Intelligent coal-fired boiler temperature control system and method based on Internet of Things

Abstract

The invention discloses a coal-fired boiler temperature intelligent control system and method based on the Internet of Things, and relates to the technical field of coal-fired boiler temperature intelligent control. The system comprises a data acquisition module, a data analysis module, a control module and a feedback module, the output end of the data acquisition module is connected with the input end of the data analysis module; the output end of the data analysis module is connected with the input end of the control module; and the output end of the control module is connected with the feedback module. The intelligent temperature control system for the coal-fired boiler can overcome the defect that an existing intelligent temperature control system for the coal-fired boiler has certain limitation in function, the self-adaption and self-learning capabilities of the intelligent temperature control system for the coal-fired boiler are improved, the dependence of the coal-fired boiler on manpower is reduced, and the fault processing efficiency is improved. Meanwhile, by controlling the temperature, the intelligent temperature control system of the coal-fired boiler reduces energy waste and pollutant emission, and brings greener, more efficient and more intelligent future for the coal-fired boiler industry.

Description

Technical Field

[0001] This invention relates to the field of intelligent temperature control technology for coal-fired boilers, specifically an intelligent temperature control method for coal-fired boilers based on the Internet of Things. Background Technology

[0002] A coal-fired boiler is a type of boiler that converts electrical energy, the chemical energy of coal fuel, and the thermal energy of high-temperature flue gas into steam and high-temperature water with a certain amount of thermal energy.

[0003] During the operation of power plants, coal-fired boilers are important heat energy supply equipment, and boiler temperature is an important control parameter. Existing intelligent temperature control systems for coal-fired boilers have certain limitations in functionality. Although intelligent elements have been introduced, they have poor adaptability and cannot automatically optimize control strategies based on historical data and real-time feedback. Most systems still rely on manual parameter setting and timed inspections, lacking the ability to remotely monitor and adjust in real time. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent temperature control method for coal-fired boilers based on the Internet of Things (IoT) to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for intelligent temperature control of a coal-fired boiler based on the Internet of Things, the method comprising the following steps:

[0006] S1. Train a neural network based on the historical database of coal-fired boilers to form a set of operating data at a certain moment, and establish a temperature prediction model and a parameter control model.

[0007] S2. Extract real-time operating data of coal-fired boilers and transmit it to the temperature prediction model to predict the predicted temperature of the coal-fired boiler at a certain moment and determine whether the predicted temperature is within the temperature threshold range.

[0008] S3. If the predicted temperature is not within the temperature threshold range, the new wind speed and water flow rate are calculated through the parameter control model.

[0009] S4. Send the new wind speed and water flow data to the host computer. After receiving the new wind speed and water flow data, the host computer issues an adjustment command to adjust the temperature of the coal-fired boiler to the target range.

[0010] According to the above-mentioned technical solution, in step S1, the historical temperature database of the coal-fired boiler includes:

[0011] The boiler's ambient temperature data, furnace temperature data, operating parameter data, and boiler status data during operation, including wind speed, water flow rate, and fuel quantity data.

[0012] According to the above technical solution, the temperature prediction model includes:

[0013] Obtain operational data from the historical database of coal-fired boilers. This operational data includes furnace temperature, wind speed, and water flow rate. Extract the furnace temperature, wind speed, and water flow rate at the same time to form a data set, denoted as [T0, V0, Q0]. i , where T0, V0, and Q0 represent the furnace temperature, wind speed, and water flow rate of the coal-fired boiler in the i-th data set, respectively;

[0014] Set the furnace temperature thresholds as T max T min T max T represents the maximum furnace temperature. min The minimum furnace temperature; obtain the historical furnace temperature T0, and extract the values ​​that satisfy T. min <T0 <T max This forms a target temperature set, and the data sets for a certain time and the next time are obtained from the set.

[0015] The temperature prediction formula is constructed as follows

[0016] T = T0 + β1 * V0 + β2 * Q0 + ∈;

[0017] Where T0 is the temperature at a certain moment, V0 is the wind speed at a certain moment, Q0 is the water flow rate at a certain moment, T is the temperature at the next moment, β1 and β2 represent the regression coefficients, where β1 > 0 and β2 < 0; ∈ represents the residual term of the first equation;

[0018] In the above technical solution, the temperature T0 at a certain moment in the data set satisfies T min <T0 <T max The data set was used for training to form a temperature prediction model that calculates the predicted temperature at the next moment based on the temperature T0, wind speed and water flow at a certain moment.

