Intelligent combustion optimization control system and method for boiler

Abstract

An intelligent combustion optimization control system and method for a boiler, relating to the technical field of boiler combustion control. The method comprises: a determination module, used for acquiring boiler combustion thermal imaging data, determining a combustion stability coefficient of a current boiler on the basis of the boiler combustion thermal imaging data, and determining, on the basis of the combustion stability coefficient of the current boiler, whether combustion optimization is needed; a strategy module, used for, if combustion optimization is needed, acquiring current boiler operation data and an operation load, and determining a current combustion optimization strategy on the basis of the current boiler operation data and the operation load; and an optimization module, used for determining a standard stability coefficient on the basis of the current combustion optimization strategy and determining a final combustion optimization strategy on the basis of the standard stability coefficient and the combustion stability coefficient of the current boiler. The present invention can accurately monitor the combustion state of a boiler during operation and, on the basis of the combustion state, formulate a combustion optimization strategy in a timely manner so that the boiler can maintain stable combustion.

Description

A boiler intelligent combustion optimization control system and method Technical Field

[0001] This application relates to the field of boiler combustion control technology, and more specifically, to an intelligent combustion optimization control system and method for boilers. Background Technology

[0002] At present, thermal power generation still occupies an important position in my country's power development. Thermal power plants mainly generate electricity through boiler combustion. In the process of converting fuel into electricity, the emission of pollutants and greenhouse gases is unavoidable. With the introduction of a series of control standards and policies for environmental pollution by the state, thermal power plants are actively and steadily promoting carbon peaking and carbon neutrality by optimizing boiler combustion efficiency. Based on my country's energy resource endowment, they are adhering to the principle of establishing before dismantling, implementing carbon peaking actions in a planned and step-by-step manner, deeply promoting the energy revolution, strengthening the clean and efficient utilization of coal, and accelerating the planning and construction of a new energy system.

[0003] In existing technologies, during the flexible peak-shaving process of the unit, the unit often operates at a low load. Due to the problem of unstable flame in the combustion system of coal-fired boilers, the fuel cannot be fully utilized, and the unit's combustion efficiency is reduced. Summary of the Invention

[0004] This invention provides an intelligent combustion optimization control system and method for boilers, which solves the problem of unstable flame in existing boiler combustion systems, including:

[0005] The judgment module is used to acquire boiler combustion thermal imaging data, determine the current combustion stability coefficient of the boiler based on the boiler combustion thermal imaging data, and determine whether combustion optimization is needed based on the current combustion stability coefficient of the boiler.

[0006] The strategy module is used to obtain the current boiler operating data and operating load if combustion optimization is required, and to determine the current combustion optimization strategy based on the current boiler operating data and operating load.

[0007] The optimization module is used to determine the standard stability coefficient based on the current combustion optimization strategy, and to determine the final combustion optimization strategy based on the standard stability coefficient and the current combustion stability coefficient of the boiler.

[0008] Furthermore, the judgment module determines the current combustion stability coefficient of the boiler based on boiler combustion thermal imaging data, including:

[0009] The boiler combustion thermal imaging data is segmented to obtain several combustion image blocks, and the pixel value of each pixel in the combustion image block is obtained.

[0010] The average pixel value of the burning image block is determined based on the pixel value of each pixel. The difference between the average pixel value of the burning image block and the average pixel value of the other burning image blocks within an 8-neighborhood is calculated. A burning direction image is then established based on the difference between the average pixel value of the burning image block and the average pixel value of the other burning image blocks within an 8-neighborhood.

[0011] Calculate the degree of each combustion image block in the combustion direction image, obtain the average value of the pixel value difference between each combustion image block and its adjacent combustion image blocks, and multiply the average value of the pixel value difference by the corresponding degree to obtain the stability coefficient of the combustion image block.

[0012] The average stability coefficient of the boiler is obtained by calculating the average stability coefficient of all combustion image blocks in the combustion direction image.

[0013] Further, the step of establishing a combustion direction image based on the difference between the average pixel value of the combustion image patch and the average pixel value of the remaining combustion image patches within an 8-neighborhood range includes:

[0014] Two burning image blocks with an average pixel value difference greater than a first preset threshold are selected, and the two burning image blocks with an average pixel value difference greater than the first preset threshold are connected to obtain a burning direction image;

[0015] Adjacent combustion image blocks that are not connected to other combustion image blocks are merged, and the merged combustion image blocks are stitched together to obtain a combustion direction image.

