CN117146982A - Sinter temperature detection method, system and electronic equipment - Google Patents
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Abstract
The invention discloses a sinter temperature detection method, a system and electronic equipment, and relates to the field of high-temperature sinter blanking temperature detection. According to the sinter temperature detection method provided by the invention, after the RGB gray data of the sintering area in the obtained visible light image of the sinter is extracted, the brightness value is determined based on the RGB gray data of the sintering area in the visible light image, then the determined brightness value is subjected to least square fitting to obtain the radiant energy value, and then the radiant energy fitting temperature distribution fitting scheme is used, so that the sinter temperature is detected by the built sinter temperature least square fitting model, the accurate detection of the real temperature of the sinter can be realized by adopting the visible light image alone, and the detection sensitivity of the sinter temperature can be ensured.
Description
Technical Field
The invention relates to the field of high-temperature sinter blanking temperature detection, in particular to a sinter temperature detection method, a system and electronic equipment.
Background
The temperature of the sinter at the interface of the sintering system and the cooling system is an important parameter for judging the sintering end point, the sintering quality and influencing the recovery efficiency of the sintering waste heat; the sintering ore blanking process cannot carry out contact temperature measurement due to the limitation of the field environment.
At present, a large amount of high-temperature and high-dust smoke is generated in the sinter blanking process, and when the infrared thermal imager is used for measuring the sinter temperature, the problem that the sinter temperature measurement precision is low because the radiation energy of the sinter is difficult to measure due to the shielding effect of the high-temperature smoke still exists.
Although the visible light image is less affected by the flue gas, the sinter can be obviously distinguished in the image, the identification of the sinter temperature is facilitated, and the sinter blanking window camera collects a large number of visible light images, so that data support can be provided for the identification of the sinter temperature image. However, most of the existing infrared temperature measurement or image identification temperature measurement methods directly use gray value to calculate temperature, for example, in the document 'an infrared thermal imaging equipment temperature detection method based on calibration fitting', infrared detection equipment is used, then a temperature-gray mapping table is constructed to calculate temperature, but the method is not suitable for sinter blanking temperature detection, because high-temperature smoke generated by a sinter high-temperature blanking site can greatly influence the detection precision of the infrared thermal imaging equipment, and secondly, the direct use of image gray value to calculate the sinter temperature can cause larger result error.
Therefore, how to accurately detect the real temperature of the sinter by adopting a visible light image in the high-temperature blanking process of the sinter and ensure the detection sensitivity and accuracy of the sinter temperature becomes a technical problem to be solved in the field.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for detecting the temperature of a sinter and electronic equipment.
In order to achieve the above object, the present invention provides the following solutions:
a sinter temperature detection method, comprising:
constructing a sintering ore temperature least square fitting model;
obtaining a visible light image of the agglomerate to be detected;
extracting RGB gray scale data of a sintering area in the visible light image;
determining a first brightness value based on RGB gray scale data of a sintering region in the visible light image;
performing least square fitting on the first brightness value to obtain a first radiant energy value;
and inputting the first radiant energy value into the sintering ore temperature least square fitting model to obtain the temperature of the sintering ore to be detected.
Optionally, constructing a sinter temperature least square fitting model, which specifically comprises the following steps:
collecting an infrared image of the experimental sinter in a sinter blanking environment simulated by an experimental platform, and collecting a visible light image and temperature data of the experimental sinter;
extracting RGB gray data of a sintering area in a visible light image of the experimental sintering ore and RGB gray data of the sintering area in an infrared image of the experimental sintering ore;
determining a second brightness value based on RGB gray scale data of a sintering region in a visible light image of the experimental sinter;
performing least square fitting on the second brightness value to obtain a second radiant energy value;
determining a third radiant energy value based on the RGB grayscale data of the sintering zone in the infrared image of the experimental sinter and the temperature data of the experimental sinter;
determining an error between the second radiant energy value and the third radiant energy value;
and when the error meets a set condition, carrying out least square fitting on the second radiant energy value to obtain the temperature of the sintering ore so as to construct and form a sintering ore temperature least square fitting model.
Optionally, the sinter temperature least squares fitting model is:
f(E)＝aE b +c；
wherein f (E) is the temperature of the sinter, E is the radiant energy value, and a, b and c are least squares fit coefficients.
