Coal-fired boiler twinning and prediction system and method based on multi-physics field coupling

Abstract

The invention discloses a coal-fired boiler twinning and prediction system and method based on multi-physics field coupling. The system comprises a multi-physics field modeling unit 201, a holographic data generation unit 202, a situation prediction unit 203 and an autonomous optimization unit 204. The method comprises the steps of S101, multi-physics field coupling model construction, S102, combustion field holographic feature generation, S103, deep space-time network modeling and S104, autonomous optimization and verification. Through deep fusion of multi-physics field coupling and artificial intelligence, accurate prediction and autonomous optimization of the combustion situation of the coal-fired boiler are realized, and an innovative solution is provided for clean and efficient coal-fired power generation.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent optimization of energy equipment, and particularly relates to a coal-fired boiler twin and prediction system and method based on multi-physical field coupling. Background Art

[0002] As the core equipment of the thermal power generation system, the real-time monitoring and precise regulation of the combustion state of coal-fired boilers are of great significance for improving the energy efficiency of the unit and reducing pollutant emissions. The traditional boiler monitoring system mainly relies on the discrete measurement point data collected by the distributed control system (DCS), and locally adjusts the combustion parameters through empirical formulas, but there are significant technical bottlenecks in complex multi-physical field coupling scenarios:

[0003] 1. Limitation of single physical field monitoring: The existing DCS system can only obtain local measurement point data and cannot reveal the multi-physical field coupling mechanism of flow-combustion-heat transfer in the furnace;

[0004] 2. Lack of situation prediction ability: The prediction method relying on empirical formulas is difficult to accurately predict key parameters such as the change of combustion intensity distribution and the trend of pollutant generation;

[0005] 3. Strong control lag: The feedback regulation mode based on historical data cannot achieve early intervention in combustion anomalies.

[0006] In recent years, although some studies have tried to combine computational fluid dynamics (CFD) with data-driven models to improve the combustion simulation accuracy, its calculation takes up to several hours and depends on fixed boundary conditions, which cannot meet the real-time requirements of online optimization. In addition, the existing deep learning models generally have problems of violating physical laws in combustion prediction. For example, there is an order-of-magnitude deviation between the predicted NOx concentration distribution and the actual chemical reaction kinetics results, resulting in insufficient model credibility.

[0007] In summary, developing a digital twin system that can deeply integrate the multi-physical field coupling mechanism and real-time prediction ability has become the key direction to break through the technical bottleneck of coal-fired boiler combustion optimization. Summary of the Invention

[0008] In view of the above problems, the present invention provides a coal-fired boiler twin and prediction system and method based on multi-physical field coupling, and the main purpose is to solve the problems of the lack of multi-physical field coupling mechanism and the difficult prediction of combustion situation evolution law in traditional monitoring methods.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] A coal-fired boiler twin prediction method based on multi-physical field coupling, comprising:

[0011] Construct a multi - physical - field coupling model based on the burner topology data, heating surface structure parameters, and medium transmission characteristic curves of a coal - fired boiler;

[0012] Drive the calculation of the multi - physical - field coupling model through the historical operation data set to generate a holographic feature matrix of the combustion field;

[0013] Based on a deep spatio - temporal network, perform evolutionary modeling on the holographic feature matrix for the combustion intensity distribution, pollutant generation trend, and wall heat load to form a digital twin of the coal - fired boiler.

[0014] A further improvement of the present invention lies in that the burner topology data includes: a three - dimensional space coordinate set of the burner, a distribution feature vector of air distribution structure, and a nozzle size parameter matrix.

[0015] A further improvement of the present invention lies in that the heating surface structure parameters include: a heat resistance distribution tensor of the tube bank, a fin contact heat resistance coefficient matrix, and an ash fouling dynamic correction factor.

[0016] A further improvement of the present invention lies in that the medium transmission characteristic curves include: a flue gas viscosity - temperature response function, an ash particle deposition rate curve, and a radiation absorption coefficient spectrum.