[0016] Furthermore, the judgment module determines whether combustion optimization is needed based on the current combustion stability coefficient of the boiler, including:

[0017] Obtain the preset combustion tolerance coefficient and calculate the difference between the preset combustion tolerance coefficient and the combustion stability coefficient;

[0018] Determine whether the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is greater than the second preset threshold. If the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is greater than the second preset threshold, then it is determined that the boiler needs to be optimized for combustion.

[0019] If the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is less than or equal to the second preset threshold, it is determined that the boiler does not need to be optimized for combustion.

[0020] Furthermore, the strategy module determines the current combustion optimization strategy based on the current boiler operating data and operating load, including:

[0021] Obtain the historical operating database of the boiler, and determine the historical operating parameters and operating load of the boiler based on the historical operating database;

[0022] A sample dataset is established based on the historical operating parameters and operating load of the boiler, and k initial cluster centers are randomly selected from the sample dataset.

[0023] Calculate the Euclidean distance from the sample values ​​in the sample dataset to the initial cluster centers, and divide each sample data into the corresponding partition based on the Euclidean distance from the sample values ​​in the sample dataset to the initial cluster centers;

[0024] Calculate the average value of the sample data within each partition, and redetermine the cluster centers based on the average value of the sample data within each partition;

[0025] Repeat the above steps until the cluster centers no longer change or the number of iterations reaches the preset iteration threshold, to obtain k final cluster centers;

[0026] Based on the final cluster center, determine the standard operating data and operating load of each cluster partition in the historical operating database. Based on the standard operating data and operating load of each cluster partition, establish a combustion optimization strategy model. Based on the combustion optimization strategy model, determine the current combustion optimization strategy.

[0027] Furthermore, the step of establishing a combustion optimization strategy model based on the standard operating data and operating load of each cluster partition, and determining the current combustion optimization strategy based on the combustion optimization strategy model, includes:

[0028] Based on the boiler historical operation database, determine the combustion optimization strategy corresponding to the standard operating data and operating load, and establish a training sample set based on the standard operating data, operating load and corresponding combustion optimization strategy.

[0029] An initial combustion optimization strategy model is established, and the initial combustion optimization strategy model is trained based on the training sample set to obtain a trained combustion optimization strategy model.

[0030] Input the current boiler operating data and operating load into the trained combustion optimization strategy model to obtain the current combustion optimization strategy.

[0031] Furthermore, the optimization module determines a standard stability coefficient based on the current combustion optimization strategy, including:

[0032] Obtain standard operating data for each cluster partition, determine the combustion thermal imaging data corresponding to the standard operating data for each cluster partition based on the boiler historical operating database, and calculate the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data.

[0033] The optimal stability coefficient is determined based on the current combustion optimization strategy. The optimal stability coefficient is then corrected based on the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data, thus obtaining the standard stability coefficient.

[0034] Furthermore, the step of correcting the optimal stability coefficient based on the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data to obtain the standard stability coefficient includes:

[0035] The optimal stability coefficient is corrected according to the stability coefficient correction formula, which is as follows:

[0036]

[0037] in, The standard stability coefficient, The optimal stability coefficient is . To preset the standard matching degree, The degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data. It is a natural exponential function.

[0038] Furthermore, the optimization module determines the final combustion optimization strategy based on the standard stability coefficient and the current boiler's combustion stability coefficient, including:

[0039] Calculate the difference between the standard stability coefficient and the current combustion stability coefficient, determine the optimization adjustment speed based on the difference between the standard stability coefficient and the current combustion stability coefficient, and determine the final combustion optimization strategy based on the optimization adjustment speed.

[0040] To achieve the above objectives, the present invention also provides an intelligent combustion optimization control method for boilers, comprising:

[0041] Acquire boiler combustion thermal imaging data, determine the current boiler combustion stability coefficient based on the boiler combustion thermal imaging data, and determine whether combustion optimization is needed based on the current boiler combustion stability coefficient;

[0042] If combustion optimization is required, obtain the current boiler operating data and operating load, and determine the current combustion optimization strategy based on the current boiler operating data and operating load;

[0043] The standard stability coefficient is determined based on the current combustion optimization strategy, and the final combustion optimization strategy is determined based on the standard stability coefficient and the current combustion stability coefficient of the boiler.