Optionally, the method further includes extracting RGB gray data of the sintering area in the visible light image of the experimental sinter and RGB gray data of the sintering area in the infrared image of the experimental sinter:
screening the visible light image of the experimental sinter and the infrared image of the experimental sinter according to the set temperature gradient to generate a training set and a testing set; the training set is used for constructing a least square fitting model of the sintering ore temperature; the test set is used for testing the sintering ore temperature least square fitting model.
Optionally, the determining the third radiant energy value based on the RGB gray scale data of the sintering region in the infrared image of the experimental sinter and the temperature data of the experimental sinter further includes:
and converting two-dimensional data formed by the RGB gray scale data of the sintering area in the visible light image of the experimental sintering ore into one-dimensional data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the sinter temperature detection method provided by the invention, after the RGB gray data of the sintering area in the obtained visible light image of the sinter is extracted, the brightness value is determined based on the RGB gray data of the sintering area in the visible light image, then the determined brightness value is subjected to least square fitting to obtain the radiant energy value, and then the radiant energy fitting temperature distribution fitting scheme is used, so that the sinter temperature is detected by the built sinter temperature least square fitting model, the accurate detection of the real temperature of the sinter can be realized by adopting the visible light image alone, and the detection sensitivity of the sinter temperature can be ensured.
The invention further provides a sinter temperature detection system, which is used for implementing the sinter temperature detection method; the system comprises:
the model construction module is used for constructing a sintering ore temperature least square fitting model;
the image acquisition module is used for acquiring a visible light image of the agglomerate to be detected;
the gray level extraction module is used for extracting RGB gray level data of a sintering area in the visible light image;
the brightness determining module is used for determining a first brightness value based on RGB gray scale data of a sintering area in the visible light image;
the radiant energy fitting module is used for carrying out least square fitting on the first brightness value to obtain a first radiant energy value;
and the temperature detection module is used for inputting the first radiation energy value into the sintering ore temperature least square fitting model to obtain the temperature of the sintering ore to be detected.
An electronic device, comprising:
a memory for storing a computer program;
and the processor is connected with the memory and is used for calling and executing the computer program so as to implement the sinter temperature detection method.
Optionally, the memory is a computer readable storage medium.
The technical effects achieved by the two structures provided by the invention are the same as those achieved by the sinter temperature detection method provided by the invention, so that the description is omitted here.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a sinter temperature detection method provided by the invention;
FIG. 2 is a flow chart of the method for detecting the temperature of the sinter according to the invention;
FIG. 3 is a graph comparing test results provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a sinter temperature detection method, a system and electronic equipment, which can accurately detect the real temperature of sinter by adopting a visible light image and can ensure the detection sensitivity of the sinter temperature.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the method for detecting the temperature of the sintering ore provided by the invention comprises the following steps:
step 100: and constructing a sintering ore temperature least square fitting model. The sintering ore temperature least square fitting model is as follows:
f(E)＝aE b +c (1)
wherein f (E) is the temperature of the sinter, E is the radiant energy value, and a, b and c are least squares fit coefficients.
In a specific application process, the specific construction process of the sintering ore temperature least square fitting model comprises the following steps:
1) And collecting an infrared image of the experimental sinter in a sinter blanking environment simulated by the experimental platform, and collecting a visible light image and temperature data of the experimental sinter.
2) And extracting RGB gray scale data of a sintering area in a visible light image of the experimental sintering ore and RGB gray scale data of the sintering area in an infrared image of the experimental sintering ore.
3) The second brightness value is determined based on RGB gray-scale data of the sintering region in the visible light image of the experimental sinter. The definition value is determined by the following formula:
wherein L is a brightness value, R is a gray value of a red channel in the RGB gray scale data, G is a gray value of a green channel in the RGB gray scale data, and B is a gray value of a blue channel in the RGB gray scale data.
4) And performing least square fitting on the second brightness value to obtain a second radiant energy value.
