An artificial intelligence-based industrial hot water boiler energy efficiency improvement system

Abstract

The present application relates to the technical field of hot water boiler control, in particular to an industrial hot water boiler energy efficiency improving system based on artificial intelligence, which comprises a combustion state optimization module, a heat exchange efficiency improving module, an operation mode adjusting module, a safety performance guaranteeing module and an operation optimizing module.In the present application, the combustion efficiency and heat transfer are matched by real-time regulation and control of air and fuel supply, reducing heat loss and combustion inefficiency, and combining with the identification of heat exchanger surface temperature and thermal resistance distribution, dynamic heat exchange regulation is implemented to enhance heat exchange capacity, and the operation mode is adjusted according to external environment temperature changes to realize efficient cooperation between operation state and heat load, potential nodes are marked by identifying abnormal heat-resistant layer temperature rise rate and pressure fluctuation risk, key safety warning is completed in advance, the cooperative relationship between the combustion system and the heat exchanger is reconstructed, the risk area and the operation strategy are integrated, and the heat utilization efficiency and the safety and stability of the boiler operation are improved.

Description

Technical Field

[0001] This invention relates to the field of hot water boiler control technology, and in particular to an energy efficiency improvement system for industrial hot water boilers based on artificial intelligence. Background Technology

[0002] The field of hot water boiler control technology involves the monitoring and regulation of the operating status of industrial and domestic hot water boilers. It mainly includes key aspects such as temperature control, water level regulation, fuel supply management, and heat exchange efficiency monitoring. By real-time adjustment of the boiler combustion status, water circulation, and heat load matching, it ensures the continuity and safety of hot water supply and the economy of energy utilization. This technology integrates multiple disciplines such as thermal engineering, automation control, and thermal measurement, and is widely used in centralized heating, industrial heating, and commercial hot water systems, playing a particularly important role in improving thermal efficiency and reducing energy consumption. Among these, the energy efficiency improvement system for traditional industrial hot water boilers addresses the problems of high heat loss and low heat exchange efficiency that hot water boilers suffer from during long-term operation. It improves energy efficiency through methods such as optimizing the surface structure of heat exchangers, improving the burner air distribution structure, installing flue gas waste heat recovery devices, and reinforcing the boiler's internal insulation materials, thereby achieving more complete fuel combustion, more efficient heat transfer, and minimized heat loss paths.

[0003] Existing technologies rely primarily on static settings for combustion regulation and heat exchange management during long-term boiler operation. This makes it difficult to cope with heat deviations and heat transfer efficiency fluctuations caused by real-time load changes. Under conditions of rapid changes in ambient temperature or drastic load fluctuations, combustion efficiency is prone to deviating from its optimal point, resulting in incomplete combustion of some fuel, leading to energy waste and increased emissions. The boiler heat exchanger structure has not been optimized to identify key thermal resistance points, resulting in localized thermal resistance accumulation that affects heat conduction capacity. Furthermore, a dynamic mechanism linking operating mode adjustments with environmental factors has not been established, causing operating condition adjustments to lag behind external changes. This results in a mismatch between heat output and demand. The safety monitoring system lacks real-time identification of abnormalities in the boiler's heat-resistant layer and pressure, posing a risk of untimely response to sudden changes in high temperature and high pressure, thus affecting the safety and stability of system operation. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing an energy efficiency improvement system for industrial hot water boilers based on artificial intelligence.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an industrial hot water boiler energy efficiency improvement system based on artificial intelligence includes: The combustion state optimization module is based on the real-time operating characteristics of the boiler combustion process, including fuel supply, air ratio and flame temperature distribution. It monitors the matching relationship between combustion efficiency and heat transfer, and generates combustion state deviation values ​​by comparing real-time combustion parameters with the theoretical optimal combustion curve. The heat exchange efficiency improvement module analyzes the surface temperature distribution and fluid flow characteristics of the heat exchanger inside the boiler based on the combustion state deviation value, collects heat conduction data and flue gas emission path of key areas of the heat exchanger, identifies abnormal areas of thermal resistance on the surface of the heat exchanger, and generates an optimized heat exchange adjustment strategy. The operation mode adjustment module extracts the boiler operating period and external environmental conditions according to the optimized heat exchange adjustment strategy, evaluates the impact of ambient temperature on boiler thermal efficiency, analyzes the priority of operation mode switching, adjusts the boiler operating conditions, and obtains the boiler operation mode adjustment table. The safety performance assurance module calls the boiler operation mode adjustment table to identify key aspects of boiler operation, detect the temperature rise rate and pressure fluctuation of the boiler inner wall heat-resistant layer, mark key points that exceed the safety range, and generate a boiler safe operation list.

