Boiler operation optimization method and system

Abstract

The invention relates to a boiler operation optimization method and system, and the method comprises the steps: obtaining a flame image of boiler operation in thermal power generation equipment; based on the flame image, analyzing the flame brightness of the boiler to obtain combustion characteristics; and based on the combustion characteristics, the combustion stability of the boiler is evaluated, and under the condition that the combustion stability does not meet the preset stability condition, the air volume of the boiler and the coal quality entering the boiler are optimized and adjusted. According to the method and the device, the boiler with unstable combustion is optimized and adjusted on the basis of the combustion characteristics of coal combustion in the boiler obtained through analysis, the judgment accuracy and optimization efficiency of the combustion stability of the boiler are improved, energy consumption is reduced, environmental pollution is reduced, the safety and economy of boiler operation are improved, and the method and the device are suitable for popularization and application. The problem of how to ensure safe, stable and efficient operation of the boiler is solved.

Description

Technical Field

[0001] This application relates to the technical field of thermal power generation, and particularly to an optimization method and system for boiler operation. Background Art

[0002] The boiler in thermal power generation equipment is a key device for converting the chemical energy of fuel into thermal energy. It generates high-temperature and high-pressure steam by burning fuels such as coal and natural gas, and then drives the steam turbine to do work and generate electricity.

[0003] With the change of power demand and the improvement of environmental protection requirements, boilers are operating under low load conditions more and more frequently. However, under low load conditions, the combustion stability of the boiler is poor, which easily leads to problems such as incomplete combustion, increased pollutant emissions, and low efficiency.

[0004] Currently, for the problem of how to ensure the safe, stable and efficient operation of boilers in related technologies, no effective solution has been proposed. Summary of the Invention

[0005] Embodiments of this application provide an optimization method and system for boiler operation to at least solve the problem of how to ensure the safe, stable and efficient operation of boilers in related technologies.

[0006] In a first aspect, embodiments of this application provide an optimization method for boiler operation, and the method includes:

[0007] Obtain the flame image of the boiler operation in thermal power generation equipment;

[0008] Based on the flame image, analyze the flame brightness of the boiler to obtain combustion characteristics;

[0009] Based on the combustion characteristics, evaluate the combustion stability of the boiler. In the case where the combustion stability does not meet the preset stability conditions, optimize and adjust the air volume and the coal quality entering the furnace of the boiler.

[0010] In some embodiments, evaluating the combustion stability of the boiler based on the combustion characteristics includes:

[0011] Based on the combustion characteristics of the flame image, calculate the average value and standard deviation of the flame center brightness of each frame of the flame image respectively;

[0012] Based on the standard deviation and average value of the flame center brightness, evaluate the combustion stability of the boiler.

[0013] In some embodiments, evaluating the combustion stability of the boiler based on the standard deviation and average value of the flame center brightness includes:

[0014] Based on the standard deviation and the average value of the brightness of the flame center, if the standard deviation of the brightness of the flame center in consecutive time series is within the first fluctuation range and the average value of the brightness of the flame center in consecutive time series is within the second fluctuation range, then the combustion stability of the boiler meets the preset stability condition;

[0015] Otherwise, the combustion stability of the boiler does not meet the preset stability condition.

[0016] In some embodiments, optimizing and adjusting the air volume and the coal quality entering the furnace of the boiler includes:

[0017] Based on the current condition of the boiler, performing operating condition optimization on the boiler through a boiler efficiency prediction model to obtain an optimization strategy;

[0018] Based on the optimization strategy, optimizing and adjusting the air volume and the coal quality entering the furnace of the boiler.

[0019] In some embodiments, based on the current condition of the boiler, performing operating condition optimization on the boiler through a boiler efficiency prediction model to obtain an optimization strategy includes:

[0020] Based on the current condition of the boiler, inputting the current load data, coal quality data entering the furnace, CO concentration data, carbon content in fly ash data, flue gas temperature data, total air volume data, and the ratio data of primary air to secondary air of the boiler into the boiler efficiency prediction model;

[0021] Executing a genetic algorithm through the boiler efficiency prediction model to obtain the coal quality entering the furnace, the total air volume, and the ratio of primary air to secondary air that meet the preset optimization conditions, where the preset optimization conditions are that the combustion stability of the boiler meets the preset stability condition and the boiler efficiency is not lower than the current boiler efficiency.

