Visualization method and system for boiler combustion state

Abstract

The invention provides a boiler combustion state visualization method, which comprises the steps of embedding a plurality of thermocouple temperature sensors in gaps of refractory bricks on the inner wall of a boiler hearth, acquiring temperature data of the hearth at different depths in real time, and transmitting the temperature data to a data acquisition terminal through a wireless communication module; fitting the normalized temperature data matrix by adopting a three-dimensional thin-plate spline interpolation algorithm to generate a hearth three-dimensional real-time temperature distribution model, performing mesh generation on the three-dimensional temperature distribution model by combining hearth geometric structure parameters obtained from computer aided design software, and constructing a hearth temperature finite element model; the hearth temperature finite element model is imported into ANSYS finite element analysis software, hearth wall surface material attributes and combustion heat load boundary condition parameters are set, transient thermal analysis of a hearth temperature field is carried out, and a temperature estimation value of any position of the hearth changing along with time is obtained.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method and system for visualizing the combustion state of a boiler. Background Art

[0002] The combustion process of thermal power plant boilers is complex, involving a series of physical and chemical processes such as mixing of fuel and air, ignition, combustion, and heat release. The temperature distribution inside the furnace directly reflects the combustion state. Uneven temperature distribution can lead to local high temperature, slagging, and wall washing problems. In severe cases, it can even cause pipe bursts, boiler damage, and other accidents. Therefore, real-time and accurate perception of the temperature distribution inside the furnace is crucial to ensure safe and economical operation of the boiler.

[0003] However, due to the large size of the boiler, the high temperature inside the furnace and the complex distribution, the traditional contact temperature measurement method is difficult to implement. Although non-contact temperature measurement technologies such as acoustic method, laser method, infrared method, etc. can realize the measurement of the temperature field inside the furnace, they all have certain limitations in practical applications. The acoustic method is easily interfered by strong noise, and the measurement accuracy of large-sized boilers is not high; the laser method is costly and easily affected by dust; the infrared method is not suitable for low-concentration flue gas areas. In addition, most of the existing temperature measurement methods can only obtain local or average temperatures in the furnace, and it is difficult to fully reflect the temperature distribution inside the entire furnace.

[0004] In summary, it is urgent to develop a new temperature measurement method that can obtain the three-dimensional temperature distribution inside the entire furnace in real time, and on this basis, use artificial intelligence algorithms to analyze and warn the combustion state, in order to achieve visual monitoring and intelligent optimization control of the boiler combustion process, and further improve the safety, economy and environmental protection of boiler operation. This is a key technical issue and challenge in the process of intelligent upgrading of thermal power plants. Summary of the invention

[0005] The present invention provides a method and system for visualizing the combustion state of a boiler, which mainly include:

[0006] Embed multiple thermocouple temperature sensors in the gaps of refractory bricks on the inner wall of the boiler furnace to collect temperature data at different depths of the furnace in real time, and transmit the temperature data to the data acquisition terminal through the wireless communication module;

[0007] The collected furnace temperature data is subjected to adaptive Kalman filtering denoising, and the temperature data is mapped in three dimensions according to the spatial position coordinates of the temperature sensor to obtain a normalized temperature data matrix. The normalized temperature data matrix contains temperature values, spatial coordinates, and timestamp information for subsequent analysis and visualization;

[0008] The normalized temperature data matrix is ​​fitted by using a three-dimensional thin plate spline interpolation algorithm to generate a three-dimensional real-time temperature distribution model of the furnace. Combined with the furnace geometric structure parameters obtained from the computer-aided design software, the three-dimensional temperature distribution model is meshed to construct a furnace temperature finite element model.

[0009] Import the furnace temperature finite element model into ANSYS finite element analysis software, set the furnace wall material properties and combustion heat load boundary condition parameters, perform transient thermal analysis of the furnace temperature field, and obtain the estimated value of the temperature at any position in the furnace that changes with time;

[0010] Extract the furnace temperature distribution data under the boiler design conditions from the design parameter database, obtain the standard furnace combustion temperature distribution data based on fuel characteristics and burner structure from the combustion model database, compare and analyze the deviations between the temperature estimation values ​​obtained by numerical simulation and the design values ​​and standard values, and trigger an audible and visual alarm signal when the temperature deviation exceeds the preset threshold value obtained based on the statistical analysis of historical operating data or a local high temperature abnormal area appears;

[0011] The Unity3D engine is used to develop a furnace 3D visualization system. The furnace geometry model, 3D temperature distribution model, key position temperature-time curve and alarm information are rendered into the same virtual scene to generate a 3D visualization interface of the furnace combustion status. The interface is presented in real time through human-computer interaction equipment. The visualization system receives and processes alarm signals and highlights abnormal temperature areas on the interface.

[0012] Based on the abnormal temperature alarm location information, combined with the furnace thermal calculation model, the burner three-dimensional flame model and historical operation data, multiple groups of combustion control parameter adjustment plans are generated through the Monte Carlo random simulation method combined with expert experience rules, and the impact of each adjustment plan on the furnace temperature distribution is predicted. The optimal combustion control parameter adjustment plan is screened out to form intelligent decision-making suggestions;

[0013] According to the three-dimensional visualization interface information and intelligent decision-making suggestions, the burner primary air damper opening, secondary air nozzle angle, fuel feed rate combustion control parameters are remotely adjusted through the distributed control system. The distributed control system includes a field controller, an operator station and an engineer station to achieve precise control of the furnace combustion process. During the adjustment process, temperature changes are continuously monitored to verify the adjustment effect. When necessary, parameter fine-tuning is performed until the abnormal furnace temperature distribution is eliminated.

[0014] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0015] The present invention discloses a method for visualizing the combustion state of a boiler. The temperature data is collected in real time by embedding a thermocouple sensor in the inner wall of the furnace, and a temperature distribution model is generated after filtering and three-dimensional spatial mapping. The abnormal temperature is alarmed by combining finite element analysis and historical data. The three-dimensional visualization technology is used to present the combustion state of the furnace in real time, and a combustion control parameter adjustment scheme is generated based on an intelligent decision-making algorithm. The burner parameters are remotely adjusted through a distributed control system to achieve precise control of the furnace temperature and improve the operating efficiency and safety of the boiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a method for visualizing boiler combustion status.

[0017] Figure 2 It is a schematic diagram of a method for visualizing boiler combustion status according to the present invention.

[0018] Figure 3 This is another schematic diagram of a method for visualizing boiler combustion status according to the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0020] like Figure 1-3 In this embodiment, a method for visualizing the combustion state of a boiler may specifically include:

[0021] Step S101, embed multiple thermocouple temperature sensors in the gaps of refractory bricks on the inner wall of the boiler furnace, collect temperature data at different depths of the furnace in real time, and transmit the temperature data to a data acquisition terminal through a wireless communication module.

[0022] According to the structure and size of the boiler furnace, the layout and quantity of the thermocouple temperature sensors are determined. The sensors are embedded in the gaps of the refractory bricks on the inner wall of the furnace to collect temperature data at different depths. The thermocouple temperature sensors collect furnace temperature data in real time, and the temperature signals are amplified and filtered through the signal conditioning circuit to obtain stable and reliable temperature measurement values. The processed temperature data is encoded and compressed using the wireless communication module, and transmitted to the data acquisition terminal through the wireless network to achieve remote transmission of temperature data. The data acquisition terminal receives the wirelessly transmitted temperature data, restores the original temperature measurement value through decoding and decompression algorithms, and performs time synchronization and data format conversion. The restored temperature data is stored in the database, and a historical record and trend analysis of the temperature data is established. Analysis, providing data support for subsequent data mining and visualization analysis. Data mining algorithms are used to analyze temperature data. Clustering, classification and other methods are used to identify the distribution characteristics and change laws of the furnace temperature field to determine whether there is combustion anomaly. If there is an obvious deviation or abnormal fluctuation in the temperature field distribution, the early warning mechanism is triggered to generate combustion anomaly early warning information. The early warning content is displayed through the human-machine interface and an audible and visual alarm is issued. After obtaining the combustion anomaly early warning information, combustion optimization adjustment suggestions are automatically generated, such as adjusting the burner angle, coal powder injection parameters, etc., to guide the operating personnel to adjust the combustion conditions. The furnace temperature field data and distribution status are displayed in real time in the form of curves, thermal maps, etc. on the human-machine interface, and the combustion conditions are intuitively presented to provide visual decision support for the optimized operation of the boiler.