[0006] As a further aspect of the present invention, the combustion state deviation value includes combustion efficiency difference, heat transfer matching degree, and deviation degree between real-time and theoretical combustion curves; the optimized heat exchange adjustment strategy includes heat exchanger surface thermal resistance optimization point, flue gas emission path improvement scheme, and fluid flow characteristic optimization direction; the boiler operation mode adjustment table includes adjusted operating conditions, operating time arrangement, mode switching priority, and environmental impact analysis results; and the boiler safe operation checklist includes key points exceeding standards, abnormal temperature rise areas of the heat-resistant layer, and pressure fluctuation risk points.

[0007] As a further aspect of the present invention, the combustion state optimization module includes: The combustion parameter capture submodule is based on the real-time operating characteristics of the boiler combustion process, including changes in fuel supply, dynamic adjustment of air ratio, and acquisition of flame temperature distribution, and converts them into a combustion node operation sequence to obtain a synchronous operation sequence of combustion nodes. The combustion difference calculation submodule matches the optimal combustion curve with real-time combustion data based on the synchronous operation sequence of the combustion nodes, extracts the difference sequence, and obtains the combustion node operation difference dataset. The combustion deviation value acquisition submodule calls the combustion node running difference dataset, combines the fuel utilization rate and heat transfer efficiency under the node, and superimposes the change in air ratio and flame temperature fluctuation to obtain the combustion state deviation value.

[0008] As a further aspect of the present invention, the heat exchange efficiency improvement module includes: The thermal resistance analysis submodule collects the surface temperature distribution and fluid flow characteristics of the heat exchanger based on the combustion state deviation value, analyzes the thermal resistance distribution law of the heat exchanger surface, and obtains the thermal resistance distribution map. The thermal resistance anomaly detection submodule analyzes the time overlap between the heat exchanger surface temperature and fluid flow characteristics based on the thermal resistance distribution map, identifies thermal resistance anomaly regions, and generates a list of thermal resistance anomaly regions. The heat exchange optimization submodule, based on the list of abnormal thermal resistance regions, optimizes the surface temperature distribution of the heat exchanger and the flue gas emission path according to the combustion state, heat exchanger structure and fluid flow characteristics, adjusts the fluid flow direction and velocity, calculates the heat exchange optimization value, and adjusts the heat exchange strategy based on the optimization value to generate an optimized heat exchange adjustment strategy.

[0009] As a further aspect of the present invention, the operating mode adjustment module includes: The runtime extraction submodule extracts the start and end times of boiler operation according to the optimized heat exchange adjustment strategy, organizes the corresponding time periods of boiler operation, and generates a boiler operation schedule. The environmental impact assessment submodule, based on the boiler operation schedule and in conjunction with real-time environmental conditions, filters environmental factors for the period in which the boiler is operating, assesses whether they exceed the operating range, marks the restricted time period, and obtains the environmental impact assessment results. Based on the environmental impact assessment results, the operation mode adjustment submodule assesses the degree of reduction in boiler thermal efficiency according to the restricted operation period, and, in conjunction with the original boiler operation plan and the postponement range, rearranges the operation mode order, identifies the adjusted boiler operation conditions, and sorts the modes to obtain the boiler operation mode adjustment table.

