Fuzzy neural network-based boiler control method, system, medium, equipment and device

Abstract

The invention provides a boiler control method and system based on a fuzzy neural network, a medium, equipment and a device. The method comprises the following steps: acquiring and processing operation data of a gas boiler; training a fuzzy neural network model based on the processed gas boiler operation data, wherein the fuzzy neural network model is used for realizing boiler control; and performing boiler control performance simulation based on the trained fuzzy neural network model to obtain an optimal control scheme of the gas boiler. According to the boiler control method and system based on the fuzzy neural network, the medium, the equipment and the device, the boiler heat efficiency can be improved, boiler combustion can be stabilized, and the method and the system have profound significance for realizing boiler system control intelligence and reducing system redundancy.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent control, and particularly relates to a boiler control method, system, medium, device and apparatus based on a fuzzy neural network. Background Art

[0002] Gas boiler equipment is a large and complex control object. In the fuel circuit and water-steam circuit of the boiler, there are multiple input parameters, such as water supply volume, fuel input volume, air supply volume, induced draft volume, etc.; at the same time, the boiler system also has multiple output parameters, such as drum pressure, furnace negative pressure, steam temperature, pressure, etc. The input and output parameters are closely related and interact with each other, which is a complex non-linear problem. The main purpose of the gas boiler automatic control system is to maintain the boiler drum water level, combustion state, and superheater outlet steam temperature and pressure within the specified range. The traditional gas boiler control uses the classical PID algorithm, and system oscillation and overshoot often occur. The method of using a neural network for boiler control is not yet mature. With the gradual expansion of the production scale of the boiler industry, the stability of gas boiler operation must be ensured.

[0003] Therefore, how to solve the above problems has become an urgent problem to be solved in this field. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a boiler control method, system, medium, device and apparatus based on a fuzzy neural network, to solve the problems in the adjustment of the existing boiler system, such as the parameters in multiple loops are closely coupled and interact with each other, there is an obvious lag phenomenon in parameter adjustment, and there is strong interference between various parameters, and also to improve the stability of boiler operation and the thermal efficiency of boiler operation.

[0005] In a first aspect, the present invention provides a boiler control method based on a fuzzy neural network, and the method includes the following steps:

[0006] Obtain and process the operation data of the gas boiler;

[0007] Train a fuzzy neural network model based on the processed operation data of the gas boiler, and the fuzzy neural network model is used to implement boiler control;

[0008] Perform a boiler control performance simulation based on the trained fuzzy neural network model to obtain an optimal control scheme for the gas boiler.

[0009] In one implementation manner of the first aspect, the obtaining and processing the operation data of the gas boiler includes the following steps:

[0010] Obtain the operation data of the gas boiler, where the operation data at least includes: the operation parameters of the feed water part, the combustion part, and the superheater of the gas boiler at different external temperatures and different times;

[0011] Use the mean imputation method to process the operation data of the gas boiler to obtain the processed operation data of the gas boiler.

[0012] In one implementation of the first aspect, training the fuzzy neural network based on the processed operation data of the gas boiler includes the following steps:

[0013] Divide the processed operation data of the gas boiler into a training set and a test set. The training set is used for training the fuzzy neural network model, and the test set is used for simulating and verifying the boiler control performance;

[0014] Train the fuzzy neural network model based on the training set;

[0015] Based on the test set, simulate and verify the trained fuzzy neural network model, and select the parameters with the best performance as the trained fuzzy neural network model.

[0016] In one implementation of the first aspect, performing boiler control performance simulation based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler includes the following steps:

[0017] Input the measured operation data of the gas boiler and the target constraint conditions into the trained fuzzy neural network model;

[0018] Obtain the optimal control scheme for the gas boiler output by the trained fuzzy neural network model.

[0019] In one implementation of the first aspect, the fuzzy neural network model adopts a BP neural network model based on the T-S fuzzy theory.

[0020] In the second aspect, the present invention provides a boiler control system based on a fuzzy neural network. The system includes a data acquisition module, a data analysis and training module, and an actual operation module;

[0021] The data acquisition module is used to obtain the operation data of the gas boiler and process it;

[0022] The data analysis and training module is used to train the fuzzy neural network based on the processed operation data of the gas boiler. The fuzzy neural network model is used to realize boiler control;

[0023] The actual operation module is used to perform boiler control performance simulation based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler.

