Method and system for determining high-temperature corrosion position of boiler based on coupled BP neural network

Abstract

The invention belongs to the field of boiler detection, and discloses a method and a system for determining a high-temperature corrosion position of a boiler based on a coupled BP neural network, which are used for mapping complex multi-dimensional data into corrosion risk distribution by utilizing boiler operation records, monitoring data and historical data and through the learning ability of the BP neural network, and quickly positioning a high-temperature corrosion high-incidence area. Compared with a traditional manual detection method, the method can cover all areas in the boiler, and human subjectivity and blind areas are avoided. According to the method, the operation condition, the fire coal component and the corrosion mechanism model are combined, the comprehensive influence of multi-factor interaction on corrosion is fully considered through BP neural network coupling analysis, and the prediction accuracy and credibility are improved.

Description

Technical Field

[0001] The invention belongs to the field of boiler detection, and in particular relates to a method and a system for determining a boiler high-temperature corrosion position based on a coupled BP neural network. Background Art

[0002] A boiler is an energy conversion device. The energy input into the boiler includes chemical energy and electrical energy in the fuel. The boiler outputs steam, high-temperature water or organic heat carrier with a certain amount of thermal energy. The original meaning of "pot" refers to a water container heated on fire, and "furnace" refers to a place where fuel is burned. The boiler consists of two major parts: the pot and the furnace.

[0003] The current method on the market for determining the location of high-temperature corrosion of boilers based on coupled BP neural networks is very difficult to observe and detect directly when inspecting boilers because the boilers are in a high-temperature environment. Some advanced inspection equipment is difficult to apply directly to the inside of the boiler, and the corrosion process in the early stages of corrosion is very concealed and difficult to detect. Due to the large filter volume, it is difficult for staff to inspect all locations when conducting corrosion location inspections. The inspection range is wide and it is difficult to determine the corrosion location. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcoming that it is difficult to inspect all positions, and to provide a method and system for determining the high-temperature corrosion position of a boiler based on a coupled BP neural network.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for determining a boiler high temperature corrosion location based on a coupled BP neural network, comprising the following steps: Obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler and historical data; Analyze the composition of coal in areas prone to high-temperature corrosion to obtain the composition of coal; Establish a corrosion model based on the composition of coal in areas prone to high-temperature corrosion; The operation records and monitoring data of the boiler are fed into the corrosion model to obtain the specific situation of the boiler's high-temperature corrosion.

[0006] A further improvement of the present invention is that the specific method of obtaining the operation records and monitoring data of the boiler and determining the high-incidence area of ​​high-temperature corrosion based on the operation records and monitoring data of the boiler combined with historical data is as follows: Obtain the operation records and monitoring data of the boiler, and perform data cleaning and standardization on the operation records and monitoring data; Perform time series analysis on the processed operating data to identify areas of abnormal temperature and pressure fluctuations; By comparing the abnormal fluctuation areas of temperature and pressure with historical data, the high-incidence areas of high-temperature corrosion are obtained.

[0007] A further improvement of the present invention is that the composition of the coal in the high-incidence area of ​​high-temperature corrosion is analyzed, and the specific method for obtaining the composition of the coal is as follows: Obtain coal from areas prone to high-temperature corrosion, crush and screen the coal, control the particle size to meet the requirements of the analytical instrument, and pre-treat the screened coal to remove moisture; The elements in the pretreated coal are determined by an element analyzer to obtain the coal composition.

[0008] A further improvement of the present invention is that, according to the coal composition in the high-incidence area of ​​high-temperature corrosion, the specific method of establishing the corrosion model is as follows: A preliminary mathematical model was established based on the chemical reaction mechanism between coal components and metal corrosion products in high-incidence areas of high-temperature corrosion. The preliminary mathematical model was trained with different coal compositions to obtain the corrosion model.

[0009] A further improvement of the present invention is that the operation record and monitoring data of the boiler are fed into the corrosion model, and the specific method for obtaining the specific situation of the high temperature corrosion of the boiler is as follows: Obtain the operation records and monitoring data of the boiler, align the operation records and monitoring data in time series, and perform standardization and feature extraction; The extracted features are fed into the corrosion model to obtain high temperature corrosion rate, corrosion depth and product distribution indicators.

