CN115760671A - Circular coal yard temperature monitoring method and system based on infrared image and storage medium - Google Patents
Circular coal yard temperature monitoring method and system based on infrared image and storage medium Download PDFInfo
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Abstract
The invention relates to a circular coal yard temperature monitoring method, a system and a storage medium based on infrared images, wherein the method comprises the following steps: s1: acquiring infrared images of different positions of a circular coal yard in real time, wherein the image contents of the infrared images of different positions cover the whole circular coal yard; s2: acquiring the total number of pictures of the whole circular coal yard jigsaw based on an overlapping matching principle, and confirming the infrared images to be spliced: s3: splicing the infrared images to be spliced to form a complete circular coal yard thermal image; s4: and extracting temperature data in the circular coal yard according to the thermal image of the circular coal yard. Compared with the prior art, the method has the advantages of being effectively suitable for the circular coal yard, high in temperature identification accuracy and the like.
Description
Technical Field
The invention relates to the field of coal yard temperature monitoring, in particular to a circular coal yard temperature monitoring method and system based on infrared images and a storage medium.
Background
Coal is used as an inflammable substance and easily generates a spontaneous combustion problem in a long-term stacking process, coal is used as a main production raw material in thermal power generation enterprises and accounts for about 70% of the operation cost of the coal, if the problem of heating and spontaneous combustion in a coal storage process cannot be well solved, huge loss of a calorific value and an extreme decomposition spontaneous combustion phenomenon are easily caused, immeasurable economic loss is brought to the enterprises, and meanwhile, toxic and harmful gases or even explosion danger can be generated to a stacking place in the spontaneous combustion process, so that great influence is generated on enterprise personnel and safe production.
The whole coal yard area is scanned in real time through installing a plurality of thermal infrared image devices, the coverage area of each infrared temperature measurement device is limited, the whole coal yard cannot be observed, the output data only comprise the current coverage area temperature extreme value and the average value, the continuous temperature detection cannot be achieved, meanwhile, the presentation mode of the plurality of thermal infrared image devices only adopts the real-time infrared video mode to present, and the visual whole coal yard temperature graph cannot be formed. The spontaneous combustion process of the coal pile is quite complex, particularly when the temperature of the coal pile is close to the spontaneous combustion point, high-order chemical reaction is involved, so that a calculation model is quite complex, the temperature change of the coal pile is in a flying trend when the temperature of the coal pile is close to the spontaneous combustion point, and the spontaneous combustion of the coal pile can happen quickly. By adopting a conventional temperature monitoring mode, the continuous change monitoring of the temperature of the coal pile cannot be realized, the detection effect is poor, and the accuracy is limited.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a circular coal yard temperature monitoring method and system based on infrared images and a storage medium.
The purpose of the invention can be realized by the following technical scheme:
a circular coal yard temperature monitoring method based on infrared images comprises the following steps:
s1: acquiring infrared images of different positions of a circular coal yard in real time, wherein the image contents of the infrared images of different positions cover the whole circular coal yard;
s2: acquiring the total number of pictures of the whole circular coal yard jigsaw based on an overlapping matching principle, and confirming the infrared images to be spliced:
s3: splicing the infrared images to be spliced to form a complete circular coal yard thermal image;
s4: and extracting temperature data in the circular coal yard according to the thermal image of the circular coal yard.
Preferably, in the step S1, a plurality of thermal infrared imaging devices are arranged in the circular coal yard to acquire infrared images, and the shooting ranges of the plurality of thermal infrared imaging devices are overlapped to cover the whole circular coal yard.
Preferably, the calculation formula for obtaining the total number of pictures in step S2 is:
L(x,y,σ)＝G(x,y,σ)*I(x i ,y i ,a,b,c,h)*N
where G (x, y, σ) is a Gaussian function of a predefined scale of variation, I (x) i ,y i A, b, c, h) is the original image, x i Is the x-direction coordinate value of the image, y i The coordinate values in the y direction of the image are L (x, y, sigma) is G (x, y, sigma) and I (x) i ,y i Convolution of a, b, c, h), x and y being position parameters of infrared thermal imaging equipment, sigma being standard deviation of Gaussian function, a, b and c being redThe current holder angle of the thermal infrared image equipment, h is the installation height of the thermal infrared image equipment, N is the total number of pictures, m and N are the dimension information of a Gaussian template, and I p Is the gray value of any point on the image, I i Is the gray value of any point on the 3-circle of the radius around the point, C i Is shown as I p And I i Is related to the magnitude of the predefined threshold t.