[0024] In a third aspect, the present invention provides an electronic device, which includes: a processor and a memory;

[0025] The memory is used to store a computer program;

[0026] The processor is used to execute the computer program stored in the memory, so that the electronic device executes the above-mentioned boiler control method based on a fuzzy neural network.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by an electronic device, it implements the above-mentioned boiler control method based on a fuzzy neural network.

[0028] In a fifth aspect, the present invention provides a boiler control device based on a fuzzy neural network, which includes a data acquisition device and an electronic device for boiler control based on a fuzzy neural network;

[0029] The data acquisition device is used to acquire the operation data of a gas boiler and provide the operation data of the gas boiler to the electronic device for boiler control based on a fuzzy neural network.

[0030] In an implementation manner of the first aspect, the data acquisition device includes a feed water data collector, a combustion part data collector, and a superheater data collector; the feed water data collector includes a boiler feed water temperature sensor for monitoring the boiler feed water temperature, a boiler feed water flow sensor for monitoring the boiler feed water flow, and a steam drum water level sensor for monitoring the steam drum water level; the combustion part data collector includes a fuel inlet flow sensor for monitoring the fuel inlet flow, a fuel inlet temperature sensor for monitoring the fuel inlet temperature, an air inlet flow sensor for monitoring the air inlet flow, and an air inlet temperature sensor for monitoring the air inlet temperature; the superheater data collector includes a temperature sensor for monitoring the superheater outlet steam temperature, a superheater outlet steam pressure sensor for monitoring the superheater outlet steam pressure, and a superheater outlet steam flow sensor for monitoring the superheater outlet steam flow.

[0031] As described above, the boiler control method, system, medium, device and apparatus based on a fuzzy neural network according to the present invention have the following beneficial effects:

[0032] The boiler control method, system, medium, device and apparatus based on a fuzzy neural network according to the present invention adopt a combination of fuzzy theory and neural network to control the operating state of the boiler, which not only solves the problems in the regulation of existing boiler systems, such as the parameters in multiple loops are closely coupled with each other, there is an obvious lag phenomenon in parameter regulation, and there is strong interference between various parameters. It can also improve the stability of boiler operation and the thermal efficiency of boiler operation. The present invention is an "intelligent" upgrade of the traditional boiler control system. A large number of simulation results show that for a control object such as a gas boiler, which is nonlinear, has closely related multiple inputs and outputs, and is difficult to establish an accurate mathematical model to obtain a numerical analytical solution, using a fuzzy neural network model can obtain better control effects, and the boiler operation efficiency, combustion stability, and system adaptability are all improved. This model can be used in the control fields of power plant coal-fired boilers, industrial gas boilers, waste heat boilers, etc., which has profound significance for realizing the intelligent control of the boiler system and reducing system redundancy. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It shows a flowchart of the boiler control method based on a fuzzy neural network according to the present invention in an embodiment;

[0034] Figure 2 It shows a schematic structural diagram of the boiler control system based on a fuzzy neural network according to the present invention in an embodiment;

[0035] Figure 3 It shows a schematic structural diagram of the electronic device according to the present invention in an embodiment;

[0036] Figure 4 It shows a schematic structural diagram of the boiler control device based on a fuzzy neural network according to the present invention in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0038] It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0039] The technical solutions in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0040] As Figure 1 shown, in an embodiment, the boiler control method based on a fuzzy neural network of the present invention includes steps S11 - S13.

[0041] Step S11, obtain and process the operation data of the gas boiler.

[0042] Specifically, obtain the operation data of the gas boiler, and the operation data at least includes: the operation parameters of the feed water part, the combustion part, and the superheater of the gas boiler at different external temperatures and different times.

[0043] Collect the operation parameters of different working states of the gas boiler at different external temperatures and different times to ensure the diversity of sample data, so that the trained neural network model has good generalization performance.

[0044] Use the mean imputation method to process the operation data of the gas boiler, that is, fill and replace the missing data and error data to obtain the processed operation data of the gas boiler.

[0045] Step S12, train the fuzzy neural network based on the processed operation data of the gas boiler, and the fuzzy neural network model is used to realize boiler control.

[0046] Specifically, divide the processed operation data of the gas boiler into a training set and a test set (80% as the training set and 20% as the test set). The training set is used for training the fuzzy neural network model, and the test set is used for simulation verification of the boiler control performance; the fuzzy neural network model adopts a BP neural network model based on the T-S fuzzy theory.

