi transmission.

[0027] Human-computer interaction module: 5-inch rotatable touch screen (resolution 1280×720) with integrated GPS, voice recording and laser ranging functions.

[0028] Power supply and protection system: Lithium-ion battery pack (life ≥ 4 hours), IP67 protective housing, explosion-proof certification Ex ic IIC T4 Gc.

[0029] The advantages of the present invention are: By using a cooled type II superlattice infrared detector, combined with an optimized cooling system and algorithm, we can achieve: Improved sensitivity: NETD≤10mK, capable of detecting leakage of 0.001ml / s; Power consumption and lifespan optimization: The detector operating temperature is increased to 120K, the cooling capacity requirement is reduced by 50%, and the device lifespan is extended to ≥16,000 hours; Lightweight and multifunctional integration: The whole machine weighs ≤ 2.5kg, can be handheld or mounted on drones, and has both explosion-proof (Ex icIIC T4 Gc) and real-time data transmission functions.

[0030] Under the premise of ensuring high sensitivity (≤10mK), the long life, low power consumption and multi-gas compatibility of the portable gas leak detection device are achieved through the cooling type II superlattice detector and system optimization.

[0031] It is worth mentioning that the technical features such as the touch screen involved in the patent application of this invention should be regarded as prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional choices in the field and should not be regarded as the inventive point of this patent. This patent will not be further elaborated.

[0032] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A portable gas leak detection device based on a refrigerated superlattice infrared detector, characterized in that: It includes a detector module, an optical lens group and a data processing unit, wherein: The detector module includes a refrigerated detector and a refrigerator. The refrigerated detector captures the infrared radiation energy distribution of the gas in the target area based on the infrared radiation absorption effect and converts it into an electrical signal. The refrigerator is used to cool the refrigerated detector to a preset temperature to reduce the thermal noise of the refrigerated detector itself. The optical lens group includes a medium-wave infrared lens and a visible light lens. The medium-wave infrared lens is used to efficiently collect medium-wave infrared radiation from the target area. When gas leaks, gas molecules near the leak source absorb infrared radiation emitted by background objects, forming an abnormal area with a different radiation intensity from the background. The medium-wave infrared lens focuses the infrared radiation from the area onto a refrigerated detector, converts it into an electrical signal, and forms an infrared image. The grayscale value of the gas leak area in the image is different from that of the background, thereby achieving visual positioning of the leak. The visible light lens synchronously collects a visible light image of the target area and fuses it with the infrared image. The visible light image provides clear scene details, and the infrared image provides location information of the gas leak. By combining the two, the specific location of the leak source in the actual scene can be accurately located. The data processing unit obtains data including infrared images and visible light images, and determines the gas leakage situation after processing through a detection algorithm.

2. A portable gas leak detection device based on a refrigerated superlattice infrared detector according to claim 1, characterized in that: The detection algorithm is implemented in the following steps: Step S1: Data input, input the infrared image sequence of the original gas, and synchronously obtain the visible light image, GPS positioning and laser ranging data; Step S2: Preprocessing, aligning the spatiotemporal references of the infrared and visible light images, and using a motion compensation algorithm to eliminate image offsets caused by device jitter or environmental vibrations; Step S3: Temporal noise suppression, performing multi-frame sliding average and temporal difference enhancement respectively; Step S4: spatial domain filtering, performing adaptive wavelet denoising and anisotropic diffusion filtering respectively; Step S5: edge enhancement, combining adaptive dual thresholds to enhance the continuity of the gas leakage edge and using morphological closing operations to fill edge breaks; Step S6: Post-processing and output, including concentration inversion and mapping, real-time alarm and visualization.

3. A portable gas leak detection device based on a refrigerated superlattice infrared detector according to claim 2, characterized in that: Step S3 is specifically implemented as follows: Step S3.1: For multi-frame sliding average, random noise is suppressed by multi-frame weighted averaging, and a stable gas signal is retained. The weighted average of N consecutive frames of images is performed to suppress random noise. The formula is: ； in, The pixel point of the t-th frame image Gray value at ; is the weight coefficient of the t-th frame; is the number of frames involved in averaging; is the averaged output image pixel value; Step S3.2: For time domain difference enhancement, the absolute difference between adjacent frames is calculated to highlight the dynamic changes of the gas leakage area and suppress static background interference. The difference image between adjacent frames is calculated to highlight the dynamic leakage area. The formula is: , is the pixel grayscale value of two adjacent frames, is the pixel value of the difference image, indicating the intensity of dynamic changes.

4. The portable gas leak detection device based on a refrigerated superlattice infrared detector according to claim 3, characterized in that: Step S4 is specifically implemented as follows: Step S4.1: For adaptive wavelet denoising, wavelet transform is used to decompose the high-frequency and low-frequency components of the image, retaining the gas edge features and filtering out high-frequency noise; and the noise coefficient is suppressed by the threshold shrinkage method. The formula is: , is the jth coefficient after wavelet decomposition, is the preset threshold, is the coefficient after threshold processing; Step S4.2: For anisotropic diffusion filtering, based on partial differential equations, the image is smoothed along the gas concentration gradient direction to preserve edge details. The formula is: , is the image gradient, is the diffusion coefficient function, controlling the smoothing intensity, is the divergence operator.

5. The portable gas leak detection device based on a refrigerated superlattice infrared detector according to claim 4, characterized in that: Step S6 is specifically implemented as follows: Step S6.1: For concentration inversion and mapping, convert the filtered grayscale value into leakage concentration according to the gas absorption rate model. The formula is: , is the background infrared light intensity when there is no gas leakage, is the infrared light intensity after passing through the leaked gas, is the absorption coefficient of the gas, which is related to the gas type and wavelength. is the optical path, is the gas concentration; Step S6.2: For real-time alarm and visualization, a heat map with the leakage area superimposed is output, and an audible and visual alarm is triggered when the concentration exceeds a preset threshold.

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