Intelligent monitoring system and method for temperature precision of roller way type sintering furnace

Abstract

The invention relates to the technical field of intelligent temperature control, in particular to a temperature precision intelligent monitoring system and method for a roller way type sintering furnace, and the system comprises a temperature data collection module, a temperature field simulation module, a hot spot recognition and adjustment module and a temperature precision control module. According to the method, temperature is monitored in real time and compared with simulation data in real time, so that temperature fluctuation in the sintering process can be accurately captured and adjusted in time, inconsistency of product quality is minimized, uniformity of material treatment and structural performance of products are guaranteed, hot spot areas are recognized in real time, actual adjustment measures are determined in combination with simulation data, and the accuracy of the sintering process is improved. The local abnormal temperature can be quickly responded, fine regulation and control can be performed, and a dynamic regulation strategy not only improves the response speed of the system, but also enables energy utilization to be more efficient, reduces energy waste, integrally optimizes the temperature control process, enhances the production efficiency and the product quality, and improves the market competitiveness of products.

Description

Technical Field

[0001] The present invention relates to the field of intelligent temperature control technology, and in particular to a temperature accuracy intelligent monitoring system and method for a roller sintering furnace. Background Art

[0002] The field of intelligent temperature control technology involves the use of advanced sensors, data analysis methods and control systems to provide efficient and precise temperature management solutions. It not only responds to basic heating or cooling needs, but also can automatically adjust and optimize its performance through machine learning and artificial intelligence algorithms. It is usually integrated in environments that require strict temperature management, such as industrial production, biotechnology laboratories, medical equipment and other scientific and technological applications. By monitoring the environment and equipment conditions in real time, analyzing the data and predicting temperature changes, it can achieve fine control of complex systems.

[0003] Among them, the temperature accuracy intelligent monitoring system of the roller sintering furnace focuses on achieving and maintaining the precise temperature control required by the sintering furnace during operation. Its main purpose is to monitor and adjust the temperature of the sintering furnace to ensure the uniformity and quality of the material processing process. By utilizing intelligent temperature control technology, the temperature fluctuation during the sintering process can be accurately controlled, thereby improving product quality, reducing energy waste and enhancing production efficiency. This is particularly critical in industries such as metal processing and ceramic manufacturing, where temperature control directly affects the structure and performance characteristics of the final product.

[0004] The existing roller sintering furnace usually adopts a slow data processing and response mechanism in the intelligent temperature control process, which makes it difficult to effectively identify and adjust temporary temperature peaks in the production process. The lack of sensitive control system makes it impossible to adjust production parameters in time, which in turn affects product quality and energy efficiency. In addition, the limitations of existing technologies in dynamic temperature control, such as the inability to accurately locate hot spots and make instant adjustments, further restrict their application in high-demand temperature control situations. Technical limitations lead to low production efficiency and increased production costs, affecting the overall competitiveness of enterprises. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a temperature accuracy intelligent monitoring system and method for a roller type sintering furnace.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A temperature accuracy intelligent monitoring system for a roller sintering furnace comprises:

[0007] The temperature data acquisition module deploys temperature sensors in key areas of the roller sintering furnace to collect temperature data in real time, screen and preliminarily analyze the data, and perform formatting and storage to generate real-time temperature data sets;

[0008] The temperature field simulation module simulates the temperature distribution in the sintering furnace through the real-time temperature data set, compares the simulation results with the collected temperature data in real time, analyzes the differences based on the real-time comparison results, determines potential adjustment points, and generates temperature field simulation analysis data;

[0009] The hot spot identification and adjustment module monitors the temperature of the sintering area through an infrared camera, decodes and processes the temperature video stream, identifies the hot spot area with abnormal temperature, determines the actual position of the hot spot based on the hot spot area identification result combined with the temperature field simulation analysis data, marks the hot spot area, and outputs the hot spot adjustment instruction;

[0010] The temperature precision control module receives the hot spot adjustment instruction, adjusts the output power and response speed of the heating element, adjusts the local heating, analyzes and records the effect and stability of the temperature adjustment, and generates the roller sintering furnace temperature control record.

[0011] As a further solution of the present invention, the step of acquiring the real-time temperature data set is:

[0012] Select the key areas of the sintering furnace, choose temperature sensors based on thermodynamic principles, install the sensors and complete initial functional tests to obtain sensor deployment configuration results;

[0013] Through the sensor deployment configuration results, the temperature data of key areas is monitored and recorded in real time, and the data is transmitted to the central database in real time to obtain real-time temperature monitoring data;

[0014] The real-time temperature monitoring data is screened and cleaned to remove outliers and noise. The formula is used.

[0015]

[0016] Calculate the average of the absolute differences between the data points and the mode T filtered , execute format storage, and obtain the real-time temperature data set, where T i is the temperature data value of the region at the target time point, T mode is the mode of the temperature data, and n is the total number of data points.

