An on-line monitoring system for the temperature of the heated body of a heat treatment furnace

Abstract

The present invention discloses an on-line monitoring system for the temperature of the heated body of a heat treatment furnace, belonging to the technical field of heat treatment furnaces. Specifically, it includes: an infrared imaging module for generating a thermal imaging map of the temperature field inside the heat treatment furnace based on infrared temperature measurement technology; a steel pipe recognition module for preprocessing the thermal imaging map, performing edge detection on the preprocessed thermal imaging map, identifying the boundary lines between each steel pipe area and the surrounding area, and obtaining the steel pipe areas in the thermal imaging map; a temperature analysis module for acquiring the temperature data of the steel pipe areas, where the temperature data includes the average temperature, the highest point temperature, and the lowest point temperature, and judging whether the steel pipe heat treatment is operating normally according to the temperature data; a cycle adjustment module for setting the monitoring cycle and the standard duration, statistically analyzing the temperature data every n monitoring cycles, calculating the cycle adjustment coefficient according to the temperature data, and calculating the duration of the next n monitoring cycles according to the cycle adjustment coefficient.

Description

Technical Field

[0001] The present invention relates to the technical field of heat treatment furnaces, and particularly to an on-line monitoring system for the temperature of the heated body in a heat treatment furnace. Background Art

[0002] In the heat treatment process, accurately monitoring the temperature of the heated body, such as a steel pipe, is an important link to ensure product quality and process stability. Traditional temperature monitoring methods mainly rely on thermocouples or other contact temperature sensors. Although these methods can provide local temperature information, contact sensors such as thermocouples can only measure the temperature at a single point and are difficult to comprehensively reflect the temperature field distribution in the heat treatment furnace. Contact sensors are easily affected by the complex atmosphere in the heat treatment furnace in a high-temperature environment, resulting in measurement errors. In addition, the installation and maintenance of the sensors may also interfere with the production process.

[0003] With the development of infrared imaging technology and image processing technology, non-contact temperature monitoring methods have gradually attracted attention. Infrared imaging technology can generate a temperature distribution image by detecting the infrared radiation on the surface of an object, providing more comprehensive temperature information for the heat treatment process.

[0004] However, when the existing technology applies infrared imaging to the temperature monitoring of a heat treatment furnace, there are still difficulties in accurately identifying the boundary of the heated steel pipe during temperature analysis of the heated body, resulting in limited accuracy of temperature data. And the existing technology often adopts a fixed monitoring period and cannot dynamically adjust the monitoring frequency according to the actual temperature change during the heat treatment process, which may lead to the omission of important information or waste of resources. Based on this, the present invention provides an efficient and accurate on-line monitoring system for the temperature of the heated body in a heat treatment furnace. Summary of the Invention

[0005] The purpose of the present invention is to provide an on-line monitoring system for the temperature of the heated body in a heat treatment furnace, and solve the following technical problems:

[0006] When infrared imaging is applied to the temperature monitoring of a heat treatment furnace, it is difficult to accurately identify the boundary of the heated steel pipe, resulting in limited accuracy of temperature data. And the existing technology often adopts a fixed monitoring period and cannot dynamically adjust the monitoring frequency according to the actual temperature change during the heat treatment process, which may lead to the omission of important information or waste of resources.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An on-line monitoring system for the temperature of the heated body in a heat treatment furnace, comprising:

[0009] An infrared imaging module, configured to generate a thermal imaging map of the temperature field in the heat treatment furnace based on infrared temperature measurement technology;

[0010] A steel pipe recognition module, which is used to preprocess the thermal imaging map, perform edge detection on the preprocessed thermal imaging map, identify the boundary lines between each steel pipe area and the surrounding area, and obtain the steel pipe areas in the thermal imaging map;

[0011] A temperature analysis module, which is used to obtain the temperature data of the steel pipe area. The temperature data includes the average temperature, the highest point temperature, and the lowest point temperature, and determines whether the heat treatment of the steel pipe is operating normally according to the temperature data;

[0012] A cycle adjustment module, which is used to set the monitoring cycle. The standard duration of the monitoring cycle is T. The temperature data is statistically analyzed every n monitoring cycles, the cycle adjustment coefficient is calculated according to the temperature data, and the duration of the next n monitoring cycles is calculated according to the cycle adjustment coefficient.