[0022] In some embodiments, based on the optimization strategy, optimizing and adjusting the air volume and the coal quality entering the furnace of the boiler includes:

[0023] Based on the coal quality entering the furnace, the total air volume, and the ratio of primary air to secondary air that meet the preset optimization conditions, optimizing and adjusting the current air volume and the coal quality entering the furnace of the boiler.

[0024] In some embodiments, analyzing the flame brightness of the boiler based on the flame image to obtain combustion characteristics includes:

[0025] Based on the flame image, analyzing the flame brightness of the boiler through preset image processing software to obtain combustion characteristics, where the preset image processing software includes MATLAB image processing software and OpenCV image processing software, and the combustion characteristics include flame shape characteristics, flame size characteristics, and brightness distribution characteristics.

[0026] In some of these embodiments, obtaining the flame image of the boiler operating at low load in a thermal power generation device includes:

[0027] Collecting the internal furnace flame image of the boiler operating at low load in the thermal power generation device through a high-temperature and high-speed camera at a rate of 1000 frames per second.

[0028] In some of these embodiments, the boiler efficiency prediction model is a model based on an artificial neural network.

[0029] In a second aspect, an embodiment of the present application provides an optimization system for boiler operation. The system is used to execute the method described in the first aspect above. The system includes a data acquisition module, a feature analysis module, and an optimization adjustment module;

[0030] The data acquisition module is used to obtain the flame image of the boiler operation in the thermal power generation device;

[0031] The feature analysis module is used to analyze the flame brightness of the boiler based on the flame image to obtain combustion characteristics;

[0032] The optimization adjustment module is used to evaluate the combustion stability of the boiler based on the combustion characteristics. In the case where the combustion stability does not meet the preset stability conditions, optimize and adjust the air volume and the coal quality entering the furnace of the boiler.

[0033] Compared with the related art, an optimization method and system for boiler operation provided by an embodiment of the present application, wherein the method includes obtaining the flame image of the boiler operation in the thermal power generation device; analyzing the flame brightness of the boiler based on the flame image to obtain combustion characteristics; evaluating the combustion stability of the boiler based on the combustion characteristics. In the case where the combustion stability does not meet the preset stability conditions, optimize and adjust the air volume and the coal quality entering the furnace of the boiler, realizing the optimization and adjustment of the unstable combustion boiler based on the analyzed combustion characteristics of coal combustion in the boiler, improving the determination accuracy and optimization efficiency of the boiler combustion stability, contributing to reducing energy consumption, reducing environmental pollution, improving the safety and economy of boiler operation, and solving the problem of how to ensure the safe, stable, and efficient operation of the boiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0035] Figure 1 is a flowchart of the steps of the optimization method for boiler operation according to an embodiment of the present application;

[0036] Figure 2 It is a schematic flowchart of a boiler operation optimization method according to an embodiment of the present application;

[0037] Figure 3 It is a schematic internal structure diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0038] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without creative efforts fall within the scope of protection of the present application.

[0039] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in this development process may be complex and time-consuming, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as insufficient disclosure of the content of the present application.

[0040] Referring to "embodiment" in the present application means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0041] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one", "the" and the like involved in this application do not indicate a quantity limitation and may represent a singular or plural number. The terms "including", "comprising", "having" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The words such as "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0042] An embodiment of this application provides an optimization method for boiler operation. Figure 1 It is a flowchart of the steps of the boiler operation optimization method according to the embodiment of this application, as Figure 1 shown. The method includes the following steps:

[0043] Step S102, obtain the flame image of the boiler operation in the thermal power generation equipment;

[0044] Specifically, step S102 of obtaining the flame image of the boiler operation at low load in the thermal power generation equipment includes:

[0045] Collect the internal flame image of the furnace of the boiler operation at low load in the thermal power generation equipment through a high-temperature and high-speed camera at a rate of 1000 frames per second.

