A multi-ccd vision-based furnace flame temperature field visualization monitoring system
By integrating multi-CCD vision technology and radiation heat transfer theory, the accuracy and adaptability issues of existing furnace temperature monitoring systems have been solved, achieving high-precision, stable, and visualized monitoring of the furnace temperature field, which is suitable for combustion optimization and safe operation of industrial furnaces.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NORTHEAST DIANLI UNIVERSITY
- Filing Date
- 2026-03-18
- Publication Date
- 2026-06-23
AI Technical Summary
Existing furnace temperature monitoring systems mostly use single-point temperature measurement or traditional optical temperature measurement methods, which cannot achieve accurate monitoring of the temperature distribution across the entire furnace cross-section. The system structure design is unreasonable, the coordination between modules is poor, and it is easily affected by interference factors such as high temperature and dust. The camera parameters are fixed and cannot be flexibly adapted, the solution algorithm is prone to ill-conditioned problems, and the operation is complex. They are difficult to meet the needs of real-time, accurate and intuitive monitoring of industrial furnace temperature fields.
A multi-CCD vision technology is used to construct a multi-CCD furnace flame visual monitoring interactive system, which realizes adaptive parameter configuration and image preprocessing. The radiation transfer matrix is solved by combining the DRESOR method and the Tikhonov regularization algorithm to reconstruct the temperature field and output visualization. Through the linkage control of the number of cameras and input permissions, rapid adaptation and high-precision monitoring of furnaces of different specifications can be achieved.
It significantly improves the accuracy, stability, and scenario adaptability of furnace temperature field monitoring, realizes comprehensive visualization monitoring of furnace cross-sectional temperature distribution and radiation characteristic parameters, reduces the cognitive load and decision-making threshold of operation and maintenance personnel, and provides accurate data support.
Smart Images
Figure CN122265808A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of furnace temperature monitoring technology, and in particular to a furnace flame temperature field visualization monitoring system based on multi-CCD vision. Background Technology
[0002] In the operation of furnaces such as industrial boilers, power plant boilers, and industrial kilns, the distribution of the temperature field across the furnace cross-section is a core parameter for assessing combustion efficiency, controlling pollutant emissions, and ensuring the safe and stable operation of furnace equipment. While existing furnace temperature monitoring technologies are widely used in the daily operation and maintenance of various industrial furnaces, existing temperature monitoring systems still have several shortcomings in practical applications, specifically: Existing systems often employ single-point temperature measurement or traditional optical temperature measurement methods, resulting in unreasonable structural design and poor inter-module coordination. This leads to low temperature monitoring efficiency, delayed response, and an inability to accurately acquire the temperature distribution across the entire furnace cross-section, failing to meet the specific scenario requirements for real-time and accurate monitoring of the furnace cross-section temperature field; their functionality is limited, lacking a complete technical link based on radiation intensity analysis and radiation source term solving, only enabling simple temperature detection and failing to simultaneously acquire internal furnace radiation parameters. Furthermore, the accuracy of temperature monitoring is easily affected by the high temperature inside the furnace. The system suffers from several drawbacks. First, it is susceptible to interference from smoke and dust. Second, the fixed camera parameters and furnace dimensions prevent flexible adjustments based on actual furnace specifications, hindering a comprehensive reflection of the overall temperature distribution and internal conditions across different furnace cross-sections, thus limiting its applicability. Third, the system is unstable, easily affected by external factors such as high temperatures, smoke and dust, radiation interference, and airflow fluctuations within the furnace, resulting in significant temperature monitoring accuracy drift. Furthermore, existing algorithms are prone to ill-conditioned problems, leading to large deviations in calculation results. The inflexible adjustment of camera and furnace dimensions further complicates its adaptability, leading to a high probability of system failure and high maintenance costs. Fourth, the system is highly complex, requiring specialized technicians for system debugging, parameter calibration, and data interpretation. The solution process for radiation source terms is particularly cumbersome, with an unreasonable calculation program design. Adjusting the number of cameras, coordinates, and furnace dimensions is inconvenient, resulting in poor usability and hindering its widespread adoption in various small- to medium-sized furnace operation and maintenance scenarios.
[0003] However, current common solutions have many drawbacks, including: existing furnace temperature monitoring systems mostly use single-point temperature measurement or traditional optical temperature measurement methods, which can only obtain local temperature information and cannot achieve accurate monitoring of the temperature distribution across the entire furnace cross-section; the system structure design is unreasonable, the coordination between modules is poor, and there is a lack of a complete technical link based on radiation intensity analysis and radiation source term inversion. The system is also limited in function and easily affected by interference factors such as high temperature and smoke; the number of cameras, installation coordinates, and furnace size parameters are fixed, making it impossible to flexibly adapt to different furnace specifications. The solution algorithm is prone to ill-conditioned problems, resulting in large deviations in calculation results. The operation is complex and lacks visualization interaction and a full-process status feedback mechanism, making it difficult to meet the actual application needs of real-time, accurate, and intuitive monitoring of industrial furnace temperature fields. Summary of the Invention
[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.