[0013] As a further solution of the present invention: The process of the infrared imaging module generating the thermal imaging map of the temperature field in the heat treatment furnace based on the infrared temperature measurement technology is as follows:

[0014] The infrared imaging module is based on an infrared detector array composed of a plurality of infrared detectors. The infrared detector array receives the infrared radiation energy in the heat treatment furnace, converts the infrared radiation at different positions in the furnace into electrical signals, amplifies and filters the electrical signals through a built-in signal processing circuit, and converts them into digital signals through analog-to-digital conversion. Based on a preset algorithm, these digital signals are calculated to obtain the temperature values corresponding to each position in the heat treatment furnace, and a thermal imaging map of the temperature field in the heat treatment furnace is generated through visualization processing.

[0015] As a further solution of the present invention: The process of the steel pipe recognition module preprocessing the thermal imaging map is as follows:

[0016] Convert the thermal imaging map into a grayscale image. Based on a 3×3 template of the Laplace operator, multiply each pixel point in the grayscale image with the adjacent 3×3 pixel points point by point and sum them. Mark the summed grayscale value result as the new grayscale value of the intermediate pixel point, and update all pixel points in the thermal imaging map respectively.

[0017] As a further solution of the present invention: The process of the steel pipe recognition module performing edge detection on the preprocessed thermal imaging map and identifying the boundary lines between each steel pipe area and the surrounding area is as follows:

[0018] Obtain the placement direction of the steel pipe. Starting from the edge parallel to the steel pipe in the grayscale image, select a column of pixel points in sequence, calculate the average grayscale value of the selected column of pixel points, and calculate the difference with the average grayscale value of the next column of pixel points. When the difference is greater than the preset threshold, mark the one with the lower average value between the two columns of pixel points as the pending boundary line and define it as the start line. Traverse the grayscale image, and mark the last pending boundary line as the end line. When the number of pending boundary lines is greater than 2, mark the pending boundary lines within the set range near the start line as the start line group, and mark the pending boundary lines within the set range near the end line as the end line group. Traverse each pending boundary line in the start line group and the end line group respectively. For each pending boundary line, starting from one end of the pending boundary line, compare the grayscale values of the pixel points with the next pixel point in sequence, and mark the smaller one of the two pixel points with a difference greater than the preset threshold as the edge point. Connect all the edge points in the start line group and the end line group respectively, perform linear fitting on the connected edge points, and mark the fitted straight line as the boundary line of the steel pipe area.

[0019] As a further solution of the present invention: When the number of pending boundary lines is equal to 2, it is directly marked as the boundary line of the steel pipe area.

[0020] As a further solution of the present invention: When the slope offset of any fitted straight line from the slope of the set direction is greater than the set deviation, an abnormal prompt is issued.

[0021] As a further solution of the present invention: The process of judging whether the heat treatment of the steel pipe is operating normally in the temperature analysis module according to the temperature data is to calculate the difference Dmax and the difference Dmax between the highest temperature and the lowest temperature and the average temperature respectively. When any difference is greater than the set difference, it is judged that the heat treatment furnace is operating abnormally.

[0022] As a further solution of the present invention: The process of the cycle adjustment module statistically analyzing the temperature data every n monitoring cycles, calculating the cycle adjustment coefficient according to the temperature data, and calculating the duration of the next n monitoring cycles according to the cycle adjustment coefficient is as follows:

[0023] During the constant temperature heat treatment of the steel pipe, statistically analyze the average temperature Ta, the difference Dmax, and the difference Dmax of the steel pipe area every n monitoring cycles, and respectively statistically analyze the proportions K 1 、K 2 and K 3 ;

[0024] Statistically analyze the weighted moving average value T 0 of the previous n monitoring cycles, and use T 0It is added to the average temperature Ta sequence, and a break point graph of the average temperature changing with the number of monitoring times is drawn according to the average temperature Ta sequence. The slope k of the line segment between every two adjacent points is obtained. Then the calculation formula of the cycle adjustment coefficient C is as follows:

[0025] ;

[0026] Among them, ki represents the slope of the i-th line segment in the break point graph, kmax represents the maximum value of the slope, kmin represents the minimum value of the slope, and c represents the compensation coefficient, which is used to prevent the adjustment coefficient value from being 0 or meaningless;

[0027] Then the duration of the next n monitoring cycles is Tnew = T / C.