[0046] Step S104, analyze the flame brightness of the boiler based on the flame image to obtain combustion characteristics;

[0047] Specifically, step S104 of analyzing the flame brightness of the boiler based on the flame image to obtain combustion characteristics, wherein the preset image processing software includes MATLAB image processing software and OpenCV image processing software, and the combustion characteristics include flame shape characteristics, flame size characteristics and brightness distribution characteristics.

[0048] Step S106: Based on the combustion characteristics, evaluate the combustion stability of the boiler. In the case where the combustion stability does not meet the preset stability conditions, optimize and adjust the air volume and the quality of the coal fed into the furnace of the boiler.

[0049] Step S106 specifically includes the following steps:

[0050] Step S1061: Based on the combustion characteristics of the flame image, calculate the average value and the standard deviation of the brightness of the flame center for each frame of the flame image respectively;

[0051] It should be noted that in Step S106, based on the combustion characteristics of coal combustion in the boiler obtained through analysis, the unstable combustion boiler is optimized and adjusted to improve the determination accuracy and optimization efficiency of the boiler combustion stability, and ensure the safety and economy of the boiler operation. Among them, the average value of the brightness of the flame center can be obtained by averaging the gray values (or color intensities) of the pixel points in the flame center area of each frame of the image, and the standard deviation of the brightness of the flame center can be obtained by calculating the standard deviation of the gray values of the pixel points in the flame center area of each frame of the image. In addition, the average value of the brightness of the flame center reflects the overall luminous intensity of the flame and is an index to measure the overall energy output of the flame. The standard deviation of the brightness of the flame center represents the uniformity of the flame brightness distribution. In other words, for a series of consecutive frames of flame images, if the average value of the brightness of the flame center remains within a relatively stable range and the fluctuation amplitude is small, it indicates that the combustion process is relatively stable; if the standard deviation of the brightness of the flame center maintains a low level, it means that the flame shape is relatively consistent and the combustion state is relatively stable.

[0052] Step S1062: Based on the standard deviation and the average value of the brightness of the flame center, evaluate the combustion stability of the boiler.

[0053] Specifically in Step S1062, based on the standard deviation and the average value of the brightness of the flame center, if the standard deviation of the brightness of the flame center in the continuous time series is within the first fluctuation range and the average value of the brightness of the flame center in the continuous time series is within the second fluctuation range, then the combustion stability of the boiler meets the preset stability conditions; otherwise, the combustion stability of the boiler does not meet the preset stability conditions.

[0054] Step S1063: In the case where the combustion stability does not meet the preset stability conditions, based on the current condition of the boiler, perform operating condition optimization on the boiler through the boiler efficiency prediction model to obtain an optimization strategy; and then optimize and adjust the air volume and the quality of the coal fed into the furnace of the boiler.

[0055] In some preferred embodiments, preferably in Step S1063, Figure 2 is a schematic flow chart of the boiler operation optimization method according to the embodiment of the present application, as Figure 2As shown, when the combustion stability of the boiler is judged by the flame monitoring device and does not meet the preset stability condition; combining with the current condition of the boiler, that is, obtaining the fly ash carbon content data in real time through the fly ash carbon content on-line detection device, obtaining the CO concentration data in real time through the CO on-line monitoring device, and obtaining the in-furnace coal quality data in real time through the (laser Raman) coal quality on-line detection device, inputting the above-mentioned data, as well as the boiler operation parameter data such as boiler load, flue gas temperature, total air volume, and ratio of primary air to secondary air into the boiler efficiency prediction model, wherein, the boiler efficiency prediction model is a model based on artificial neural network;

[0056] As Figure 2 shown, execute the genetic algorithm through the boiler efficiency prediction model to obtain the in-furnace coal quality, total air volume, and ratio of primary air to secondary air that meet the preset optimization conditions, wherein the preset optimization conditions are that the combustion stability of the boiler meets the preset stability condition and the boiler efficiency is not lower than the current boiler efficiency;

[0057] As Figure 2 shown, based on the in-furnace coal quality, total air volume, and ratio of primary air to secondary air that meet the preset optimization conditions, optimize and adjust the current air volume and in-furnace coal quality of the boiler.