[0044] The beneficial effects of this invention are as follows:

[0045] By applying the above technical solutions, this invention obtains an accurate combustion stability coefficient by analyzing boiler combustion thermal imaging data, and dynamically adjusts the boiler's combustion optimization strategy based on the combustion stability coefficient, enabling the boiler to maintain stable combustion and improve boiler thermal efficiency. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 shows a schematic diagram of the results of an intelligent combustion optimization control system for boilers proposed in an embodiment of the present invention;

[0048] Figure 2 shows the overall flowchart of an intelligent combustion optimization control method for boilers proposed in an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] This application provides an intelligent combustion optimization control system for a boiler, as shown in Figure 1, including:

[0051] The judgment module is used to acquire boiler combustion thermal imaging data, determine the current combustion stability coefficient of the boiler based on the boiler combustion thermal imaging data, and determine whether combustion optimization is needed based on the current combustion stability coefficient of the boiler. The strategy module is used to acquire the current boiler operating data and operating load if combustion optimization is needed, and determine the current combustion optimization strategy based on the current boiler operating data and operating load. The optimization module is used to determine the standard stability coefficient based on the current combustion optimization strategy, and determine the final combustion optimization strategy based on the standard stability coefficient and the current boiler combustion stability coefficient.

[0052] In this embodiment, a fixed infrared thermal imaging camera is used to collect boiler combustion thermal imaging data. Based on the real-time combustion stability coefficient, it is determined whether combustion optimization is required. If combustion optimization is required, the current boiler operating data and operating load are collected to establish a combustion optimization strategy. At the same time, the combustion optimization strategy is adjusted by comparing the standard stability coefficient with the current boiler combustion stability coefficient to determine the final combustion optimization strategy.

[0053] In some embodiments of this application, the determination module determines the current combustion stability coefficient of the boiler based on boiler combustion thermal imaging data, including: segmenting the boiler combustion thermal imaging data to obtain several combustion image blocks, and obtaining the pixel value of each pixel in the combustion image block; determining the average pixel value of the combustion image block based on the pixel value of each pixel, calculating the difference between the average pixel value of the combustion image block and the average pixel value of the remaining combustion image blocks within an 8-neighborhood, and establishing a combustion direction image based on the difference between the average pixel value of the combustion image block and the average pixel value of the remaining combustion image blocks within an 8-neighborhood; calculating the degree of each combustion image block in the combustion direction image, obtaining the average value of the pixel value difference between each combustion image block and the adjacent combustion image block in the combustion direction image, multiplying the average value of the pixel value difference by the corresponding degree to obtain the stability coefficient of the combustion image block; and calculating the average value of the stability coefficients of all combustion image blocks in the combustion direction image to obtain the combustion stability coefficient of the boiler.

[0054] In this embodiment, the boiler combustion thermal imaging data is divided into several small image blocks according to the size of the thermal imaging data. A combustion direction image is established by the difference between the average pixel value of the combustion image block and the average pixel value of the other combustion image blocks within an 8-neighborhood. The degree of each combustion image block is the number of other combustion image blocks connected to it. The combustion stability coefficient of the boiler is calculated by multiplying the average difference of the pixel values ​​of each combustion image block by the corresponding degree. The larger the value, the greater the temperature fluctuation of the boiler combustion.

[0055] In some embodiments of this application, the step of establishing a combustion direction image based on the difference between the average pixel value of a combustion image block and the average pixel value of other combustion image blocks within an 8-neighborhood range includes: selecting two combustion image blocks whose average pixel value difference is greater than a first preset threshold, connecting the two combustion image blocks whose average pixel value difference is greater than the first preset threshold to obtain a combustion direction image; merging adjacent combustion image blocks that are not connected to other combustion image blocks, and stitching the merged combustion image blocks together to obtain a combustion direction image.

[0056] In this embodiment, by filtering two combustion image blocks whose average pixel value difference is greater than a first preset threshold, two combustion image blocks with large temperature differences are obtained and connected, and unconnected combustion image blocks are merged to obtain a combustion direction image. The combustion direction image can more intuitively confirm the temperature fluctuation of boiler combustion.