[0023] For example, according to the structural dimensions of the boiler furnace with a diameter of 8 meters and a height of 45 meters, the uniform distribution method is used to determine the 18 positions where the thermocouple temperature sensors are arranged in the gaps of the refractory bricks on the inner wall of the furnace. They are arranged in a circular manner at 10 meters, 20 meters, 30 meters, and 40 meters from the bottom of the furnace, and 6 sensors are arranged in each section to collect temperature data at different depths of 2 meters, 5 meters, and 1 meter in the furnace. The thermocouple temperature sensor adopts K type, with a range of 0 to 1600℃ and an accuracy of ±75%. The weak thermoelectric potential signal is amplified 100 times by the signal conditioning circuit, and is converted into a 4 to 20mA standard current signal after a 5Hz low-pass filter and transmitted to the data acquisition terminal. The wireless communication module adopts the Zigbee protocol, the data encoding uses Manchester encoding, and the data compression uses run-length encoding, with a compression rate of 30%. The 18 sensor nodes are networked to form a wireless sensor network, and the transmission distance can reach 500 meters. The data acquisition terminal receives the temperature data transmitted wirelessly through the RS-485 bus, uses the least squares method to decode and decompress the data, and realizes the time synchronization of the sensor nodes through linear interpolation, and stores the temperature data in JSON format. The K-means clustering algorithm is used to analyze the temperature data, and the cluster to which each data point belongs is determined by Euclidean distance calculation to obtain the distribution characteristics of the furnace temperature field, and the temperature change curve is fitted using cubic spline interpolation to determine whether the temperature gradient exceeds 30℃ / meter. If the temperature rises and falls sharply by more than 50℃ or the temperature gradient exceeds 30℃ / meter, a combustion abnormality warning information is generated, displayed through a pop-up window on the human-machine interface, and the sound and light alarm device is linked. The system automatically generates combustion optimization adjustment suggestions based on the temperature abnormality, combined with the burner structural parameters and the coal powder injection amount, using the fuzzy control algorithm, such as adjusting the burner swing angle to 15 degrees and increasing the coal powder injection amount to 5 tons per hour. The human-machine interface is developed with the WPF framework and uses dynamic scaling technology to adapt to different resolutions. It uses a two-dimensional curve graph to display the temperature change trend of 18 sensors in real time, a three-dimensional thermal map to intuitively present the distribution of the furnace temperature field, and a bar graph to display the flue gas temperature, oxygen content and other parameters of each flue, providing visual decision support for boiler combustion optimization.

[0024] Step S102, the collected furnace temperature data is subjected to adaptive Kalman filtering denoising, and the temperature data is mapped in three dimensions according to the spatial position coordinates of the temperature sensor to obtain a normalized temperature data matrix. The normalized temperature data matrix contains temperature values, spatial coordinates, and timestamp information for subsequent analysis and visualization.

[0025] According to the boiler structure and combustion mode, several infrared temperature probes are arranged around the furnace to collect furnace temperature data. The collected furnace temperature data is subjected to adaptive Kalman filtering denoising to eliminate random noise interference in the temperature data. The spatial position coordinates of each temperature sensor are obtained, and the denoised temperature data are mapped to three-dimensional space according to the sensor spatial position. Through three-dimensional spatial mapping, a normalized temperature data matrix is ​​obtained, which contains temperature values, spatial coordinates and timestamp information. The image recognition algorithm is used to analyze the normalized temperature data matrix to determine the combustion uniformity in the furnace. If it is determined that the furnace has abnormal combustion conditions such as partial burning, an early warning message is generated and an alarm is issued to prompt the operator. According to the normalized temperature data matrix, the furnace combustion state is visualized through three-dimensional visualization technology. The historical curve of the temperature field data is obtained, and the historical data export function is provided to facilitate subsequent analysis and optimization. The early warning information, visualization results and historical data are integrated into the boiler combustion abnormal state early warning system to provide decision support for the optimization control of boiler operation.

[0026] For example, 20 infrared temperature probes are arranged around the boiler furnace to collect furnace temperature data with a sampling frequency of 1 second. The collected temperature data is subjected to adaptive Kalman filtering denoising. The process noise covariance matrix of the filter is Q, and the measurement noise covariance R is set to 1, which effectively eliminates the random noise interference in the temperature data. The spatial coordinates (x, y, z) of each temperature sensor are obtained, and the denoised temperature data are mapped into three-dimensional space to obtain a 20×4 normalized temperature data matrix containing temperature values, x, y, z coordinates and timestamp information. The convolutional neural network algorithm is used to perform image recognition analysis on the matrix. Through binarization processing and connected domain analysis, it is determined that there is a biased burning phenomenon in the furnace. The average temperature of the biased burning area is 150°C higher than that of the normal area. Warning information is generated and the sound and light alarm device is triggered. The OpenGL library is used to realize the three-dimensional visualization of the combustion state of the furnace. Different colors are mapped according to the temperature value to intuitively display the combustion distribution inside the furnace. The temperature field historical data of the past 24 hours is obtained, and the temperature curve is generated and exported in CSV format. Finally, the above information is integrated into the early warning system to provide data support and decision-making basis for optimized boiler operation.

[0027] Step S103, a three-dimensional thin plate spline interpolation algorithm is used to fit the normalized temperature data matrix to generate a three-dimensional real-time temperature distribution model of the furnace. Combined with the furnace geometric structure parameters obtained from the computer-aided design software, the three-dimensional temperature distribution model is meshed to construct a furnace temperature finite element model.

[0028] According to the boiler structure and combustion mode, several infrared probes are arranged around the furnace to collect furnace temperature data in real time to form an original temperature data matrix. The original temperature data matrix is ​​preprocessed, outliers are removed, and standardized to obtain a normalized temperature data matrix. The normalized temperature data matrix is ​​fitted using a three-dimensional thin plate spline interpolation algorithm to generate a three-dimensional real-time temperature distribution model of the furnace. The furnace geometry parameters are obtained from the computer-aided design software, the three-dimensional temperature distribution model is meshed, and the furnace temperature finite element model is constructed. Through finite element analysis, the temperature gradient and temperature distribution uniformity index of each point inside the furnace are calculated to determine whether there is an abnormal combustion state such as partial burning. If the temperature gradient and uniformity index exceed the preset threshold, the partial burning warning is triggered, and the location of the partial burning area is determined in combination with the furnace geometry. According to the location of the partial burning area and the parameters of the boiler combustion control system, the combustion adjustment strategy is calculated using an intelligent optimization algorithm to generate an adjustment instruction. The combustion adjustment instruction is sent to the boiler control system to realize automatic adjustment of the combustion state and continue to monitor the changes in the furnace temperature distribution in real time. The furnace temperature distribution model, bias burning warning information, combustion adjustment instructions and other data are stored in the historical database for subsequent analysis and optimization.

[0029] For example, 20 infrared temperature probes are arranged around the boiler furnace, the sampling frequency of the probe is 10Hz, the temperature range of each probe is 0-1600℃, and the accuracy is ±1%. The furnace temperature data of 1 minute is collected to form a 20×600 original temperature data matrix. The original data is preprocessed, and the 3σ criterion is used to eliminate abnormal values ​​beyond the normal range, and the maximum and minimum value normalization method is used to map the temperature data to the interval of 0-1 to obtain a normalized temperature data matrix. The three-dimensional thin plate spline interpolation algorithm is used to fit the normalized temperature data, and the number of interpolation nodes is 50×50×50, and a three-dimensional real-time temperature distribution model of the furnace is generated. The furnace geometry graphic file is imported from the AutoCAD software, and the key parameters such as the furnace length, width, and height are extracted. The temperature distribution model is matched with the geometric model, and the furnace temperature field is tetrahedral meshed with a unit size of 1m to generate a furnace temperature finite element model. Through finite element solution, the temperature values ​​and temperature gradients of 10,000 grid nodes inside the furnace are calculated, and the temperature distribution uniformity index Jt is calculated. When Jt is greater than 85, the temperature distribution is determined to be uniform; when Jt is less than 7, it is determined that there is a risk of partial burning. Combined with the geometric structure of the furnace, the position of the partial burning area is located, and according to the temperature distribution characteristics of the partial burning area, the particle swarm optimization algorithm is used to calculate the combustion adjustment strategy, including the ratio of primary air to secondary air, the burner swing angle, etc., to generate combustion control instructions. The DCS system issues combustion control instructions to adjust the air damper opening, burner angle, etc., to achieve automatic optimization of the furnace combustion state, and continuously monitor the changes in the furnace temperature distribution until the temperature distribution uniformity index reaches above 85. The furnace temperature distribution model, partial burning warning information, combustion adjustment instructions and other data at each moment are uploaded to the historical database of the industrial cloud platform, and big data analysis technology is used to explore the changing law of furnace temperature distribution, optimize the combustion control strategy, and improve the boiler operation efficiency.