[0058] Through the above steps in the embodiments of the present application, comprehensively utilize means such as flame monitoring, CO on-line monitoring, coal quality on-line monitoring, and fly ash carbon content on-line monitoring to determine the stability of the boiler's low-load combustion in real time, and ensure the safe and efficient operation of the boiler by optimizing the combustion control strategy; realize the optimization and adjustment of the unstable combustion boiler based on the combustion characteristics of coal combustion in the boiler obtained by analysis, improve the judgment accuracy and optimization efficiency of the boiler combustion stability, contribute to reducing energy consumption, reducing environmental pollution, improving the safety and economy of the boiler operation, and solve the problem of how to ensure the safe, stable, and efficient operation of the boiler.

[0059] The embodiments of the present application provide an optimization method for boiler operation. If there is no flame monitoring device installed in the above method to evaluate the combustion stability of the boiler, optionally, a flame stability model is constructed to evaluate the combustion stability of the boiler. The specific construction steps are as follows:

[0060] S1, establish a flame stability model.

[0061] S2, construct a historical database. The inputs of the flame stability model include but are not limited to load, total air volume, total coal volume, coal quality information, furnace temperature, damper opening, furnace CO concentration, etc. Each set of data corresponds to a flame stability state. According to the actual operation situation and the fire detection signal, the stability state is divided into: very stable, stable, unstable, very unstable.

[0062] S3. Establish a flame stability model based on an artificial neural network, and perform training and verification. Optimize the model performance by adjusting parameters, cross-validation, etc.

[0063] S4. Use the trained model to analyze real-time data, predict combustion stability, and set a threshold. When the model predicts that the combustion state may be unstable, trigger an alarm.

[0064] S5. Perform timely combustion optimization according to the prediction results to ensure flame stability.

[0065] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0066] The embodiment of the present application provides an optimization system for boiler operation. The system is used to execute the method of the first aspect above. The system includes a data acquisition module, a feature analysis module, and an optimization adjustment module.

[0067] The data acquisition module is used to obtain the flame image of the boiler operation in the thermal power generation equipment.

[0068] The feature analysis module is used to analyze the flame brightness of the boiler based on the flame image to obtain combustion characteristics.

[0069] The optimization adjustment module is used to evaluate the combustion stability of the boiler according to the combustion characteristics, and optimize and adjust the air volume and the coal quality entering the furnace of the boiler when the combustion stability does not meet the preset stability conditions.

[0070] Through the data acquisition module, feature analysis module, and optimization adjustment module in the embodiment of the present application, it is realized to optimize and adjust the unstable combustion boiler based on the combustion characteristics of coal combustion in the boiler obtained by analysis, improve the determination accuracy and optimization efficiency of boiler combustion stability, contribute to reducing energy consumption, reducing environmental pollution, improving the safety and economy of boiler operation, and solving the problem of how to ensure the safe, stable and efficient operation of the boiler.

[0071] It should be noted that the above-mentioned each module can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned each module can be located in the same processor; or the above-mentioned each module can also be located in different processors in any combined form.

[0072] This embodiment also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any of the above method embodiments.

[0073] Optionally, the above electronic device may further include a transmission device and an input / output device. The transmission device is connected to the processor, and the input / output device is connected to the processor.

[0074] It should be noted that the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated herein.

[0075] In addition, in combination with the optimization method for boiler operation in the above embodiments, an embodiment of the present application can provide a storage medium to implement it. A computer program is stored on the storage medium; when the computer program is executed by a processor, it implements any one of the optimization methods for boiler operation in the above embodiments.

[0076] In one embodiment, a computer device is provided. The computer device may be a terminal. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements an optimization method for boiler operation. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0077] In one embodiment, Figure 3 is a schematic internal structure diagram of an electronic device according to an embodiment of the present application. As Figure 3 shown, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as Figure 3As shown. The electronic device includes a processor, a network interface, an internal memory, and a non-volatile memory connected by an internal bus. Among them, the non-volatile memory stores an operating system, computer programs, and a database. The processor is used to provide computing and control capabilities. The network interface is used to communicate with external terminals through a network connection. The internal memory is used to provide an environment for the operation of the operating system and computer programs. The computer program, when executed by the processor, implements an optimization method for boiler operation. The database is used to store data.