[0005] In view of the problems existing in the current furnace flame temperature field visualization monitoring system based on multi-CCD vision, the present invention is proposed.
[0006] Therefore, the purpose of this invention is to provide a visualization monitoring system for furnace flame temperature field based on multi-CCD vision. This system addresses the problems of existing furnace temperature monitoring systems, which often employ single-point temperature measurement or traditional optical temperature measurement methods, only acquiring local temperature information and failing to achieve accurate monitoring of the temperature distribution across the entire furnace cross-section; unreasonable system structure design, poor inter-module coordination, lack of a complete technical link based on radiation intensity analysis and radiation source term inversion, limited functionality, and susceptibility to interference from high temperatures, smoke, and dust; fixed camera numbers, installation coordinates, and furnace size parameters, making it inflexible for adapting to different furnace specifications; error-prone algorithms leading to large deviations in calculation results; high operational complexity; and a lack of visualization interaction and full-process status feedback mechanisms, making it difficult to meet the practical application needs of real-time, accurate, and intuitive monitoring of industrial furnace temperature fields.
[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for visual monitoring of furnace flame temperature field based on multi-CCD vision, comprising the following steps: Step 1: Constructing a multi-CCD furnace flame visual monitoring interactive system, completing the initialization of global system parameters, configuration of basic interface components, and initial resource loading, providing a standardized interactive carrier for full-process monitoring; Step 2: Establishing a core monitoring parameter input and calibration system for furnace size, camera spatial coordinates, and platform elevation, embedding linkage control logic for the number of cameras and parameter input permissions, and achieving precise parameter configuration under different monitoring scenarios; Step 3: Performing standardized preprocessing on furnace flame images, identifying the number of effective monitoring cameras and camera calibration parameters, and simultaneously completing adaptive matching of interactive system parameters, providing accurate input for temperature field reconstruction; Step 4: Constructing a standardized matrix model of furnace-camera parameters, calling the core algorithm for flame temperature field reconstruction to complete the calculation, and outputting basic data of furnace cross-section temperature field and core operating characteristic parameters; Step 5: Performing interpolation fitting and visualization rendering on the temperature field data, simultaneously realizing real-time feedback of the operating status of the entire monitoring process and intuitive display of core results.
[0008] As a preferred embodiment of the furnace flame temperature field visualization monitoring method based on multi-CCD vision described in this invention, in step one, the interactive system adopts a mutually exclusive single-selection architecture to control the number of cameras, setting three mutually exclusive selectable levels: 4 cameras, 6 cameras, and 8 cameras. When the system starts, the 4-camera level is selected by default. At the same time, the window lifecycle management logic is configured to complete the release of global parameters and resource reclamation when the window is closed.
[0009] As a preferred embodiment of the multi-CCD vision-based visualization monitoring method for furnace flame temperature field described in this invention, step two specifically includes: Construction of a furnace basic parameter calibration unit: setting input channels for three core basic parameters—furnace depth, furnace width, and monitoring platform elevation—to accurately calibrate the physical spatial dimensions of the furnace; Construction of a multi-CCD camera spatial coordinate calibration unit: configuring eight independent camera three-dimensional coordinate input channels, each corresponding to parameter inputs in the X, Y, and Z axes, to achieve accurate calibration of the spatial position of each monitoring camera; Implantation of camera quantity-input permission linkage control logic: establishing a linkage relationship between camera quantity levels and coordinate input channel permissions. When a 4-camera level is selected, the coordinate input channels for cameras 5-8 are disabled and their parameters are set to zero; when a 6-camera level is selected, the input channels for cameras 5-6 are enabled, and the input channels for cameras 7-8 are disabled and their parameters are set to zero; when an 8-camera level is selected, all camera coordinate input channels are fully enabled.
[0010] As a preferred embodiment of the multi-CCD vision-based visualization monitoring method for furnace flame temperature field described in this invention, in step three, the original furnace flame image is preprocessed for noise reduction through a standardized image processing workflow, and flame radiation feature data, effective monitoring camera count, and camera intrinsic parameter calibration data are extracted. Based on the identified effective camera count, the selected status of the camera count level in the interactive system is automatically updated, and the permission configuration of the corresponding coordinate input channel is adjusted synchronously to achieve adaptive matching between monitoring parameters and the actual acquisition scene.