[0030] Step S104, importing the furnace temperature finite element model into ANSYS finite element analysis software, setting the furnace wall material properties and combustion heat load boundary condition parameters, performing transient thermal analysis of the furnace temperature field, and obtaining an estimated value of the temperature at any position in the furnace that changes with time.

[0031] According to the boiler furnace structure and combustion mode, a three-dimensional geometric model of the furnace is established, and the mesh is divided to generate a finite element model. The finite element model of the furnace temperature is imported into the software using ANSYS finite element analysis software. The physical property parameters of the furnace wall material, such as thermal conductivity, specific heat capacity, etc., are obtained, and the furnace wall material properties are set in the software. By analyzing the boiler combustion system and operating parameters, the combustion heat load distribution in the furnace is determined, and the combustion heat load is set as the boundary condition in the software. If it is necessary to analyze the change of furnace temperature over time, the time step and total duration of transient thermal analysis are set in the software. The finite element equation of the furnace temperature field is solved using ANSYS software to obtain the numerical solution of the furnace temperature field. Through the post-processing module, the node temperature values ​​at any position of the furnace are extracted to generate temperature cloud maps and temperature curves. It is judged whether the furnace temperature field distribution meets the boiler combustion requirements. If not, return to step 4 and adjust the combustion heat load boundary conditions. Obtain the furnace temperature field distribution results that meet the requirements, and export the estimated value of the temperature at any position of the furnace that changes with time to provide data support for subsequent boiler combustion optimization control.

[0032] For example, according to the boiler furnace structure and combustion mode, the SolidWorks software is used to establish a three-dimensional geometric model of the furnace, and the model size is 10 meters long, 8 meters wide, and 15 meters high. The hexahedral meshing method is adopted, and the mesh size is set to 1 meter to generate a finite element model of about 500,000 mesh units. The finite element model is imported into the ANSYS finite element analysis software. According to the refractory brick material of the furnace wall, the thermal conductivity is 8W / (m·K) and the specific heat capacity is 0.5kJ / (kg·℃) in the software material library, and it is set as the furnace wall material property. By analyzing the coal burning capacity of the boiler is 50t / h, and the front and rear wall counter-combustion method is adopted, according to the empirical formula: q=ηQ / V, where η is the furnace thermal efficiency, which is taken as 85, Q is the low calorific value of the fuel, which is taken as 23000kJ / kg, and V is the furnace volume. Substituting the data, the furnace combustion heat load q=175kW / m 3 , set as the internal heat load boundary condition of the furnace in the software. If it is necessary to analyze the change of furnace temperature over time, take 10 seconds as a time step, and set the total time to 120 minutes to simulate the boiler startup process. The furnace temperature field is solved by ANSYS finite element analysis software, and the converged temperature field numerical solution is obtained through iterative calculation. The node temperature values ​​of each wall and typical section of the furnace are extracted to generate temperature cloud diagrams and temperature curves. Compare and analyze the temperature field distribution with the boiler combustion requirements. If the highest temperature exceeds the allowable temperature of refractory bricks of 1400℃ or the lowest temperature is lower than 800℃, it is necessary to return to adjust the combustion heat load boundary conditions until the furnace temperature field distribution results that meet the requirements are obtained. Finally, the estimated values ​​of the temperature of each part of the furnace that changes with time are exported to guide the design and operation adjustment of the boiler combustion optimization control system.

[0033] Step S105, extract the furnace temperature distribution data under the boiler design condition from the design parameter database, and obtain the standard furnace combustion temperature distribution data based on the fuel characteristics and burner structure from the combustion model database. The temperature estimation value obtained by numerical simulation is compared with the design value and the standard value for deviation analysis. When the temperature deviation exceeds the preset threshold value obtained based on the statistical analysis of historical operation data or a local high temperature abnormal area appears, an audible and visual alarm signal is triggered.

[0034] According to the boiler structure and combustion mode specified in the project document, the corresponding boiler model design parameter database is matched and searched to obtain the furnace temperature distribution data under the design working conditions of the boiler model. The fuel characteristics and burner structure data given in the project document are used to connect the combustion model database to obtain the standard furnace combustion temperature distribution data under this condition. If the boiler is a boiler with "several infrared probes arranged" as mentioned in the project document, then according to the infrared probes arranged around the furnace, an intelligent algorithm is constructed and executed to obtain a furnace combustion visualization image and real-time temperature field data. According to the preset numerical simulation calculation program, the real-time temperature field data obtained by the infrared probe is input into the numerical simulation model to calculate the temperature estimate. The temperature estimate obtained by the numerical simulation calculation is used as input, and the "statistical analysis based on historical operation data" link mentioned in the project document is used. The program for comparison and analysis with the design value and the standard value is adopted to calculate and obtain the temperature deviation value between the three. If the temperature deviation value exceeds the "preset threshold value based on the statistical analysis of historical operating data" mentioned in the technical goal of "establishing an early warning system" in the project file, the alarm program will be triggered and the alarm information processing link will be entered. If the temperature deviation value is within the preset threshold value, the furnace temperature is judged to be normal, the state is maintained, and the process starting from the third step is continued. If the alarm program determines that there is a "local high temperature abnormal area" mentioned in the project file in the current furnace, a control signal is sent to the corresponding hardware device to obtain an audible and visual alarm signal. Obtain the audible and visual alarm signal and the current boiler temperature data, and combine the function point of "being able to display the historical curve of temperature field data" mentioned in the project file to generate the corresponding temperature field data historical curve chart. The temperature field data historical curve chart and the audible and visual alarm record are displayed through the chart display system. Combined with the function of "historical data can be exported" mentioned in the project file, the relevant data within this time period is output and saved in the specified database.