[0010] In a second aspect, the present invention provides a system for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network, comprising: The data acquisition module is used to obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler combined with historical data; The component analysis module is used to analyze the composition of the coal in the high-incidence area of ​​high-temperature corrosion to obtain the composition of the coal; Model building module, used to build corrosion models based on the composition of coal in areas prone to high-temperature corrosion; The data processing module is used to input the operation records and monitoring data of the boiler into the corrosion model to obtain the specific situation of the high-temperature corrosion of the boiler.

[0011] A further improvement of the present invention is that the function of the component analysis module is realized by the following method: Obtain coal from areas prone to high-temperature corrosion, crush and screen the coal, control the particle size to meet the requirements of the analytical instrument, and pre-treat the screened coal to remove moisture; The elements in the pretreated coal are determined by an element analyzer to obtain the coal composition.

[0012] A further improvement of the present invention is that the model building module obtains coal from a high-incidence area of ​​high-temperature corrosion, crushes and screens the coal, controls the particle size to meet the requirements of the analytical instrument, and pre-treats the screened coal to eliminate moisture; The elements in the pretreated coal are determined by an element analyzer to obtain the coal composition.

[0013] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for determining a location of high-temperature corrosion of a boiler based on a coupled BP neural network are implemented.

[0014] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for determining a location of high-temperature corrosion of a boiler based on a coupled BP neural network.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses boiler operation records, monitoring data and historical data, and through the learning ability of BP neural network, maps complex multidimensional data into corrosion risk distribution, and quickly locates high-incidence areas of high-temperature corrosion. Compared with traditional manual detection methods, the present invention can cover all areas inside the boiler, avoiding the existence of human subjectivity and blind spots. The present invention combines operating conditions, coal composition and corrosion mechanism model, and through BP neural network coupling analysis, fully considers the comprehensive impact of multi-factor interaction on corrosion, and improves the accuracy and credibility of prediction. Under complex operating conditions, the model of the present invention can automatically learn nonlinear relationships and adapt to dynamic changes in boiler operating conditions, such as load fluctuations, coal ratio adjustment, etc. The present invention directly inputs boiler operation records and monitoring data into the corrosion model, automatically outputs corrosion locations and risk distribution, and significantly reduces the frequency and intensity of manual detection. The present invention does not need to frequently shut down for comprehensive inspections, but only needs to focus on high-risk areas for key inspections, thereby improving the operating efficiency of the boiler and reducing economic losses. Through accurate prediction of high-temperature corrosion locations, the present invention can take targeted measures (such as local anti-corrosion coatings, adding preservatives, etc.), effectively reducing resource waste and unnecessary comprehensive protection. After the present invention predicts the corrosion situation, the operating personnel can formulate a maintenance plan in advance according to the results, optimize the maintenance cycle, and avoid sudden equipment damage due to unpredicted corrosion problems. The BP neural network continuously optimizes the corrosion model by learning new boiler operation data and actual detection feedback, and improves the prediction ability and adaptability. In summary, the present invention utilizes the nonlinear mapping and self-learning ability of the BP neural network, and realizes the accurate prediction of the high-temperature corrosion location of the boiler by coupling the boiler operation data, coal composition and corrosion mechanism. It has the comprehensive advantages of high efficiency, accuracy, economy and safety, and provides a scientific basis and optimization plan for boiler operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 This is a system diagram of Example 5. DETAILED DESCRIPTION

[0017] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0018] See also Figure 1 , a method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network, comprising the following steps: S1, obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler combined with historical data.

[0019] S2, analyzing the composition of the coal in the high-incidence area of ​​high-temperature corrosion to obtain the composition of the coal.

[0020] S3, establish a corrosion model based on the composition of coal in areas with high incidence of high-temperature corrosion.

[0021] S4, sending the operation records and monitoring data of the boiler into the corrosion model to obtain the specific situation of the high-temperature corrosion of the boiler.

