Boiler early warning system and method under low-load working condition

Abstract

The invention discloses a boiler early warning system and method under a low-load working condition, and the system comprises a real-time monitoring and data collection system, a central monitoring system and a data processing platform, and the real-time monitoring and data collection system completes the detection of the operation state of a boiler through a sensor; the central monitoring system and the data processing platform are in signal connection with the real-time monitoring and data acquisition system; according to the boiler early warning system and method under the low-load working condition, by completing real-time monitoring and data collection, safe, stable and efficient operation of a boiler can be ensured, meanwhile, important decision support and early warning information can be provided for maintenance personnel, effective monitoring and early warning are conducted according to the operation working condition of the boiler, safety and redundancy design is additionally arranged, and the boiler early warning system and method are suitable for being popularized and applied. The stability and reliability of the system can be ensured; in combination with a data analysis and automatic control system for low-load working condition recognition, early recognition of the low-load working condition can be achieved, corresponding measures are taken to optimize operation of the boiler, and operation efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of boiler operating condition early warning, and in particular to a boiler early warning system and method under low load operating conditions. Background Art

[0002] Boilers play an important role in industrial production, but under low-load conditions, the operating efficiency of the boiler may be reduced, and problems such as incomplete combustion, reduced thermal efficiency, and system instability may occur, which may even lead to equipment damage or safety accidents. Therefore, it is very important to develop a method that can monitor, warn, and optimize control in real time based on the operating characteristics of the boiler under low-load conditions.

[0003] The prior art has the following defects or problems:

[0004] The existing boiler operating condition early warning system cannot respond quickly to low load conditions, affecting the normal operation of the boiler, and cannot effectively handle the operating conditions.

[0005] It should be noted that the above contents belong to the technical knowledge of the inventor and do not necessarily constitute prior art. Summary of the invention

[0006] The purpose of the present invention is to provide a boiler early warning system and method under low load conditions in view of the deficiencies of the prior art, so as to solve the problems raised in the background technology.

[0007] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a boiler early warning system under low-load conditions, a real-time monitoring and data acquisition system, wherein the real-time monitoring and data acquisition system detects the boiler operation status through sensors;

[0008] A central monitoring system and a data processing platform, wherein the central monitoring system and the data processing platform are signal-connected to a real-time monitoring and data acquisition system;

[0009] The central monitoring system and data processing platform are used to analyze the boiler operation status and make rapid responses. It is specifically composed of a data processing and analysis module, a low-load identification and decision-making module, and a control model formulation module, and is connected to the data storage cloud via a wireless network.

[0010] As a preferred technical solution of the present invention, the real-time monitoring and data acquisition system includes:

[0011] The sensor data acquisition assembly is specifically composed of various sensors, including: temperature, steam, pressure, flue gas composition, and combustion status sensors;

[0012] Various data collection units, each of which is built into each type of sensor to collect data collected by each type of sensor;

[0013] The signal adjustment and conversion module is used to adjust the signal structure and can convert analog signals into digital signals to facilitate the processing of the central monitoring system and the data processing platform. At the same time, the data can be amplified, filtered and denoised;

[0014] A wireless data transmission module, which is used to transmit various data to the data storage cloud and complete the processing through the central monitoring system and data processing platform;

[0015] The wireless data transmission module uses one of a local area network and the Internet, and the industrial communication protocol uses Modbus TCP / IP to implement.

[0016] As a preferred technical solution of the present invention, the data processing and analysis module includes:

[0017] Safety and redundancy design module, the safety and redundancy design module is used to process the received data information, and is composed of data redundancy, communication redundancy, and system fault tolerance and automatic recovery. The data redundancy adopts multiple data acquisition devices and data storage backup, the communication redundancy adopts dual-link redundancy, and the system fault tolerance and automatic recovery design fault tolerance mechanism can quickly recover and continue monitoring when a fault occurs;

[0018] The data conversion module can convert the analyzed data into visual information and display it through a real-time monitoring dashboard.