[0017] As a further solution of the present invention, the step of real-time comparison between the simulation results and the collected temperature data is:

[0018] Using the real-time temperature data set, initializing the temperature field simulation environment, setting boundary conditions and initial parameters, simulating the temperature distribution in the sintering furnace, and generating the temperature field simulation results in the sintering furnace;

[0019] The temperature field simulation results in the sintering furnace are compared point by point with the real-time temperature data set, using the formula,

[0020] ΔT=|T sim -T real |

[0021] Calculate the temperature difference ΔT of the data points to generate a deviation data set; where T sim Represents the simulated temperature value, T real Represents the actual temperature value;

[0022] The deviation data set is analyzed to identify areas where the temperature difference exceeds a set threshold, mark them as areas of concern, and generate a list of areas of concern.

[0023] As a further solution of the present invention, the step of acquiring the temperature field simulation analysis data is:

[0024] Based on the list of regions of interest, performing a difference analysis, calculating a statistical deviation between an actual measured temperature and a simulated temperature in the region of interest, and generating a difference analysis record;

[0025] According to the difference analysis records, check the causes of the deviations, determine the operating points that need to be optimized and adjusted, and generate a list of potential adjustment points;

[0026] According to the potential adjustment point list, the input parameters of the temperature field simulation are updated, the changes in the temperature distribution in the simulation results are observed, and it is confirmed that the adjusted simulation process matches the actual measurement data to generate temperature field simulation analysis data.

[0027] As a further solution of the present invention, the step of identifying the hot spot area is:

[0028] Install infrared cameras at key locations in the sintering area, adjust the camera angle and focal length to cover the sintering area, collect infrared radiation video, and send video data streams in real time through the data transmission interface to generate infrared video data streams;

[0029] Based on the infrared video data stream, each frame of the image is decoded, the decoded image is converted into a temperature matrix, and the temperature value is extracted to obtain the temperature distribution data of each frame;

[0030] Analyze the temperature distribution data of each frame, compare the temperature peak with the surrounding temperature, and use the formula:

[0031]

[0032] Calculate the average excess temperature T of the hot spot area hot , identify the abnormal temperature hotspot area, and obtain the hotspot area identification result, where T pixel Represents the temperature of each pixel identified as a hot spot, T avg is the average temperature of the entire temperature matrix, N hotis the number of pixels in the hotspot area.

[0033] As a further solution of the present invention, the step of obtaining the hotspot adjustment instruction is:

[0034] Based on the hot spot area identification result, the spatial position of the hot spot is determined by comparing it with the temperature field simulation analysis data, and the hot spot data with confirmed position is obtained;

[0035] Using the hotspot data confirmed at the location, identifying the shape and boundary of each hotspot, visually marking the hotspot according to its geometric features, displaying the location and range of each hotspot, and generating a marked hotspot view;

[0036] The marked hot spot view is dynamically analyzed, and the temperature adjustment value is calculated according to the temperature change trend and related risk analysis of the hot spot area, the temperature management of the entire sintering process is optimized, and the hot spot adjustment instruction is output.

[0037] As a further solution of the present invention, the step of obtaining the temperature control record of the roller sintering furnace is:

[0038] Parsing the hotspot adjustment instruction, adjusting the output power and response speed of the heating element according to the instruction content, confirming the adjustment parameters of each heating area, and generating heating element adjustment data;

[0039] Based on the heating element adjustment data, the actual output of the heating element is dynamically adjusted using the formula,

[0040]

[0041] Calculate the new output power P new , generates a real-time heating element status record, where P old represents the original output power, δ represents the adjustment increment, and c is the normalization constant;

[0042] The real-time heating element status record is used in combination with the real-time monitored temperature data to evaluate the effect and stability of the temperature adjustment and generate a temperature control record for the roller sintering furnace.

[0043] A temperature accuracy intelligent monitoring method for a roller sintering furnace comprises the following steps:

[0044] S1: Select the key areas of the sintering furnace, install sensors and complete initial functional tests, monitor and record the temperature data of the key areas in real time, perform data screening and cleaning, perform formatting and storage, and obtain real-time temperature data sets;

[0045] S2: using the real-time temperature data set, initializing the temperature field simulation environment, simulating the temperature distribution in the sintering furnace, comparing it point by point with the real-time temperature data set, identifying areas where the temperature difference exceeds the set threshold, marking them as areas of interest, and generating a list of areas of interest;

[0046] S3: Based on the list of areas of interest, calculate the statistical deviation between the actual measured temperature and the simulated temperature in the area of ​​interest, check the cause of the deviation, determine the potential adjustment point, update the input parameters of the temperature field simulation, observe the change of temperature distribution in the simulation results, and generate temperature field simulation analysis data;

[0047] S4: Install infrared cameras at key locations in the sintering area to collect infrared radiation videos, decode each frame of the image and extract the temperature value, analyze the temperature peak and compare it with the surrounding temperature to identify the abnormal temperature hotspot area and obtain the hotspot area identification result;