[0022] See also Figure 2 , a system for determining the location of high-temperature corrosion of boilers based on coupled BP neural networks, including: The data acquisition module is used to obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler combined with historical data; The component analysis module is used to analyze the composition of the coal in the high-incidence area of ​​high-temperature corrosion to obtain the composition of the coal; Model building module, used to build corrosion models based on the composition of coal in areas prone to high-temperature corrosion; The data processing module is used to input the operation records and monitoring data of the boiler into the corrosion model to obtain the specific situation of the high-temperature corrosion of the boiler.

[0023] Embodiment 1: Obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler and historical data as follows: Collect boiler operation records, including key parameters (such as tube wall temperature, flue gas temperature, combustion pressure, steam flow rate, etc.) and long-term historical maintenance data. Extract monitoring data, such as flue gas composition (SO2, HCl, etc.), deposit thickness, and temperature difference changes. Summarize historical fault and maintenance records, and mark the specific locations and characteristics of high-temperature corrosion.

[0024] Remove invalid, missing or outlier values ​​to ensure data quality. Align the data time axis to enable comparison across parameters and devices.

[0025] Group the data by location, time period and operating status to establish a unified database, providing a basis for subsequent analysis.

[0026] Perform time series analysis on operating data to identify areas of abnormal temperature and pressure fluctuations. Compare historical data to find operating patterns related to high-temperature corrosion, such as overheating and combustion fluctuations.

[0027] Combine the coal composition, combustion conditions and historical corrosion locations to analyze the coupling effect of temperature and chemical composition. Use algorithms such as regression analysis or neural networks to explore the correlation between corrosion areas and parameters and find potential corrosion causes.

[0028] Based on the analysis results, the boiler area is divided into high, medium and low corrosion risk areas, and the corresponding parameter characteristics are recorded.

[0029] Embodiment 2: The specific method for analyzing the composition of coal in areas with high incidence of high-temperature corrosion is as follows: In the coal supply chain corresponding to the high-incidence areas of high-temperature corrosion, select typical representative samples, including raw coal and blended coal from different sources. Follow standard sampling methods (such as GB / T 474-2008 "Preparation Method of Coal Samples") to ensure that the sampling is representative and consistent.

[0030] The collected coal samples are crushed and sieved to control the particle size to meet the requirements of the analytical instrument (such as 200 mesh powder). The samples are pre-treated using drying equipment to eliminate the interference of moisture on subsequent analysis.

[0031] Each batch of samples is numbered, and its source, sampling time and boiler operating conditions are recorded to facilitate subsequent correlation analysis.

[0032] Elemental analyzers (such as CHN analyzers) are used to determine the carbon (C), hydrogen (H), nitrogen (N) and sulfur (S) content in coal and identify potential corrosive components (such as sulfur and chlorine). Alkali metal elements such as sodium (Na) and potassium (K) in coal are determined by atomic absorption spectrometry (AAS) or inductively coupled plasma mass spectrometry (ICP-MS).

[0033] The ash content of coal is measured using a high-temperature furnace, and the mineral composition of the ash (such as sulfates and chlorides) is analyzed in combination with X-ray diffraction (XRD). The content of corrosive salts (such as KCl and NaCl) in the ash is quantitatively measured to assess the corrosion risk of coal combustion.

[0034] Thermogravimetric analysis-gas chromatography / mass spectrometry (TGA-GC / MS) technology was used to determine the release characteristics of sulfide and chloride during coal heating.

[0035] Establish the corresponding relationship between coal combustion products and corrosive gases, and predict the highly corrosive substances produced in actual operation.

[0036] The coal composition is correlated with boiler operation data (such as flue gas composition and temperature distribution in high-temperature areas) to analyze the distribution and effects of corrosive components in the boiler.

[0037] Regression analysis or multivariate statistical models are used to evaluate the contribution of sulfur, chlorine, sodium, potassium and other components in coal to high-temperature corrosion.

[0038] Combine the coal composition analysis results with historical data to construct a coal corrosion risk level system (such as low, medium, and high risk).

[0039] Output data charts (such as component distribution diagrams, risk assessment reports) to intuitively display key information for operators.