[0019] Based on the temperature prediction model, predict the temperature data for the next moment. If the predicted temperature T does not meet the condition T... min <T0<T max In this case, parameter adjustment is performed through a parameter control model.

[0020] According to the above technical solution, the parameter control model includes:

[0021] Obtain the target temperature set, and extract the data sets for a certain time and the next time from the target temperature set;

[0022] The formula for controlling the wind speed parameters is as follows:

[0023]

[0024] Where V0 is the wind speed at a certain moment, V is the wind speed at the next moment, T0 is the temperature at a certain moment, T is the temperature at the next moment, and k1 represents the rate of change of the square difference of temperature with wind speed.

[0025] The formula for controlling the water flow rate is as follows:

[0026]

[0027] Where Q0 is the water flow rate at a certain moment, Q is the water flow rate at the next moment, T0 is the temperature at a certain moment, T is the temperature at the next moment, and k2 represents the rate of change of the square difference of temperature with the water flow rate.

[0028] When the predicted temperature T does not meet T min <T<T max At a given moment, the wind speed and water flow rate are used to calculate the new wind speed and water flow rate for the coal-fired boiler at the next moment using parameter control formulas.

[0029] In the above technical solution, the temperature T0 at a certain moment in the data set satisfies T min <T0 <T max The data sets were used for training to form a parameter control model that calculates the wind speed at the next moment based on the temperature T0 and wind speed at a certain moment and the temperature T at the next moment, and a parameter control model that calculates the water flow rate at the next moment based on the temperature T0 and water flow rate at a certain moment and the temperature T at the next moment.

[0030] According to the above technical solution, step S4 also includes:

[0031] The host computer includes a PC-based host computer and a mobile host computer, both of which are used to receive new wind speed and water flow data and send adjustment commands to automatically adjust the temperature of the coal-fired boiler to the target range. At the same time, it can view the real-time operating status diagram, historical data curves and alarm information of the coal-fired boiler, and can also manually adjust the wind speed and water flow through remote operation.

[0032] In the above technical solution, after receiving new wind speed and water flow, the host computer issues adjustment commands, thereby controlling the wind speed regulating valve and the water flow regulating valve to adjust the temperature of the coal-fired boiler to the target range, reducing manual real-time monitoring and enhancing the intelligence of the coal-fired boiler.

[0033] An intelligent temperature control system for a coal-fired boiler based on the Internet of Things (IoT) includes: a data acquisition module, a data analysis module, an automatic control module, and a human-machine interaction module.

[0034] The data acquisition module is equipped with sensor devices for detecting and collecting furnace temperature, wind speed, and water flow rate; the data analysis module is used for temperature prediction and calculating the required wind speed and water flow rate based on the predicted temperature and current wind speed and water flow rate; the automatic control module is used to control the water flow rate and air volume of the coal-fired boiler in real time according to the results of the data analysis module, and adjust the temperature of the coal-fired boiler to the corresponding range; the human-machine interaction module is used to transmit the adjusted boiler operating status information to the PC and mobile host computers, and monitor the boiler operating status and control effect in real time.

[0035] The output of the data acquisition module is connected to the input of the data analysis module; the output of the data analysis module is connected to the input of the automatic control module; and the automatic control module is bidirectionally connected to the human-machine interaction module.

[0036] According to the above technical solution, the data acquisition module includes a temperature data acquisition unit, a wind speed data acquisition unit, and a water flow rate data acquisition unit;

[0037] The temperature data acquisition unit, i.e., the temperature sensor acquisition device, is installed inside the furnace of the coal-fired boiler to sense changes in furnace temperature and convert the sensed physical changes into digital signals through a signal processing module; the wind speed data acquisition unit, i.e., the wind speed sensor acquisition device, is installed at the air inlet of the boiler to calculate the effective wind speed by measuring the speed and direction of air flow; the water flow data acquisition unit, i.e., the water flow sensor acquisition device, is installed in the water system of the boiler to monitor the speed of water flow in real time and obtain water flow data through data processing.