[0048] S5: Based on the hot spot area identification result, compare it with the temperature field simulation analysis data, determine the spatial position of the hot spot, identify the shape and boundary of each hot spot, calculate the temperature adjustment value according to the temperature change trend and related risk analysis of the hot spot area, and output the hot spot adjustment instruction;

[0049] S6: Analyze the hotspot adjustment instruction, adjust the output power and response speed of the heating element, dynamically adjust the actual output of the heating element, combine the real-time monitored temperature data, evaluate the effect and stability of the temperature adjustment, and generate the temperature control record of the roller sintering furnace.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, by real-time monitoring of temperature and real-time comparison with simulation data, temperature fluctuations during the sintering process can be accurately captured and adjusted in time, minimizing the inconsistency of product quality, ensuring the uniformity of material processing and the structural performance of the product, identifying hot spots in real time, and determining actual adjustment measures in combination with simulation data. It can respond quickly to local abnormal temperatures and perform fine regulation. The dynamic adjustment strategy not only improves the response speed of the system, but also makes energy utilization more efficient, reduces energy waste, optimizes the temperature control process as a whole, enhances production efficiency and product quality, and improves the market competitiveness of products. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a system flow chart of the present invention;

[0053] Figure 2 A flowchart of obtaining a real-time temperature data set according to the present invention;

[0054] Figure 3A flow chart for real-time comparison between the simulation results of the present invention and the collected temperature data;

[0055] Figure 4 This is a flow chart for obtaining temperature field simulation analysis data of the present invention;

[0056] Figure 5 It is a flow chart for identifying hotspot areas of the present invention;

[0057] Figure 6 A flowchart of obtaining hotspot adjustment instructions of the present invention;

[0058] Figure 7 The present invention is a flow chart for obtaining the temperature control record of the roller type sintering furnace. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 , a temperature accuracy intelligent monitoring system for a roller sintering furnace includes:

[0062] The temperature data acquisition module deploys temperature sensors in key areas of the roller sintering furnace to collect temperature data in real time, screen and preliminarily analyze the data, and perform formatting and storage to generate real-time temperature data sets;

[0063] The temperature field simulation module simulates the temperature distribution in the sintering furnace through the real-time temperature data set, compares the simulation results with the collected temperature data in real time, analyzes the differences based on the real-time comparison results, determines the potential adjustment points, and generates temperature field simulation analysis data;

[0064] The hot spot identification and adjustment module monitors the temperature of the sintering area through an infrared camera, decodes and processes the temperature video stream, identifies the hot spot area with abnormal temperature, determines the actual location of the hot spot based on the hot spot area identification results combined with the temperature field simulation analysis data, marks the hot spot area, and outputs the hot spot adjustment instruction;

[0065] The temperature precision control module receives hot spot adjustment instructions, adjusts the output power and response speed of the heating element, adjusts local heating, analyzes and records the effect and stability of temperature adjustment, and generates temperature control records of the roller sintering furnace.

[0066] The real-time temperature data set includes temperature value, timestamp and sensor location. The temperature field simulation analysis data includes temperature distribution diagram, difference analysis results and adjustment suggestions. The hotspot adjustment instructions include hotspot location, adjustment parameters and operation priority. The roller sintering furnace temperature control record includes the adjusted temperature value, stability evaluation results and adjustment time point.

[0067] See also Figure 2 ,The steps to obtain the real-time temperature data set are,

[0068] Select the key areas of the sintering furnace, choose temperature sensors based on thermodynamic principles, install the sensors and complete initial functional tests to obtain sensor deployment configuration results;

[0069] When deploying temperature sensors adapted to high-temperature environments in key areas of the selected sintering furnace, thermodynamic principles and environmental tolerance were taken into consideration. The sensor model was selected first. Considering the high temperature characteristics in the working environment, a sensor that can stably work at temperatures up to 1000 degrees Celsius was selected. The sensor has the characteristics of high precision and fast response, and can accurately capture temperature changes in real time. Subsequently, sensors were installed at strategic locations such as the feed port, combustion zone, and cooling zone to ensure that the key thermal areas of the sintering furnace can be fully covered. Functional tests were performed after installation to verify the response speed and accuracy of the sensor to ensure that the system can provide reliable data in subsequent operations and obtain sensor deployment configuration results.

[0070] Through the sensor deployment configuration results, the temperature data of key areas is monitored and recorded in real time, and the data is transmitted to the central database in real time to obtain real-time temperature monitoring data;

[0071] Through the deployed sensor configuration, the temperature data of key areas of the sintering furnace are monitored and recorded in real time. The latest wireless transmission technology is used in the implementation process to support high-speed data transmission and reduce data loss, and effectively send the temperature data collected from each sensor to the central database in real time. In addition, data buffering technology is also applied, which can handle large amounts of data collected at a high frequency and avoid possible delays in the data transmission process. This series of measures ensures the real-time and integrity of the data and obtains real-time temperature monitoring data.

[0072] The real-time temperature monitoring data is screened and cleaned to remove outliers and noise. The formula is used.