[0019] As a preferred technical solution of the present invention, the low-load identification and decision module includes:

[0020] Comparison of load and standard load. The load and standard load are connected to the data storage cloud and are used to compare the difference between the previous boiler load and the current data. The judgment content is as follows:

[0021] Boiler load: Low load conditions usually refer to boiler steam output and thermal power below a certain threshold;

[0022] Fuel consumption: The fuel consumption of the boiler is significantly lower than that of normal working conditions;

[0023] Combustion efficiency: The combustion efficiency of the boiler is low, which is reflected in high fuel consumption and low heat output;

[0024] The combustion consumption and heat generation analysis module can be used to detect whether the load is low or not. If the same fuel consumption corresponds to a lower heat output, it may be a sign of low load.

[0025] Boiler operating pressure and temperature: Under low-load operation, the internal pressure and temperature of the boiler are lower than the normal load setting value;

[0026] The pressure and temperature anomaly detection module can be used to detect pressure and temperature. The steam pressure and temperature of the boiler may be low when the load is low. By monitoring the changes in pressure and temperature in real time, the low-load condition can be identified.

[0027] The criteria for defining low-load conditions can be determined by comparing the rated load of the boiler with the actual load, thereby setting a threshold, for example, 60-70% below the rated load of the boiler. In addition to steam production, fuel consumption, pressure and temperature, other factors can also be considered, such as oxygen content and flue gas composition.

[0028] As a preferred technical solution of the present invention, the low-load identification and decision module further includes:

[0029] Warning level setting, the warning level setting can be divided into:

[0030] Primary warning: the system detects that the boiler load is slightly below the set threshold;

[0031] Level 2 warning: when the load continues to be below the threshold and is expected to cause equipment loss in the near future;

[0032] Level 3 warning: when the system determines that the boiler has entered a serious low-load condition and the load cannot be restored;

[0033] An early warning processing and anomaly detection module, which can process the collected data in real time, including data cleaning, noise removal and normalization, and complete anomaly detection;

[0034] The self-response alarm module can perform an alarm operation according to the warning level setting based on the information detected by the warning processing and abnormality detection module.

[0035] As a preferred technical solution of the present invention, the control model module includes:

[0036] A control model embedding module, wherein a built-in control model in the control model embedding module is composed of a physical model, a data-driven model and a control algorithm model;

[0037] The physical model mainly describes the basic operation process of the boiler, such as the combustion process, heat exchange process and heat load changes;

[0038] Data-driven models can predict boiler load demand and optimize parameters by collecting historical data and implementing data and using machine learning to build data-driven models;

[0039] Algorithm optimization module, which is used to select a suitable optimization algorithm, select a PID control algorithm according to the specific needs and working conditions of the boiler, and adjust the water supply, gas supply and combustion volume of the boiler to keep the system output near the set value;

[0040] Real-time data monitoring and feedback module, which realizes real-time monitoring of system data and timely feedback;

[0041] The optimization objectives are set according to the operation requirements of the boiler. The optimization objectives include:

[0042] By adjusting the ratio of air and fuel, optimal combustion is achieved, thereby reducing energy waste;

[0043] Control oxygen content and combustion temperature to reduce emissions of pollutants such as NOx and CO;

[0044] Optimize boiler load regulation so that the system can maximize fuel savings while meeting demand;

[0045] Dynamically adjust boiler output according to load fluctuations to avoid equipment damage due to low load;

[0046] Intelligent adjustment and self-learning to gradually optimize the control effect;

[0047] Data analysis and historical optimization: the data analysis and historical optimization can analyze various data and then compare them with the data storage cloud.