[0010] As a further aspect of the present invention, the security performance protection module includes: The heat-resistant layer inspection submodule monitors the operating status of the heat-resistant layer on the inner wall of the boiler based on the boiler operation mode adjustment table, checks whether the temperature rise rate and pressure fluctuation of the heat-resistant layer meet the safety standards, records parameter changes, and obtains the heat-resistant layer operating parameter record. The risk list generation submodule identifies key points where the temperature rise rate or pressure fluctuation exceeds the safe range based on the recorded operating parameters of the heat-resistant layer, marks them as areas with safety risks, calculates the safe operation qualification rate of the heat-resistant layer, and generates a boiler safe operation list.

[0011] As a further aspect of the present invention, the system also includes an operation optimization module: The operation optimization module extracts areas with risks based on the boiler safety operation list, rearranges associated heat exchangers and burners, assesses potential operational hazards, and optimizes boiler operating time based on real-time conditions to obtain a boiler optimal operation table. The boiler optimization operation schedule includes the optimized operating time, the rearranged heat exchangers and burners, and the results of the operational hazard assessment.

[0012] As a further aspect of the present invention, the operation optimization module includes: The regional screening submodule extracts areas with risks based on the boiler safe operation list, filters associated heat exchangers and burners, extracts corresponding operation information for each area, analyzes the causes of regional risks, and generates a list of areas to be optimized. Based on the list of areas to be optimized, the equipment scheduling submodule rearranges associated heat exchangers and burners, schedules currently available equipment resources, analyzes equipment performance and operating progress, and generates an equipment scheduling plan. The operation optimization submodule, based on the equipment scheduling plan and considering the current real-time progress and environmental conditions, assesses potential operational risks, optimizes the operation sequence and time allocation, and obtains the boiler optimal operation table.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by coordinating the supply of air and fuel based on the real-time characteristics of the combustion process, the combustion efficiency and heat transfer are better matched, avoiding heat loss and incomplete combustion. Combined with the accurate identification of the surface temperature and thermal resistance distribution of the heat exchanger, a dynamic heat exchange control strategy is implemented to enhance the heat exchange capacity of the heat conduction area. Furthermore, based on the consideration of changes in the external ambient temperature, the operating mode is dynamically adjusted to achieve efficient coordination between the operating state and the external heat load. By identifying abnormal temperature rise rate and pressure fluctuation risks in the heat-resistant layer, potential risk nodes are marked, and key safety warnings in thermal energy management are completed in advance. Finally, through the mutual reconstruction between the combustion system and the heat exchanger, risk areas and optimization strategies are integrated to improve the thermal utilization efficiency and the overall safety and stability of the boiler during operation. Attached Figure Description

[0014] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart illustrating the acquisition process of the combustion state optimization module in this invention. Figure 3 This is a flowchart illustrating the acquisition process of the heat exchange efficiency improvement module in this invention. Figure 4 This is a flowchart illustrating the acquisition process of the operating mode adjustment module in this invention. Figure 5 This is a flowchart illustrating the acquisition process of the safety performance assurance module in this invention. Figure 6 This is a flowchart of the process for obtaining the optimization module in this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Please see Figure 1 An artificial intelligence-based energy efficiency improvement system for industrial hot water boilers includes: The combustion state optimization module is based on the real-time operating characteristics of the boiler combustion process, including fuel supply, air ratio and flame temperature distribution. It monitors the matching relationship between combustion efficiency and heat transfer, determines whether there is incomplete combustion or excessive heat loss, and generates combustion state deviation values ​​by comparing real-time combustion parameters with the theoretical optimal combustion curve. The heat exchange efficiency improvement module analyzes the surface temperature distribution and fluid flow characteristics of the heat exchanger inside the boiler based on the combustion state deviation value, collects heat conduction data and flue gas emission path of key areas of the heat exchanger, identifies abnormal areas of thermal resistance on the surface of the heat exchanger, and generates an optimized heat exchange adjustment strategy. The operation mode adjustment module extracts the boiler operating period and external environmental conditions based on the optimized heat exchange adjustment strategy, evaluates the impact of ambient temperature on boiler thermal efficiency, analyzes the priority of operation mode switching, adjusts the boiler operating conditions, and obtains the boiler operation mode adjustment table. The safety performance assurance module calls the boiler operation mode adjustment table to identify key aspects of boiler operation, detects the temperature rise rate and pressure fluctuation of the boiler inner wall heat-resistant layer, marks key points that exceed the safety range, and generates a boiler safe operation checklist. The operation optimization module extracts areas with risks based on the boiler safety operation checklist, rearranges associated heat exchangers and burners, assesses potential operational hazards, and optimizes boiler operating time based on real-time conditions to obtain the boiler optimal operation schedule.