[0011] As a preferred embodiment of the multi-CCD vision-based visualization monitoring method for furnace flame temperature field described in this invention, step four specifically includes: Matrix modeling of monitoring parameters: converting the calibrated furnace dimensions, number of effective cameras, and camera spatial coordinate parameters into a standardized parameter matrix to complete the coupled modeling of the furnace space and the visual monitoring system; Core calculation for three-dimensional temperature field reconstruction: calling the flame radiation transfer equation solution and the core algorithm for temperature field reconstruction to complete the inversion calculation of flame radiation characteristic data, outputting a discrete data matrix of the furnace cross-section temperature field, and simultaneously extracting core operating characteristic parameters, including the highest furnace temperature, the average furnace temperature, the flame absorption coefficient, the flame scattering coefficient, and the number of algorithm iterations; Standardized output of core parameters: formatting the core operating characteristic parameters and synchronously updating them to the corresponding display area of the interactive system to achieve intuitive and accurate output of monitoring results.
[0012] As a preferred embodiment of the multi-CCD vision-based visualization monitoring method for furnace flame temperature field described in this invention, the core algorithm for temperature field reconstruction specifically includes the following: constructing a geometric and physical model of the furnace; discretizing the furnace spatial medium region using a structured grid; and discretizing the imaging pixels of the multi-CCD flame image detector; solving the radiation transfer equation within the furnace using the DRESOR method; establishing a quantitative functional relationship between the flame image radiation intensity and the furnace internal temperature and radiation parameters; completing the construction of the radiation imaging forward problem model; and discretizing the radiation imaging matrix equation as follows:
[0013] in, This represents the measured value of the boundary radiation intensity; The coefficient matrix is uniquely determined by the furnace geometry, mesh generation, medium radiation parameters, and DRESOR number. This represents the blackbody radiation intensity matrix of each unit within the furnace. Furnace flame images from different perspectives are simultaneously acquired using a multi-CCD flame image detector. After preprocessing, the image grayscale values are converted to absolute radiation intensity using a camera calibration model, yielding the measured values of the furnace boundary radiation intensity. Initial values for the uniform radiation parameters of the furnace medium are set, and the Tikhonov regularization algorithm is used to solve for the furnace radiation source term, transforming the inverse problem into a functional minimization problem. The calculation formula is as follows:
[0014] in, For radiation sources inside the furnace; For the imaging matrix; For imaging matrix Transpose of; The regularization coefficient is used. This is the regularization matrix; The measured boundary radiation intensity is represented by the blackbody radiation intensity matrix obtained by inversion. The theoretical boundary radiation intensity is calculated by substituting the inverted blackbody radiation intensity matrix into the forward problem model. The root mean square residual (RMSE) is calculated based on the measured boundary radiation intensity. A residual convergence threshold is preset, and the RMSE is compared with the convergence threshold. If the RMSE is greater than the preset residual convergence threshold, the absorption coefficient and scattering coefficient of the furnace medium are iteratively updated using an optimization algorithm, and the radiation source term is inverted again using the updated radiation parameters. If the RMSE is less than or equal to the preset residual convergence threshold, the temperature value of each grid cell in the furnace is obtained by inverse solving the blackbody radiation law based on the converged blackbody radiation intensity matrix.
[0015] As a preferred embodiment of the furnace flame temperature field visualization monitoring method based on multi-CCD vision described in this invention, step five specifically includes the following: Temperature field data interpolation and fitting: A standardized uniform grid is generated based on the basic dimensions of the furnace. A high-precision interpolation algorithm is used to fit the discrete temperature field data to generate continuous furnace cross-sectional temperature field distribution data; Temperature field visualization rendering: A custom red-blue gradient color scheme is used to draw the furnace cross-sectional temperature field distribution through multi-level filled contour lines. Color labels, coordinate axis labels, and distribution titles are added to achieve an intuitive visualization of the temperature field distribution; Full-process operation status feedback: The operation status prompt information is updated synchronously throughout the monitoring process, and the full-process status of flame image processing startup, core calculation in progress, monitoring completion, and iteration count is output sequentially.
[0016] Secondly, to further address the aforementioned technical problems, this invention provides a multi-CCD vision-based visualization monitoring system for furnace flame temperature fields. This system includes: an interactive system construction module, which builds a multi-CCD furnace flame visual monitoring interactive system, completes global parameter initialization, interface basic component configuration, and initial resource loading, providing a standardized interactive carrier for full-process monitoring; a furnace camera calibration module, which establishes a core monitoring parameter input and calibration system for furnace dimensions, camera spatial coordinates, and platform elevation, embedding linkage control logic for the number of cameras and parameter input permissions to achieve precise parameter configuration under different monitoring scenarios; an image preprocessing module, which performs standardized preprocessing on furnace flame images, identifies the number of effective monitoring cameras and camera calibration parameters, and simultaneously completes adaptive matching of interactive system parameters, providing accurate input for temperature field reconstruction; a temperature field reconstruction module, which constructs a standardized matrix model of furnace-camera parameters, calls the core algorithm for flame temperature field reconstruction to complete calculations, and outputs basic data and core operating characteristic parameters of the furnace cross-section temperature field; and a visualization feedback module, which performs interpolation fitting and visualization rendering on the temperature field data, simultaneously achieving real-time feedback on the operating status of the entire monitoring process and intuitive display of core results.