[0028] As a further solution of the present invention: statistically calculate the weighted moving average value T of the previous n monitoring cycles 0 The specific process is as follows:

[0029] Obtain the average temperature XT of the previous n monitoring cycles, and assign corresponding weights to each average temperature XT. The weights increase gradually according to the time sequence. Then the weighted moving average value T 0 The calculation formula is as follows:

[0030] ;

[0031] Among them, XTi represents the i-th average temperature XT, and ωi represents the weight of the i-th average temperature XT.

[0032] The beneficial effects of the present invention:

[0033] The present invention generates a thermal imaging map of the temperature field inside the heat treatment furnace based on infrared temperature measurement technology through an infrared imaging module, which can display the temperature distribution inside the furnace in real time and intuitively. The steel pipe recognition module preprocesses and performs edge detection on the thermal imaging map, and can accurately identify the boundary lines between each steel pipe area and the surrounding areas, and accurately obtain the steel pipe areas in the thermal imaging map. In its preprocessing process, the Laplacian operator is used to enhance the image, effectively highlighting the edge information; the edge detection method can adapt to steel pipes in different placement directions through a series of detailed calculations and judgments, and accurately mark the boundary lines. This accurate steel pipe recognition function provides a guarantee for accurately obtaining the temperature data of the steel pipe area subsequently, ensuring the accuracy of temperature analysis. Dynamic cycle adjustment: The cycle adjustment module can dynamically adjust the monitoring cycle according to the temperature data during the heat treatment of the steel pipe. The temperature data is statistically analyzed every n monitoring cycles, the cycle adjustment coefficient is calculated, and then the duration of the next n monitoring cycles is obtained. This dynamic adjustment mechanism fully considers the temperature changes during the heat treatment process. When the temperature fluctuates greatly or shows an abnormal trend, the monitoring cycle can be shortened to strengthen the temperature monitoring; while when the temperature is relatively stable, the monitoring cycle is appropriately extended to improve the monitoring efficiency, and at the same time, the monitoring resources can be reasonably allocated to reduce the system operation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings.

[0035] Figure 1 It is a schematic diagram of the modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0037] Please refer to Figure 1 As shown, the present invention is an on-line temperature monitoring system for the heated body of a heat treatment furnace, including:

[0038] Infrared imaging module, the infrared imaging module is a core component based on infrared temperature measurement technology, which is used to generate thermal imaging images of the temperature field in the heat treatment furnace. The module consists of an array of several infrared detectors that can receive infrared radiation energy at different positions in the heat treatment furnace. When the surface temperature of an object is higher than absolute zero, it will emit infrared radiation. The infrared detector captures this radiation energy and converts it into electrical signals. Subsequently, these electrical signals are amplified and filtered by the built-in signal processing circuit to reduce noise and interference. The preliminarily processed signals are then converted into digital signals by an analog-to-digital converter (ADC).

[0039] Based on the preset algorithm, the system calculates these digital signals to obtain the temperature values ​​corresponding to each position in the heat treatment furnace. In order to present these temperature data in an intuitive way, the system will perform visualization processing to generate a thermal image of the temperature field in the heat treatment furnace. This thermal image can not only clearly display the temperature distribution, but also mark different temperature areas with different colors through pseudo-color coding technology, so that operators can quickly identify abnormal temperature areas. In addition, the infrared imaging module also has the characteristics of high sensitivity and high resolution, which can detect tiny temperature changes and is suitable for complex industrial environments.

[0040] Steel pipe identification module is the key part of this system for processing thermal images and identifying steel pipe areas. Its core function is to pre-process thermal images to enhance the edge information of the image, which is convenient for subsequent steel pipe area identification and boundary extraction. The specific process of pre-processing is as follows:

[0041] First, the thermal image is converted into a grayscale image. This process is achieved by simplifying the color information of each pixel in the color image into a grayscale value, thereby reducing the amount of data and highlighting the brightness information of the image. The grayscale image retains the basic characteristics of the temperature distribution while reducing the computational complexity, providing a basis for subsequent edge detection.

[0042] Next, the module uses a 3×3 template based on the Laplacian operator to process the grayscale image. The Laplacian operator is a commonly used edge detection operator that can effectively detect areas where the grayscale value changes in the image, thereby highlighting the contours and boundaries of the object. In the specific operation, the 3×3 template of the Laplacian operator is convolved with each pixel in the grayscale image and its adjacent 3×3 pixels. The convolution operation process is to multiply each value in the template with the corresponding pixel grayscale value point by point, and sum the product results. The summed grayscale value result is marked as the new grayscale value of the middle pixel, thereby completing the update of the pixel.