[0048] Another technical problem to be solved by the present invention is to provide a boiler early warning method under low load conditions, comprising the following steps:

[0049] Step 1: Complete the installation of various sensors to ensure that they can be put into use normally and stably;

[0050] Step 2: Complete the collection of boiler operation status data information through various data collection units;

[0051] Step 3: The signal adjustment and conversion module adjusts the signal structure into a digital signal, and amplifies, filters and removes noise on the data, which is then transmitted to the data storage cloud via a wireless network and analyzed through the central monitoring system and data processing platform;

[0052] Step 4: The built-in security and redundancy design module of the data processing and analysis module completes the processing of data information, including data redundancy, communication redundancy, system fault tolerance and automatic recovery. The data is then converted into visual information through the data conversion module and displayed through the real-time monitoring dashboard.

[0053] Step 5: The low-load identification and decision module can compare the current boiler data with the previous load data, including boiler load, fuel consumption, combustion efficiency, boiler operating pressure and temperature, and set the low-load operating standard;

[0054] Step 6: Then, the collected data is processed in real time through the early warning processing and anomaly detection module to complete anomaly detection. When the data is abnormal, the threshold is compared to effectively influence it, and three groups of early warning levels are set at the same time;

[0055] Step 7: Use the self-response alarm module to implement alarms based on data anomalies and three warning levels, as follows:

[0056] Primary warning: the system detects that the boiler load is slightly lower than the set threshold and issues a primary warning to indicate that the boiler may enter a low-load state;

[0057] Level 2 warning: When the load continues to be lower than the threshold and it is expected that equipment will be damaged or efficiency will be reduced in the near future, a level 2 warning will be issued. At this time, some adjustment measures can be taken;

[0058] Level 3 warning: When the system determines that the boiler has entered a serious low-load condition and the load cannot be restored, a level 3 warning is issued. At this time, emergency measures should be taken immediately, such as manual intervention, adjustment of the operating mode, or shutdown inspection;

[0059] Step 8: After the warning is completed, the staff will complete the processing and embed the physical model, data-driven model and control algorithm model into the system through the control model embedding module.

[0060] The PID control algorithm is used to adjust the water supply, gas supply and combustion volume of the boiler to keep the system output close to the set value;

[0061] Among them, the proportional part (P) adjusts the control amount according to the current error value to adapt to the changes in the system.

[0062] Integral part (I): eliminates the long-term error of the system and makes the system more accurate.

[0063] Derivative part (D): predict the changing trend of the error and improve the system response speed;

[0064] Adjust the boiler operation status in line with the optimization target, and compare various data with the data stored in the cloud through data analysis and historical optimization.

[0065] Compared with the prior art, the present invention provides a boiler early warning system and method under low load conditions, which has the following beneficial effects:

[0066] The boiler early warning system and method under low-load conditions can ensure the safety, stability and efficiency of boiler operation by completing real-time monitoring and data collection, and can provide important decision support and early warning information for maintenance personnel, conduct effective monitoring and early warning for boiler operation conditions, and set up three sets of thresholds to improve the early warning effect. At the same time, additional safety and redundancy designs can ensure the stability and reliability of the system.

[0067] The key to identifying low-load conditions is to identify whether the boiler is in a low-load state by real-time monitoring of various operating parameters of the boiler (such as steam output, temperature, pressure, fuel consumption, etc.). Combining data analysis and automatic control systems, early identification of low-load conditions can be achieved, and corresponding measures can be taken to optimize the operation of the boiler and improve operating efficiency and safety;

[0068] Timely warning and optimization adjustment can effectively prevent safety hazards caused by low-load operation of the boiler. By optimizing the boiler's adjustment strategy, the combustion efficiency under low-load conditions can be improved, thereby reducing energy effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0070] Figure 1 This is a schematic diagram of a boiler early warning system under low load conditions of the present invention;

[0071] Figure 2 This is a schematic diagram of the data processing and analysis module of the present invention;

[0072] Figure 3 It is a schematic diagram of the low-load identification and decision module of the present invention;

[0073] Figure 4 Schematic diagram of the control module of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] See also Figure 1-4 In this embodiment: a boiler early warning system under low load conditions, comprising:

[0076] A real-time monitoring and data acquisition system, which detects the operating status of the boiler through sensors;

[0077] A central monitoring system and a data processing platform, wherein the central monitoring system and the data processing platform are signal-connected to a real-time monitoring and data acquisition system;

[0078] The central monitoring system and data processing platform are used to analyze the boiler operation status and make rapid responses. It is specifically composed of a data processing and analysis module, a low-load identification and decision-making module, and a control model formulation module, and is connected to the data storage cloud via a wireless network.