[0017] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in the first aspect of the present invention.
[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in the first aspect of the present invention.
[0019] The beneficial effects of this invention are as follows: By deeply integrating multi-CCD vision with radiation heat transfer theory, this invention constructs a complete technical closed loop from parameter adaptive configuration, image preprocessing, radiation transfer matrix solution to temperature field inversion and visualization output, significantly improving the accuracy, stability, and scene adaptability of furnace temperature field monitoring; at the parameter level, through the linkage control of camera quantity and input permissions and the parameter adaptive matching mechanism, it achieves rapid adaptation to furnaces of different specifications and avoids the source of human error; at the algorithm level, it innovatively combines the high-precision radiation transfer forward problem model constructed by the DRESOR method with the Tikhonov regularization algorithm. This approach effectively suppresses ill-conditioned characteristics in the solution process of the radiation inverse problem, and ensures high accuracy and convergence stability of temperature field inversion through iterative optimization of absorption and scattering coefficients. At the human-computer interaction level, through discrete data interpolation fitting, red-blue gradient contour rendering, and full-process status feedback, abstract radiation data is transformed into intuitive temperature field cloud maps and traceable calculation processes, greatly reducing the cognitive load and decision-making threshold for operation and maintenance personnel. Ultimately, it achieves comprehensive, visualized, and highly reliable monitoring of furnace cross-sectional temperature distribution, radiation characteristic parameters, and combustion conditions, providing accurate data support for combustion optimization and safe operation of industrial furnaces. Attached Figure Description
[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating the implementation method of a furnace flame temperature field visualization monitoring system based on multi-CCD vision according to the present invention.
[0021] Figure 2 This is a flowchart of the core algorithm for temperature field reconstruction in a furnace flame temperature field visualization monitoring system based on multi-CCD vision, according to the present invention.
[0022] Figure 3 This is an example of a furnace temperature field cloud map visualization of a furnace flame temperature field visualization monitoring system based on multi-CCD vision according to the present invention.
[0023] Figure 4 This is a schematic diagram of the layout of the visualization interface of a furnace flame temperature field visualization monitoring system based on multi-CCD vision according to the present invention. Detailed Implementation
[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0025] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.
[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.
[0027] Example 1 Reference Figure 1 ~ Figure 4 This is the first embodiment of the present invention, which provides a method for visually monitoring the furnace flame temperature field based on multi-CCD vision, including the following steps: S1: Construct a multi-CCD furnace flame visual monitoring and interactive system, complete the initialization of global system parameters, configuration of basic interface components and initial resource loading, and provide a standardized interactive carrier for full-process monitoring.
[0028] Preferably, in step one, the interactive system adopts a mutually exclusive single-selection architecture to control the number of cameras, setting three mutually exclusive selectable levels: 4 cameras, 6 cameras, and 8 cameras. When the system starts, the 4-camera level is selected by default. At the same time, the window lifecycle management logic is configured to release global parameters and reclaim resources when the window is closed.
[0029] Furthermore, the global parameters include at least the image acquisition frame rate, communication port number, default storage path, and log recording level, and the basic interface components include at least a menu bar, a status bar, a display area for real-time display of multiple camera images, and a panel for parameter input.
[0030] Specifically, the window lifecycle management logic refers to the following: when a window closing command is detected, the system first interrupts the currently executing calculation thread, then releases the drive resources occupied by the multiple CCD cameras, disconnects from the local database, writes the current interface configuration to a temporary backup file, and finally completes the destruction of the window.
[0031] S2: Establish a core monitoring parameter input and calibration system for furnace size, camera spatial coordinates, and platform elevation, and embed linkage control logic between the number of cameras and parameter input permissions to achieve accurate parameter configuration under different monitoring scenarios.