[0057] In some embodiments of this application, the determination module determines whether combustion optimization is needed based on the current combustion stability coefficient of the boiler, including: obtaining a preset combustion tolerance coefficient and calculating the difference between the preset combustion tolerance coefficient and the combustion stability coefficient; determining whether the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is greater than a second preset threshold; if the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is greater than the second preset threshold, then the boiler needs to be optimized for combustion; if the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is less than or equal to the second preset threshold, then the boiler does not need to be optimized for combustion.

[0058] In this embodiment, the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is used to determine whether the boiler needs combustion optimization, so as to realize the dynamic adjustment of the boiler combustion strategy.

[0059] In some embodiments of this application, the strategy module determines the current combustion optimization strategy based on the current boiler operating data and operating load, including: acquiring a historical boiler operating database; determining historical boiler operating parameters and operating load based on the historical boiler operating database; establishing a sample dataset based on the historical boiler operating parameters and operating load; randomly selecting k initial cluster centers from the sample dataset; calculating the Euclidean distance from the sample values ​​in the sample dataset to the initial cluster centers; dividing each sample data into corresponding partitions based on the Euclidean distance from the sample values ​​in the sample dataset to the initial cluster centers; calculating the average value of the sample data in each partition; re-determining the cluster centers based on the average value of the sample data in each partition; repeating the above steps iteratively until the cluster centers no longer change or the number of iterations reaches a preset iteration threshold, obtaining k final cluster centers; determining the standard operating data and operating load of each cluster partition in the historical operating database based on the final cluster centers; establishing a combustion optimization strategy model based on the standard operating data and operating load of each cluster partition; and determining the current combustion optimization strategy based on the combustion optimization strategy model.

[0060] In some embodiments of this application, the step of establishing a combustion optimization strategy model based on the standard operating data and operating load of each cluster partition, and determining the current combustion optimization strategy based on the combustion optimization strategy model, includes: determining the combustion optimization strategy corresponding to the standard operating data and operating load based on the boiler historical operating database; establishing a training sample set based on the standard operating data and operating load and the corresponding combustion optimization strategy; establishing an initial combustion optimization strategy model; training the initial combustion optimization strategy model based on the training sample set to obtain a trained combustion optimization strategy model; and inputting the current boiler operating data and operating load into the trained combustion optimization strategy model to obtain the current combustion optimization strategy.

[0061] In this embodiment, the historical operating parameters and corresponding operating loads of the boiler are clustered based on the k-means clustering algorithm to obtain standard operating data and operating loads under various operating conditions. An initial combustion optimization strategy model is established through a deep learning neural network model. The initial combustion optimization strategy model is trained using the standard operating data and operating loads. The current boiler operating data and operating loads are then input into the trained combustion optimization strategy model to obtain the current combustion optimization strategy.

[0062] In some embodiments of this application, the optimization module determines the standard stability coefficient based on the current combustion optimization strategy, including: acquiring standard operating data for each cluster partition; determining the combustion thermal imaging data corresponding to the standard operating data for each cluster partition based on the boiler historical operating database; calculating the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data; determining the optimal stability coefficient based on the current combustion optimization strategy; and correcting the optimal stability coefficient based on the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data to obtain the standard stability coefficient.

[0063] In this embodiment, a preset optimal combustion thermal imaging data is established using modeling software. The degree of matching between the combustion thermal imaging data corresponding to the standard operating data of each cluster partition and the preset optimal combustion thermal imaging data is calculated using the combustion thermal imaging data corresponding to the standard operating data of each cluster partition. The optimal stability coefficient of the current combustion optimization strategy is then corrected to obtain the standard stability coefficient.

[0064] In some embodiments of this application, the step of correcting the optimal stability coefficient based on the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data to obtain the standard stability coefficient includes:

[0065] The optimal stability coefficient is corrected according to the stability coefficient correction formula, which is as follows:

[0066]

[0067] in, The standard stability coefficient, The optimal stability coefficient is . To preset the standard matching degree, The degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data. It is a natural exponential function.

[0068] In this embodiment, the optimal stability coefficient is corrected by the difference between the matching degree of the combustion thermal imaging data corresponding to the preset standard matching degree and the preset optimal combustion thermal imaging data. The larger the difference, the larger the corresponding standard stability coefficient.

[0069] In some embodiments of this application, the optimization module determines the final combustion optimization strategy based on the standard stability coefficient and the current combustion stability coefficient of the boiler, including: calculating the difference between the standard stability coefficient and the current combustion stability coefficient, determining the optimization adjustment speed based on the difference between the standard stability coefficient and the current combustion stability coefficient, and determining the final combustion optimization strategy based on the optimization adjustment speed.