[0047] Train the fuzzy neural network model based on the training set, and train multiple fuzzy neural network models according to different data combinations.

[0048] Perform simulation verification on the trained fuzzy neural network model based on the test set, and select the parameters with the best performance as the trained fuzzy neural network model, that is, select the model with the smallest error between the predicted value and the actual value as the fuzzy neural network prediction control algorithm model of the gas boiler. Further, the fuzzy neural network prediction control algorithm model of the gas boiler system uses the fuel calorific value, fuel inlet flow rate, fuel inlet temperature, air inlet temperature, air inlet flow rate, and superheater outlet steam flow rate as the input units of the neural network; uses the superheater outlet steam temperature and pressure as the output units of the neural network.

[0049] Step S13: Perform boiler control performance simulation based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler.

[0050] Specifically, input the measured operation data and target constraint conditions of the gas boiler into the trained fuzzy neural network model; obtain the optimal control scheme for the gas boiler output by the trained fuzzy neural network model. Use the measured operation data to verify the trained fuzzy neural network model. After successful verification, input the target constraint conditions, and calculate the optimal operation scheme of the gas boiler through the fuzzy neural network model.

[0051] The protection scope of the boiler control method based on fuzzy neural network described in the embodiments of the present invention is not limited to the execution order of the steps listed in this embodiment. Any solution achieved by adding or reducing steps of the prior art and replacing steps according to the principles of the present invention is included in the protection scope of the present invention.

[0052] The embodiments of the present invention also provide a boiler control system based on fuzzy neural network. The boiler control system based on fuzzy neural network can implement the boiler control method based on fuzzy neural network described in the present invention. However, the implementation devices of the boiler control system based on fuzzy neural network described in the present invention include but are not limited to the structures of the boiler control system based on fuzzy neural network listed in this embodiment. Any structural deformation and replacement of the prior art made according to the principles of the present invention are included in the protection scope of the present invention.

[0053] As Figure 2 shown, in one embodiment, the boiler control system based on fuzzy neural network of the present invention includes a data acquisition module 21, a data analysis and training module 22, and an actual operation module 23.

[0054] The data acquisition module 21 is used to obtain and process the operation data of the gas boiler;

[0055] The data analysis and training module 22 is used to train the fuzzy neural network model based on the processed operation data of the gas boiler, and the fuzzy neural network model is used to implement boiler control;

[0056] The actual operation module 23 is used to perform boiler control performance simulation based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler.

[0057] The data acquisition module 21 is connected to the data analysis and training module 22, and the actual operation module 23 is connected to the data analysis and training module.

[0058] The data acquisition module 21 includes a feedwater data collector, a combustion section data collector, and a superheater data collector: The feedwater data collector includes a feedwater temperature sensor, a boiler feedwater flow sensor, and a steam drum water level sensor; The combustion section data collector includes a fuel input flow sensor, a fuel input temperature sensor, an air input flow sensor, and an air input temperature sensor; The superheater data collector includes a superheater outlet steam temperature sensor, a superheater outlet steam pressure sensor, and a superheater outlet steam flow sensor.

[0059] Among them, the feedwater temperature sensor is used to monitor the boiler feedwater temperature, the boiler feedwater flow sensor is used to monitor the boiler feedwater flow, and the steam drum water level sensor is used to monitor the steam drum water level; The data of the feedwater temperature sensor, the boiler feedwater flow sensor, and the steam drum water level sensor are transmitted to the data processing unit of the data analysis and training module 22;

[0060] Among them, the fuel input flow sensor is used to monitor the fuel input flow, the fuel input temperature sensor is used to monitor the fuel input temperature, the air input flow sensor is used to monitor the air input flow, and the air input temperature sensor is used to monitor the air input temperature; The data of the fuel input flow sensor, the air input flow sensor, and the air input temperature sensor are transmitted to the data processing unit of the data analysis and training module 22;

[0061] Among them, the superheater outlet steam temperature sensor is used to monitor the superheater outlet steam temperature, the superheater outlet steam pressure sensor is used to monitor the superheater outlet steam pressure, and the superheater outlet steam flow sensor is used to monitor the superheater outlet steam flow. The data of the superheater outlet steam temperature sensor, the superheater outlet steam pressure sensor, and the superheater outlet steam flow sensor are transmitted to the data processing unit of the data analysis and training module 22.