[0079] It should be noted that:

[0080] In this embodiment, the real-time monitoring and data acquisition system includes:

[0081] The sensor data acquisition assembly is specifically composed of various sensors, including: temperature, steam, pressure, flue gas composition, and combustion status sensors;

[0082] Various data collection units, each of which is built into each type of sensor to collect data collected by each type of sensor;

[0083] The signal adjustment and conversion module is used to adjust the signal structure and can convert analog signals into digital signals to facilitate the processing of the central monitoring system and the data processing platform. At the same time, the data can be amplified, filtered and denoised;

[0084] A wireless data transmission module, which is used to transmit various data to the data storage cloud and complete the processing through the central monitoring system and data processing platform;

[0085] The wireless data transmission module uses one of a local area network and the Internet, and the industrial communication protocol uses Modbus TCP / IP to implement.

[0086] In this embodiment, the data processing and analysis module includes:

[0087] Safety and redundancy design module, the safety and redundancy design module is used to process the received data information, and is composed of data redundancy, communication redundancy, and system fault tolerance and automatic recovery. The data redundancy adopts multiple data acquisition devices and data storage backup, the communication redundancy adopts dual-link redundancy, and the system fault tolerance and automatic recovery design fault tolerance mechanism can quickly recover and continue monitoring when a fault occurs;

[0088] The data conversion module can convert the analyzed data into visual information and display it through a real-time monitoring dashboard.

[0089] In this embodiment, the low load identification and decision module includes:

[0090] Comparison of load and standard load. The load and standard load are connected to the data storage cloud and are used to compare the difference between the previous boiler load and the current data. The judgment content is as follows:

[0091] Boiler load: Low load conditions usually refer to boiler steam output and thermal power below a certain threshold;

[0092] Fuel consumption: The fuel consumption of the boiler is significantly lower than that of normal working conditions;

[0093] Combustion efficiency: The combustion efficiency of the boiler is low, which is reflected in high fuel consumption and low heat output;

[0094] The combustion consumption and heat generation analysis module can be used to detect whether the load is low or not. If the same fuel consumption corresponds to a lower heat output, it may be a sign of low load.

[0095] Boiler operating pressure and temperature: Under low-load operation, the internal pressure and temperature of the boiler are lower than the normal load setting value;

[0096] The pressure and temperature anomaly detection module can be used to detect pressure and temperature. The steam pressure and temperature of the boiler may be low when the load is low. By monitoring the changes in pressure and temperature in real time, the low-load condition can be identified.

[0097] The criteria for defining low-load conditions can be determined by comparing the rated load of the boiler with the actual load, thereby setting a threshold, for example, 60-70% below the rated load of the boiler. In addition to steam production, fuel consumption, pressure and temperature, other factors can also be considered, such as oxygen content and flue gas composition.

[0098] In this embodiment, the low load identification and decision module further includes:

[0099] Warning level setting, the warning level setting can be divided into:

[0100] Primary warning: the system detects that the boiler load is slightly below the set threshold;

[0101] Level 2 warning: when the load continues to be below the threshold and is expected to cause equipment loss in the near future;

[0102] Level 3 warning: when the system determines that the boiler has entered a serious low-load condition and the load cannot be restored;

[0103] An early warning processing and anomaly detection module, which can process the collected data in real time, including data cleaning, noise removal and normalization, and complete anomaly detection;

[0104] The self-response alarm module can perform an alarm operation according to the warning level setting based on the information detected by the warning processing and abnormality detection module.