[0032] Preferably, step two specifically includes: Construction of furnace basic parameter calibration unit: Set up input channels for three core basic parameters: furnace depth, furnace width, and monitoring platform elevation to complete the accurate calibration of the physical space dimensions of the furnace; Construction of multi-CCD camera spatial coordinate calibration unit: Configure 8 independent camera 3D coordinate input channels, each channel corresponds to the parameter input of three spatial dimensions of X-axis, Y-axis and Z-axis, to achieve accurate calibration of the spatial position of each monitoring camera; Camera quantity - input permission linkage control logic implementation: Establish a linkage relationship between camera quantity level and coordinate input channel permissions. When 4 camera levels are selected, disable the coordinate input channels of cameras 5-8 and set the parameters to zero. When the 6-camera setting is selected, camera input channels 5 and 6 are enabled, camera input channels 7 and 8 are disabled, and the parameters are set to zero. When the 8-camera setting is selected, all camera coordinate input channels are fully enabled.
[0033] Specifically, the elevation of the monitoring platform refers to the vertical distance between the common mounting plane of all CCD cameras and the bottom of the furnace (reference zero point). This parameter is used to subsequently transform the pixel coordinate system of the cameras to the world coordinate system of the furnace.
[0034] Furthermore, setting the parameters to zero not only includes disabling the input boxes on the interface and resetting the values to zero, but also includes shielding the data flow of the corresponding channels in the underlying data structure to ensure that they do not contribute to the matrix modeling process in step four, thereby avoiding interference from invalid data to the calculation model.
[0035] S3: Standardize and preprocess the furnace flame images, identify the number of effective monitoring cameras and camera calibration parameters, and simultaneously complete the adaptive matching of interactive system parameters to provide accurate input for temperature field reconstruction.
[0036] Preferably, in step three, the noise reduction preprocessing of the original furnace flame image is completed through a standardized image processing workflow, and flame radiation feature data, effective monitoring camera count, and camera intrinsic parameter calibration data are extracted. Based on the number of valid cameras identified, the selected status of the camera quantity level in the interactive system is automatically updated, and the permission configuration of the corresponding coordinate input channel is adjusted synchronously to achieve adaptive matching between the monitoring parameters and the actual acquisition scene.
[0037] Furthermore, the standardized image processing flow includes: firstly, using a median filtering algorithm to denoise the original image to eliminate salt and pepper noise caused by dust particles in the furnace; then, based on the camera intrinsic parameters (distortion coefficients) calibrated in step two, using the Zhang Zhengyou calibration method to perform distortion correction and restore the true geometric relationship of the image.
[0038] Specifically, the number of valid cameras identified refers to the system automatically identifying the number of cameras that are actually connected and working normally by detecting the valid data packet identifiers carried in the image data stream or by using an image sharpness evaluation function (such as the Tenengrad gradient function) to determine whether there is a valid flame image in the current channel.
[0039] S4: Construct a standardized matrix model of furnace-camera parameters, call the core algorithm for flame temperature field reconstruction to complete the calculation, and output the basic data of furnace cross-sectional temperature field and core operating characteristic parameters.
[0040] Preferably, step four specifically includes: Matrix modeling of monitoring parameters: The calibrated furnace dimensions, number of effective cameras, and camera spatial coordinate parameters are transformed into a standardized parameter matrix to complete the coupled modeling of the furnace space and the visual monitoring system; Core calculation for 3D temperature field reconstruction: The core algorithm for solving the flame radiation transfer equation and reconstructing the temperature field is called to complete the inversion calculation of flame radiation characteristic data, output the discrete data matrix of the furnace cross-section temperature field, and extract the core operating characteristic parameters, including the maximum furnace temperature, the average furnace temperature, the flame absorption coefficient, the flame scattering coefficient and the number of algorithm iterations. Standardized output of core parameters: After formatting the core operating characteristic parameters, they are synchronously updated to the corresponding display area of the interactive system to achieve intuitive and accurate output of monitoring results.
[0041] Specifically, the core algorithm for temperature field reconstruction is as follows: A geometric and physical model of the furnace is constructed, and the structured mesh discretization of the furnace space medium region is performed. At the same time, the imaging pixels of the multi-CCD flame image detector are discretized. The radiation transfer equation inside the furnace is solved using the DRESOR method, establishing a quantitative functional relationship between the radiation intensity of the flame image and the internal temperature and radiation parameters of the furnace. This completes the construction of the forward problem model for radiation imaging, and the discretized radiation imaging matrix equation is as follows:
[0042] in, This represents the measured value of the boundary radiation intensity; The coefficient matrix is uniquely determined by the furnace geometry, mesh generation, medium radiation parameters, and DRESOR number. This represents the blackbody radiation intensity matrix of each unit within the furnace. The furnace flame images are acquired simultaneously from different perspectives by a multi-CCD flame image detector. After preprocessing, the image grayscale values are converted into absolute radiation intensity by a camera calibration model to obtain the measured value of the radiation intensity at the furnace boundary. Initial values for the uniform radiation parameters of the furnace medium are set, and the Tikhonov regularization algorithm is used to solve for the furnace radiation source term, transforming the inverse problem into a functional minimization problem. The calculation formula is as follows:
[0043] in, For radiation sources inside the furnace; For the imaging matrix; For imaging matrix Transpose of; The regularization coefficient is used. This is the regularization matrix; This represents the measured value of the boundary radiation intensity; Substitute the blackbody radiation intensity matrix obtained by inversion into the forward problem model to calculate the theoretical boundary radiation intensity, and calculate the root mean square residual (RMSE) based on the measured boundary radiation intensity. A preset residual convergence threshold is set, and the RMSE is compared with the convergence threshold: If the RMSE is greater than the preset residual convergence threshold, the optimization algorithm is used to iteratively update the absorption coefficient and scattering coefficient of the furnace medium, and the updated radiation parameters are used to perform radiation source term inversion again. If the RMSE is less than or equal to the preset residual convergence threshold, the temperature value of each grid cell in the furnace is obtained by combining the inverse solution of the blackbody radiation law based on the converged blackbody radiation intensity matrix.