[0073]

[0074] Calculate the average of the absolute differences between the data points and the mode T filtered , execute format storage, and obtain the real-time temperature data set, where T i is the temperature data value of the region at the target time point, T mode is the mode of temperature data, and n is the total number of data points;

[0075] There are five temperature data points: 102 degrees Celsius, 100 degrees Celsius, 98 degrees Celsius, 101 degrees Celsius, and 100 degrees Celsius. The mode T mode is 100 degrees Celsius and the total number of data points n is 5.

[0076] According to the formula, first calculate the absolute value of the difference between each point and the mode:

[0077] |102-100|=2

[0078] |100-100|=0

[0079] |98-100|=2

[0080] |101-100|=1

[0081] |100-100|=0

[0082] Adding the absolute values ​​gives:

[0083] 2+0+2+1+0=5

[0084] but:

[0085]

[0086] The results show that after removing or reducing the impact of anomalies and noise, the observed temperature data deviates from its most common value (mode) by an average of 1 degree Celsius. This value helps understand the degree of temperature fluctuation in the data set, provides a more reliable data basis for subsequent data analysis and process control, and helps to improve the sensitivity and accuracy of the system's response to real temperature changes.

[0087] See also Figure 3 ,The real time comparison steps between the simulation results and the collected temperature data are,

[0088] Using the real-time temperature data set, initialize the temperature field simulation environment, set the boundary conditions and initial parameters, simulate the temperature distribution in the sintering furnace, and generate the temperature field simulation results in the sintering furnace;

[0089] When initializing the temperature field simulation environment, the initial state inside the sintering furnace is first determined based on the real-time temperature data set, including the temperature value of each measurement point and its spatial position, which directly affects the starting conditions of the simulation. Using the data, a three-dimensional geometric model of the space inside the furnace is established, including the physical structure and material properties of the furnace body. The specific material thermal conductivity, specific heat capacity, density and other thermophysical parameters of each area are set according to the material standards or historical data provided by the factory. Boundary conditions are set, such as the temperature of the furnace wall, the ambient temperature outside the furnace, and the input temperature and rate of the sintering material. The parameters are directly obtained from the production line control system to ensure the authenticity of the simulation environment. The finite difference method or finite element method is used to dynamically simulate the temperature distribution inside the sintering furnace. During the simulation process, the boundary conditions and the internally generated heat sources, such as the heat released by the combustion reaction, are updated in real time to reflect the actual operation changes in the production process. The simulation result is the temperature value of each grid point during the simulation time period. These data will provide a basis for the subsequent comparison of the simulation results with the actual temperature data.

[0090] The temperature field simulation results in the sintering furnace are compared point by point with the real-time temperature data set, using the formula,

[0091] ΔT=|T sim -T real |

[0092] Calculate the temperature difference ΔT of the data points to generate a deviation data set; where T sim Represents the simulated temperature value, T real Represents the actual temperature value;

[0093] The simulated temperature collected at position x, y, z is 450 degrees Celsius; T real is the actual temperature value measured directly by the sensor at the same location, assuming it is 440 degrees Celsius. The difference calculated by the formula is:

[0094] ΔT=|450-440|=10

[0095] The results show that there is a deviation of 10 degrees Celsius between the simulated temperature and the actual temperature at this point, indicating that the accuracy of the simulation at this location needs further adjustment.

[0096] Analyze the deviation data set, identify the areas where the temperature difference exceeds the set threshold, mark them as areas of concern, and generate a list of areas of concern;

[0097] After obtaining the temperature difference data set of each monitoring point, these differences are compared with the preset thresholds to identify those areas where the difference exceeds the standard. First, a reasonable temperature difference threshold, such as 5 degrees, is set. The threshold is based on the historical operation data and typical temperature fluctuation analysis in the furnace to ensure that both abnormalities can be captured and frequent false alarms can be avoided. For each data point, the absolute difference between its simulated temperature and the actual measured temperature is calculated, and the result is compared with the threshold. All points exceeding the threshold are marked as abnormal points, which may indicate sensor failure, data transmission problems, or inaccurate simulation parameters. To further verify the abnormal points, a secondary check will be conducted, including recalibrating the sensor readings and checking the relevant simulation parameter settings. In addition, the distribution pattern of the abnormal points is also analyzed to determine whether there are systematic error sources, such as structural problems or improper operation of a part of the furnace. Through this process, not only can the current problems be identified and corrected, but also the simulation parameters can be optimized to improve the accuracy of future simulations. The list of areas of concern generated in the end provides the technical team with clear goals to guide actual operations and continuous improvement.