[0105] In this embodiment, the control model module includes:

[0106] A control model embedding module, wherein a built-in control model in the control model embedding module is composed of a physical model, a data-driven model and a control algorithm model;

[0107] The physical model mainly describes the basic operation process of the boiler, such as the combustion process, heat exchange process and heat load changes;

[0108] Data-driven models can predict boiler load demand and optimize parameters by collecting historical data and implementing data and using machine learning to build data-driven models;

[0109] Algorithm optimization module, which is used to select a suitable optimization algorithm, select a PID control algorithm according to the specific needs and working conditions of the boiler, and adjust the water supply, gas supply and combustion volume of the boiler to keep the system output near the set value;

[0110] Real-time data monitoring and feedback module, which realizes real-time monitoring of system data and timely feedback;

[0111] The optimization objectives are set according to the operation requirements of the boiler. The optimization objectives include:

[0112] By adjusting the ratio of air and fuel, optimal combustion is achieved, thereby reducing energy waste;

[0113] Control oxygen content and combustion temperature to reduce emissions of pollutants such as NOx and CO;

[0114] Optimize boiler load regulation so that the system can maximize fuel savings while meeting demand;

[0115] Dynamically adjust boiler output according to load fluctuations to avoid equipment damage due to low load;

[0116] Intelligent adjustment and self-learning to gradually optimize the control effect;

[0117] Data analysis and historical optimization: the data analysis and historical optimization can analyze various data and then compare them with the data storage cloud.

[0118] Based on the above-mentioned boiler early warning system under low load conditions, a boiler early warning method under low load conditions is proposed:

[0119] It specifically includes the following steps:

[0120] Step 1: Complete the installation of various sensors to ensure that they can be put into use normally and stably;

[0121] Step 2: Complete the collection of boiler operation status data information through various data collection units;

[0122] Step 3: The signal adjustment and conversion module adjusts the signal structure into a digital signal, and amplifies, filters and removes noise on the data, which is then transmitted to the data storage cloud via a wireless network and analyzed through the central monitoring system and data processing platform;

[0123] Step 4: The built-in security and redundancy design module of the data processing and analysis module completes the processing of data information, including data redundancy, communication redundancy, system fault tolerance and automatic recovery. The data is then converted into visual information through the data conversion module and displayed through the real-time monitoring dashboard.

[0124] Step 5: The low-load identification and decision module can compare the current boiler data with the previous load data, including boiler load, fuel consumption, combustion efficiency, boiler operating pressure and temperature, and set the low-load operating standard;

[0125] Step 6: Then, the collected data is processed in real time through the early warning processing and anomaly detection module to complete anomaly detection. When the data is abnormal, the threshold is compared to effectively influence it, and three groups of early warning levels are set at the same time;

[0126] Step 7: Use the self-response alarm module to implement alarms based on data anomalies and three warning levels, as follows:

[0127] Primary warning: the system detects that the boiler load is slightly lower than the set threshold and issues a primary warning to indicate that the boiler may enter a low-load state;

[0128] Level 2 warning: When the load continues to be lower than the threshold and it is expected that equipment will be damaged or efficiency will be reduced in the near future, a level 2 warning will be issued. At this time, some adjustment measures can be taken;

[0129] Level 3 warning: When the system determines that the boiler has entered a serious low-load condition and the load cannot be restored, a level 3 warning is issued. At this time, emergency measures should be taken immediately, such as manual intervention, adjustment of operating mode or shutdown inspection;

[0130] Step 8: After the warning is completed, the staff will complete the processing and embed the physical model, data-driven model and control algorithm model into the system through the control model embedding module.

[0131] The PID control algorithm is used to adjust the water supply, gas supply and combustion volume of the boiler to keep the system output close to the set value;

[0132] Among them, the proportional part (P) adjusts the control amount according to the current error value to adapt to the changes in the system.

[0133] Integral part (I): eliminates the long-term error of the system and makes the system more accurate.