Preferably, the step S3 specifically includes:
s31: performing texture recognition and splicing, and splicing images with the texture matching degree higher than a preset value according to the texture condition of the images to be spliced;
s32: and performing similarity identification and splicing, performing feature extraction on the images which cannot be subjected to texture identification and splicing, and splicing the similar images.
Preferably, in step S32, feature extraction and similar image stitching are performed on the images that cannot be subjected to texture recognition stitching based on a rannac algorithm.
Preferably, the step S32 specifically includes:
s321: gridding the image which can not be subjected to texture recognition and splicing, and establishing a central coordinate system in each grid;
s322: performing gray level calculation according to four dimensional directions, matching gray levels between four adjacent grids, and taking grid intersection points with high similarity as feature points;
s323: and carrying out linear transformation matrix transformation on the characteristic points, substituting the transformation matrix result into the position information of each image for fine adjustment, and forming images which can be spliced for graph splicing.
Preferably, the linear transformation matrix in step S323 is:
wherein k is the weight of the matching point in the first image, α 'and β' are image coordinate values of the matching point in the first image, α and β are image coordinate values of the pixel point in the second image, α τ 、β τ The coordinate value of the image of the new image after transformation, H is the weight of the matching point in the image two, H 1 、h 2 、h 3 、h 4 、h 5 、h 6 、h 7 、h 8 Is a transformation matrix.
Preferably, the formula of the position information of the image in step S323 is:
wherein,
in order to stitch the completed images together,
for the image one and the image two to be spliced,
is the coordinate value of the image in the x direction, gamma is the coordinate value of the image in the y direction, w 1 、w 2 The identification weights of the image I and the image II are respectively used for identifying the coincidence degree requirement of matching the feature points in each image.
A circular coal yard temperature monitoring system based on infrared images comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the circular coal yard temperature monitoring method based on infrared images.
A computer readable storage medium, which stores a program that can be loaded and executed by a processor to implement the above-mentioned circular coal yard temperature monitoring method based on infrared images.
Compared with the prior art, the invention has the following advantages:
(1) The invention can monitor the temperature data of the round coal yard in real time, acquire the temperature data in the round coal yard, realize the combination of the temperature data and the stacking condition data, and provide a data basis for researching the coal type and the temperature change condition;
(2) According to the invention, when infrared image splicing is carried out, the influence of gray level difference between images on a fusion result is reduced as much as possible, so that the finally obtained target image graph is accurate and natural, a multistage splicing form is adopted, splicing processing is carried out through texture conditions, then similarity identification splicing is carried out, the splicing effect is effectively improved, the accuracy and reliability of temperature data in a coal yard are effectively improved based on the fact that the whole image is identified subsequently, and continuous temperature data acquisition and monitoring of the temperature of the circular coal yard are realized.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a similarity identification stitching process according to the present invention;
FIG. 3 is a schematic structural diagram of a circular coal yard temperature monitoring system based on infrared images in the embodiment of the invention;
FIG. 4 is a complete circular coal yard thermal image completed by the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
A circular coal yard temperature monitoring method based on infrared images, because of the limitations of the special coal yard and the infrared thermal imaging equipment, all the equipment images must be spliced to realize the display of the infrared video images of the whole coal yard, the image data of different positions of the coal yard, which are acquired by a plurality of equipment in the coal yard, are spliced to acquire the completed circular coal yard thermal image images for temperature monitoring, as shown in figure 1, the method comprises the following steps:
s1: the method comprises the steps of acquiring infrared images of different positions of a circular coal yard in real time, wherein the image contents of the infrared images of different positions cover the whole circular coal yard.
In the step S1, a plurality of thermal infrared image devices are arranged in the circular coal yard to acquire infrared images, and the shooting ranges of the plurality of thermal infrared image devices are overlapped to cover the whole circular coal yard.
In the embodiment, the internal maintenance platform of the closed structure of the circular storage yard is used as a supporting structure, three double-visual locatable high-precision infrared thermal image temperature detection devices are equally arranged on the platform, and each set of device is respectively responsible for monitoring the temperature of a part of coal piles in the storage yard. The infrared thermal image equipment adopts 320 x 240 image elements, 25um pixel size, 7.5-13 um response wave band, working temperature of-40-60 ℃, storage temperature of-45-70 ℃, 360-degree rotation angle of a holder, 180-degree pitching angle, 0.01-degree precision and 50-meter installation height.