[0098] See also Figure 4 , the steps to obtain temperature field simulation analysis data are as follows:

[0099] Based on the list of concerned areas, difference analysis is performed, the statistical deviation between the actual measured temperature and the simulated temperature in the concerned area is calculated, and a difference analysis record is generated;

[0100] Calculate the statistical deviation between the actual measured temperature and the simulated temperature in the region of interest using the formula

[0101]

[0102] Calculate and collect 3 measurement points (m) to simulate the temperature value (T sim,i ) are 400 degrees Celsius, 405 degrees Celsius, 410 degrees Celsius, and the actual temperature value (T real,i ) are 395 degrees Celsius, 400 degrees Celsius, and 407 degrees Celsius, respectively. Substitute them into the formula and first calculate the square of the difference at each point:

[0103] Point 1:

[0104] (400-395) 2 =25

[0105] Point 2:

[0106] (405-400) 2 =25

[0107] Point 3:

[0108] (410-407) 2 =9

[0109] Compute the mean and deviation:

[0110]

[0111]

[0112] The results show that the average deviation between the simulated temperature and the actual temperature is about 4.44 degrees Celsius, which indicates the level of consistency between the simulation and actual observations, and helps to identify and adjust potential errors in the simulation process.

[0113] Based on the difference analysis records, check the causes of deviations, determine the operating points that need to be optimized and adjusted, and generate a list of potential adjustment points;

[0114] Key information was extracted from the differential analysis records. Through discussions with operators on the production line and checking standard procedures in the operating manual, it was found that, for example, the uneven distribution of the material layer might be the main cause of the temperature simulation deviation. This discovery prompted further inspection of the function of the heat supply system, and it was found that the local heat supply was indeed insufficient. In addition, instrument calibration error was also identified as an influencing factor. After integrating the information, potential adjustment points were determined, such as the hot air outlet position adjustment and the change in material layer thickness as potential adjustment points. Based on this, a targeted list of potential adjustment points was generated, providing a clear direction for the next step of operation optimization.

[0115] Based on the list of potential adjustment points, update the input parameters of the temperature field simulation, observe the changes in the temperature distribution in the simulation results, confirm that the adjusted simulation process matches the actual measurement data, and generate temperature field simulation analysis data;

[0116] Using the suggestions in the list of potential adjustment points, relevant parameters of the temperature field simulation model were updated, such as adjusting the heat source distribution and material properties. The adjustments were optimized based on feedback information collected from actual operations. After re-running the model, it was observed that the temperature distribution in the simulation results had changed significantly, especially in the areas previously identified as having large deviations. The adjusted simulation temperature was closer to the actual measured data, verifying the effectiveness of the adjustment measures. The final generated temperature field simulation analysis data showed the improvement effect of the optimized sintering process, providing strong support for the improvement of production quality.

[0117] See also Figure 5 ,The steps for identifying hot spots are,

[0118] Install infrared cameras at key locations in the sintering area, adjust the camera angle and focal length to cover the sintering area, collect infrared radiation video, and send video data streams in real time through the data transmission interface to generate infrared video data streams;

[0119] Infrared cameras are installed at key locations in the sintering area, and the camera angle and focal length are adjusted to fully cover the sintering area. The process involves precise spatial positioning and field of view coverage calculation. Installation engineers need to select the best installation position and angle based on the specific size of the sintering area and the technical specifications of the camera, such as the maximum resolution and optimal working distance of the infrared camera, to ensure that every point in the monitoring area can be effectively covered. In addition, environmental factors such as temperature, dust and other factors that may affect the performance of the camera must also be considered, and protective measures must be taken to protect the equipment from damage and collect and generate infrared video data streams.

[0120] Based on the infrared video data stream, each frame of the image is decoded, the decoded image is converted into a temperature matrix, and the temperature value is extracted to obtain the temperature distribution data of each frame;

[0121] The infrared video data stream is decoded and image processed, and each frame of the image is decoded with high precision. During the decoding process, the original data captured by the infrared camera needs to be converted into a format, and the video stream needs to be processed in real time. In the process of image decoding and conversion into a temperature matrix, the grayscale value of each pixel will be converted into a corresponding temperature value according to the preset color temperature comparison table. This conversion process requires precise color temperature calibration to ensure the accuracy of the temperature data and obtain the temperature distribution data of each frame.

[0122] Analyze the temperature distribution data of each frame, compare the temperature peak with the surrounding temperature, and use the formula:

[0123]

[0124] Calculate the average excess temperature T of the hot spot area hot , identify the abnormal temperature hotspot area, and obtain the hotspot area identification result, where T pixel Represents the temperature of each pixel identified as a hot spot, T avg is the average temperature of the entire temperature matrix, N hot is the number of pixels in the hotspot area;

[0125] In a specific case, the monitored hot spot area contains 10 pixels, and the temperature of each pixel is 450 degrees Celsius, 460 degrees Celsius, 445 degrees Celsius, 470 degrees Celsius, 430 degrees Celsius, 435 degrees Celsius, 440 degrees Celsius, 455 degrees Celsius, 465 degrees Celsius, and 450 degrees Celsius, respectively. avg is the average temperature of the entire temperature matrix, set to 400 degrees Celsius, N hot is the number of pixels in the hotspot area, here it is 10. The calculation process is as follows:

[0126]

[0127] The results show that the average exceeded temperature in the hotspot areas is 2560 degrees Celsius, indicating that the temperature fluctuations in these areas are significantly higher than the average temperature. The corresponding hotspot area identification results mean that these areas require special attention and further analysis.