[0035] For example, assuming that the project document specifies that the boiler is a 600MW supercritical coal-fired boiler with a four-corner tangential combustion method, the corresponding information is retrieved in the boiler model design parameter database by entering this parameter. Under the condition that the type of coal burned is bituminous coal, the boiler is rated power, and points are taken on different horizontal sections for detection. The model selects a total of 156 points for testing. The data is recorded to generate a list and a temperature distribution diagram, and it is known that the center temperature of the furnace is 1650 degrees Celsius and the outlet flue gas temperature is 1350 degrees Celsius under the design conditions. In order to form a chain of thought, the data obtained is entered into the database and associated. The coal quality composition is used for analysis, for example: the volatile matter of the coal is 30%, the fixed carbon is 40%, the ash content is 20%, and the sulfur content is 6%. The combustion model database is connected to calculate that the combustion of this type of fuel coal can release a standard temperature value of 1600 degrees Celsius under theoretical conditions. According to the multiple infrared probes arranged around the furnace, assuming that each probe collects data at a frequency of 30 frames per second, the resolution of each frame image is 1280x720 pixels, and each pixel represents a temperature measurement point, the scene three-dimensional temperature distribution fast reconstruction algorithm is used to build and execute the intelligent algorithm model, and the furnace combustion visualization image and temperature field data are obtained in real time, and the furnace three-dimensional dynamic temperature cloud map and specific temperature values ​​are obtained. After obtaining the information for internal calculation, in order to connect the thinking chain with subsequent business, the data is automatically synchronized to the numerical simulation module. Take each frame of temperature data obtained as a data packet, and input it into the numerical simulation model for calculation through the preset numerical simulation program based on finite element analysis. The model divides the furnace into 100x100x200 grids, each grid size is 5 meters x5 meters x5 meters, and selects the fast solution algorithm for calculation. After the infrared monitoring data is calculated to obtain the actual monitoring value, in order to facilitate the subsequent calculation link. The data is organized and stored according to a specific data structure to facilitate data extraction in the comparison link. By analyzing and processing the historical operation data using the moving average algorithm, the operation data of the past 30 days are selected as samples, and the average value is calculated as a benchmark with one data point per hour. The temperature distribution data under the design conditions, the standard furnace combustion temperature distribution data, and the temperature field estimated value calculated by real-time monitoring and numerical simulation are compared to obtain specific deviation values. For example, the current furnace center temperature is 1685 degrees Celsius calculated by numerical simulation. The deviation value calculation program obtains the deviation from the design value through (1685-1650) / 1650=12%, and the deviation from the standard value is obtained through (1685-1600) / 1600=31%. Based on the operation data of the past 15 days, statistical analysis is performed, and the quartile method is applied to obtain the 95% confidence interval. The warning threshold of the temperature deviation is set to fluctuate up and down by 5%. The currently calculated temperature deviation value exceeds the preset threshold upper limit of 5%, and enters the warning processing program. According to the deviation value, this deviation is classified as a third-level warning. The execution code is set for this state to start the warning.In order to establish the subsequent logic, the execution code will automatically execute the matching algorithm through the database through the alarm record and the furnace temperature distribution data to establish a connection. There is a judgment logic module in the early warning information processing program. This module has preset alarm rules and corresponding equipment action scripts. By executing this alarm rule script, logical judgment is automatically performed to detect that there is a local high temperature abnormal area in the current furnace. Based on this situation, the program will send a three-level alarm signal to the DCS system through the CAN bus and notify the corresponding alarm device control circuit to trigger the sound and light alarm. The alarm light starts to flash at a frequency of 1 Hz, and the buzzer emits an alarm sound of 90 decibels. The sound and light alarm is triggered to record the alarm information and status at the same time, which is convenient for information analysis. When the alarm action is triggered, the process is started in parallel in the background for mapping operations. The process runs the mapping program. The program obtains the temperature value in the database through an automatic query statement based on the occurrence time and end time of the alarm. At the same time, the furnace temperature data of the current time and the previous period, such as the furnace temperature data in the past hour, is obtained and read from the furnace temperature real-time monitoring system. The values ​​are bound to the data, and the temperature values ​​are generated into a line graph. At the same time, they are matched and bound with the time points of the sound and light alarm signals to generate a comprehensive chart of the associated sound and light alarm signals. After the process is completed, the chart is output to the main monitoring module. In order to form a complete data flow closed loop, the main monitoring module starts the chart generation process and the data saving process. On the graphic workstation where the chart is displayed, the operator can view the real-time updated temperature field data history curve chart and the sound and light alarm records. The program also includes the function of packaging and storing the data and sound and light alarm records during this period, and the export file format is standard. csv format. After clicking the button to package, the operator can also export all relevant data from 10 to 11 on March 15, 2023 in the format of a CSV file. These data can then be automatically uploaded to the company's central data storage system. These data packets automatically contain detailed text descriptions, original data forms, and analysis results. Convenient for subsequent query calls and secondary processing.

[0036] Step S106, using Unity3D engine to develop a furnace 3D visualization system, rendering the furnace geometry model, 3D temperature distribution model, key position temperature time curve and alarm information into the same virtual scene, generating a 3D visualization interface of the furnace combustion state, and presenting it in real time through human-computer interaction equipment. The visualization system receives and processes the alarm signal, and highlights the abnormal temperature area on the interface.

[0037] Obtain the furnace geometry data, design the infrared temperature measurement array according to the boiler structure and combustion mode, arrange several infrared probes, and obtain the furnace geometry model. If the total project budget of 3.8 million yuan is confirmed, then according to the furnace geometry model, import the furnace CAD drawing data, build a three-dimensional model through the modeling tool in the Unity3D engine, and determine the basic model of the furnace three-dimensional visualization system. According to the infrared probe, collect the temperature data in the furnace in real time. If the historical data export is confirmed, perform statistical analysis on the temperature information data at multiple locations in the furnace to obtain a three-dimensional temperature distribution model. According to the temperature data at each location in the three-dimensional temperature distribution model, use the interpolation algorithm to generate continuous temperature field data to obtain a smooth temperature distribution curve. Obtain the temperature distribution curve and historical temperature data, establish a temperature threshold database according to the plan of 50,000 yuan expert consultation fee budget, and determine the judgment standard of abnormal temperature. Use the real-time temperature data continuously detected by the installed infrared probe supported by the on-site construction fee budget of 160,000 yuan to compare with the temperature threshold database. If it exceeds the threshold range, an alarm signal will be issued. Through the rendering function of the Unity3D engine, the furnace geometry model, three-dimensional temperature distribution model and alarm signal are integrated together to obtain a unified virtual scene. According to the data relationship of each object in the virtual scene, the script code is written to control the display and interaction logic of each object in the virtual scene, and a visual interface with key position temperature time curve and alarm information rendering effect is obtained. Through the data acquisition hardware supported by the equipment fee of 3.14 million yuan, various signals such as temperature and image are collected, and the collected real-time data is transmitted to the established visualization system using a graphical user interface to obtain and present a real-time updated three-dimensional visualization interface of the furnace combustion status.

[0038] For example, first, the technical team needs to use a laser scanner to scan the inside of a boiler furnace of a certain model DG-130 / 38-II9, obtain the three-dimensional point cloud data of the inner wall surface of the furnace, and control the accuracy within 5 mm. Combined with the design drawings of the boiler, determine the basic geometric parameters of the furnace, such as 15 meters in height, 8 meters in width, and 6 meters in depth, and determine the preliminary layout plan of the infrared probe according to the characteristics of the four-corner tangential combustion method. Based on this plan, an infrared thermal imaging temperature probe is arranged every 5 meters on the four walls of the furnace and the top water-cooled wall, and each temperature measurement point is calibrated. A total of 120 probes are arranged and connected to the special sealing joint of the furnace water-cooled wall pipe using an infrared lens assembly. Then, based on the confirmed project budget of 3.8 million yuan, CAD data conversion and modeling are carried out, and detailed furnace three-dimensional CAD drawings are imported with an accuracy of 1 mm. The imported furnace three-dimensional model can meet the LOD3 standard. In this step, a high-precision three-dimensional model can be constructed using modeling software. At this stage, it is necessary to strictly define the size, shape, material, position and connection of each component such as burner, water wall and superheater in the virtual environment, and set the resolution of the virtual scene to 1920x1080 to ensure the display clarity of 120 probe points. After completing the construction of the three-dimensional basic model of the furnace, the infrared probe data analysis begins. It is necessary to obtain the data collected by the infrared probe every 5 seconds and save the data of the last 7 days. The relational database is used to store structured data, and real-time records are recorded, such as the temperature value at the position of probe No. 37 is 850 degrees Celsius, and the real-time temperature detected by probe No. 68 is 932 degrees Celsius. The user needs to analyze the historical data. After the user confirms, the historical data in CSV format is exported through the ODBC interface, and the data preprocessing and denoising based on fast Fourier transform are implemented through Python programming. Then the multivariate linear regression method is used for statistical analysis to establish the interaction relationship between key parameters, and the average temperature of these probe positions in the past month is 800 degrees Celsius, with a standard deviation of 50 degrees Celsius. Then, the data of 120 infrared probes were spatially interpolated using the inverse distance weighted interpolation algorithm to form the temperature field distribution. The spatial resolution was set to 5 meters, and the weighted average calculation was performed based on the coordinates of each point in the space and the data of the nearest five known temperature probes around it, and a smooth three-dimensional temperature field surface map was drawn. It was found that the temperature in the center of the furnace was about 120 degrees Celsius higher than the surrounding area on average. Then, combined with the experience that the temperature in the center of the furnace usually fluctuates around 1200 degrees Celsius and the fluctuation range does not exceed 150 degrees Celsius, three experts with senior professional titles in the field were invited to conduct on-site evaluation and discussion to jointly discuss and confirm the safe operating range of the temperature in each monitoring area.According to the opinions given by experts and considering the consulting fee of 50,000 yuan, based on the existing temperature data and temperature curve data, the normal temperature range of the upper area of ​​the furnace is set to 800 to 1000 degrees Celsius, and the lower combustion area is set to 1100 to 1300 degrees Celsius, and the upper and lower limits of normal working conditions are formulated. On this basis, a database containing different areas and different working conditions is established. According to the abnormal temperature judgment conditions of different working conditions, the red alarm threshold (too high temperature) is set for 20 points in the confirmed normal range, and the yellow alarm threshold (pay attention to the temperature increase trend) is set for 15 points. Through real-time monitoring of the 120 monitoring points that are continuously uploaded to the database by infrared probes, when the 45th probe detects that the temperature reaches 1050 degrees Celsius and continues to rise for 5 minutes, it will trigger a yellow warning. If the temperature continues to rise to 1100 degrees Celsius and lasts for more than 1 minute, it will be upgraded to a red alarm. Based on the red alarm, the information is sent to the DCS system and the operator's mobile terminal, and the sound alarm signal is triggered synchronously. In the 3D model integration phase, a graphics API that supports DirectX11 is selected, and high dynamic range rendering HDR technology is used to simulate real lighting conditions, and the obtained furnace temperature information is standardized and color-coded. The temperature color range is set in the furnace overheating zone: 800 degrees Celsius is blue, and more than 1300 degrees Celsius is red. Different temperatures are distinguished by color, so that the real-time visual monitoring of the furnace temperature at all points and under different working conditions can be achieved through the virtual reality interface. Programming is carried out for the above-mentioned abnormal furnace temperature monitoring scenario, and about 1,000 lines of C# code are written to handle alarm triggering conditions and interface interactive control. For example, clicking a specific position can display the corresponding real-time temperature data, such as the current temperature value of 895 degrees Celsius at the 35th probe position, and the temperature change curve in the past 24 hours. The curve refresh rate is set to 1 time per second. Finally, the data acquisition end transmits the temperature, flame images, etc. to the computer of the visualization system in real time through the high-speed Ethernet card and multi-channel parallel data acquisition equipment with a total value of 3.14 million yuan. The 120 infrared temperature measurement signals, 5 high-definition videos, and 60 pressure signals on site are interconnected with the temperature acquisition control system through the CAN bus and UDP transmission protocol to ensure a transmission delay of less than 1 second. The collected data is then connected to the visualization system, and the OpenGL graphics library is used for efficient rendering to synchronize the data to the visualization model of the three-dimensional scene. According to the set rules, the data is updated in real time, and the alarm signal and related text and chart descriptions are pushed to the front-end display interface. The operating personnel can see the latest and intuitive three-dimensional visualization interface of the furnace combustion status through the 65-inch 4K high-definition display installed in the monitoring center.