[0040] Embodiment 3: According to the coal composition in the high-incidence area of ​​high-temperature corrosion, the specific method of establishing the corrosion model is as follows: Collect coal composition data (such as sulfur, chlorine, sodium, potassium content, etc.) corresponding to high-temperature corrosion areas. Synchronously integrate boiler operation data, including tube wall temperature, combustion gas composition, sediment chemical composition, and relevant historical corrosion cases and regional failure records.

[0041] The quantitative relationship between coal composition and corrosion rate is extracted using experimental data (such as metal weight loss experiments and microstructure analysis). The formation process and key influencing factors (such as melting point and volatility) of corrosive deposits in high-temperature areas are analyzed.

[0042] Standardize the coal composition and operating condition data to eliminate the dimensional differences between different data sources and provide consistent input for model construction.

[0043] According to the chemical reaction mechanism between coal components (such as S, Cl, Na, K) and metal corrosion products, a preliminary mathematical model (such as oxidation, sulfidation and chlorination reaction kinetic model) is established. The regulating effect of operating conditions such as temperature, pressure and combustion atmosphere (oxygen partial pressure, flue gas composition) on the corrosion rate is considered, and the coupling effect of environmental factors is supplemented. Key parameters such as corrosion reaction rate constant and activation energy are fitted using experimental data to ensure the applicability of the model to actual working conditions. Through multi-region data training, the adaptability of the corrosion rate under different coal compositions and operating conditions is determined.

[0044] Based on model calculation, the corrosion rate distribution of the high-temperature area of ​​the boiler under different coal ratios and working conditions is simulated. The corrosion risk map is output to identify high-risk areas and provide direction for subsequent protection.

[0045] Reproduce the typical operating environment of the boiler in the laboratory, test the effect of the concentration of key components in the coal (such as S and Cl) on the corrosion rate, and verify the reliability of the model prediction results. Collect actual corrosion data in the high-temperature area of ​​the boiler, compare the model calculation values, evaluate the error range and adjust the model parameters.

[0046] The model is embedded in the online monitoring system, and the coal composition and boiler operating parameters are input in real time to generate dynamic corrosion prediction data. The monitoring data is compared with the model output, and the model is regularly calibrated and deviations are recorded to improve the applicability of the model.

[0047] Expand the model’s applicability to different coal types and boiler operating modes (e.g., different load conditions). Incorporate machine learning algorithms, use long-term boiler monitoring data to continuously optimize the model, and build an adaptive corrosion prediction system.

[0048] Embodiment 4: The specific method of feeding the boiler's operation records and monitoring data into the corrosion model to obtain the specific situation of the boiler's high-temperature corrosion is as follows: The pre-processed boiler operation data is input into the corrosion model to simulate the corrosion behavior in the high temperature area under the current combustion conditions. The model calculates specific indicators such as high temperature corrosion rate, corrosion depth and product distribution based on the coal composition, operating parameters and historical data.

[0049] Output the corrosion rate distribution diagram of different parts of the boiler (such as superheater, economizer, etc.) under the current operating conditions. According to the risk assessment module in the corrosion model, mark the high-risk areas and their corresponding corrosion characteristics (such as sulfate deposition, chloride corrosion).

[0050] During the model operation, the impact of boiler load fluctuations or changes in coal composition on high-temperature corrosion is simulated, and the dynamic changes of corrosion rate under different conditions are evaluated.

[0051] Based on the corrosion distribution and rate data output by the model, a boiler high-temperature corrosion diagnosis report is generated, including specific corrosion location, rate, depth and main cause analysis. High-risk areas are marked first to provide a basis for maintenance and operation optimization.

[0052] Compare the model output results with the actual boiler inspection data (such as ultrasonic thickness measurement and infrared thermal imaging results) to verify the accuracy of the model prediction. Analyze the prediction error and optimize the key parameters in the model (such as corrosion rate constant and influencing factor weight).

[0053] According to the corrosion analysis results, targeted operation adjustment suggestions are put forward, such as optimizing the coal ratio, reducing local overheating or adjusting the flue gas composition. Based on the model verification, the corrosion monitoring plan is dynamically adjusted to provide closed-loop support for real-time prediction and control.