[0134] Derivative part (D): predict the changing trend of the error and improve the system response speed;

[0135] Adjust the boiler operation status in line with the optimization target, and compare various data with the data stored in the cloud through data analysis and historical optimization.

[0136] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A boiler early warning system under low load conditions, characterized in that: include: A real-time monitoring and data acquisition system, which detects the operating status of the boiler through sensors; A central monitoring system and a data processing platform, wherein the central monitoring system and the data processing platform are signal-connected to a real-time monitoring and data acquisition system; The central monitoring system and data processing platform are used to analyze the boiler operation status and make rapid responses. It is specifically composed of a data processing and analysis module, a low-load identification and decision-making module, and a control model formulation module, and is connected to the data storage cloud via a wireless network.

2. A boiler early warning system under low load conditions according to claim 1, characterized in that: The real-time monitoring and data acquisition system comprises: The sensor data acquisition assembly is specifically composed of various sensors, including: temperature, steam, pressure, flue gas composition, and combustion status sensors; Various data collection units, each of which is built into each type of sensor to collect data collected by each type of sensor; The signal adjustment and conversion module is used to adjust the signal structure and can convert analog signals into digital signals to facilitate the processing of the central monitoring system and the data processing platform. At the same time, the data can be amplified, filtered and denoised; A wireless data transmission module, which is used to transmit various data to the data storage cloud and complete the processing through the central monitoring system and data processing platform; The wireless data transmission module uses one of a local area network and the Internet, and the industrial communication protocol is implemented using Modbus TCP / IP.

3. The boiler early warning system under low load conditions according to claim 1 is characterized in that: The data processing and analysis module includes: Safety and redundancy design module, the safety and redundancy design module is used to process the received data information, and is composed of data redundancy, communication redundancy, and system fault tolerance and automatic recovery. The data redundancy adopts multiple data acquisition devices and data storage backup, the communication redundancy adopts dual-link redundancy, and the system fault tolerance and automatic recovery design fault tolerance mechanism can quickly recover and continue monitoring when a fault occurs; The data conversion module can convert the analyzed data into visual information and display it through a real-time monitoring dashboard.

4. The boiler early warning system under low load conditions according to claim 1 is characterized in that: The low-load identification and decision module includes: Comparison of load and standard load. The load and standard load are connected to the data storage cloud and are used to compare the difference between the previous boiler load and the current data. The judgment content is as follows: Boiler load: Low load conditions usually refer to boiler steam output and thermal power below a certain threshold; Fuel consumption: The fuel consumption of the boiler is significantly lower than that of normal working conditions; Combustion efficiency: The combustion efficiency of the boiler is low, which is reflected in high fuel consumption and low heat output; The combustion consumption and heat generation analysis module can be used to detect whether the load is low or not. If the same fuel consumption corresponds to a lower heat output, it may be a sign of low load. Boiler operating pressure and temperature: Under low-load operation, the internal pressure and temperature of the boiler are lower than the normal load setting value; The pressure and temperature anomaly detection module can be used to detect pressure and temperature. The steam pressure and temperature of the boiler may be low when the load is low. By monitoring the changes in pressure and temperature in real time, the low-load condition can be identified. The criteria for defining low-load conditions can be determined by comparing the rated load of the boiler with the actual load, thereby setting a threshold, for example, 60-70% below the rated load of the boiler. In addition to steam production, fuel consumption, pressure and temperature, other factors can also be considered, such as oxygen content and flue gas composition.

5. A boiler early warning system under low load conditions according to claim 4, characterized in that: The low-load identification and decision module also includes: Warning level setting, the warning level setting can be divided into: Primary warning: the system detects that the boiler load is slightly below the set threshold; Level 2 warning: when the load continues to be below the threshold and is expected to cause equipment loss in the near future; Level 3 warning: when the system determines that the boiler has entered a serious low-load condition and the load cannot be restored; An early warning processing and anomaly detection module, which can process the collected data in real time, including data cleaning, noise removal and normalization, and complete anomaly detection; The self-response alarm module can perform an alarm operation according to the warning level setting based on the information detected by the warning processing and abnormality detection module.