[0039] Step S107, based on the abnormal temperature alarm location information, combined with the furnace thermal calculation model, the burner three-dimensional flame model and historical operation data, a plurality of groups of combustion control parameter adjustment schemes are generated through the Monte Carlo random simulation method combined with expert experience rules, and the influence of each adjustment scheme on the furnace temperature distribution is predicted, and the optimal combustion control parameter adjustment scheme is screened out to form intelligent decision-making recommendations.

[0040] According to the boiler structure and combustion mode, several infrared probes are arranged around the furnace to collect the furnace temperature field distribution data in real time. The collected temperature field distribution data is analyzed by image recognition algorithm to determine whether there is abnormal combustion state such as partial burning. If combustion abnormality is detected, the position coordinate information of the abnormal area is obtained. According to the coordinates of the abnormal position, the theoretical temperature value at the position is calculated in combination with the furnace thermal calculation model and the burner three-dimensional flame model. The theoretical temperature value is compared with the actual measured temperature value to obtain the temperature deviation data. The Monte Carlo random simulation method is used to generate multiple groups of combustion control parameter adjustment schemes in combination with historical operation data and expert experience rules. For each group of adjustment schemes, the furnace thermal calculation model is used to predict its impact on the furnace temperature field distribution. The predicted effects of each adjustment scheme are compared to select the optimal scheme that can minimize the temperature deviation. The optimal combustion control parameter adjustment scheme is formed into an intelligent decision suggestion and sent to the DCS control system for execution, and an alarm is issued to remind the operator.

[0041] Exemplarily, 8 infrared probes are arranged around the boiler furnace, and the furnace temperature field distribution data is collected every 5 seconds. The collected temperature field distribution image is analyzed by the convolutional neural network algorithm to determine whether there is a biased burning area with a temperature deviation exceeding 50°C. If biased burning is detected, the center position coordinates of the biased burning area are obtained by the image segmentation algorithm. According to the coordinates, the theoretical temperature value at the biased burning position is calculated in combination with the furnace thermal calculation model and the burner three-dimensional flame model. The theoretical temperature value is compared with the actual temperature value measured by the infrared probe to obtain the temperature deviation data. If the deviation exceeds 30°C, it is judged to be abnormal. In order to seek the best combustion control parameter adjustment scheme, the Monte Carlo random simulation method is used to randomly generate 1000 groups of adjustment schemes within the value range of parameters such as air-fuel ratio, primary and secondary air volume ratio, and burner swing angle. For each group of schemes, the furnace thermal calculation model is used to predict its impact on the furnace temperature field distribution, and the root mean square error (RMSE) is used to evaluate the deviation between the predicted temperature field and the target temperature field. Finally, the one with the smallest RMSE was selected from the 1,000 solutions as the optimal adjustment solution, forming an intelligent decision-making suggestion, which was sent to the DCS system in JSON format for execution. At the same time, an alarm was issued when the deviation exceeded 50°C to prompt the operator to pay attention.

[0042] Step S108: Based on the three-dimensional visualization interface information and intelligent decision-making suggestions, the primary air damper opening, secondary air nozzle angle, and fuel supply combustion control parameters of the burner are remotely adjusted through the distributed control system. The distributed control system includes a field controller, an operator station, and an engineer station to achieve precise control of the furnace combustion process. During the adjustment process, the temperature changes are continuously monitored to verify the adjustment effect, and the parameters are fine-tuned when necessary until the abnormal distribution of furnace temperature is eliminated.

[0043] Step 1: Design an infrared temperature measurement array according to the boiler structure and combustion mode, obtain the data of several infrared probes around the furnace, and obtain the basic data of the furnace temperature field data. Step 2: Obtain the basic data of the furnace temperature field data, use intelligent algorithms to process and analyze the data, determine whether there is abnormal distribution of furnace temperature, and obtain the diagnosis results of abnormal temperature status. Step 3: If the furnace temperature status is normal, maintain the control parameters of the current distributed control system to obtain a stable boiler combustion process. Step 4: If the furnace temperature is abnormally distributed, use an image recognition algorithm to analyze the combustion uniformity in the furnace, and combine it with the computer vision boiler combustion abnormal state warning system to obtain the type and location information of the combustion abnormality, and obtain the specific situation of abnormal conditions such as partial burning. Step 5: According to the specific situation of abnormal conditions such as partial burning and the three-dimensional visualization interface information, combined with the preset intelligent decision rule library, determine whether to adjust the combustion control parameters of the burner primary air damper opening, secondary air nozzle angle, and fuel coal supply, and obtain intelligent decision suggestions. Step 6: If the combustion control parameters are not adjusted, return to step 4 to continuously monitor the abnormal state. Step 7: If the combustion control parameter is adjusted, the current value of the corresponding control parameter is obtained through the distributed control system field controller to obtain the initial state. Step 8: According to the initial state, combined with the logic setting of the control algorithm, the burner primary air damper opening, secondary air nozzle angle, and fuel supply are automatically adjusted, and the control parameter target value is determined through the communication and calculation of the operator station and engineer station of the distributed control system. Step 9: According to the infrared probe data, the furnace temperature change is monitored in real time, and the adjustment effect is judged in combination with the previous data to obtain the temperature anomaly elimination situation.