[0018] Combustion state deviation values ​​include differences in combustion efficiency, heat transfer matching degree, and the degree of deviation between real-time and theoretical combustion curves. Optimized heat exchange adjustment strategies include optimization points for heat exchanger surface thermal resistance, improvement schemes for flue gas emission paths, and optimization directions for fluid flow characteristics. The boiler operation mode adjustment table includes adjusted operating conditions, operating time arrangements, mode switching priorities, and environmental impact analysis results. The boiler safety operation checklist includes key points exceeding standards, abnormal temperature rise areas in the heat-resistant layer, and pressure fluctuation risk points. The boiler optimized operation table includes optimized operating time, rearranged heat exchangers and burners, and operational hazard assessment results.

[0019] Please see Figure 2 The combustion state optimization module includes: The combustion parameter capture submodule is based on the real-time operating characteristics of the boiler combustion process, including changes in fuel supply, dynamic adjustment of air ratio, and acquisition of flame temperature distribution, and converts them into a combustion node operation sequence to obtain a synchronous operation sequence of combustion nodes. Based on the real-time operating characteristics of the boiler combustion process, the operation of a coal-fired boiler first captures these characteristics, specifically including changes in fuel supply, dynamic adjustments to the air-fuel ratio, and the acquisition of flame temperature distribution. For changes in fuel supply, the pulverized coal flow rate at the pulverizer outlet is monitored. For example, when the boiler load requirement increases from 75% to 80%, the pulverized coal supply increases from 45 tons / hour to 48 tons / hour. For dynamic adjustments to the air-fuel ratio, the oxygen content in the flue gas at the furnace outlet is monitored using an oxygen analyzer, and the frequencies of the forced draft fan and induced draft fan are adjusted according to a set target value (e.g., an excess air coefficient set to 1.15). For example, when the furnace oxygen content drops from 4.5% to 4.2%, the forced draft is increased to adjust the oxygen content back to 4.5%. For the acquisition of flame temperature distribution, real-time flame temperature data is obtained through an array of infrared temperature sensors installed at different heights in the furnace. For example, the average temperature measured by the sensors at the bottom, middle, and top of the furnace is recorded. The temperatures are 1250℃, 1300℃, and 1280℃ respectively. The real-time operating characteristics are uniformly converted into combustion node operating sequences. Among them, the data on changes in fuel supply, dynamic adjustment of air ratio, and flame temperature distribution are all digitized and serialized according to timestamps. For example, within a monitoring cycle (such as every 5 seconds), a set of data points {timestamp, fuel supply, air ratio, furnace bottom temperature, furnace middle temperature, furnace top temperature} is recorded. The data points constitute the combustion node operating sequence. For example, a sequence may be: {[2025-06-27 15:40:00, 45 tons / hour, 1.15, 1250℃, 1300℃, 1280℃], [2025-06-27 15:40:05, 46 tons / hour, 1.16, 1255℃, 1305℃, 1285℃]}. The sequence is further synchronized to ensure that the time of all sensor data is aligned, thereby obtaining the combustion node synchronized operating sequence.