6. The boiler early warning system under low load condition according to claim 1 is characterized in that: The control model module includes: A control model embedding module, wherein a built-in control model in the control model embedding module is composed of a physical model, a data-driven model and a control algorithm model; The physical model mainly describes the basic operation process of the boiler, such as the combustion process, heat exchange process and heat load changes; Data-driven models can predict boiler load demand and optimize parameters by collecting historical data and implementing data and using machine learning to build data-driven models; Algorithm optimization module, which is used to select a suitable optimization algorithm, select a PID control algorithm according to the specific needs and working conditions of the boiler, and adjust the water supply, gas supply and combustion volume of the boiler to keep the system output near the set value; Real-time data monitoring and feedback module, which realizes real-time monitoring of system data and timely feedback; The optimization objectives are set according to the operation requirements of the boiler. The optimization objectives include: By adjusting the ratio of air and fuel, optimal combustion is achieved, thereby reducing energy waste; Control oxygen content and combustion temperature to reduce emissions of pollutants such as NOx and CO; Optimize boiler load regulation so that the system can maximize fuel savings while meeting demand; Dynamically adjust boiler output according to load fluctuations to avoid equipment damage due to low load; Intelligent adjustment and self-learning to gradually optimize the control effect; Data analysis and historical optimization: the data analysis and historical optimization can analyze various data and then compare them with the data storage cloud.

7. A boiler early warning method under low load conditions, characterized in that: The steps include: Step 1: Complete the installation of various sensors to ensure that they can be put into use normally and stably; Step 2: Complete the collection of boiler operation status data information through various data collection units; Step 3: The signal adjustment and conversion module adjusts the signal structure into a digital signal, and amplifies, filters and removes noise on the data, which is then transmitted to the data storage cloud via a wireless network and analyzed through the central monitoring system and data processing platform; Step 4: The built-in security and redundancy design module of the data processing and analysis module completes the processing of data information, including data redundancy, communication redundancy, system fault tolerance and automatic recovery. The data is then converted into visual information through the data conversion module and displayed through the real-time monitoring dashboard. Step 5: The low-load identification and decision module can compare the current boiler data with the previous load data, including boiler load, fuel consumption, combustion efficiency, boiler operating pressure and temperature, and set the low-load operating condition standard; Step 6: Then, the collected data is processed in real time through the early warning processing and anomaly detection module to complete anomaly detection. When the data is abnormal, the threshold is compared to effectively influence it, and three groups of early warning levels are set at the same time; Step 7: Use the self-response alarm module to implement alarms based on data anomalies and three warning levels, as follows: Primary warning: the system detects that the boiler load is slightly lower than the set threshold and issues a primary warning to indicate that the boiler may enter a low-load state; Level 2 warning: When the load continues to be lower than the threshold and it is expected that equipment will be damaged or efficiency will be reduced in the near future, a level 2 warning will be issued. At this time, some adjustment measures can be taken; Level 3 warning: When the system determines that the boiler has entered a serious low-load condition and the load cannot be restored, a level 3 warning is issued. At this time, emergency measures should be taken immediately, such as manual intervention, adjustment of operating mode or shutdown inspection; Step 8: After the warning is completed, the staff will complete the processing and embed the physical model, data-driven model and control algorithm model into the system through the control model embedding module. The PID control algorithm is used to adjust the water supply, gas supply and combustion volume of the boiler so that the system output remains close to the set value; Among them, the proportional part (P) adjusts the control amount according to the current error value to adapt to the changes in the system. Integral part (I): eliminates the long-term error of the system and makes the system more accurate. Derivative part (D): predict the changing trend of the error and improve the system response speed; Adjust the boiler operation status in line with the optimization target, and compare various data with the data stored in the cloud through data analysis and historical optimization.

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