S2: and acquiring the total number of the pictures of the whole circular coal yard jigsaw based on an overlapping matching principle, and confirming the infrared images to be spliced.
The calculation formula for obtaining the total number of pictures in the step S2 is as follows:
L(x,y,σ)＝G(x,y,σ)*I(x i ,y i ,a,b,c,h)*N
wherein G (x, y, σ) is a Gaussian function of a predefined scale of variation, I (x) i ,y i A, b, c, h) is the original image, x i Is the x-direction coordinate value of the image, y i Is the y-direction coordinate value of the image, and L (x, y, sigma) is G (x, y, sigma) and I (x) i ,y i Convolution of a, b, c and h), x and y are position parameters of the infrared thermal image equipment, sigma is standard deviation of Gaussian function, and a, b and c are infrared thermal image equipmentH is the installation height of the thermal infrared image equipment, N is the total number of pictures, m and N are the dimension information of the Gaussian template, and I p Is the gray value of any point on the image, I i Is the gray value of any point on the 3-circle of the radius around the point, C i Is I p And I i Is related to the magnitude of the predefined threshold t.
The infrared thermal imaging device converts visible light image information into thermal imaging information through infrared radiation through atmospheric transmission, optical imaging, photoelectric conversion and gray processing, so that real-time image information acquired by infrared thermal imaging is a gray image, the resolution is low, the outline edge is not obvious, in order to ensure that multiple adjacent pictures really restore a scene in the splicing process, the infrared image needs to be preprocessed, the multiple adjacent pictures are subjected to image matching, the adjacent pictures are overlapped as much as possible, and the splicing of the multiple pictures is completed. In the image acquisition process, the more the adjacent images are overlapped, the better the splicing effect is after the images are overlapped, but after the images are overlapped more, the number of infrared images is increased, the image processing speed is reduced, and the system operation time is increased. Therefore, in order to quickly realize the matching of the picture information in the preprocessing process and not reduce the overall operation efficiency of the system, the picture information is acquired according to the principle of 50% overlapping matching of adjacent pictures. According to the model, the purpose that a photo data model needing to be matched is obtained by utilizing the resolution ratio, the focal length, the coal yard size and the equipment installation position of the thermal infrared imagery equipment is achieved, a specified number of images in the coverage area of the equipment are shot by each thermal infrared imagery equipment within a specified time, the total number of the images needed for completing the picture splicing of the whole coal yard is calculated, the amplitude and the frequency of the action of a holder of each equipment are calculated according to the condition of each equipment, and the infrared images to be spliced are confirmed.
S3: and splicing the infrared images to be spliced to form a complete circular coal yard thermal image, as shown in FIG. 4.
The purpose of image splicing is to superpose a plurality of thermal image images to finally form a storage yard thermal image completion graph, and in the process, smooth transition processing needs to be carried out on the splicing process to complete fusion on gray level. Images acquired in the shooting process show slight difference in picture gray scale due to different directions, angles, illumination and the like, so that matching errors exist in the image matching process, and accurate matching cannot be achieved. The image splicing and fusing process is to reduce the influence of the gray level difference between the images on the fusion result as much as possible, so that the finally obtained target image is accurate and natural in graph. Therefore, various conditions in the spliced image need to be analyzed, and different conditions are respectively processed.
The step of S3 specifically comprises:
s31: and performing texture recognition and splicing, and splicing the images with the texture matching degree higher than a preset value according to the texture condition of the images to be spliced. Texture is a main element forming a picture, and is widely used in visible light image processing, but because the spatial resolution and the detection capability of a thermal imaging system are lower than those of a visible light imaging system, the infrared image has low contrast compared with the visible light image, the proportion of a target scene in the image is small, and the target is not easy to recognize. Meanwhile, the interference of the external environment and the thermal imaging system have noise sources, so that various noises exist in the infrared image, and the noises directly influence the extraction of textures in the image. In the invention, in the process of image splicing, by comparing a plurality of overlapped graphs and utilizing the matching degree of texture recognition, the graphs which are more matched are directly spliced, and the graphs which have problems are subjected to image matching in a mode of building a reasonable similarity model.