[0062] The data analysis and training module 22 includes a data processing unit, a neural network input unit, and a neural network output unit. Among them, the data processing unit is used to process the data transmitted by the data acquisition module 21, fill and replace the missing data and incorrect data using the mean imputation method, and transmit the input data and output data of the boiler system to the neural network input unit and the neural network output unit respectively; The neural network input unit is used to receive the input data of the boiler system transmitted by the data processing unit for training; The neural network output unit is used to receive the output data of the boiler system transmitted by the data processing unit for training.

[0063] The actual operation module 23 is used to verify the neural network model trained by the data analysis and training module 22, and perform subsequent boiler operation performance simulation.

[0064] In one embodiment, the data acquisition module 21 acquires the operation data of the boiler and sends it to the data processing unit. After the data processing unit processes the data, a part of the data is transmitted to the neural network input unit and the neural network output unit as a training set for neural network training. After the trained neural network model is tested with a test set, the actual operation module 23 is used to verify the neural network model. After successful verification, the constraint conditions of the target are input, and the optimal operation plan of the gas boiler is obtained through the calculation of the fuzzy neural network model.

[0065] In several embodiments provided by the present invention, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or modules or units can be in an electrical, mechanical or other form.

[0066] The modules / units described as separate components may or may not be physically separated. The components shown as modules / units may or may not be physical modules, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present invention. For example, in each embodiment of the present invention, the various functional modules / units can be integrated in a processing module, or each module / unit can exist physically alone, or two or more modules / units can be integrated in one module / unit.

[0067] Those of ordinary skill in the art should also further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0068] Embodiments of the present invention also provide a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing a processor through a program. The program can be stored in a computer-readable storage medium. The storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid state disk (SSD)), etc.

[0069] Embodiments of the present invention also provide an electronic device. The electronic device includes a processor and a memory.

[0070] The memory is used to store a computer program.

[0071] The memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card, or optical disc.

[0072] The processor is connected to the memory and is configured to execute the computer program stored in the memory, so that the electronic device executes the above-mentioned boiler control method of the fuzzy neural network.

[0073] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it can also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0074] Such as Figure 3As shown, the electronic device of the present invention is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: one or more processors or processing units 31, a memory 32, and a bus 33 that connects different system components (including the memory 32 and the processing unit 31).

[0075] The bus 33 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0076] The electronic device typically includes a variety of computer system-readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0077] The memory 32 may include computer system-readable media in the form of volatile memory, such as random access memory (RAM) 521 and / or cache memory 322. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 323 may be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 3 not shown, commonly referred to as a "hard disk drive"). Although Figure 3 not shown in the figure, a disk drive for reading and writing on a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing on a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 33 through one or more data media interfaces. The memory 32 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the embodiments of the present invention.

[0078] A program / utility 324 having a set (at least one) of program modules 3241 may be stored, for example, in the memory 32. Such program modules 3241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. The program modules 3241 generally perform the functions and / or methods described in the embodiments of the present invention.

[0079] The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or communicate with any device that enables the electronic device to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 34. Moreover, the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 35. As Figure 3 shown, the network adapter 35 communicates with other modules of the electronic device through the bus 33. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0080] An embodiment of the present invention also provides a boiler control device based on a fuzzy neural network. As Figure 4 shown, in one embodiment, the boiler control device based on a fuzzy neural network of the present invention includes a data acquisition device 41 and an electronic device 42 for boiler control based on a fuzzy neural network;

[0081] The data acquisition device 41 is used to acquire the operation data of the gas boiler and provide the operation data of the gas boiler to the electronic device 42 for boiler control based on a fuzzy neural network.

[0082] Specifically, the data acquisition device 41 includes a feed water data collector 411, a combustion part data collector 412, and a superheater data collector 413;

[0083] The feed water data collector 411 includes a boiler feed water temperature sensor for monitoring the boiler feed water temperature, a boiler feed water flow sensor for monitoring the boiler feed water flow, and a steam drum water level sensor for monitoring the steam drum water level; the data collected by the boiler feed water temperature sensor, the boiler feed water flow sensor, and the steam drum water level sensor are provided to the electronic device for boiler control based on a fuzzy neural network.

[0084] The combustion part data collector 412 includes a fuel inlet flow sensor for monitoring the fuel inlet flow, a fuel inlet temperature sensor for monitoring the fuel inlet temperature, an air inlet flow sensor for monitoring the air inlet flow, and an air inlet temperature sensor for monitoring the air inlet temperature; the data collected by the fuel inlet flow sensor, the fuel inlet temperature sensor, the air inlet flow sensor, and the air inlet temperature sensor are provided to the electronic device for boiler control based on a fuzzy neural network.