[0044] Specifically, the structured grid discretization adopts a uniform grid resolution of 20×20. This resolution is the preferred option determined after comprehensively considering the furnace size (accuracy up to 0.1 meters) and the real-time performance of the calculation. It can capture the core distribution characteristics of the flame temperature field and meet the real-time requirements of industrial sites.
[0045] S5: Performs interpolation fitting and visualization rendering on temperature field data, simultaneously enabling real-time feedback on the operational status of the entire monitoring process and intuitive display of core results.
[0046] Specifically, step five contains the following details: Temperature field data interpolation and fitting: Based on the basic dimensions of the furnace, a standardized uniform grid is generated, and a high-precision interpolation algorithm is used to fit the discrete temperature field data to generate continuous furnace cross-sectional temperature field distribution data. Temperature field visualization rendering: A custom red and blue gradient color scheme is adopted. The temperature field distribution of the furnace section is drawn by filling contour lines at multiple levels. Color mark legends, coordinate axis labels and distribution titles are added to achieve an intuitive visualization of the temperature field distribution. Full-process operation status feedback: The operation status prompts are updated synchronously throughout the entire monitoring process, and the full-process status of flame image processing startup, core calculation in progress, monitoring completion and iteration count are output sequentially.
[0047] Specifically, the high-precision interpolation algorithm adopts the bicubic interpolation algorithm, which generates a smoother and more consistent temperature field distribution that conforms to the actual physical laws by considering the gray value changes of the 4×4 neighboring pixels around the point to be interpolated, thus avoiding the "mosaic" effect produced by low-order interpolation.
[0048] Specifically, the custom red-blue gradient color scheme follows the visual convention of "low temperature blue, medium temperature green, and high temperature red". The lowest temperature value of the furnace cross section (such as 950℃) is mapped to RGB(0,0,255) blue through linear mapping, and the highest temperature value (such as 1300℃) is mapped to RGB(255,0,0) red. The intermediate temperature values are generated by linear interpolation of the HSL color space to ensure the continuity and readability of the temperature field cloud map.
[0049] In summary, this invention, through the deep integration of multi-CCD vision and radiative heat transfer theory, constructs a complete technical closed loop from parameter adaptive configuration, image preprocessing, radiative transfer matrix solution to temperature field inversion and visualization output, significantly improving the accuracy, stability, and scene adaptability of furnace temperature field monitoring. At the parameter level, through the linkage control of camera quantity and input permissions, and a parameter adaptive matching mechanism, it achieves rapid adaptation to furnaces of different specifications and avoids the source of human error. At the algorithm level, it innovatively combines a high-precision radiative transfer forward problem model constructed using the DRESOR method with the Tikhonov regularization algorithm. It effectively suppressed the ill-conditioned characteristics in the solution process of the radiation inverse problem, and ensured the high accuracy and convergence stability of temperature field inversion through iterative optimization of absorption and scattering coefficients. At the human-computer interaction level, through discrete data interpolation fitting, red-blue gradient contour rendering, and full-process status feedback, abstract radiation data is transformed into intuitive temperature field cloud maps and traceable calculation processes, which greatly reduces the cognitive load and decision threshold of operation and maintenance personnel. Finally, it realizes comprehensive, visualized, and highly reliable monitoring of furnace cross-sectional temperature distribution, radiation characteristic parameters, and combustion conditions, providing accurate data support for combustion optimization and safe operation of industrial furnaces.