S32: as shown in fig. 2, similarity recognition and stitching are performed, feature extraction is performed on images which cannot be subjected to texture recognition and stitching, and similar images are stitched.
In a special environment of a coal yard space, a holder is adopted to drive a thermal imager to act, the change of the visual angle and the posture of an imaging lens can cause the space geometric change among images, the main geometric change comprises the displacement and the rotation among the images and the zooming caused by the different focal lengths of the lenses, and the factors cause that the image splicing can only be partially matched or can not be matched due to the different shooting time and angles. In the invention, reasonable similarity characteristic points are searched to determine the similarity of the pictures to be matched, the reasonable similarity characteristic points are subjected to rationality analysis through coal types of a coal yard, firstly, pictures which cannot be subjected to texture analysis and peripheral pictures thereof are obtained, the pictures are subjected to characteristic extraction, the rationality characteristic points in the pictures are found out through a Ranpac algorithm, and the matching of the pictures is carried out by combining the characteristic point conditions in a plurality of pictures, and the method specifically comprises the following steps:
s321: gridding the image which can not be subjected to texture recognition and splicing, and establishing a central coordinate system in each grid;
s322: performing gray level calculation according to four dimensional directions, matching gray levels between four adjacent grids, and taking grid intersection points with high similarity as feature points;
s323: carrying out linear transformation matrix transformation on the characteristic points, substituting the transformation matrix result into the position information of each image for fine adjustment, forming images which can be spliced for image splicing, wherein the linear transformation matrix is as follows:
wherein k is the weight of the matching point in the first image, α 'and β' are the image coordinate values of the matching point in the first image, α and β are the image coordinate values of the pixel point in the second image τ 、β τ The coordinate value of the image of the new image after transformation, H is the weight of the matching point in the image two, H 1 、h 2 、h 3 、h 4 、h 5 、h 6 、h 7 、h 8 Is a transformation matrix.
The formula of the position information of the image is:
wherein,
in order to stitch the completed images together,
the images to be spliced are the first image and the second image,
is the coordinate value of the image in the x direction, gamma is the coordinate value of the image in the y direction, w 1 、w 2 The identification weights of the image I and the image II are respectively used for identifying the coincidence degree requirement of matching the feature points in each image.
S4: and extracting temperature data in the circular coal yard according to the thermal image of the circular coal yard.
The method has the advantages that the continuous and complete temperature monitoring result of the whole area of the circular coal yard coal storage yard can be carried out by utilizing the spliced complete coal infrared temperature thermal image graph, although the temperature figure is the surface temperature of the coal pile, when the temperature of any position (bottom or middle of the coal bed) of the coal pile is increased under normal conditions, the temperature is vertically upward and directly reaches the surface of the coal bed, and then the manual measurement of the internal temperature of the coal pile is combined, according to the data discovery of the continuous monitoring result for a period of time, if the temperature change of the coal surface reaches 2 ℃, the maximum temperature inside the coal bed can be basically judged to have the change of about 20 ℃. According to the situation, a linear correlation change model of the internal temperature and the surface temperature of the coal pile is made, the continuous monitoring data of the temperature of the coal pile is introduced into model data, and then the continuous monitoring data of the temperature of the coal pile is formed by combining the consideration of external factors such as the storage time, the season, the coal type and the like of the coal pile in the coal yard.
The invention also provides a circular coal yard temperature monitoring system based on the infrared image, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the circular coal yard temperature monitoring method based on the infrared image. Specifically, as shown in fig. 3, the monitoring system includes a plurality of thermal infrared imagers and an infrared monitoring client, the memory and the processor are mounted in the infrared monitoring client, and infrared images acquired by the thermal infrared imagers are sent to the infrared monitoring client through the field control cabinet and the central switch for processing, splicing and acquiring temperature data. In addition, the system is further provided with a display and a video monitor, video monitoring is carried out under the control of the infrared monitoring client, and the infrared images of the circular coal yard are displayed. The invention is additionally provided with a video server and a data server for storing and processing the monitoring video and the acquired temperature data.
In addition, the invention also provides a computer readable storage medium, which stores a program capable of being loaded and executed by a processor to implement the method for monitoring the temperature of the circular coal yard based on the infrared image.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the scope of the technical idea of the present invention.