[0043] In this way, the module updates all pixel points in the thermal image sequentially. For the image processed by the Laplace operator, its edge information will be significantly enhanced while the background noise is suppressed. This enhanced grayscale image can clearly show the boundary between the steel pipe and the surrounding environment, providing a high-quality image basis for subsequent steel pipe area recognition and boundary extraction.

[0044] In the preprocessed thermal image, the steel pipe recognition module identifies the boundary lines between each steel pipe area and the surrounding area through a series of complex edge detection algorithms, thereby accurately extracting the steel pipe areas in the thermal image. The specific process is as follows:

[0045] First, the system obtains the placement direction of the steel pipes in the heat treatment furnace. This is achieved by analyzing the overall layout of the grayscale image or preset process parameters. After determining the placement direction, the system starts from the edge parallel to the steel pipe and sequentially selects a column of pixel points in the grayscale image. For each column of pixel points, the system calculates the mean value of its grayscale values and calculates the difference with the mean value of the grayscale values of the next column of pixel points. This process aims to detect the sudden change of grayscale values, thereby identifying the boundary between the steel pipe and the surrounding environment.

[0046] When the difference between the mean grayscale values of two adjacent columns of pixel points is greater than the preset threshold, the system marks the column with the lower mean value between these two columns of pixel points as the "pending boundary line" and defines it as the "starting line". Subsequently, the system continues to traverse the grayscale image column by column until it finds the last pending boundary line and marks it as the "ending line".

[0047] If the number of pending boundary lines is found to be greater than 2 during the traversal process, the system will further process these pending boundary lines. Specifically, the system marks the pending boundary lines within the set range near the starting line as the "starting line group" and marks the pending boundary lines within the set range near the ending line as the "ending line group". This set range is determined in advance according to the size and placement density of the steel pipes to ensure that different steel pipe areas can be accurately distinguished.

[0048] Next, the system traverses each pending boundary line in the starting line group and the ending line group point by point. For each pending boundary line, the system starts from one end and sequentially compares the grayscale values of adjacent pixel points. When the difference between the grayscale values of two adjacent pixel points is greater than the preset threshold, the system marks the pixel point with the smaller grayscale value between these two pixel points as the "edge point". This process further refines the position of the boundary by detecting the local change of grayscale values.

[0049] Subsequently, the system connects all the edge points in the start line group and the end line group respectively, and performs linear fitting on the connected edge points. The fitted straight lines are marked as the boundary lines of the steel pipe area. In this way, the system can accurately depict the contour of the steel pipe, thereby realizing the identification and extraction of the steel pipe area.

[0050] In some cases, the number of boundary lines to be determined may be only 2. At this time, the system directly marks these two edge lines to be determined as the boundary lines of the steel pipe, without further linear fitting processing.

[0051] In addition, the system also has an anomaly detection function. When the slope of the fitted straight line deviates from the slope of the preset steel pipe placement direction by more than the set threshold, the system will issue an anomaly prompt. This function can timely detect the position deviation or other abnormal conditions of the steel pipe during the heat treatment process, thereby ensuring the stability of the heat treatment process and the product quality.

[0052] Temperature analysis module. The temperature analysis module is the core part of the heat treatment furnace temperature monitoring system. Its main function is to obtain the temperature data of the steel pipe area and judge whether the operation status of the heat treatment furnace is normal by calculating and analyzing these data. Specifically, this module can extract the average temperature, the highest point temperature, and the lowest point temperature of the steel pipe area. Based on these temperature data, the module calculates the difference between the highest point temperature and the average temperature (denoted as Dmax) and the difference between the lowest point temperature and the average temperature (also denoted as Dmax) respectively.

[0053] When any difference Dmax exceeds the set threshold, the system will judge that the heat treatment furnace is in an abnormal operation state. This judgment criterion is based on the requirement of temperature distribution uniformity during the heat treatment process. During normal operation, the temperature distribution in the heat treatment furnace should be relatively uniform, and the deviation between the highest and lowest point temperatures and the average temperature should be within the allowable range. If the deviation is too large, it may mean that there are uneven temperature distribution, heating element failure or other abnormal conditions in the furnace.