[0038] According to the above technical solution, the data analysis module includes a temperature prediction unit and a parameter control unit;

[0039] The temperature prediction unit includes a temperature prediction model for calculating and predicting the furnace temperature, and the parameter control unit includes a parameter control model for calculating the required air volume and water flow rate; the air volume and water flow rate are adjusted based on the temperature prediction model and the parameter control model.

[0040] According to the above technical solution, the automatic control module includes a wind speed regulation unit and a water flow regulation unit;

[0041] The wind speed regulating unit is used to acquire the wind speed parameters of the intelligent control system for temperature of the coal-fired boiler, and automatically adjust the wind speed regulating valve according to the required wind speed; the water flow regulating unit is used to acquire the water flow parameters of the intelligent control system for temperature of the coal-fired boiler, and automatically adjust the water flow regulating valve according to the required water flow; based on the control commands output by the automatic control module to the regulating valve of the coal-fired boiler, the coal-fired boiler is automatically controlled.

[0042] According to the above technical solution, the human-computer interaction module includes an information receiving unit and an input instruction unit;

[0043] The receiving information unit is used by users to view the boiler's operating status and historical data in real time, including receiving alarm information; the input command unit allows users to send control commands and set parameters to the control system through the human-machine interface, and the control system responds accordingly to realize remote control of the boiler.

[0044] Compared with existing technologies, the beneficial effects of this invention are: This invention can overcome the functional limitations of existing intelligent temperature control systems for coal-fired boilers, improve the self-adaptive and self-learning capabilities of these systems, reduce reliance on manual intervention, and increase fault handling efficiency. Simultaneously, by controlling temperature, the intelligent temperature control system for coal-fired boilers reduces energy waste and pollutant emissions, bringing a greener, more efficient, and intelligent future to the coal-fired boiler industry. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a flowchart illustrating an intelligent temperature control method for a coal-fired boiler based on the Internet of Things according to the present invention.

[0047] Figure 2 This is a schematic diagram of the structure of an intelligent temperature control system for a coal-fired boiler based on the Internet of Things according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figure 1 In this first embodiment, an intelligent temperature control system and method for a coal-fired boiler based on the Internet of Things is provided. The method includes: the historical temperature database of the coal-fired boiler includes: ambient temperature data, furnace temperature data, operating parameter data and boiler status data during boiler operation. The operating parameter data includes wind speed, water flow rate and fuel quantity data.

[0050] A neural network is trained based on a historical database of coal-fired boilers to form a set of operating data at a certain moment, and a temperature prediction model and a parameter control model are established.

[0051] The temperature prediction model includes:

[0052] Obtain operational data from the historical database of coal-fired boilers. This operational data includes furnace temperature, wind speed, and water flow rate. Extract the furnace temperature, wind speed, and water flow rate at the same time to form a data set, denoted as [T0, V0, Q0]. i , where T0, V0, and Q0 represent the furnace temperature, wind speed, and water flow rate of the coal-fired boiler in the i-th data set, respectively;

[0053] Set the furnace temperature thresholds as T max T min T max T represents the maximum furnace temperature. min The minimum furnace temperature; obtain the historical furnace temperature T0, and extract the values ​​that satisfy T. min <T0 <T max This forms a target temperature set, and the data sets for a certain time and the next time are obtained from the set.

[0054] For example, there exist three sets of data at adjacent time points: [T1, V1, Q1]1, [T2, V2, Q2]2, and [T3, V3, Q3]3, where T... min <T1<T max T min <T2<T max Then, obtain [T1, V1, Q1]1 and [T2, V2, Q2]2.

[0055] The temperature prediction formula is constructed as follows:

[0056] T = T0 + β1 * V0 + β2 * Q0 + ∈;

[0057] Where T0 is the temperature at a certain moment, V0 is the wind speed at a certain moment, Q0 is the water flow rate at a certain moment, T is the temperature at the next moment, β1 and β2 represent the regression coefficients, where β1 > 0 and β2 < 0; ∈ represents the residual term of the first equation;

[0058] Based on the temperature prediction model, predict the temperature data for the next moment. If the predicted temperature T does not meet the condition T... min <T0<T max In this case, parameter adjustment is performed through a parameter control model.