[0044] For example, according to the boiler structure and combustion mode, an infrared temperature measurement array consisting of 120 infrared probes is designed. The probes are evenly distributed around the furnace with a sampling frequency of 1 second. The convolutional neural network algorithm is used to process and analyze the acquired furnace temperature field data, and the furnace temperature distribution is judged to be abnormal by comparing it with the temperature field data under standard working conditions. When the furnace temperature state is normal, the distributed control system maintains the current control parameters such as the burner primary air damper opening, secondary air nozzle angle, and fuel coal supply unchanged. When the furnace temperature is monitored to have an abnormal distribution, the improved FasterR-CNN algorithm is used to segment and identify the flame image in the furnace to determine the specific location of the biased burning. Combined with the expert knowledge base of combustion optimization, through fuzzy reasoning, intelligent decision-making suggestions for adjusting the burner primary air damper opening, secondary air nozzle angle, and fuel coal supply are given. If it is recommended not to adjust the combustion control parameters, the abnormal state of the furnace temperature is continuously monitored. If it is recommended to adjust the combustion control parameters, the current burner damper opening is 30%, the secondary air nozzle angle is 15°, and the fuel supply is 45t / h through the field controller of the distributed control system. Using the model predictive control algorithm, the target values ​​of the burner damper opening, secondary air nozzle angle, and fuel supply are calculated to be 35%, 18°, and 43t / h, respectively. The distributed control system automatically adjusts the combustion control parameters according to the set target values, and monitors the furnace temperature changes in real time. By comparing and analyzing the temperature data before adjustment, it is confirmed that the abnormal temperature state has been eliminated and the combustion process has returned to normal.

[0045] The present invention further provides a boiler combustion status visualization system, the system performing the following steps:

[0046] Embed multiple thermocouple temperature sensors in the gaps of refractory bricks on the inner wall of the boiler furnace to collect temperature data at different depths of the furnace in real time, and transmit the temperature data to the data acquisition terminal through the wireless communication module;

[0047] The collected furnace temperature data is subjected to adaptive Kalman filtering denoising, and the temperature data is mapped in three dimensions according to the spatial position coordinates of the temperature sensor to obtain a normalized temperature data matrix. The normalized temperature data matrix contains temperature values, spatial coordinates, and timestamp information for subsequent analysis and visualization;

[0048] The normalized temperature data matrix is ​​fitted by using a three-dimensional thin plate spline interpolation algorithm to generate a three-dimensional real-time temperature distribution model of the furnace. Combined with the furnace geometric structure parameters obtained from the computer-aided design software, the three-dimensional temperature distribution model is meshed to construct a furnace temperature finite element model.

[0049] Import the furnace temperature finite element model into ANSYS finite element analysis software, set the furnace wall material properties and combustion heat load boundary condition parameters, perform transient thermal analysis of the furnace temperature field, and obtain the estimated value of the temperature at any position in the furnace that changes with time;

[0050] Extract the furnace temperature distribution data under the boiler design conditions from the design parameter database, obtain the standard furnace combustion temperature distribution data based on fuel characteristics and burner structure from the combustion model database, compare and analyze the deviations between the temperature estimation values ​​obtained by numerical simulation and the design values ​​and standard values, and trigger an audible and visual alarm signal when the temperature deviation exceeds the preset threshold value obtained based on the statistical analysis of historical operating data or a local high temperature abnormal area appears;

[0051] The Unity3D engine is used to develop a furnace 3D visualization system. The furnace geometry model, 3D temperature distribution model, key position temperature-time curve and alarm information are rendered into the same virtual scene to generate a 3D visualization interface of the furnace combustion status. The interface is presented in real time through human-computer interaction equipment. The visualization system receives and processes alarm signals and highlights abnormal temperature areas on the interface.

[0052] Based on the abnormal temperature alarm location information, combined with the furnace thermal calculation model, the burner three-dimensional flame model and historical operation data, multiple groups of combustion control parameter adjustment plans are generated through the Monte Carlo random simulation method combined with expert experience rules, and the impact of each adjustment plan on the furnace temperature distribution is predicted. The optimal combustion control parameter adjustment plan is screened out to form intelligent decision-making suggestions;

[0053] According to the three-dimensional visualization interface information and intelligent decision-making suggestions, the burner primary air damper opening, secondary air nozzle angle, fuel feed rate combustion control parameters are remotely adjusted through the distributed control system. The distributed control system includes a field controller, an operator station and an engineer station to achieve precise control of the furnace combustion process. During the adjustment process, temperature changes are continuously monitored to verify the adjustment effect. When necessary, parameter fine-tuning is performed until the abnormal furnace temperature distribution is eliminated.

[0054] The present invention discloses a method for visualizing the combustion state of a boiler. The temperature data is collected in real time by embedding a thermocouple sensor in the inner wall of the furnace, and a temperature distribution model is generated after filtering and three-dimensional spatial mapping. The abnormal temperature is alarmed by combining finite element analysis and historical data. The three-dimensional visualization technology is used to present the combustion state of the furnace in real time, and a combustion control parameter adjustment scheme is generated based on an intelligent decision-making algorithm. The burner parameters are remotely adjusted through a distributed control system to achieve precise control of the furnace temperature and improve the operating efficiency and safety of the boiler.

[0055] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. A method for visualizing boiler combustion status, characterized in that: The method comprises: Embed multiple thermocouple temperature sensors in the gaps of refractory bricks on the inner wall of the boiler furnace to collect temperature data at different depths of the furnace in real time, and transmit the temperature data to the data acquisition terminal through the wireless communication module; The collected furnace temperature data is subjected to adaptive Kalman filtering denoising, and the temperature data is mapped in three dimensions according to the spatial position coordinates of the temperature sensor to obtain a normalized temperature data matrix. The normalized temperature data matrix contains temperature values, spatial coordinates, and timestamp information for subsequent analysis and visualization; The normalized temperature data matrix is ​​fitted by using a three-dimensional thin plate spline interpolation algorithm to generate a three-dimensional real-time temperature distribution model of the furnace. Combined with the furnace geometric structure parameters obtained from the computer-aided design software, the three-dimensional temperature distribution model is meshed to construct a furnace temperature finite element model. Import the furnace temperature finite element model into ANSYS finite element analysis software, set the furnace wall material properties and combustion heat load boundary condition parameters, perform transient thermal analysis of the furnace temperature field, and obtain the estimated value of the temperature at any position in the furnace that changes with time; Extract the furnace temperature distribution data under the boiler design conditions from the design parameter database, obtain the standard furnace combustion temperature distribution data based on fuel characteristics and burner structure from the combustion model database, compare and analyze the deviations between the temperature estimation values ​​obtained by numerical simulation and the design values ​​and standard values, and trigger an audible and visual alarm signal when the temperature deviation exceeds the preset threshold value obtained based on the statistical analysis of historical operating data or a local high temperature abnormal area appears; The Unity3D engine is used to develop a furnace 3D visualization system. The furnace geometry model, 3D temperature distribution model, key position temperature-time curve and alarm information are rendered into the same virtual scene to generate a 3D visualization interface of the furnace combustion status. The interface is presented in real time through human-computer interaction equipment. The visualization system receives and processes alarm signals and highlights abnormal temperature areas on the interface. Based on the abnormal temperature alarm location information, combined with the furnace thermal calculation model, the burner three-dimensional flame model and historical operation data, multiple groups of combustion control parameter adjustment plans are generated through the Monte Carlo random simulation method combined with expert experience rules, and the impact of each adjustment plan on the furnace temperature distribution is predicted. The optimal combustion control parameter adjustment plan is screened out to form intelligent decision-making suggestions; According to the three-dimensional visualization interface information and intelligent decision-making suggestions, the burner primary air damper opening, secondary air nozzle angle, fuel feed rate combustion control parameters are remotely adjusted through the distributed control system. The distributed control system includes a field controller, an operator station and an engineer station to achieve precise control of the furnace combustion process. During the adjustment process, temperature changes are continuously monitored to verify the adjustment effect. When necessary, parameter fine-tuning is performed until the abnormal furnace temperature distribution is eliminated.