[0054] Period adjustment module. The main function of the period adjustment module is to set and adjust the duration of the monitoring period. During the heat treatment process, especially during the constant temperature heat treatment of the steel pipe, the system statistically analyzes the temperature data of the steel pipe area every n monitoring periods. These temperature data include the average temperature Ta, the difference Dmax between the highest point and the average temperature, and the difference Dmin between the lowest point and the average temperature. The module will respectively count the proportions K 1 、K 2 and K 3 of these temperature data exceeding the preset threshold range, so as to evaluate the stability and uniformity of the temperature.

[0055] Calculation of weighted moving average

[0056] To more accurately reflect the temperature change trend, the cycle adjustment module calculates the weighted moving average T of the previous n monitoring cycles. 0 The specific process is as follows:

[0057] Obtain the average temperature sequence: The system first obtains the average temperature XT of the previous n monitoring cycles, which reflects the temperature level in the steel pipe area during each monitoring cycle.

[0058] Assign weights: To attach more importance to recent temperature data, the system assigns a weight ω to each average temperature XT. i These weights gradually increase in chronological order, that is, the temperature data closer to the current moment has a higher weight. This weighting method enables the system to more sensitively capture the recent temperature change trend.

[0059] Calculate the weighted moving average T 0 : The calculation formula for the weighted moving average T 0 is as follows:

[0060] ;

[0061] where XTi represents the i-th average temperature XT, and ωi represents the weight of the i-th average temperature XT.

[0062] To further optimize the monitoring cycle, the system plots a break point graph based on the weighted moving average T 0 and the average temperature T a sequence. The break point graph visually shows the temperature change trend over time by connecting the average temperature points of each monitoring cycle. The system calculates the slope k of the line segment between every two adjacent points to evaluate the rate of temperature change. The calculation formula for the cycle adjustment coefficient C is as follows:

[0063] ;

[0064] where ki represents the slope of the i-th line segment in the break point graph, kmax represents the maximum value of the slope, kmin represents the minimum value of the slope, and c represents a compensation coefficient used to prevent the adjustment coefficient value from being 0 or meaningless; then the duration of the next n monitoring cycles is Tnew = T / C. In this way, the system can shorten the monitoring cycle when the temperature changes violently to collect data more frequently; while when the temperature changes smoothly, it can extend the monitoring cycle, thereby optimizing resource utilization.

[0065] Through the above process, the cycle adjustment module can dynamically adjust the monitoring cycle according to the actual temperature change during the heat treatment process. This method not only improves the monitoring efficiency but also ensures that temperature anomalies can be captured in a timely manner during critical stages, thus providing a strong guarantee for the stability of the heat treatment process and product quality.

[0066] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0067] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0068] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0069] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0070] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0071] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0072] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0073] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0074] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0075] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An online monitoring system for the temperature of a heated object in a heat treatment furnace, characterized in that: include: Infrared imaging module, used to generate thermal imaging of the temperature field in the heat treatment furnace based on infrared temperature measurement technology; A steel pipe identification module is used to preprocess the thermal image, perform edge detection on the preprocessed thermal image, identify the boundary line between each steel pipe area and the surrounding area, and obtain the steel pipe area in the thermal image; The temperature analysis module is used to obtain the temperature data of the steel pipe area, the temperature data includes the average temperature, the highest point temperature and the lowest point temperature, and judge whether the heat treatment of the steel pipe is running normally according to the temperature data; A cycle adjustment module is used to set a monitoring cycle, the standard duration of the monitoring cycle is T, the temperature data is counted every n monitoring cycles, a cycle adjustment coefficient is calculated according to the temperature data, and the duration of the next n monitoring cycles is calculated according to the cycle adjustment coefficient; The steel pipe identification module performs edge detection on the preprocessed thermal image to identify the boundary line between each steel pipe area and the surrounding area as follows: Get the placement direction of the steel pipe, start from the edge of the grayscale image parallel to the steel pipe, select a column of pixels in turn, calculate the grayscale value mean of the selected column of pixels, and perform difference calculation with the grayscale value mean of the next column of pixels. When the difference is greater than the preset threshold, the lower mean between the two columns of pixels is marked as a pending boundary line and defined as the start line. Traverse the grayscale image and mark the last pending boundary line as the end line. When the number of pending boundary lines is greater than 2, mark the pending boundary lines within the set range near the start line as the start line group, and define the end line group as the end line group. The pending boundary lines within the set range near the beam line are marked as the end line group, and each pending boundary line in the start line group and the end line group is traversed respectively. For each pending boundary line, starting from one end of the pending boundary line, the grayscale value of the pixel point is compared with the grayscale value of the next pixel point in turn, and the smaller pixel point between the two pixel points whose difference is greater than the preset threshold is marked as an edge point. All edge points in the start line group and the end line group are connected, and the connected edge points are linearly fitted, and the fitted straight line is marked as the boundary line of the steel pipe area; When the deviation between the slope of any fitted straight line and the slope of the set direction is greater than the set deviation, an abnormal prompt is issued.