[0059] The parameter control model includes:

[0060] Obtain the target temperature set, and extract the data sets for a certain time and the next time from the target temperature set;

[0061] The formula for controlling the wind speed parameters is as follows:

[0062]

[0063] Where V0 is the wind speed at a certain moment, V is the wind speed at the next moment, T0 is the temperature at a certain moment, T is the temperature at the next moment, and k1 represents the rate of change of the square difference of temperature with wind speed.

[0064] The formula for controlling the water flow rate is as follows:

[0065]

[0066] Where Q0 is the water flow rate at a certain moment, Q is the water flow rate at the next moment, T0 is the temperature at a certain moment, T is the temperature at the next moment, and k2 represents the rate of change of the square difference of temperature with the water flow rate.

[0067] When the predicted temperature T does not meet T min <T0 <T max At a given moment, the wind speed and water flow rate are used to calculate the new wind speed and water flow rate for the coal-fired boiler at the next moment using parameter control formulas.

[0068] The new wind speed and water flow data are sent to the host computer. After receiving the new wind speed and water flow data, the host computer issues an adjustment command to adjust the temperature of the coal-fired boiler to the target range.

[0069] The host computer includes a PC-based host computer and a mobile host computer, both of which are used to receive new wind speed and water flow data and send adjustment commands to automatically adjust the temperature of the coal-fired boiler to the target range. At the same time, it can view the real-time operating status diagram, historical data curves and alarm information of the coal-fired boiler, and can also manually adjust the wind speed and water flow through remote operation.

[0070] For example, given a temperature of 1000℃, wind speed of 30m / s, and water flow rate of 150t / h at a certain moment, a temperature prediction model predicts the next temperature to be 1100℃; however, if T does not meet these conditions... min =800℃<1100℃<T max At 1100℃, the wind speed of the coal-fired boiler at the next moment is obtained as 25 m / s using the wind speed parameter control formula, and the water flow rate at the next moment is obtained as 160 t / h using the water flow rate parameter control formula. The wind speed and water flow rate at the next moment are sent to the host computer. After receiving the new wind speed and water flow rate, the host computer issues commands to the wind speed regulating valve and the water flow regulating valve, which are then adjusted to 25 m / s and 160 t / h, respectively.

[0071] Please see Figure 2 In this second embodiment, an intelligent temperature control system for a coal-fired boiler based on the Internet of Things is also provided. The system includes: a data acquisition module, a data analysis module, an automatic control module, and a human-computer interaction module.

[0072] The data acquisition module is equipped with sensor devices for detecting and collecting furnace temperature, wind speed, and water flow rate; the data analysis module is used for temperature prediction and calculating the required wind speed and water flow rate based on the predicted temperature and current wind speed and water flow rate; the automatic control module is used to control the water flow regulating valve and air flow regulating valve of the coal-fired boiler in real time according to the results of the data analysis module, so as to adjust the temperature of the coal-fired boiler to the corresponding range; the human-machine interaction module is used to transmit the adjusted boiler operating status information to the PC and mobile host computers to monitor the boiler operating status and control effect in real time.

[0073] The output of the data acquisition module is connected to the input of the data analysis module; the output of the data analysis module is connected to the input of the automatic control module; and the automatic control module is bidirectionally connected to the human-machine interaction module.

[0074] The data acquisition module includes a temperature data acquisition unit, a wind speed data acquisition unit, and a water flow rate data acquisition unit.

[0075] The temperature data acquisition unit, i.e., the temperature sensor acquisition device, is installed inside the furnace of the coal-fired boiler to sense changes in furnace temperature and convert the sensed physical changes into digital signals through a signal processing module; the wind speed data acquisition unit, i.e., the wind speed sensor acquisition device, is installed at the air inlet of the boiler to calculate the effective wind speed by measuring the speed and direction of air flow; the water flow data acquisition unit, i.e., the water flow sensor acquisition device, is installed in the water system of the boiler to monitor the speed of water flow in real time and obtain water flow data through data processing.