5) And determining a third radiant energy value based on the RGB gray scale data of the sintering zone in the infrared image of the experimental sinter and the temperature data of the experimental sinter. Wherein, the calculation formula of the radiant energy is as follows:
wherein E (lambda, T) represents the radiant energy at a radiant wavelength lambda of T, lambda is the radiant wavelength, T is the absolute temperature of the blackbody, C 1 、C 2 Are pixel locations in the one-dimensional data.
6) An error between the second radiant energy value and the third radiant energy value is determined.
7) And when the error meets the set condition, carrying out least square fitting on the second radiant energy value to obtain the temperature of the sinter, so as to construct a sinter temperature least square fitting model.
Step 101: and obtaining a visible light image of the agglomerate to be detected.
Step 102: RGB gray scale data of a sintering region in a visible light image is extracted.
Step 103: a first brightness value is determined based on RGB gray scale data of a sintered region in a visible light image.
Step 104: and performing least square fitting on the first brightness value to obtain a first radiant energy value. Wherein the radiant energy E (L) is determined by the following formula:
E（L)＝aL b +c (4)
wherein L is the calculated brightness value of the image, and a, b and c are fitting coefficients.
Step 105: and inputting the first radiant energy value into a sintering ore temperature least square fitting model to obtain the temperature of the sintering ore to be detected.
Based on the description, before the sinter temperature detection, the sinter temperature least square fitting model is constructed in a mode of sinter temperature least square fitting, so that the purpose that the real temperature of the sinter can be accurately detected by only adopting a visible light image can be achieved by the constructed fitting model. Based on the above, the invention provides a specific implementation mode, and a mode of least square fitting of the sinter temperature provided by the invention is described in detail.
As shown in fig. 2, the implementation process of the sinter temperature least square fitting method based on image recognition provided in this embodiment mainly includes three working aspects of data acquisition, data processing and temperature fitting.
Step S1: in data acquisition, an experimental platform is firstly required to be built, a high-temperature blanking environment of the sinter is simulated, and an image of the sinter red ore is acquired. The visible light detection equipment FLUKE TIX660 is used for collecting visible light images and temperature data of the sinter, and a thermocouple thermometer is used for correcting the collected temperature data.
Step S2: in the data processing, the acquired high-temperature sinter images are subjected to data preprocessing (such as image enhancement, denoising and the like), proper temperature gradients are set for selecting the high-temperature sinter images to form a picture library, and the image gray data of the sinter areas in the visible light and infrared light images are extracted. The temperature gradient is to set high temperature sinter image between the highest temperature and the lowest temperature, to keep the same set of temperature data at close interval, to ensure the subsequent fitting data smooth as possible.
Specifically, the obtained temperature distribution diagram of each sample sinter is divided into a training set and a testing set. For example, 77 high-temperature sinter images collected on a laboratory bench are subjected to sintering temperature fitting by setting a proper temperature gradient to screen out 66 images, wherein 50 images are obtained in a training set, the highest temperature of the images in the training set is 889.8 ℃, the lowest temperature of the images in the training set is 577.9 ℃, and the average temperature of the images is 719.4 ℃. The test set of 16 pictures, the highest temperature of the pictures in the test set is 818.6 ℃, the lowest temperature is 548.9 ℃, and the average temperature is 664.7 ℃. And carrying out formula fitting on the characteristics of the sinter images (namely the visible light images and the infrared light images) in the training set and the temperature by using a least square method to obtain coefficients in a fitting formula, and predicting the sinter temperature by using the characteristics of the images in the test set after error verification is carried out on the obtained fitting formula. Specific:
r, G, B gray data of a sinter area (a coordinate range set by people) in the visible light image are extracted in batches through calculation software programming, and the calculation software can be Matlab or Python according to the visible light image parameter database composed of the sinters with different temperature gradients. For example, RGB gray data of a sinter area in a visible light image is extracted in batches through Matlab platform programming so as to calculate a brightness value of the image, and gray values and temperature data of a corresponding area are extracted through an infrared image. Because the height difference exists between the visible light lens and the infrared lens of the infrared thermal imager, the height difference exists between the visible light image and the infrared light image, and the coordinate data of the pixel points in the image cannot be shared, a manual control computer programming selection mode (namely a manual calibration mode) is needed to determine the effective areas of the sintering ores in the visible light image and the infrared image. For example, the regions of [267, 165, 38, 25], [341, 156, 19, 31] in the infrared image, the regions of [279, 221, 38, 25], [350, 215, 19, 31] in the visible image, the first two data in the vector are the upper left corner coordinates x, y of the rectangular box, and the last two are the width and height of the rectangular box are selected using computing software.