[0128] See also Figure 6 ,The steps to obtain the hotspot adjustment instruction are:

[0129] Based on the hot spot area identification results, the spatial position of the hot spot is determined by comparing with the temperature field simulation analysis data, and the hot spot data with confirmed position is obtained;

[0130] Based on the spatial comparative analysis of the hot spot identification results and simulation data in the sintering zone, the temperature data of the current sintering zone is first obtained from the real-time monitoring system. The data is presented in a graphical form for easy observation. Then it is compared with the temperature field data previously obtained through computational simulation, focusing on the temperature difference in the hot spot area. Using advanced graphics processing software, the temperature data of the two are superimposed and displayed in the same coordinate system for easy analysis. For the hot spot area, the specific location of the temperature anomaly is identified and marked by calculating the difference between the two data sets. Furthermore, based on the difference value, a detailed numerical analysis is performed to determine the intensity and scope of the hot spot. On this basis, a dynamic adjustment plan is formed to ensure that the location of the hot spot and the temperature distribution can accurately correspond.

[0131] Using the hotspot data with confirmed locations, identify the shape and boundaries of each hotspot, visually mark the hotspots according to their geometric features, display the location and range of each hotspot, and generate a marked hotspot view;

[0132] The confirmed hotspot location data is collected, including the coordinates, temperature characteristics and surrounding environment information of each hotspot. The corresponding visual marks are generated by analyzing these data. The specific operation includes using shape recognition algorithms to identify the contour, area and boundary of the hotspot. Subsequently, based on the temperature characteristics of the hotspot, color coding is used to classify hotspots in different temperature ranges. For example, red is used to represent high temperature areas, orange is used to represent medium temperature areas, and green is used to represent normal areas. These visual marks not only intuitively present the specific location and status of the hotspot, but also enable the monitoring interface to reflect changes in real time through dynamic updates. In addition, combined with the interactive design of the user interface, users can obtain detailed data of the area, such as current temperature, change trend, etc., by clicking on a hotspot mark. The graphical marking method not only improves the convenience and effectiveness of monitoring, but also enhances the operator's overall grasp of the hotspot area.

[0133] Dynamically analyze the marked hot spot view, calculate the temperature adjustment value according to the temperature change trend and related risk analysis of the hot spot area, optimize the temperature management of the entire sintering process, and output the hot spot adjustment instructions;

[0134] The temperature adjustment value is based on the following formula:

[0135]

[0136] Calculate, where T current represents the current average temperature of the hotspot, which is set to 450 degrees Celsius; ΔW represents the predetermined temperature adjustment, which is set to -20 degrees Celsius, meaning cooling; T rate represents the temperature change rate, set to 0.05; R risk Represents the risk assessment value, set to 2. Then:

[0137]

[0138] The calculation process showed that the adjusted hotspot temperature would drop to 438.35 degrees Celsius, providing a scientific basis for adjustment based on actual risks and temperature change rates, making hotspot management more precise and adaptable.

[0139] See also Figure 7 , the steps for obtaining the temperature control record of the roller sintering furnace are:

[0140] Parse the hotspot adjustment instructions, adjust the output power and response speed of the heating element according to the instruction content, confirm the adjustment parameters of each heating area, and generate heating element adjustment data;

[0141] According to the content of the hotspot adjustment instruction, instruction parsing and data conversion are performed. First, the received hotspot adjustment instruction is parsed into usable data, and the key parameters contained therein, such as target temperature, zone location, etc., are identified. Then the data is converted into the adjustment parameters required by the heating element. This process involves matching the data in the instruction with the actual response capability of the element, adjusting the input data according to the performance characteristics of the element, and making the settings of output power and response speed more accurate. The heating element adjustment data finally generated can be directly applied to actual temperature control operations to ensure that the heating efficiency and accuracy are optimized.

[0142] Based on the heating element adjustment data, the actual output of the heating element is dynamically adjusted using the formula,

[0143]

[0144] Calculate the new output power P new , generates a real-time heating element status record, where P old represents the original output power, δ represents the adjustment increment, and c is the normalization constant;

[0145] In the actual operating environment, P old =100 units, an increment of δ=0.1 represents a 10% increase in power, and a normalization constant c=2 is used to adjust the calculation result to conform to the physical output range.

[0146] Calculate the adjustment factor: 1+δ=1.1

[0147] Calculate the new power product:

[0148] P old 1.1 = 110

[0149] Applying the formula we get:

[0150]

[0151] It shows that the adjusted output power is 5.244 units. This result shows that by properly adjusting the original power, a more effective thermal control response can be achieved, and the stability and efficiency of the heating process can be optimized.