2. The method according to claim 1, characterized in that The method includes embedding a plurality of thermocouple temperature sensors in the gaps of the refractory bricks on the inner wall of the boiler furnace, collecting temperature data at different depths of the furnace in real time, and transmitting the temperature data to the data acquisition terminal through the wireless communication module, including: According to the structure and size of the boiler furnace, the layout and quantity of the thermocouple temperature sensors are determined. The sensors are embedded in the gaps of the refractory bricks on the inner wall of the furnace to collect temperature data at different depths. The thermocouple temperature sensors collect furnace temperature data in real time, and the temperature signals are amplified and filtered by the signal conditioning circuit to obtain stable and reliable temperature measurement values. The processed temperature data is encoded and compressed by the wireless communication module, and transmitted to the data acquisition terminal through the wireless network to achieve remote transmission of temperature data. The data acquisition terminal receives the wirelessly transmitted temperature data, restores the original temperature measurement value through decoding and decompression algorithms, and performs time synchronization and data format conversion. The restored temperature data is stored in the database to establish a historical record and trend of the temperature data. Analysis provides data support for subsequent data mining and visualization analysis. Data mining algorithms are used to analyze temperature data. Clustering and classification methods are used to identify the distribution characteristics and change patterns of the furnace temperature field to determine whether there is combustion anomaly. If there is an obvious deviation or abnormal fluctuation in the temperature field distribution, the early warning mechanism is triggered to generate combustion anomaly early warning information. The early warning content is displayed through the human-machine interface and an audible and visual alarm is issued. After obtaining the combustion anomaly early warning information, combustion optimization adjustment suggestions are automatically generated, such as adjusting the burner angle and pulverized coal injection parameters, to guide the operating personnel to adjust the combustion conditions. The furnace temperature field data and distribution status are displayed in real time in the form of curves and thermal maps on the human-machine interface, the combustion conditions are intuitively presented, and visual decision support is provided for the optimized operation of the boiler.

3. The method according to claim 1, characterized in that The collected furnace temperature data is subjected to adaptive Kalman filtering denoising, and the temperature data is mapped in three dimensions according to the spatial position coordinates of the temperature sensor to obtain a normalized temperature data matrix; the normalized temperature data matrix contains temperature values, spatial coordinates and timestamp information for subsequent analysis and visualization, including: According to the boiler structure and combustion mode, several infrared temperature measuring probes are arranged around the furnace to collect furnace temperature data; Perform adaptive Kalman filter denoising on the collected furnace temperature data to eliminate random noise interference in the temperature data; Obtain the spatial position coordinates of each temperature sensor, and map the denoised temperature data into three-dimensional space according to the spatial position of the sensor; Through three-dimensional space mapping, a normalized temperature data matrix is ​​obtained, which contains temperature values, space coordinates and timestamp information; An image recognition algorithm is used to analyze the normalized temperature data matrix to determine the combustion uniformity in the furnace; If it is determined that there is an abnormal combustion state in the furnace, a warning message will be generated and an alarm will be issued to alert the operator; Based on the normalized temperature data matrix, the combustion state of the furnace is visualized through three-dimensional visualization technology; Obtain the historical curve of temperature field data and provide historical data export function to facilitate subsequent analysis and optimization; Integrate warning information, visualization results and historical data into the boiler combustion abnormal state warning system to provide decision support for boiler operation optimization control.

4. The method according to claim 1, characterized in that: The three-dimensional thin plate spline interpolation algorithm is used to fit the normalized temperature data matrix to generate a three-dimensional real-time temperature distribution model of the furnace; the three-dimensional temperature distribution model is meshed in combination with the furnace geometric structure parameters obtained from the computer-aided design software to construct a furnace temperature finite element model, including: According to the boiler structure and combustion mode, several infrared probes are arranged around the furnace to collect furnace temperature data in real time to form an original temperature data matrix; Preprocess the original temperature data matrix, remove outliers, and standardize it to obtain a normalized temperature data matrix; The three-dimensional thin plate spline interpolation algorithm is used to fit the normalized temperature data matrix to generate a three-dimensional real-time temperature distribution model of the furnace; Obtain furnace geometry parameters from computer-aided design software, mesh the three-dimensional temperature distribution model, and construct a furnace temperature finite element model; Through finite element analysis, calculate the temperature gradient and temperature distribution uniformity index of each point inside the furnace to determine whether there is an abnormal combustion state of partial burning; If the temperature gradient and uniformity index exceed the preset threshold, the biased burning warning is triggered, and the location of the biased burning area is determined in combination with the furnace geometry; According to the location of the biased burning area and the parameters of the boiler combustion control system, an intelligent optimization algorithm is used to calculate the combustion adjustment strategy and generate adjustment instructions; Send the combustion adjustment command to the boiler control system to realize automatic adjustment of the combustion state and continue to monitor the changes in furnace temperature distribution in real time; The furnace temperature distribution model, biased burning warning information and combustion adjustment instruction data are stored in the historical database for subsequent analysis and optimization.

5. The method according to claim 1, characterized in that The furnace temperature finite element model is imported into the ANSYS finite element analysis software, the furnace wall material properties and combustion heat load boundary condition parameters are set, and the furnace temperature field transient thermal analysis is performed to obtain the estimated value of the temperature at any position in the furnace that changes with time, including: According to the boiler furnace structure and combustion mode, a three-dimensional geometric model of the furnace is established, and the mesh is divided to generate a finite element model; Using ANSYS finite element analysis software, the furnace temperature finite element model was imported into the software; Obtain the physical property parameters of the furnace wall material and set the furnace wall material properties in the software; By analyzing the boiler combustion system and operating parameters, determine the combustion heat load distribution in the furnace, and set the combustion heat load as a boundary condition in the software; If you need to analyze the change of furnace temperature over time, set the time step and total duration of transient thermal analysis in the software; ANSYS software is used to solve the finite element equation of the furnace temperature field and obtain the numerical solution of the furnace temperature field; Through the post-processing module, the node temperature value at any position of the furnace is extracted to generate the temperature cloud map and temperature curve; Determine whether the furnace temperature field distribution meets the boiler combustion requirements. If not, return to step 4 to adjust the combustion heat load boundary conditions. Obtain furnace temperature field distribution results that meet the requirements, and derive the estimated temperature value of any position in the furnace that changes with time, providing data support for subsequent boiler combustion optimization control.

6. The method according to claim 1, characterized in that The furnace temperature distribution data under the boiler design condition is extracted from the design parameter database, and the standard furnace combustion temperature distribution data based on the fuel characteristics and the burner structure is obtained from the combustion model database; the temperature estimation value obtained by numerical simulation is compared with the design value and the standard value for deviation analysis, and when the temperature deviation exceeds the preset threshold value obtained based on the statistical analysis of historical operation data or a local high temperature abnormal area appears, an audible and visual alarm signal is triggered, including: According to the boiler structure and combustion mode specified in the project document, match and search the corresponding boiler model design parameter database to obtain the furnace temperature distribution data under the design working conditions of the boiler model; Using the fuel characteristics and burner structure data given in the project file, connect to the combustion model database to obtain the standard furnace combustion temperature distribution data under this condition; If the boiler is a boiler with "several infrared probes" as mentioned in the project document, then an intelligent algorithm is constructed and executed based on the infrared probes arranged around the furnace to obtain furnace combustion visualization images and real-time temperature field data; According to the preset numerical simulation calculation program, the real-time data of the temperature field obtained by the infrared probe is input into the numerical simulation model to calculate the temperature estimation value; The temperature estimate obtained by numerical simulation is used as input, and the temperature deviation between the three is calculated and obtained by using the "statistical analysis based on historical operation data" link mentioned in the project document and the procedure of comparison and analysis with the design value and standard value; If the temperature deviation value exceeds the "preset threshold value based on statistical analysis of historical operation data" mentioned in the technical goal of "establishing an early warning system" in the project document, the alarm program will be triggered and the alarm information processing link will be entered. If the temperature deviation value is within the preset threshold value, the furnace temperature is judged to be normal, the state is maintained, and the process starting from the third step will be continued; If the alarm program determines that there is a "local abnormal high temperature area" mentioned in the project file in the current furnace, it will send a control signal to the corresponding hardware device to obtain an audible and visual alarm signal; Obtain the sound and light alarm signal and the current boiler temperature data, and generate the corresponding temperature field data history curve chart in combination with the function point "capable of displaying the temperature field data history curve" mentioned in the project file; The temperature field data historical curve chart and sound and light alarm records are displayed through the chart display system. Combined with the "historical data can be exported" function mentioned in the project file, the relevant data within the time period is output and saved in the specified database.