[0054] Embodiment 5: See also Figure 3As shown, the present invention also provides an electronic device 100 for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0055] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network as described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0056] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0057] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for determining a boiler high temperature corrosion location based on a coupled BP neural network, and the processor 102 can execute the plurality of instructions to implement: Obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler and historical data; Analyze the composition of coal in areas prone to high-temperature corrosion to obtain the composition of coal; Establish a corrosion model based on the composition of coal in areas prone to high-temperature corrosion; The operation records and monitoring data of the boiler are fed into the corrosion model to obtain the specific situation of the boiler's high-temperature corrosion.

[0058] Embodiment 6: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0059] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network, characterized in that: The following steps are involved: Obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler and historical data; Analyze the composition of coal in areas prone to high-temperature corrosion to obtain the composition of coal; Establish a corrosion model based on the composition of coal in areas prone to high-temperature corrosion; The operation records and monitoring data of the boiler are fed into the corrosion model to obtain the specific situation of the boiler's high-temperature corrosion.

2. The method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to claim 1 is characterized in that: Obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler and historical data as follows: Obtain the operation records and monitoring data of the boiler, and perform data cleaning and standardization on the operation records and monitoring data; Perform time series analysis on the processed operating data to identify areas of abnormal temperature and pressure fluctuations; By comparing the abnormal fluctuation areas of temperature and pressure with historical data, the high-incidence areas of high-temperature corrosion are obtained.

3. The method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to claim 1 is characterized in that: The specific method for analyzing the composition of coal in areas with high incidence of high-temperature corrosion is as follows: Obtain coal from areas prone to high-temperature corrosion, crush and screen the coal, control the particle size to meet the requirements of the analytical instrument, and pre-treat the screened coal to remove moisture; The elements in the pretreated coal are determined by an element analyzer to obtain the coal composition.

4. The method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to claim 1, characterized in that: According to the coal composition in the high-incidence area of ​​high-temperature corrosion, the specific method of establishing the corrosion model is as follows: A preliminary mathematical model was established based on the chemical reaction mechanism between coal components and metal corrosion products in high-incidence areas of high-temperature corrosion. The preliminary mathematical model was trained with different coal compositions to obtain the corrosion model.

5. The method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to claim 1, characterized in that: The specific method of feeding the boiler's operation records and monitoring data into the corrosion model to obtain the specific situation of the boiler's high-temperature corrosion is as follows: Obtain the operation records and monitoring data of the boiler, align the operation records and monitoring data in time series, and perform standardization and feature extraction; The extracted features are fed into the corrosion model to obtain high temperature corrosion rate, corrosion depth and product distribution indicators.

6. A system for determining the location of high-temperature corrosion of boilers based on coupled BP neural networks, characterized in that: include: The data acquisition module is used to obtain the operation records and monitoring data of the boiler, and determine the high-incidence areas of high-temperature corrosion based on the operation records and monitoring data of the boiler combined with historical data; The component analysis module is used to analyze the composition of the coal in the high-incidence area of ​​high-temperature corrosion to obtain the composition of the coal; Model building module, used to build corrosion models based on the composition of coal in areas prone to high-temperature corrosion; The data processing module is used to input the operation records and monitoring data of the boiler into the corrosion model to obtain the specific situation of the high-temperature corrosion of the boiler.

7. The system for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to claim 6 is characterized in that: The functions of the component analysis module are implemented by the following methods: Obtain coal from areas prone to high-temperature corrosion, crush and screen the coal, control the particle size to meet the requirements of the analytical instrument, and pre-treat the screened coal to remove moisture; The elements in the pretreated coal are determined by an element analyzer to obtain the coal composition.

8. The system for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to claim 6, characterized in that: Model building module, obtains coal from areas with high incidence of high-temperature corrosion, crushes and screens the coal, controls the particle size to meet the requirements of the analytical instrument, and pre-treats the screened coal to eliminate moisture; The elements in the pretreated coal are determined by an element analyzer to obtain the coal composition.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to any one of claims 1 to 5 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining the location of high-temperature corrosion of a boiler based on a coupled BP neural network according to any one of claims 1 to 5 are implemented.

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