[0152] Use real-time heating element status records, combined with real-time monitored temperature data, to evaluate the effectiveness and stability of temperature adjustments and generate temperature control records for roller sintering furnaces;

[0153] Real-time heating element status records and real-time monitoring temperature data are used for data analysis and effect evaluation. The status data collected from the heating element is compared with the data monitored in real time by the temperature sensor. The data of both are standardized and analyzed for correlation to ensure that the effect of temperature adjustment can be accurately evaluated. Through in-depth analysis of real-time data, any possible temperature control deviations are identified, and the operating parameters of the sintering furnace are adjusted according to the analysis results. The temperature control record of the roller sintering furnace finally generated not only records the parameters and effects of each temperature adjustment in detail, but also provides data support for future temperature control strategies.

[0154] A temperature accuracy intelligent monitoring method for a roller sintering furnace comprises the following steps:

[0155] S1: Select the key areas of the sintering furnace, install sensors and complete initial functional tests, monitor and record the temperature data of the key areas in real time, perform data screening and cleaning, perform formatting and storage, and obtain real-time temperature data sets;

[0156] S2: Using the real-time temperature data set, initialize the temperature field simulation environment, simulate the temperature distribution in the sintering furnace, compare it point by point with the real-time temperature data set, identify the area where the temperature difference exceeds the set threshold, mark it as the area of ​​interest, and generate a list of the areas of interest;

[0157] S3: Based on the list of areas of interest, calculate the statistical deviation between the actual measured temperature and the simulated temperature in the area of ​​interest, check the cause of the deviation, determine the potential adjustment point, update the input parameters of the temperature field simulation, observe the change of temperature distribution in the simulation results, and generate temperature field simulation analysis data;

[0158] S4: Install infrared cameras at key locations in the sintering area to collect infrared radiation videos, decode each frame of the image and extract the temperature value, analyze the temperature peak and compare it with the surrounding temperature to identify the abnormal temperature hotspot area and obtain the hotspot area identification result;

[0159] S5: Based on the hotspot area identification results, compare with the temperature field simulation analysis data, determine the spatial location of the hotspot, identify the shape and boundary of each hotspot, calculate the temperature adjustment value according to the temperature change trend and related risk analysis of the hotspot area, and output the hotspot adjustment instruction;

[0160] S6: Analyze the hot spot adjustment instructions, adjust the output power and response speed of the heating element, dynamically adjust the actual output of the heating element, combine the real-time monitored temperature data, evaluate the effect and stability of the temperature adjustment, and generate the temperature control record of the roller sintering furnace.

[0161] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A temperature accuracy intelligent monitoring system for a roller sintering furnace, characterized in that: The system comprises: The temperature data acquisition module deploys temperature sensors in key areas of the roller sintering furnace to collect temperature data in real time, screen and preliminarily analyze the data, and perform formatting and storage to generate real-time temperature data sets; The temperature field simulation module simulates the temperature distribution in the sintering furnace through the real-time temperature data set, compares the simulation results with the collected temperature data in real time, analyzes the differences based on the real-time comparison results, determines potential adjustment points, and generates temperature field simulation analysis data; The hot spot identification and adjustment module monitors the temperature of the sintering area through an infrared camera, decodes and processes the temperature video stream, identifies the hot spot area with abnormal temperature, determines the actual position of the hot spot based on the hot spot area identification result combined with the temperature field simulation analysis data, marks the hot spot area, and outputs the hot spot adjustment instruction; The temperature precision control module receives the hot spot adjustment instruction, adjusts the output power and response speed of the heating element, adjusts the local heating, analyzes and records the effect and stability of the temperature adjustment, and generates the roller sintering furnace temperature control record.

2. The temperature accuracy intelligent monitoring system of a roller sintering furnace according to claim 1 is characterized in that: The steps of acquiring the real-time temperature data set are: Select the key areas of the sintering furnace, choose temperature sensors based on thermodynamic principles, install the sensors and complete initial functional tests to obtain sensor deployment configuration results; Through the sensor deployment configuration results, the temperature data of key areas is monitored and recorded in real time, and the data is transmitted to the central database in real time to obtain real-time temperature monitoring data; The real-time temperature monitoring data is screened and cleaned to remove outliers and noise. The formula is used: Calculate the average of the absolute differences between the data points and the mode T filtered , execute format storage, and obtain the real-time temperature data set, where T i is the temperature data value of the region at the target time point, T mode is the mode of the temperature data, and n is the total number of data points.

3. The temperature accuracy intelligent monitoring system of a roller sintering furnace according to claim 2 is characterized in that: The step of real-time comparison between the simulation results and the collected temperature data is: Using the real-time temperature data set, initializing the temperature field simulation environment, setting boundary conditions and initial parameters, simulating the temperature distribution in the sintering furnace, and generating the temperature field simulation results in the sintering furnace; The temperature field simulation results in the sintering furnace are compared point by point with the real-time temperature data set, using the formula, ΔT=|T sim -T real | Calculate the temperature difference ΔT of the data points to generate a deviation data set; where T sim Represents the simulated temperature value, T real Represents the actual temperature value; The deviation data set is analyzed to identify areas where the temperature difference exceeds a set threshold, mark them as areas of concern, and generate a list of areas of concern.