[0085] The superheater data collector 413 described above includes a temperature sensor for monitoring the superheater outlet steam temperature, a superheater outlet steam pressure sensor for monitoring the superheater outlet steam pressure, and a superheater outlet steam flow sensor for monitoring the superheater outlet steam flow rate. The data collected by the temperature sensor for the superheater outlet steam temperature, the superheater outlet steam pressure sensor, and the superheater outlet steam flow sensor are provided to the electronic device for boiler control based on a fuzzy neural network.

[0086] The above embodiments are only illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. A boiler control method based on a fuzzy neural network, characterized in that The method includes the following steps: Obtain the operation data of the gas boiler and process it; Train a fuzzy neural network model based on the processed operation data of the gas boiler, where the fuzzy neural network model is used to achieve boiler control; Conduct a simulation of the boiler control performance based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler.

2. The boiler control method based on a fuzzy neural network according to claim 1, characterized in that: The obtaining of the operation data of the gas boiler and the processing thereof include the following steps: Obtain the operation data of the gas boiler, where the operation data at least includes: the operation parameters of the water supply part, the combustion part, and the superheater of the gas boiler at different external temperatures and different times; Process the operation data of the gas boiler by using the mean imputation method to obtain the processed operation data of the gas boiler.

3. The boiler control method based on a fuzzy neural network according to claim 1, characterized in that: The training of the fuzzy neural network based on the processed operation data of the gas boiler includes the following steps: Divide the processed operation data of the gas boiler into a training set and a test set. The training set is used for the training of the fuzzy neural network model, and the test set is used for the simulation verification of the boiler control performance; Train the fuzzy neural network model based on the training set; Conduct a simulation verification of the trained fuzzy neural network model based on the test set, and select the parameters with the best performance as the trained fuzzy neural network model.

4. The boiler control method based on a fuzzy neural network according to claim 1, characterized in that: The conducting of the boiler control performance simulation based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler includes the following steps: Input the measured operation data of the gas boiler and the target constraint conditions into the trained fuzzy neural network model; Obtain the optimal control scheme for the gas boiler output by the trained fuzzy neural network model.

5. The boiler control method based on a fuzzy neural network according to claim 1, characterized in that: The fuzzy neural network model adopts a BP neural network model based on the T-S fuzzy theory.

6. A boiler control system based on a fuzzy neural network, characterized in that, The system includes a data acquisition module, a data analysis and training module, and an actual operation module; The data acquisition module is used to obtain the operation data of the gas boiler and process it; The data analysis and training module is used to train a fuzzy neural network model based on the processed operation data of the gas boiler, where the fuzzy neural network model is used to achieve boiler control; The actual operation module is used to conduct a simulation of the boiler control performance based on the trained fuzzy neural network model to obtain the optimal control scheme for the gas boiler.

7. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory is used to store a computer program; The processor is used to execute the computer program stored in the memory, so that the electronic device executes the boiler control method based on a fuzzy neural network according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the electronic device, it implements the boiler control method based on a fuzzy neural network according to any one of claims 1 to 5.

9. A boiler control device based on a fuzzy neural network, characterized in that: It includes a data acquisition device and the electronic device according to claim 7; The data acquisition device is used to collect the operation data of the gas boiler and provide the operation data of the gas boiler to the electronic device.

10. The boiler control device based on a fuzzy neural network according to claim 9, characterized in that: The data acquisition device includes a feed water data collector, a combustion section data collector, and a superheater data collector; the feed water data collector includes a boiler feed water temperature sensor for monitoring the boiler feed water temperature, a boiler feed water flow sensor for monitoring the boiler feed water flow rate, and a steam drum water level sensor for monitoring the steam drum water level; the combustion section data collector includes a fuel inlet flow sensor for monitoring the fuel inlet flow rate, a fuel inlet temperature sensor for monitoring the fuel inlet temperature, an air inlet flow sensor for monitoring the air inlet flow rate, and an air inlet temperature sensor for monitoring the air inlet temperature; the superheater data collector includes a temperature sensor for monitoring the superheater outlet steam temperature, a superheater outlet steam pressure sensor for monitoring the superheater outlet steam pressure, and a superheater outlet steam flow sensor for monitoring the superheater outlet steam flow rate.

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