[0078] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0079] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application may include non-volatile and / or volatile memories. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0080] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0081] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A method for optimizing boiler operation, characterized in that: The method comprises: Obtain flame images of boilers in thermal power generation equipment; Based on the flame image, analyzing the flame brightness of the boiler to obtain combustion characteristics; Based on the combustion characteristics, the combustion stability of the boiler is evaluated. When the combustion stability does not meet the preset stability condition, the air volume and the quality of coal entering the boiler are optimized and adjusted.

2. The method according to claim 1, characterized in that Based on the combustion characteristics, evaluating the combustion stability of the boiler includes: Based on the combustion characteristics of the flame image, respectively calculating the average value and standard deviation of the flame center brightness of each frame of the flame image; The combustion stability of the boiler is evaluated based on the standard deviation and the average value of the flame center brightness.

3. The method according to claim 2, characterized in that Based on the standard deviation and average value of the flame center brightness, the combustion stability of the boiler is evaluated including: Based on the standard deviation and average value of the flame center brightness, if the standard deviation of the flame center brightness in the continuous time series is within the first fluctuation range, and the average value of the flame center brightness in the continuous time series is within the second fluctuation range, then the combustion stability of the boiler meets the preset stability condition; Otherwise, the combustion stability of the boiler does not meet the preset stability condition.

4. The method according to claim 1, characterized in that Optimizing and adjusting the air volume and coal quality of the boiler includes: Based on the current status of the boiler, optimizing the operating conditions of the boiler through a boiler efficiency prediction model to obtain an optimization strategy; Based on the optimization strategy, the air volume and the quality of coal entering the boiler are optimized and adjusted.

5. The method according to claim 4, characterized in that Based on the current status of the boiler, the operating condition of the boiler is optimized through the boiler efficiency prediction model, and the optimization strategy includes: Based on the current status of the boiler, the current load data of the boiler, the quality data of the coal entering the boiler, the CO concentration data, the carbon content of the fly ash data, the flue gas temperature data, the total air volume data and the ratio data of the primary air and the secondary air are input into the boiler efficiency prediction model; By executing a genetic algorithm through the boiler efficiency prediction model, the coal quality entering the furnace, the total air volume, and the ratio of primary air to secondary air that meet preset optimization conditions are obtained, wherein the preset optimization conditions are that the combustion stability of the boiler meets the preset stability conditions, and the boiler efficiency is not lower than the current boiler efficiency.

6. The method according to claim 5, characterized in that Based on the optimization strategy, optimizing and adjusting the air volume and coal quality of the boiler includes: Based on the inlet coal quality, total air volume and ratio of primary air to secondary air that meet the preset optimization conditions, the current air volume and inlet coal quality of the boiler are optimized and adjusted.

7. The method according to claim 1, characterized in that Based on the flame image, the flame brightness of the boiler is analyzed to obtain combustion characteristics including: Based on the flame image, the flame brightness of the boiler is analyzed by preset image processing software to obtain combustion characteristics, wherein the preset image processing software includes MATLAB image processing software and OpenCV image processing software, and the combustion characteristics include flame shape characteristics, flame size characteristics and brightness distribution characteristics.

8. The method according to claim 1, characterized in that Obtaining flame images of boilers in thermal power generation equipment at low load includes: The flame images inside the furnace of the boiler in the thermal power generation equipment at low load are collected by a high-temperature resistant high-speed camera at a rate of 1000 frames per second.

9. The method according to claim 4, characterized in that The boiler efficiency prediction model is a model based on artificial neural network.

10. A boiler operation optimization system, characterized in that: The system is used to execute the method according to any one of claims 1 to 9, and the system comprises a data acquisition module, a feature analysis module and an optimization and adjustment module; The data acquisition module is used to obtain flame images of boilers in thermal power generation equipment; The feature analysis module is used to analyze the flame brightness of the boiler according to the flame image to obtain combustion features; The optimization and adjustment module is used to evaluate the combustion stability of the boiler according to the combustion characteristics, and optimize and adjust the air volume and coal quality of the boiler when the combustion stability does not meet the preset stability conditions.

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