[0050] Example 2, an embodiment of the present invention, provides a furnace flame temperature field visualization monitoring system based on multi-CCD vision, comprising: an interactive system construction module, which constructs a multi-CCD furnace flame visual monitoring interactive system, completes the initialization of global system parameters, configuration of basic interface components, and initial resource loading, providing a standardized interactive carrier for full-process monitoring; a furnace camera calibration module, which establishes a core monitoring parameter input and calibration system for furnace size, camera spatial coordinates, and platform elevation, and embeds linkage control logic for the number of cameras and parameter input permissions to achieve accurate parameter configuration under different monitoring scenarios; an image preprocessing module, which performs standardized preprocessing on furnace flame images, identifies the number of effective monitoring cameras and camera calibration parameters, and simultaneously completes adaptive matching of interactive system parameters to provide accurate input for temperature field reconstruction; a temperature field reconstruction module, which constructs a standardized matrix model of furnace-camera parameters, calls the core algorithm for flame temperature field reconstruction to complete the calculation, and outputs basic data of furnace cross-section temperature field and core operating characteristic parameters; and a visualization feedback module, which performs interpolation fitting and visualization rendering on temperature field data, and simultaneously realizes real-time feedback of the operating status of the entire monitoring process and intuitive display of core results.
[0051] Example 3 is an embodiment of the present invention, which differs from the previous embodiment in that: If the aforementioned functions are implemented as software functional units 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 this invention, essentially, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0052] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0053] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0054] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0055] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for visually monitoring the furnace flame temperature field based on multi-CCD vision, characterized in that: include: Step 1: Construct a multi-CCD furnace flame visual monitoring and interactive system, complete the initialization of global system parameters, configuration of basic interface components and initial resource loading, and provide a standardized interactive carrier for full-process monitoring; Step 2: Establish a core monitoring parameter input and calibration system for furnace dimensions, camera spatial coordinates, and platform elevation, and embed linkage control logic between the number of cameras and parameter input permissions to achieve accurate parameter configuration under different monitoring scenarios; Step 3: Standardize and preprocess the furnace flame images, identify the number of effective monitoring cameras and camera calibration parameters, and simultaneously complete the adaptive matching of interactive system parameters to provide accurate input for temperature field reconstruction; Step 4: Construct a standardized matrix model of furnace-camera parameters, call the core algorithm for flame temperature field reconstruction to complete the calculation, and output the basic data of furnace cross-sectional temperature field and core operating characteristic parameters; Step 5: Interpolate and fit the temperature field data and visualize and render it, so as to realize real-time feedback on the operation status of the entire monitoring process and intuitive display of the core results.
2. The method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in claim 1, characterized in that: In step one, the interactive system adopts a mutually exclusive single-selection architecture to control the number of cameras. It sets three mutually exclusive selectable levels: 4 cameras, 6 cameras, and 8 cameras. When the system starts, the 4-camera level is selected by default. At the same time, the window lifecycle management logic is configured to release global parameters and reclaim resources when the window is closed.
3. The method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in claim 1, characterized in that: Step two specifically includes: Construction of furnace basic parameter calibration unit: Set up input channels for three core basic parameters: furnace depth, furnace width, and monitoring platform elevation to complete the accurate calibration of the physical space dimensions of the furnace; Construction of multi-CCD camera spatial coordinate calibration unit: Configure 8 independent camera 3D coordinate input channels, each channel corresponds to the parameter input of three spatial dimensions of X-axis, Y-axis and Z-axis, to achieve accurate calibration of the spatial position of each monitoring camera; Camera quantity - input permission linkage control logic implementation: Establish a linkage relationship between camera quantity level and coordinate input channel permissions. When 4 camera levels are selected, disable the coordinate input channels of cameras 5-8 and set the parameters to zero. When the 6-camera setting is selected, camera input channels 5 and 6 are enabled, camera input channels 7 and 8 are disabled, and the parameters are set to zero. When the 8-camera setting is selected, all camera coordinate input channels are fully enabled.
4. The method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in claim 1, characterized in that: In step three, the noise reduction preprocessing of the original furnace flame image is completed through a standardized image processing workflow, and flame radiation feature data, effective monitoring camera count and camera intrinsic parameter calibration data are extracted. Based on the number of valid cameras identified, the selected status of the camera quantity level in the interactive system is automatically updated, and the permission configuration of the corresponding coordinate input channel is adjusted synchronously to achieve adaptive matching between the monitoring parameters and the actual acquisition scene.
5. The method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in claim 1, characterized in that: Step four specifically includes: Matrix modeling of monitoring parameters: The calibrated furnace dimensions, number of effective cameras, and camera spatial coordinate parameters are transformed into a standardized parameter matrix to complete the coupled modeling of the furnace space and the visual monitoring system; Core calculation for 3D temperature field reconstruction: The core algorithm for solving the flame radiation transfer equation and reconstructing the temperature field is called to complete the inversion calculation of flame radiation characteristic data, output the discrete data matrix of the furnace cross-section temperature field, and extract the core operating characteristic parameters, including the maximum furnace temperature, the average furnace temperature, the flame absorption coefficient, the flame scattering coefficient and the number of algorithm iterations. Standardized output of core parameters: After formatting the core operating characteristic parameters, they are synchronously updated to the corresponding display area of the interactive system to achieve intuitive and accurate output of monitoring results.