[0076] The data analysis module includes a temperature prediction unit and a parameter control unit;

[0077] The temperature prediction unit is used to calculate and predict the furnace temperature, and includes a temperature prediction model; the parameter control unit is used to calculate the new air volume and water flow rate, and includes a parameter control model.

[0078] The automatic control module includes a wind speed regulation unit and a water flow regulation unit;

[0079] The wind speed regulating unit is used to acquire the wind speed parameters of the intelligent control system for temperature of the coal-fired boiler, and automatically adjust the wind speed regulating valve according to the required wind speed; the water flow regulating unit is used to acquire the water flow parameters of the intelligent control system for temperature of the coal-fired boiler, and automatically adjust the water flow regulating valve according to the required water flow; based on the control commands output by the automatic control module to the regulating valve of the coal-fired boiler, the coal-fired boiler is automatically controlled.

[0080] The human-computer interaction module includes an information receiving unit and an input command unit;

[0081] The receiving information unit is used by users to view the boiler's operating status and historical data in real time, including receiving alarm information; the input command unit allows users to send control commands and set parameters to the control system through the human-machine interface, and the control system responds accordingly to realize remote control of the boiler.

[0082] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for intelligent temperature control of a coal-fired boiler based on the Internet of Things, characterized in that: The method includes the following steps: S1. Train a neural network based on the historical database of coal-fired boilers to form a set of operating data at a certain moment, and establish a temperature prediction model and a parameter control model. S2. Extract real-time operating data of coal-fired boilers and transmit it to the temperature prediction model to predict the predicted temperature of the coal-fired boiler at a certain moment and determine whether the predicted temperature is within the temperature threshold range. S3. If the predicted temperature is not within the temperature threshold range, the new wind speed and water flow rate are calculated through the parameter control model. S4. Send the new wind speed and water flow data to the host computer. After receiving the new wind speed and water flow data, the host computer issues an adjustment command to adjust the temperature of the coal-fired boiler to the target range.

2. The method for intelligent temperature control of a coal-fired boiler based on the Internet of Things according to claim 1, characterized in that: In step S1, the historical database of the coal-fired boiler includes: The boiler's ambient temperature data, furnace temperature data, operating parameter data, and boiler status data during operation, including wind speed, water flow rate, and fuel quantity data.

3. The method for intelligent temperature control of a coal-fired boiler based on the Internet of Things according to claim 1, characterized in that: The temperature prediction model includes: Obtain operational data from the historical database of coal-fired boilers. This operational data includes furnace temperature, wind speed, and water flow rate. Extract the furnace temperature, wind speed, and water flow rate at the same time to form a data set, denoted as [T0, V0, Q0]. i , where T0, V0, and Q0 represent the furnace temperature, wind speed, and water flow rate of the coal-fired boiler in the i-th data set, respectively; Set the furnace temperature thresholds as T max T min T max T represents the maximum furnace temperature. min The minimum furnace temperature; obtain the historical furnace temperature T0, and extract the values ​​that satisfy T. min <T0 <T max This forms a target temperature set, and the data sets for a certain time and the next time are obtained from the set. The temperature prediction formula is constructed as follows T = T0 + β1 * V0 + β2 * Q0 + ∈; Where T0 is the temperature at a certain moment, V0 is the wind speed at a certain moment, Q0 is the water flow rate at a certain moment, T is the temperature at the next moment, β1 and β2 represent the regression coefficients, where β1 > 0 and β2 < 0; ∈ represents the residual term of the first equation; Based on the temperature prediction model, predict the temperature data for the next moment. If the predicted temperature T does not meet the condition T... min <T0<T max In this case, parameter adjustment is performed through a parameter control model.

4. The method for intelligent temperature control of a coal-fired boiler based on the Internet of Things according to claim 3, characterized in that: The parameter control model includes: Obtain the target temperature set, and extract the data sets for a certain time and the next time from the target temperature set; The formula for controlling the wind speed parameters is as follows: Where V0 is the wind speed at a certain moment, V is the wind speed at the next moment, T0 is the temperature at a certain moment, T is the temperature at the next moment, and k1 represents the rate of change of the square difference of temperature with wind speed. The formula for controlling the water flow rate is as follows: Where Q0 is the water flow rate at a certain moment, Q is the water flow rate at the next moment, T0 is the temperature at a certain moment, T is the temperature at the next moment, and k2 represents the rate of change of the square difference of temperature with the water flow rate. When the predicted temperature T does not meet T min <T0 <T max At a given moment, the wind speed and water flow rate are used to calculate the new wind speed and water flow rate for the coal-fired boiler using parameter control formulas.