[0070] In this embodiment, a mapping table is preset between the stability coefficient difference and the corresponding optimization adjustment speed. The boiler optimization speed is adjusted by the difference between the standard stability coefficient and the current combustion stability coefficient. The larger the difference, the higher the corresponding optimization adjustment speed, so that the combustion optimization adjustment effect of the boiler is better and the boiler thermal efficiency is effectively improved.

[0071] Based on the same technical concept, as shown in Figure 2, the present invention also provides an intelligent combustion optimization control method for boilers, comprising:

[0072] S101, acquire boiler combustion thermal imaging data, determine the current boiler combustion stability coefficient based on the boiler combustion thermal imaging data, and determine whether combustion optimization is needed based on the current boiler combustion stability coefficient;

[0073] S102, If combustion optimization is required, obtain the current boiler operating data and operating load, and determine the current combustion optimization strategy based on the current boiler operating data and operating load;

[0074] S103, determine the standard stability coefficient based on the current combustion optimization strategy, and determine the final combustion optimization strategy based on the standard stability coefficient and the current combustion stability coefficient of the boiler.

[0075] By applying the above technical solutions, this invention employs a judgment module to acquire boiler combustion thermal imaging data, determine the current boiler combustion stability coefficient based on the data, and determine whether combustion optimization is necessary based on the current boiler combustion stability coefficient. A strategy module, if combustion optimization is required, acquires the current boiler operating data and operating load, and determines the current combustion optimization strategy based on these data. An optimization module determines a standard stability coefficient based on the current combustion optimization strategy, and then determines the final combustion optimization strategy based on the standard stability coefficient and the current boiler combustion stability coefficient. This invention can accurately monitor the combustion state during boiler operation and promptly formulate combustion optimization strategies based on the combustion state, enabling the boiler to maintain stable combustion.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An intelligent combustion optimization control system for boilers, characterized in that, include: The judgment module is used to acquire boiler combustion thermal imaging data, determine the current combustion stability coefficient of the boiler based on the boiler combustion thermal imaging data, and determine whether combustion optimization is needed based on the current combustion stability coefficient of the boiler. The strategy module is used to obtain the current boiler operating data and operating load if combustion optimization is required, and to determine the current combustion optimization strategy based on the current boiler operating data and operating load. The optimization module is used to determine the standard stability coefficient based on the current combustion optimization strategy, and to determine the final combustion optimization strategy based on the standard stability coefficient and the current combustion stability coefficient of the boiler.

2. The intelligent combustion optimization control system for boilers according to claim 1, characterized in that, The judgment module determines the current combustion stability coefficient of the boiler based on boiler combustion thermal imaging data, including: The boiler combustion thermal imaging data is segmented to obtain several combustion image blocks, and the pixel value of each pixel in the combustion image block is obtained. The average pixel value of the burning image block is determined based on the pixel value of each pixel. The difference between the average pixel value of the burning image block and the average pixel value of the other burning image blocks within an 8-neighborhood is calculated. A burning direction image is then established based on the difference between the average pixel value of the burning image block and the average pixel value of the other burning image blocks within an 8-neighborhood. Calculate the degree of each combustion image block in the combustion direction image, obtain the average value of the pixel value difference between each combustion image block and its adjacent combustion image blocks, and multiply the average value of the pixel value difference by the corresponding degree to obtain the stability coefficient of the combustion image block. The average stability coefficient of the boiler is obtained by calculating the average stability coefficient of all combustion image blocks in the combustion direction image.

3. The intelligent combustion optimization control system for boilers according to claim 2, characterized in that, The step of establishing a combustion direction image based on the difference between the average pixel value of the combustion image patch and the average pixel value of the remaining combustion image patches within an 8-neighborhood range includes: Two burning image blocks with an average pixel value difference greater than a first preset threshold are selected, and the two burning image blocks with an average pixel value difference greater than the first preset threshold are connected to obtain a burning direction image; Adjacent combustion image blocks that are not connected to other combustion image blocks are merged, and the merged combustion image blocks are stitched together to obtain a combustion direction image.