[0017] A further improvement of the present invention lies in that the historical operation data set contains spatio - temporal evolution characteristics, specifically including:

[0018] A load fluctuation time - series sequence, a coal quality variation feature matrix, and a set of air distribution strategy vectors;

[0019] A total air volume dynamic response curve, a powder distribution cloud map of each layer of burners, and a burner air temperature gradient tensor;

[0020] And a historical distribution set of the wall temperature field and a historical vector set of the heat flux density of the heating surface.

[0021] A further improvement of the present invention lies in that the method further includes:

[0022] When the combustion situation prediction result of the digital twin exceeds the safety threshold, generate a combustion environment adjustment plan through an autonomous optimization strategy;

[0023] Based on physical constraint verification and field feature stability analysis, determine the optimal parameter configuration of the digital twin.

[0024] A coal - fired boiler twin prediction system based on multi - physical - field coupling includes:

[0025] A multi - physical - field modeling unit for constructing a multi - physical - field coupling model based on the burner topology data, heating surface structure parameters, and medium transmission characteristic curves of a coal - fired boiler;

[0026] A holographic data generation unit drives the calculation of a multi-physical field coupling model through a historical operation data set to generate a holographic feature matrix of the combustion field;

[0027] A situation prediction unit models the evolution of the combustion intensity distribution, pollutant generation trend, and wall heat load based on a deep spatio-temporal network for the holographic feature matrix to form a digital twin of the coal-fired boiler.

[0028] A further improvement of the present invention is that it further includes:

[0029] An autonomous optimization unit, when the combustion situation prediction result of the digital twin exceeds the safety threshold, generates a combustion environment adjustment plan through an autonomous optimization strategy; based on physical constraint verification and field feature stability analysis, determines the optimal parameter configuration of the digital twin.

[0030] An electronic device includes: a processor and a memory coupled to the processor, the memory stores a computer program, and when the computer program is executed by the processor, the steps of the coal-fired boiler twin prediction method based on multi-physical field coupling are implemented.

[0031] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the coal-fired boiler twin prediction method based on multi-physical field coupling are implemented.

[0032] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0033] The present invention realizes the digital reconstruction of the combustion field of the coal-fired boiler through multi-physical field coupling modeling technology, and constructs a high-fidelity digital twin in combination with a deep spatio-temporal prediction network, breaking through the technical limitations of traditional single-physical field monitoring.

[0034] The specific technical effects are as follows:

[0035] 1. Multi-physical field collaborative mapping: Through the real-time coupled solution of the flow field - temperature field - component field, accurately reproduce the cross-scale interaction mechanism of in-furnace combustion;

[0036] 2. Holographic situation prediction: Based on the spatio-temporal attention mechanism, capture the dynamic evolution characteristics of the combustion intensity distribution and NOx generation rate, and the prediction accuracy is improved by 2 orders of magnitude compared with the empirical model;

[0037] 3. Autonomous decision-making closed-loop: The optimization strategy output by the digital twin can be directly injected into the DCS control system to realize end-to-end closed-loop management from situation awareness to control execution;

[0038] 4. Dynamic reliability guarantee: Ensure that the prediction result of the digital twin strictly conforms to the boiler operation safety boundary through the physical constraint verification mechanism.

[0039] In summary, the coal-fired boiler twin and prediction system and method based on multi-physical field coupling provided by the present invention provide a new generation of digital solutions for the combustion optimization of coal-fired boilers through the deep integration of holographic modeling and intelligent prediction of the combustion field. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a schematic flowchart of a coal-fired boiler twin prediction method based on multi-physical field coupling provided by an embodiment of the present invention;

[0042] Figure 2 It is a schematic block diagram of the composition of a coal-fired boiler twin prediction system based on multi-physical field coupling provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In the following, only some exemplary embodiments are briefly described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present invention. Therefore, the drawings and the description are considered to be exemplary in nature and not restrictive.

[0044] In the description of the present invention, it should be understood that when used in this specification and the appended claims, the terms "comprises" and "comprising" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0045] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0046] It should be further understood that the term " / and / " used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0047] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual requirements.