6. The method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in claim 5, characterized in that: The core algorithm for temperature field reconstruction is detailed below: A geometric and physical model of the furnace is constructed, and the structured mesh discretization of the furnace space medium region is performed. At the same time, the imaging pixels of the multi-CCD flame image detector are discretized. The radiation transfer equation inside the furnace is solved using the DRESOR method, establishing a quantitative functional relationship between the radiation intensity of the flame image and the internal temperature and radiation parameters of the furnace. This completes the construction of the forward problem model for radiation imaging, and the discretized radiation imaging matrix equation is as follows: in, This represents the measured value of the boundary radiation intensity; The coefficient matrix is uniquely determined by the furnace geometry, mesh generation, medium radiation parameters, and DRESOR number. This represents the blackbody radiation intensity matrix of each unit within the furnace. The furnace flame images are acquired simultaneously from different perspectives by a multi-CCD flame image detector. After preprocessing, the image grayscale values are converted into absolute radiation intensity by a camera calibration model to obtain the measured value of the radiation intensity at the furnace boundary. Initial values for the uniform radiation parameters of the furnace medium are set, and the Tikhonov regularization algorithm is used to solve for the furnace radiation source term, transforming the inverse problem into a functional minimization problem. The calculation formula is as follows: in, For radiation sources inside the furnace; For the imaging matrix; For imaging matrix transpose; The regularization coefficient is used. This is the regularization matrix; This represents the measured value of the boundary radiation intensity; Substitute the blackbody radiation intensity matrix obtained by inversion into the forward problem model to calculate the theoretical boundary radiation intensity, and calculate the root mean square residual (RMSE) based on the measured boundary radiation intensity. A preset residual convergence threshold is set, and the RMSE is compared with the convergence threshold: If the RMSE is greater than the preset residual convergence threshold, the optimization algorithm is used to iteratively update the absorption coefficient and scattering coefficient of the furnace medium, and the updated radiation parameters are used to perform radiation source term inversion again. If the RMSE is less than or equal to the preset residual convergence threshold, the temperature value of each grid cell in the furnace is obtained by combining the inverse solution of the blackbody radiation law based on the converged blackbody radiation intensity matrix.
7. The method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in claim 1, characterized in that: The specific details of step five are as follows: Temperature field data interpolation and fitting: Based on the basic dimensions of the furnace, a standardized uniform grid is generated, and a high-precision interpolation algorithm is used to fit the discrete temperature field data to generate continuous furnace cross-sectional temperature field distribution data. Temperature field visualization rendering: A custom red and blue gradient color scheme is adopted. The temperature field distribution of the furnace section is drawn by filling contour lines at multiple levels. Color mark legends, coordinate axis labels and distribution titles are added to achieve an intuitive visualization of the temperature field distribution. Full-process operation status feedback: The operation status prompts are updated synchronously throughout the entire monitoring process, and the full-process status of flame image processing startup, core calculation in progress, monitoring completion and iteration count are output sequentially.
8. A furnace flame temperature field visualization monitoring system based on multi-CCD vision, based on the furnace flame temperature field visualization monitoring method based on any one of claims 1 to 7, characterized in that: include, The interactive system construction module builds a multi-CCD furnace flame visual monitoring interactive system, completes the initialization of global system parameters, configuration of basic interface components and initial resource loading, and provides a standardized interactive carrier for full-process monitoring. The furnace camera calibration module establishes a core monitoring parameter input and calibration system for furnace size, camera spatial coordinates, and platform elevation, and incorporates linkage control logic between the number of cameras and parameter input permissions to achieve accurate parameter configuration under different monitoring scenarios. The image preprocessing module performs standardized preprocessing on the furnace flame image, identifies the number of effective monitoring cameras and camera calibration parameters, and simultaneously completes adaptive matching of interactive system parameters to provide accurate input for temperature field reconstruction. The temperature field reconstruction module constructs a standardized matrix model of furnace-camera parameters, calls the core algorithm for flame temperature field reconstruction to complete the calculation, and outputs basic data of furnace cross-section temperature field and core operating characteristic parameters. The visualization feedback module performs interpolation fitting and visualization rendering on the temperature field data, simultaneously providing real-time feedback on the operational status of the entire monitoring process and intuitive display of core results.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for visual monitoring of furnace flame temperature field based on multi-CCD vision as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the furnace flame temperature field visualization monitoring method based on multi-CCD vision as described in any one of claims 1 to 7.