When the radiation energy is calculated by utilizing the image of the selected area of the sinter, data is required to be extracted from the two-dimensional sinter image, the data is converted into a one-dimensional array, the Matlab calculation software is used for converting the image features extracted from the training set into the one-dimensional array, and the one-dimensional array is substituted into a formula to calculate the radiation energy of the image.
Since human perception of external changes generally conforms to weber's law, human eye perception of brightness is a power function of the actual physical optical power. And obtaining brightness values through a conversion formula when the obtained visible light image gray data of each sample sinter are matched with temperature. In the practical application process, the image brightness value needs to be obtained by converting and calculating the RGB gray values in the extracted image features, and the specific calculation formula is shown in the above formula (2).
Step S3: in the temperature fitting, the object radiant energy value is fitted as an intermediate value. Namely fitting the brightness (L) value to the radiant energy by using a least square method, wherein the radiant energy is further fitted with the temperature, specifically:
(1) fitting of the brightness (L) value to the radiant energy value.
(2) The radiant energy values are then used to fit the sinter temperature,
specifically, the radiation energy value is obtained by bringing the temperature of the pixel point into the planck's law and then integrating the visible light band, and the radiation energy calculation formula can be referred to the above formula (3). And the radiation energy obtained by calculation is only used as a reference value, and error analysis is carried out on the radiation energy and the fitting result.
The distribution fitting strategy starts from the object radiation law and the imaging principle, and the functional relation distribution fitting among the visible light image brightness, the radiation energy and the sinter temperature comprises the following steps:
A. the brightness (L) value and the radiant energy are in a power function relationship, and a least square fitting model is shown in the formula (4).
And performing next temperature fitting after the radiation energy obtained by fitting and the radiation energy error obtained by calculation are analyzed.
B. The radiation energy value and the temperature are in a power function relation, namely a sintering ore temperature least square fitting model, and the specific expression form can be seen in the formula (1).
Substituting the data in the test set into the formula (3) and the formula (1) for temperature prediction verification analysis, wherein the error of fitting the brightness distribution of the image to the temperature of the sinter is reduced to 2.10% from 3.51% of the temperature of the sinter by direct fitting of the gray scale of the image, the temperature prediction precision is higher, the root mean square error of the temperature is reduced to 17.2098 from 28.2381, and the temperature prediction is more stable. The determination coefficient is increased from 0.8595 to 0.9465, which shows that the fitting formula accords with the temperature change rule. The specific changes of the values in the test results are shown in fig. 3.
Based on the above description, compared with the prior art, the technical solution provided by the present invention further has the following advantages:
1. according to the invention, the visible light image brightness value is adopted to perform sintering ore temperature distribution fitting, so that a large amount of high-temperature and high-dust smoke can be generated in the sintering ore blanking process, and the non-contact temperature fitting can be performed on the sintering ore blanking under the condition that the infrared thermal imager is inaccurate in measuring radiation energy.
2. According to the distribution fitting scheme provided by the invention, the image gray data is extracted and converted into the brightness value and then is fitted with the radiant energy value, and the accuracy of temperature detection can be ensured by using the distribution fitting scheme of radiant energy fitting temperature.
3. The invention realizes temperature detection by adopting a mode of combining image recognition, least square fitting and image brightness fitting temperature, and has better adaptability and innovation in the field of sinter temperature detection in the future intellectualization of the steel industry.
Furthermore, the invention also provides a sinter temperature detection system which is used for implementing the sinter temperature detection method. The system comprises:
and the model construction module is used for constructing a sintering ore temperature least square fitting model.
The image acquisition module is used for acquiring visible light images of the sintering ores to be detected.
And the gray level extraction module is used for extracting RGB gray level data of the sintering area in the visible light image.
And the brightness determining module is used for determining a first brightness value based on RGB gray scale data of a sintering area in the visible light image.
And the radiant energy fitting module is used for carrying out least square fitting on the first brightness value to obtain a first radiant energy value.