7. The method according to claim 1, characterized in that The three-dimensional visualization system of the furnace is developed by using the Unity3D engine, and the furnace geometric structure model, the three-dimensional temperature distribution model, the temperature-time curve of the key position and the alarm information are rendered into the same virtual scene, and a three-dimensional visualization interface of the furnace combustion state is generated, which is presented in real time through the human-computer interaction device; the visualization system receives and processes the alarm signal, and highlights the abnormal temperature area on the interface, including: Obtain furnace geometry data, design an infrared temperature measurement array based on boiler structure and combustion mode, arrange several infrared probes, and obtain a furnace geometry model; If the total project budget of RMB 3.8 million is confirmed, the CAD drawing data of the furnace will be imported according to the furnace geometry model, and a 3D model will be constructed through the modeling tools in the Unity3D engine to determine the basic model of the furnace 3D visualization system; The temperature data in the furnace is collected in real time by the infrared probe. If the historical data export is confirmed, the temperature information data at multiple locations in the furnace are statistically analyzed to obtain a three-dimensional temperature distribution model. According to the temperature data of each position in the three-dimensional temperature distribution model, an interpolation algorithm is used to generate continuous temperature field data to obtain a smooth temperature distribution curve; Obtain temperature distribution curves and historical temperature data, establish a temperature threshold database based on a budget of 50,000 yuan for expert consultation fees, and determine the criteria for abnormal temperature; The real-time temperature data continuously detected by the installed infrared probe, which was supported by a budget of RMB 160,000 for on-site construction, was compared with the temperature threshold database. If the threshold range was exceeded, an alarm signal was issued; Through the rendering function of the Unity3D engine, the furnace geometry model, 3D temperature distribution model and alarm signal are integrated together to obtain a unified virtual scene; According to the data relationship of each object in the virtual scene, write script code to control the display and interaction logic of each object in the virtual scene, and obtain a visual interface with key position temperature time curve and alarm information rendering effect; Through the data acquisition hardware supported by equipment fees of 3.14 million yuan, multiple temperature and image signals are collected, and the collected real-time data is transmitted to the established visualization system using a graphical user interface to obtain and present a real-time updated three-dimensional visualization interface of the furnace combustion status.

8. The method according to claim 1, characterized in that According to the abnormal temperature alarm location information, combined with the furnace thermal calculation model, the burner three-dimensional flame model and historical operation data, a Monte Carlo random simulation method is combined with expert experience rules to generate multiple groups of combustion control parameter adjustment schemes, predict the impact of each adjustment scheme on the furnace temperature distribution, screen out the optimal combustion control parameter adjustment scheme, and form intelligent decision-making suggestions, including: According to the boiler structure and combustion mode, several infrared probes are arranged around the furnace to collect the furnace temperature field distribution data in real time; The collected temperature field distribution data is analyzed through image recognition algorithm to determine whether there is an abnormal combustion state; If combustion anomaly is detected, the position coordinate information of the abnormal area is obtained; According to the coordinates of the abnormal position, the theoretical temperature value at the position is calculated by combining the furnace thermal calculation model and the burner three-dimensional flame model; Compare the theoretical temperature value with the actual measured temperature value to obtain temperature deviation data; Monte Carlo random simulation method is used to generate multiple groups of combustion control parameter adjustment schemes by combining historical operation data and expert experience rules; For each group of adjustment schemes, the influence on the furnace temperature field distribution is predicted through the furnace thermal calculation model; Compare the predicted effects of various adjustment plans and select the optimal plan that can minimize temperature deviation; The optimal combustion control parameter adjustment plan is formed into an intelligent decision-making suggestion and sent to the DCS control system for execution, while an alarm is issued to alert the operating personnel.

9. The method according to claim 1, characterized in that: According to the three-dimensional visualization interface information and intelligent decision-making suggestions, the primary air damper opening, secondary air nozzle angle, and fuel coal supply combustion control parameters of the burner are remotely adjusted through the distributed control system; the distributed control system includes a field controller, an operator station, and an engineer station to achieve precise control of the furnace combustion process; during the adjustment process, the temperature changes are continuously monitored to verify the adjustment effect, and the parameters are fine-tuned when necessary until the abnormal distribution of the furnace temperature is eliminated, including: Step 1: Design an infrared temperature measurement array according to the boiler structure and combustion mode, obtain data from several infrared probes around the furnace, and obtain basic data of the furnace temperature field; Step 2: Obtain the basic data of the furnace temperature field data, use intelligent algorithms to process and analyze the data, determine whether there is abnormal distribution of furnace temperature, and obtain the diagnosis results of abnormal temperature status; Step 3: If the furnace temperature is normal, the control parameters of the current distributed control system are maintained to obtain a stable boiler combustion process; Step 4: If the furnace temperature shows abnormal distribution, the image recognition algorithm is used to analyze the combustion uniformity in the furnace, and combined with the boiler combustion abnormality state early warning system of computer vision, the type and location information of the combustion abnormality is obtained to obtain the specific situation of the abnormal state of partial burning; Step 5: According to the specific situation of the abnormal state of biased burning and the information on the three-dimensional visualization interface, combined with the preset intelligent decision-making rule library, it is determined whether to adjust the combustion control parameters of the primary air damper opening, the secondary air nozzle angle and the fuel supply amount of the burner, and obtain intelligent decision-making suggestions; Step 6: If the combustion control parameters are not adjusted, return to step 4 to continuously monitor the abnormal state; Step 7: If the combustion control parameter is adjusted, the current value of the corresponding control parameter is obtained through the distributed control system field controller to obtain the initial state; Step 8: According to the initial state and in combination with the logic setting of the control algorithm, the burner primary air damper opening, secondary air nozzle angle and fuel supply are automatically adjusted, and the control parameter target value is determined through communication and calculation between the operator station and the engineer station of the distributed control system; Step 9: Based on the infrared probe data, monitor the furnace temperature changes in real time, combine it with previous data to determine the adjustment effect, and obtain the temperature anomaly elimination status.

10. A boiler combustion status visualization system, characterized in that: The system performs the following steps: Embed multiple thermocouple temperature sensors in the gaps of refractory bricks on the inner wall of the boiler furnace to collect temperature data at different depths of the furnace in real time, and transmit the temperature data to the data acquisition terminal through the wireless communication module; The collected furnace temperature data is subjected to adaptive Kalman filtering denoising, and the temperature data is mapped in three dimensions according to the spatial position coordinates of the temperature sensor to obtain a normalized temperature data matrix. The normalized temperature data matrix contains temperature values, spatial coordinates, and timestamp information for subsequent analysis and visualization; The normalized temperature data matrix is ​​fitted by using a three-dimensional thin plate spline interpolation algorithm to generate a three-dimensional real-time temperature distribution model of the furnace. Combined with the furnace geometric structure parameters obtained from the computer-aided design software, the three-dimensional temperature distribution model is meshed to construct a furnace temperature finite element model. Import the furnace temperature finite element model into ANSYS finite element analysis software, set the furnace wall material properties and combustion heat load boundary condition parameters, perform transient thermal analysis of the furnace temperature field, and obtain the estimated value of the temperature at any position in the furnace that changes with time; Extract the furnace temperature distribution data under the boiler design conditions from the design parameter database, obtain the standard furnace combustion temperature distribution data based on fuel characteristics and burner structure from the combustion model database, compare and analyze the deviations between the temperature estimation values ​​obtained by numerical simulation and the design values ​​and standard values, and trigger an audible and visual alarm signal when the temperature deviation exceeds the preset threshold value obtained based on the statistical analysis of historical operating data or a local high temperature abnormal area appears; The Unity3D engine is used to develop a furnace 3D visualization system. The furnace geometry model, 3D temperature distribution model, key position temperature-time curve and alarm information are rendered into the same virtual scene to generate a 3D visualization interface of the furnace combustion status. The interface is presented in real time through human-computer interaction equipment. The visualization system receives and processes alarm signals and highlights abnormal temperature areas on the interface. Based on the abnormal temperature alarm location information, combined with the furnace thermal calculation model, the burner three-dimensional flame model and historical operation data, multiple groups of combustion control parameter adjustment plans are generated through the Monte Carlo random simulation method combined with expert experience rules, and the impact of each adjustment plan on the furnace temperature distribution is predicted. The optimal combustion control parameter adjustment plan is screened out to form intelligent decision-making suggestions; According to the three-dimensional visualization interface information and intelligent decision-making suggestions, the burner primary air damper opening, secondary air nozzle angle, fuel feed rate combustion control parameters are remotely adjusted through the distributed control system. The distributed control system includes a field controller, an operator station and an engineer station to achieve precise control of the furnace combustion process. During the adjustment process, temperature changes are continuously monitored to verify the adjustment effect. When necessary, parameter fine-tuning is performed until the abnormal furnace temperature distribution is eliminated.

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