2. The online monitoring system for the temperature of a heated object in a heat treatment furnace according to claim 1, characterized in that: The process of generating a thermal imaging image of the temperature field in the heat treatment furnace by the infrared imaging module based on the infrared temperature measurement technology is as follows: The infrared imaging module is based on an infrared detector array composed of several infrared detectors. The infrared detector array receives infrared radiation energy in the heat treatment furnace, converts infrared radiation at different positions in the furnace into electrical signals, amplifies and filters the electrical signals through a built-in signal processing circuit, and converts analog-to-digital signals into digital signals. These digital signals are calculated based on a preset algorithm to obtain the temperature values ​​corresponding to each position in the heat treatment furnace, and generate a thermal imaging map of the temperature field in the heat treatment furnace through visualization processing.

3. The online monitoring system for the temperature of a heated object in a heat treatment furnace according to claim 1, characterized in that: The process of preprocessing the thermal image by the steel pipe recognition module is as follows: The thermal image is converted into a grayscale image. Based on the 3×3 template of the Laplace operator, each pixel in the grayscale image is multiplied and summed with the adjacent 3×3 pixels point by point. The summed grayscale value result is marked as the new grayscale value of the middle pixel, and all the pixels in the thermal image are updated respectively.

4. The online monitoring system for the temperature of a heated object in a heat treatment furnace according to claim 1, characterized in that: When the number of pending boundary lines is equal to 2, it is directly marked as the steel pipe area boundary line.

5. The online monitoring system for the temperature of a heated object in a heat treatment furnace according to claim 1, characterized in that: The process of judging whether the heat treatment of the steel pipe is operating normally according to the temperature data in the temperature analysis module is as follows: The difference Dmax and Dmin between the highest point temperature and the lowest point temperature and the average temperature are calculated respectively. When any difference is greater than the set difference, it is judged that the heat treatment furnace is operating abnormally.

6. The online monitoring system for the temperature of a heated object in a heat treatment furnace according to claim 1, characterized in that: The cycle adjustment module collects the temperature data every n monitoring cycles, calculates the cycle adjustment coefficient according to the temperature data, and calculates the duration of the next n monitoring cycles according to the cycle adjustment coefficient as follows: During the constant temperature heat treatment of the steel pipe, the average temperature Ta, difference Dmax, and difference Dmin of the steel pipe area are counted every n monitoring cycles, and the proportions K1, K2, and K3 of the average temperature Ta, difference Dmax, and difference Dmin exceeding the corresponding set interval are counted respectively; The weighted moving average value T0 of n monitoring cycles is statistically calculated, and T0 is added to the average temperature Ta sequence. A point diagram of the average temperature changing with the number of monitoring times is drawn according to the average temperature Ta sequence, and the slope k of the line segment between every two adjacent points is obtained. The calculation formula of the cycle adjustment coefficient C is: ； Among them, k i represents the slope of the i-th line segment in the inflection point graph, k max represents the maximum value of the slope, k min Indicates the minimum value of the slope, c indicates the compensation coefficient, which is used to prevent the adjustment coefficient from being 0 or meaningless; Then the duration of the next n monitoring cycles is T new =T / C.

7. The online monitoring system for the temperature of a heated object in a heat treatment furnace according to claim 6, characterized in that: The specific process of the weighted moving average T0 of n monitoring periods is statistically as follows: Get the average temperature XT of the last n monitoring cycles, and assign a corresponding weight to each average temperature XT. The weight increases gradually according to the time sequence. The calculation formula of the weighted moving average value T0 is: ； Among them, XT i represents the i-th average temperature XT, ω i represents the weight of the i-th average temperature XT.

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