Claims (10)
1. A circular coal yard temperature monitoring method based on infrared images is characterized by comprising the following steps:
s1: acquiring infrared images of different positions of a circular coal yard in real time, wherein the image contents of the infrared images of different positions cover the whole circular coal yard;
s2: acquiring the total picture quantity of the whole circular coal yard jigsaw based on the overlapping matching principle, and confirming the infrared images to be spliced:
s3: splicing the infrared images to be spliced to form a complete circular coal yard thermal image;
s4: and extracting temperature data in the circular coal yard according to the thermal image of the circular coal yard.
2. The circular coal yard temperature monitoring method based on infrared images as claimed in claim 1, wherein in step S1, a plurality of thermal infrared imaging devices are arranged in the circular coal yard to acquire infrared images, and the shooting ranges of the plurality of thermal infrared imaging devices are overlapped to cover the whole circular coal yard.
3. The circular coal yard temperature monitoring method based on infrared images as claimed in claim 1, wherein the calculation formula for obtaining the total number of pictures in step S2 is:
L(x,y,σ)＝G(x,y,σ)*I(x i ,y i ,a,b,c,h)*N
where G (x, y, σ) is a Gaussian function of a predefined scale of variation, I (x) i ,y i A, b, c, h) is the original image, x i Is the x-direction coordinate value of the image, y i Is the y-direction coordinate value of the image, and L (x, y, sigma) is G (x, y, sigma) and I (x) i ,y i Convolution of a, b, c and h), x and y are position parameters of the thermal infrared image equipment, sigma is a standard deviation of a Gaussian function, a, b and c are current holder angles of the thermal infrared image equipment, h is an installation height of the thermal infrared image equipment, N is the total number of pictures, m and N are dimension information of a Gaussian template, I p Is the gray value of any point on the image, I i Is the gray value of any point on the 3-circle of the radius around the point, C i Is shown as I p And I i Is related to the magnitude of the predefined threshold t.
4. The circular coal yard temperature monitoring method based on infrared images as claimed in claim 1, wherein the step S3 specifically includes:
s31: performing texture recognition and splicing, and splicing images with the texture matching degree higher than a preset value according to the texture condition of the images to be spliced;
s32: and performing similarity identification splicing, performing feature extraction on the images which cannot be subjected to texture identification splicing, and splicing the similar images.
5. The method for monitoring the temperature of the circular coal yard based on the infrared image as claimed in claim 4, wherein in the step S32, the images which cannot be subjected to texture recognition and splicing are subjected to feature extraction and similar image splicing based on a Ranpac algorithm.
6. The circular coal yard temperature monitoring method based on infrared images as claimed in claim 4, wherein said step S32 specifically includes:
s321: gridding the image which cannot be subjected to texture recognition and splicing, and establishing a central coordinate system in each grid;
s322: performing gray level calculation according to four dimensional directions, matching gray levels between four adjacent grids, and taking grid intersection points with high similarity as feature points;
s323: and carrying out linear transformation matrix transformation on the characteristic points, substituting the transformation matrix result into the position information of each image for fine adjustment, and forming images which can be spliced for graph splicing.
7. The method for monitoring the temperature of the circular coal yard based on the infrared image as claimed in claim 6, wherein the linear transformation matrix in the step S323 is:
wherein k is the weight of the matching point in the first image, α ', β' are the image coordinate values of the matching point in the first image, α, β,Beta is the image coordinate value of the pixel point in the second image, alpha τ 、β τ Is the image coordinate value of the new image after transformation, H is the weight of the matching point in the second image, H 1 、h 2 、h 3 、h 4 、h 5 、h 6 、h 7 、h 8 Is a transformation matrix.
8. The method for monitoring the temperature of the circular coal yard based on the infrared image as claimed in claim 6, wherein the formula of the position information of the image in the step S323 is:
wherein,
in order to stitch the completed images together,
for the image one and the image two to be spliced,
is the x-direction coordinate value of the image, gamma is the y-direction coordinate value of the image, w 1 、w 2 The identification weights of the image I and the image II are respectively used for identifying the coincidence degree requirement of matching the feature points in each image.
9. A circular coal yard temperature monitoring system based on infrared images is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the circular coal yard temperature monitoring method based on infrared images as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that it stores a program which, when being loaded and executed by a processor, implements a method for monitoring the temperature of a circular coal yard based on infrared images as claimed in any one of claims 1 to 8.
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