5. The method for intelligent temperature control of a coal-fired boiler based on the Internet of Things according to claim 1, characterized in that: Step S4 also includes: The host computer includes a PC-based host computer and a mobile host computer, both of which can be used to receive new wind speed and water flow data and send adjustment commands to automatically adjust the temperature of the coal-fired boiler to the target range. At the same time, it can view the real-time operating status diagram, historical data curves and alarm information of the coal-fired boiler, and can also manually adjust the wind speed and water flow through remote operation.

6. An IoT-based intelligent temperature control system for a coal-fired boiler, applied to the IoT-based intelligent temperature control method for a coal-fired boiler as described in any one of claims 1-5, characterized in that: The system includes: a data acquisition module, a data analysis module, an automatic control module, and a human-computer interaction module; The data acquisition module is equipped with sensor devices for detecting and collecting furnace temperature, wind speed, and water flow rate; the data analysis module is used for temperature prediction and calculating new wind speed and water flow rate based on the current temperature, wind speed, water flow rate, and predicted temperature; the automatic control module is used to control the water flow regulating valve and air flow regulating valve of the coal-fired boiler in real time according to the results of the data analysis module, so as to adjust the temperature of the coal-fired boiler to the corresponding range; the human-machine interaction module is used to transmit the adjusted boiler operating status information to the PC and mobile host computers, and monitor and adjust the boiler operating status in real time. The output of the data acquisition module is connected to the input of the data analysis module; the output of the data analysis module is connected to the input of the automatic control module; and the automatic control module is bidirectionally connected to the human-machine interaction module.

7. The intelligent temperature control system for a coal-fired boiler based on the Internet of Things according to claim 6, characterized in that: The data acquisition module includes a temperature data acquisition unit, a wind speed data acquisition unit, and a water flow rate data acquisition unit. The temperature data acquisition unit, i.e., the temperature sensor acquisition device, is installed inside the furnace of the coal-fired boiler to sense changes in furnace temperature and convert the sensed physical changes into digital signals through a signal processing module; the wind speed data acquisition unit, i.e., the wind speed sensor acquisition device, is installed at the air inlet of the boiler to calculate the effective wind speed by measuring the speed and direction of air flow; the water flow data acquisition unit, i.e., the water flow sensor acquisition device, is installed in the water system of the boiler to monitor the speed of water flow in real time and obtain water flow data through data processing.

8. The intelligent temperature control system for a coal-fired boiler based on the Internet of Things according to claim 6, characterized in that: The data analysis module includes a temperature prediction unit and a parameter control unit; The temperature prediction unit is used to calculate and predict the furnace temperature, and includes a temperature prediction model; the parameter control unit is used to calculate the new air volume and water flow rate, and includes a parameter control model.

9. The intelligent temperature control system for a coal-fired boiler based on the Internet of Things according to claim 6, characterized in that: The automatic control module includes a wind speed regulation unit and a water flow regulation unit; The wind speed regulating unit is used to receive instructions from the host computer and adjust the wind speed regulating valve according to the instructions; the water flow regulating unit is used to receive instructions from the host computer and adjust the water flow regulating valve according to the instructions; based on the control instructions output by the automatic control module to the regulating valve of the coal-fired boiler, the coal-fired boiler is automatically controlled.

10. The intelligent temperature control system for a coal-fired boiler based on the Internet of Things according to claim 6, characterized in that: The human-computer interaction module includes an information receiving unit and an input command unit; The receiving information unit is used to receive new wind speed and water flow data, and users can view the boiler's operating status and historical data in real time, including alarm information; the input command unit allows users to send control commands and set parameters to the control system through the human-machine interface, and the control system responds accordingly to realize remote control of the boiler.

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