[0048] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Embodiment 1

[0050] As Figure 1 shown, the method for twin and prediction of a coal-fired boiler based on multi-physical field coupling provided by the present invention includes the following specific implementation steps:

[0051] Step S101: Construction of a multi-physical field coupling model

[0052] Based on the burner topology data of the coal-fired boiler, the heating surface structure parameters, and the medium transmission characteristic curve, a three-multi-physical field coupling model is constructed. Specifically, it includes:

[0053] 1. Integration of burner topology data: Extract the three-dimensional space coordinate set of the burner from the boiler design drawings, and establish a distribution eigenvector of the air distribution structure including the axial spacing of the burner and the swirl angle distribution. Reconstruct the nozzle size parameter matrix through the laser scanning point cloud data to accurately characterize the geometric heterogeneity of the burner.

[0054] 2. Modeling of heating surface parameters: Calculate the heat transfer efficiency of the heating surface based on the tube row thermal resistance distribution tensor, and dynamically correct the contact heat loss in combination with the fin contact thermal resistance coefficient matrix. Introduce an ash fouling dynamic correction factor (calculation formula: α fouling = 1 - β·e -γ·t , where β is the initial ash fouling coefficient, γ is the ash deposition rate constant, and t is the operation duration) to update the heat transfer boundary conditions in real time.

[0055] 3. Coupling of medium transmission characteristic curves: Describe the rheological characteristics of the flue gas through the flue gas viscosity-temperature response function (using polynomial fitting: μ(T) = aT 3 + bT 2 + cT + d), and combine the ash particle deposition rate curve and the radiation absorption coefficient spectrum to construct a multi-physical field coupling control equation.

[0056] Step S102: Generation of holographic features of the combustion field

[0057] Drive the multi-physical field coupling model calculation through the historical operation data set to generate a holographic feature matrix of the combustion field;

[0058] 1. Data preprocessing: Perform wavelet denoising on the load fluctuation time series to eliminate the DCS signal acquisition noise. Use principal component analysis (PCA) to reduce the dimension of the coal quality variation feature matrix and extract key influencing factors such as sulfur content and volatile matter.

[0059] 2. Field feature calculation: The multi-physical field coupling model based on the air distribution strategy vector group and the total air volume dynamic response curve includes: Solve the Navier-Stokes equation using the finite volume method and output the vorticity distribution cloud map of the combustion field. Combine the component transport model to calculate the CO / NOx concentration gradient and generate the temperature-component coupling field characteristics.

[0060] 3. Dynamic response modeling: Based on the powder quantity distribution cloud map of each layer of burners and the wind temperature gradient tensor, construct a wind-powder mixing dynamic response model. Invert the heat flux density vector through the historical distribution set of the wall temperature field and establish a spatio-temporal evolution database of the heat load of the heating surface.

[0061] Step S103: Deep spatio-temporal network modeling

[0062] Based on the deep spatio-temporal network, perform evolution modeling on the combustion intensity distribution, pollutant generation trend, and wall heat load of the holographic feature matrix to form a digital twin of the coal-fired boiler.

[0063] 1. Network architecture design: Construct a hybrid neural network including a three-dimensional convolutional layer (extracting spatial features), an LSTM layer (capturing time dependencies), and a self-attention module (assigning weights to key features). The input layer receives a field feature tensor of 128×128×50 and outputs a heat map of the combustion intensity distribution for the next 5 minutes.

[0064] 2. Pollutant prediction modeling: Introduce prior knowledge of chemical reactions in the output layer and ensure that the NOx generation rate prediction conforms to the Arrhenius equation through a constraint layer (formula: [NOx] pred = f(T, O2, residence time)).

[0065] 3. Model training strategy: Adopt the transfer learning method, perform pre-training based on 300 sets of historical operating condition data, and update the parameters of the wall heat load prediction module in real time through an online learning mechanism.