4. The intelligent combustion optimization control system for boilers according to claim 3, characterized in that, The judgment module determines whether combustion optimization is needed based on the current combustion stability coefficient of the boiler, including: Obtain the preset combustion tolerance coefficient and calculate the difference between the preset combustion tolerance coefficient and the combustion stability coefficient; Determine whether the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is greater than the second preset threshold. If the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is greater than the second preset threshold, then it is determined that the boiler needs to be optimized for combustion. If the difference between the preset combustion tolerance coefficient and the combustion stability coefficient is less than or equal to the second preset threshold, it is determined that the boiler does not need to be optimized for combustion.

5. The intelligent combustion optimization control system for boilers according to claim 1, characterized in that, The strategy module determines the current combustion optimization strategy based on the current boiler operating data and operating load, including: Obtain the historical operating database of the boiler, and determine the historical operating parameters and operating load of the boiler based on the historical operating database; A sample dataset is established based on the historical operating parameters and operating load of the boiler, and k initial cluster centers are randomly selected from the sample dataset. Calculate the Euclidean distance from the sample values ​​in the sample dataset to the initial cluster centers, and divide each sample data into the corresponding partition based on the Euclidean distance from the sample values ​​in the sample dataset to the initial cluster centers; Calculate the average value of the sample data within each partition, and redetermine the cluster centers based on the average value of the sample data within each partition; Repeat the above steps until the cluster centers no longer change or the number of iterations reaches the preset iteration threshold, to obtain k final cluster centers; Based on the final cluster center, determine the standard operating data and operating load of each cluster partition in the historical operating database. Based on the standard operating data and operating load of each cluster partition, establish a combustion optimization strategy model. Based on the combustion optimization strategy model, determine the current combustion optimization strategy.

6. The intelligent combustion optimization control system for boilers according to claim 5, characterized in that, The process of establishing a combustion optimization strategy model based on the standard operating data and operating load of each cluster partition, and determining the current combustion optimization strategy based on the combustion optimization strategy model, includes: Based on the boiler historical operation database, determine the combustion optimization strategy corresponding to the standard operating data and operating load, and establish a training sample set based on the standard operating data, operating load and corresponding combustion optimization strategy. An initial combustion optimization strategy model is established, and the initial combustion optimization strategy model is trained based on the training sample set to obtain a trained combustion optimization strategy model. Input the current boiler operating data and operating load into the trained combustion optimization strategy model to obtain the current combustion optimization strategy.

7. The intelligent combustion optimization control system for boilers according to claim 6, characterized in that, The optimization module determines the standard stability coefficient based on the current combustion optimization strategy, including: Obtain standard operating data for each cluster partition, determine the combustion thermal imaging data corresponding to the standard operating data for each cluster partition based on the boiler historical operating database, and calculate the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data. The optimal stability coefficient is determined based on the current combustion optimization strategy. The optimal stability coefficient is then corrected based on the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data, thus obtaining the standard stability coefficient.

8. The intelligent combustion optimization control system for boilers according to claim 7, characterized in that, The process of correcting the optimal stability coefficient based on the degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data to obtain the standard stability coefficient includes: The optimal stability coefficient is corrected according to the stability coefficient correction formula, which is as follows: ； in, The standard stability coefficient, The optimal stability coefficient is... To preset the standard matching degree, The degree of matching between the combustion thermal imaging data corresponding to the standard operating data and the preset optimal combustion thermal imaging data. It is a natural exponential function.

9. The intelligent combustion optimization control system for boilers according to claim 1, characterized in that, The optimization module determines the final combustion optimization strategy based on the standard stability coefficient and the current boiler's combustion stability coefficient, including: Calculate the difference between the standard stability coefficient and the current combustion stability coefficient, determine the optimization adjustment speed based on the difference between the standard stability coefficient and the current combustion stability coefficient, and determine the final combustion optimization strategy based on the optimization adjustment speed.

10. A method for intelligent combustion optimization control of a boiler, characterized in that, include: Acquire boiler combustion thermal imaging data, determine the current boiler combustion stability coefficient based on the boiler combustion thermal imaging data, and determine whether combustion optimization is needed based on the current boiler combustion stability coefficient; If combustion optimization is required, obtain the current boiler operating data and operating load, and determine the current combustion optimization strategy based on the current boiler operating data and operating load; The standard stability coefficient is determined based on the current combustion optimization strategy, and the final combustion optimization strategy is determined based on the standard stability coefficient and the current combustion stability coefficient of the boiler.

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