4. The temperature accuracy intelligent monitoring system of a roller sintering furnace according to claim 3 is characterized in that: The steps for obtaining the temperature field simulation analysis data are as follows: Based on the list of regions of interest, performing a difference analysis, calculating a statistical deviation between an actual measured temperature and a simulated temperature in the region of interest, and generating a difference analysis record; According to the difference analysis records, check the causes of the deviations, determine the operating points that need to be optimized and adjusted, and generate a list of potential adjustment points; According to the potential adjustment point list, the input parameters of the temperature field simulation are updated, the changes in the temperature distribution in the simulation results are observed, and it is confirmed that the adjusted simulation process matches the actual measurement data to generate temperature field simulation analysis data.

5. The temperature accuracy intelligent monitoring system of a roller sintering furnace according to claim 4 is characterized in that: The steps of identifying the hotspot area are: Install infrared cameras at key locations in the sintering area, adjust the camera angle and focal length to cover the sintering area, collect infrared radiation video, and send video data streams in real time through the data transmission interface to generate infrared video data streams; Based on the infrared video data stream, each frame of the image is decoded, the decoded image is converted into a temperature matrix, and the temperature value is extracted to obtain the temperature distribution data of each frame; Analyze the temperature distribution data of each frame, compare the temperature peak with the surrounding temperature, and use the formula: Calculate the average excess temperature T of the hot spot area hot , identify the abnormal temperature hotspot area, and obtain the hotspot area identification result, where T pixel Represents the temperature of each pixel identified as a hot spot, T avg is the average temperature of the entire temperature matrix, N hot is the number of pixels in the hotspot area.

6. The temperature accuracy intelligent monitoring system of a roller sintering furnace according to claim 5 is characterized in that: The step of obtaining the hotspot adjustment instruction is: Based on the hot spot area identification result, the spatial position of the hot spot is determined by comparing it with the temperature field simulation analysis data, and the hot spot data with confirmed position is obtained; Using the hotspot data confirmed at the location, identifying the shape and boundary of each hotspot, visually marking the hotspot according to its geometric features, displaying the location and range of each hotspot, and generating a marked hotspot view; The marked hot spot view is dynamically analyzed, and the temperature adjustment value is calculated according to the temperature change trend and related risk analysis of the hot spot area, the temperature management of the entire sintering process is optimized, and the hot spot adjustment instruction is output.

7. The temperature accuracy intelligent monitoring system of a roller sintering furnace according to claim 6 is characterized in that: The steps for obtaining the temperature control record of the roller sintering furnace are: Parsing the hotspot adjustment instruction, adjusting the output power and response speed of the heating element according to the instruction content, confirming the adjustment parameters of each heating area, and generating heating element adjustment data; Based on the heating element adjustment data, the actual output of the heating element is dynamically adjusted using the formula, Calculate the new output power P new , generates a real-time heating element status record, where P old represents the original output power, δ represents the adjustment increment, and c is the normalization constant; The real-time heating element status record is used in combination with the real-time monitored temperature data to evaluate the effect and stability of the temperature adjustment and generate a temperature control record for the roller sintering furnace.

8. A temperature accuracy intelligent monitoring method for a roller sintering furnace, characterized in that: According to any one of claims 1 to 7, the temperature accuracy intelligent monitoring system of a roller sintering furnace is implemented, comprising the following steps: Select the key areas of the sintering furnace, install sensors and complete initial functional tests, monitor and record the temperature data of the key areas in real time, perform data screening and cleaning, perform formatting and storage, and obtain real-time temperature data sets; Using the real-time temperature data set, initialize the temperature field simulation environment, simulate the temperature distribution in the sintering furnace, compare point by point with the real-time temperature data set, identify the area where the temperature difference exceeds the set threshold, mark it as a focus area, and generate a focus area list; Based on the list of areas of interest, calculate the statistical deviation between the actual measured temperature and the simulated temperature in the area of ​​interest, check the cause of the deviation, determine the potential adjustment point, update the input parameters of the temperature field simulation, observe the change of temperature distribution in the simulation results, and generate temperature field simulation analysis data; Install infrared cameras at key locations in the sintering area to collect infrared radiation videos, decode each frame of the image and extract the temperature value, analyze the temperature peak and compare it with the surrounding temperature to identify the abnormal temperature hotspot area and obtain the hotspot area identification results; Based on the hot spot area identification result, compared with the temperature field simulation analysis data, the spatial position of the hot spot is determined, the shape and boundary of each hot spot are identified, and the temperature adjustment value is calculated according to the temperature change trend and related risk analysis of the hot spot area, and the hot spot adjustment instruction is output; The hot spot adjustment instruction is parsed, the output power and response speed of the heating element are adjusted, the actual output of the heating element is dynamically adjusted, and the effect and stability of the temperature adjustment are evaluated in combination with the real-time monitored temperature data to generate the temperature control record of the roller sintering furnace.

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