[0066] Step S104: Autonomous optimization and verification

[0067] When the combustion situation prediction result of the digital twin exceeds the safety threshold, generate a combustion environment adjustment plan through an autonomous optimization strategy; determine the optimal parameter configuration of the digital twin based on physical constraint verification and field feature stability analysis.

[0068] 1. Strategy Generation: When the predicted value of the local temperature field exceeds the temperature resistance limit of the material, the autonomous optimization unit calculates the adjustment amount of the secondary air damper opening based on the gradient descent method (optimization objective function: min∑(T local -T safe )) 2 ), and generates an optimized burnout air ratio plan.

[0069] 2. Physical Constraint Verification: Verify the feasibility of the optimization strategy through the flow field continuity equation to ensure that the Reynolds number is in the stable turbulent combustion range (Re>2300) after the air volume adjustment.

[0070] 3. Parameter Tuning: Use the Bayesian optimization algorithm to perform sensitivity analysis on the turbulence model constants of the digital twin (such as the Cμ value in the k-ε model) to determine the optimal parameter configuration that minimizes the prediction error.

[0071] Example 2

[0072] As Figure 2 shown, the multi-physical field coupling-based coal-fired boiler twin and prediction system provided by the present invention includes:

[0073] A multi-physical field modeling unit 201 for constructing a multi-physical field coupling model based on the burner topology data, heating surface structure parameters, and medium transmission characteristic curves of the coal-fired boiler;

[0074] A holographic data generation unit 202 that drives the multi-physical field coupling model calculation through the historical operation data set to generate a combustion field holographic feature matrix;

[0075] A situation prediction unit 203 for performing evolutionary modeling of the combustion intensity distribution, pollutant generation trend, and wall heat load on the holographic feature matrix based on a deep spatio-temporal network to form a digital twin of the coal-fired boiler;

[0076] An autonomous optimization unit 204 that generates a combustion environment adjustment plan through an autonomous optimization strategy when the combustion situation prediction result of the digital twin exceeds the safety threshold; determines the optimal parameter configuration of the digital twin based on physical constraint verification and field feature stability analysis.

[0077] Example 3

[0078] The measured data of a 660MW supercritical boiler shows that:

[0079] The combustion efficiency prediction error of the coal-fired boiler twin and prediction system and method based on multi-physical field coupling provided by the present invention is reduced by 62% compared with the traditional single-field simulation;

[0080] The prediction accuracy of the deep spatio-temporal network for the NOx generation rate reaches ±3mg / Nm 3 , which is two orders of magnitude higher than the empirical model;

[0081] The independent optimization strategy reduces the superheater wall temperature fluctuation by 45% under variable load conditions of the boiler, significantly extending the service life of the heating surface.

[0082] The coal-fired boiler twin and prediction system and method based on multi-physical field coupling provided by the present invention realize the accurate prediction and independent optimization of the combustion state of the coal-fired boiler through the deep integration of multi-physical field coupling and artificial intelligence, providing an innovative solution for clean and efficient coal-fired power generation.

[0083] Example 4

[0084] An electronic device provided by the present invention includes: a processor and a memory coupled to the processor, the memory stores a computer program, and when the computer program is executed by the processor, the steps of the coal-fired boiler twin prediction method based on multi-physical field coupling are implemented.

[0085] The electronic device may further include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0086] Among them, the processor is used to control the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory is used to store various types of data to support the operation of the electronic device. These data may include, for example, instructions for any application or method operating on the electronic device, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component may include a screen and an audio component. Among them, the screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal can be further stored in the memory or sent through the communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, and the above other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them. Therefore, the corresponding communication component may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0087] In an exemplary embodiment, the electronic device can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the storage medium sharing method.

[0088] Embodiment 5

[0089] A computer-readable storage medium provided by the present invention stores a computer program, and when the computer program is executed by a processor, the steps of the coal-fired boiler twin prediction method based on multi-physical field coupling are implemented.

[0090] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of methods, systems, and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate a system for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a system for implementing the functions specified in one block or multiple blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means embodying the functionality specified in the flowchart(s) Figure 1 a flow or flows and / or block(s) Figure 1 or blocks specified in the block diagram(s).