The temperature detection module is used for inputting the first radiation energy value into the sintering ore temperature least square fitting model to obtain the temperature of the sintering ore to be detected.
An electronic device, comprising:
and a memory for storing a computer program.
And the processor is connected with the memory and is used for retrieving and executing the computer program to implement the sinter temperature detection method.
Furthermore, the computer program in the above-described memory may be stored in a computer-readable storage medium when it is implemented in the form of a software functional unit and sold or used as a separate product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A sinter temperature detection method, characterized by comprising:
constructing a sintering ore temperature least square fitting model;
obtaining a visible light image of the agglomerate to be detected;
extracting RGB gray scale data of a sintering area in the visible light image;
determining a first brightness value based on RGB gray scale data of a sintering region in the visible light image;
performing least square fitting on the first brightness value to obtain a first radiant energy value;
and inputting the first radiant energy value into the sintering ore temperature least square fitting model to obtain the temperature of the sintering ore to be detected.
2. The sinter temperature detection method as claimed in claim 1, wherein the constructing of the sinter temperature least squares fitting model specifically comprises:
collecting an infrared image of the experimental sinter in a sinter blanking environment simulated by an experimental platform, and collecting a visible light image and temperature data of the experimental sinter;
extracting RGB gray data of a sintering area in a visible light image of the experimental sintering ore and RGB gray data of the sintering area in an infrared image of the experimental sintering ore;
determining a second brightness value based on RGB gray scale data of a sintering region in a visible light image of the experimental sinter;
performing least square fitting on the second brightness value to obtain a second radiant energy value;
determining a third radiant energy value based on the RGB grayscale data of the sintering zone in the infrared image of the experimental sinter and the temperature data of the experimental sinter;
determining an error between the second radiant energy value and the third radiant energy value;
and when the error meets a set condition, carrying out least square fitting on the second radiant energy value to obtain the temperature of the sintering ore so as to construct and form a sintering ore temperature least square fitting model.
3. The sinter temperature detection method as claimed in claim 1, wherein the sinter temperature least squares fitting model is:
f(E)＝aE b +c；
wherein f (E) is the temperature of the sinter, E is the radiant energy value, and a, b and c are least squares fit coefficients.
4. The sinter temperature detection method as claimed in claim 2, wherein the RGB gray-scale data of the sintering region in the visible light image of the experimental sinter and the RGB gray-scale data of the sintering region in the infrared image of the experimental sinter are extracted, and the method further comprises:
screening the visible light image of the experimental sinter and the infrared image of the experimental sinter according to the set temperature gradient to generate a training set and a testing set; the training set is used for constructing a least square fitting model of the sintering ore temperature; the test set is used for testing the sintering ore temperature least square fitting model.
5. The sinter temperature detection method as claimed in claim 2, wherein the determining of the third radiant energy value based on the RGB gray scale data of the sintering region in the infrared image of the experimental sinter and the temperature data of the experimental sinter further comprises:
and converting two-dimensional data formed by the RGB gray scale data of the sintering area in the visible light image of the experimental sintering ore into one-dimensional data.
6. A sinter temperature detection system for performing the sinter temperature detection method as claimed in any one of claims 1 to 5; the system comprises:
the model construction module is used for constructing a sintering ore temperature least square fitting model;
the image acquisition module is used for acquiring a visible light image of the agglomerate to be detected;
the gray level extraction module is used for extracting RGB gray level data of a sintering area in the visible light image;
the brightness determining module is used for determining a first brightness value based on RGB gray scale data of a sintering area in the visible light image;
the radiant energy fitting module is used for carrying out least square fitting on the first brightness value to obtain a first radiant energy value;
and the temperature detection module is used for inputting the first radiation energy value into the sintering ore temperature least square fitting model to obtain the temperature of the sintering ore to be detected.
7. An electronic device, comprising:
a memory for storing a computer program;
a processor, connected to the memory, for retrieving and executing the computer program to implement the sinter temperature detection method as claimed in any one of claims 1 to 5.
8. The electronic device of claim 7, wherein the memory is a computer-readable storage medium.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202311115812.6A CN117146982A (en) | 2023-08-30 | 2023-08-30 | Sinter temperature detection method, system and electronic equipment |
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