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functionality specified in the flowchart(s) Figure 1 a flow or flows and / or block(s) Figure 1 or blocks specified in the block diagram(s).

[0094] The foregoing has shown and described the basic principles, principal features and advantages of the present invention. To those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and that without departing from the spirit or essential characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in whatever aspect, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description. Accordingly, all changes that fall within the meaning and scope of the equivalents of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0095] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment contains only a single technical solution, and this narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of the present invention and cannot limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A coal-fired boiler twin prediction method based on multi-physics field coupling, characterized in that: include: A multi-physics coupling model is constructed based on the burner topology data, heating surface structural parameters and medium transmission characteristic curves of coal-fired boilers; Drive multi-physics coupling model calculations through historical operation data sets to generate a holographic characteristic matrix of the combustion field; Based on the deep spatiotemporal network, the holographic feature matrix is ​​used to model the combustion intensity distribution, pollutant generation trend and wall heat load evolution to form a digital twin of the coal-fired boiler.

2. The coal-fired boiler twin prediction method based on multi-physical field coupling according to claim 1 is characterized in that: The burner topology data includes: a burner three-dimensional space coordinate set, an air distribution structure characteristic vector and a nozzle size parameter matrix.

3. The coal-fired boiler twin prediction method based on multi-physical field coupling according to claim 1 is characterized in that: The heating surface structural parameters include: tube row thermal resistance distribution tensor, fin contact thermal resistance coefficient matrix and dust accumulation dynamic correction factor.

4. The coal-fired boiler twin prediction method based on multi-physical field coupling according to claim 1 is characterized in that: The medium transmission characteristic curve includes: a flue gas viscosity-temperature response function, an ash particle deposition rate curve and a radiation absorption coefficient spectrum.

5. The coal-fired boiler twin prediction method based on multi-physical field coupling according to claim 1 is characterized in that: The historical operation data set contains spatiotemporal evolution characteristics, specifically including: Load fluctuation time series, coal quality variation characteristic matrix and air distribution strategy vector group; Total air volume dynamic response curve, burner powder volume distribution cloud map at each layer and burner air temperature gradient tensor; As well as the historical distribution set of the wall temperature field and the historical vector set of the heat flux density of the heated surface.

6. The coal-fired boiler twin prediction method based on multi-physical field coupling according to claim 1 is characterized in that: The method further comprises: When the combustion situation prediction result of the digital twin exceeds the safety threshold, a combustion environment adjustment plan is generated through an autonomous optimization strategy; Based on physical constraint verification and field characteristic stability analysis, the optimal parameter configuration of the digital twin is determined.

7. The coal-fired boiler twin prediction system based on multi-physics field coupling is characterized by: include: Multi-physics modeling unit, used to build a multi-physics coupling model based on the burner topology data, heating surface structural parameters and medium transmission characteristic curve of the coal-fired boiler; The holographic data generation unit drives the calculation of the multi-physics field coupling model through the historical operation data set to generate the holographic characteristic matrix of the combustion field; The situation prediction unit models the combustion intensity distribution, pollutant generation trend and wall heat load evolution of the holographic feature matrix based on a deep space-time network to form a digital twin of the coal-fired boiler.

8. The coal-fired boiler twin prediction system based on multi-physical field coupling according to claim 7 is characterized in that: Also includes: The autonomous optimization unit generates a combustion environment adjustment plan through an autonomous optimization strategy when the combustion situation prediction result of the digital twin exceeds the safety threshold; Based on physical constraint verification and field characteristic stability analysis, the optimal parameter configuration of the digital twin is determined.

9. An electronic device, characterized in that: include: A processor and a memory coupled to the processor, the memory storing a computer program, and the computer program, when executed by the processor, implements the steps of the coal-fired boiler twin prediction method based on multi-physical field coupling as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the coal-fired boiler twin prediction method based on multi-physical field coupling described in any one of claims 1 to 7.

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