[0020] The combustion difference calculation submodule matches the optimal combustion curve with real-time combustion data based on the synchronous operation sequence of the combustion nodes, extracts the difference sequence, and obtains the combustion node operation difference dataset. Based on the synchronous operation sequence of the combustion nodes, for example, if the synchronous operation sequence at a certain moment shows a fuel supply of 46 tons / hour, an air ratio of 1.16, and an average furnace temperature of 1290℃, the optimal combustion curve is matched with real-time combustion data. The optimal combustion curve is established through historical operation data and theoretical models, representing the best combustion state under a specific load. For example, when the boiler load is 80%, the optimal combustion curve corresponds to a fuel supply of 47 tons / hour, an air ratio of 1.15, and an average furnace temperature of 1300℃. The real-time combustion data is compared with the optimal combustion curve, and the differences of various parameters are calculated. For example, the difference in fuel supply is 46-47=-1 ton / hour, the difference in air ratio is 1.16-1.15=0.01, and the difference in average furnace temperature is 1290-1300=-10℃. The differences form a difference sequence, for example, [-1 ton / hour, 0.01, -10℃]. By aggregating the difference sequences at multiple time points, a combustion node operation difference dataset is obtained.

[0021] The combustion deviation value acquisition submodule calls the combustion node to run the difference dataset, combines the fuel utilization rate and heat transfer efficiency under the node, and superimposes the change in air ratio and flame temperature fluctuation to obtain the combustion state deviation value. The combustion node is invoked to run the difference dataset. For example, for the dataset mentioned above where the fuel supply difference is -1 ton / hour, the air ratio difference is 0.01, and the furnace average temperature difference is -10℃, the fuel utilization rate and heat transfer efficiency at the node are combined. The fuel utilization rate represents the efficiency of converting fuel into heat energy, and its baseline value is set at 98%. When the actual fuel supply deviates from the optimal value by 1 ton / hour, the fuel utilization rate will decrease by 0.5%. For example, the current fuel utilization rate is 97.5%. The heat transfer efficiency represents the efficiency of transferring heat from the flame to the heated surface, and its baseline value is set at 92%. When the average furnace temperature deviates from the optimal value by 10℃, the heat transfer efficiency will decrease by 0.3%. For example, if the current heat transfer efficiency is 91.7%, the changes in air ratio and flame temperature fluctuation are weighted and summed. The change in air ratio refers to the difference between the real-time air ratio and the optimal air ratio, for example, 0.01. The flame temperature fluctuation refers to the difference between the real-time flame temperature and the optimal flame temperature, for example, -10℃. The values ​​are weighted and summed to obtain the combustion state deviation value. The influence weights of fuel utilization rate and heat transfer efficiency are set to 0.4, and the weight of air ratio change is set to 0. 1. The weight of flame temperature fluctuation is set to 0.1. For example, the combustion state deviation value = (-1 ton / hour × 0.5% + 0.01 × 100% + (-10℃) × 0.3%) × 0.4 + (0.01 × 100%) × 0.1 + ((-10℃) / 100) × 0.1. The specific calculation process is as follows: First, multiply the fuel supply difference, air ratio difference, and flame temperature difference by their corresponding influence coefficients: the fuel utilization rate influence coefficient is 0.005 (meaning that for every 1 ton / hour deviation in fuel supply, the fuel utilization rate decreases by 0.5%). The heat transfer efficiency influence coefficient is 0.003 (meaning that for every 1°C deviation from the average furnace temperature, the heat transfer efficiency decreases by 0.3%). Then, a weighted sum is performed according to the preset weights. For example, the fuel utilization deviation caused by the fuel supply is -1×0.005=-0.005, the air ratio change is 0.01, and the flame temperature fluctuation is -10. Combining the fuel utilization rate of 97.5% and the heat transfer efficiency of 91.7% at the node, the weighted deviation value is calculated, and the combustion state deviation value is finally obtained. For example, the final calculated combustion state deviation value is 0.035.

[0022] Please see Figure 3 The heat exchange efficiency improvement module includes: The thermal resistance analysis submodule collects the surface temperature distribution and fluid flow characteristics of the heat exchanger based on the combustion state deviation value, analyzes the thermal resistance distribution law of the heat exchanger surface, and obtains the thermal resistance distribution map. Based on the combustion state deviation value, for example, 0.035, the surface temperature distribution and fluid flow characteristics of the heat exchanger are collected. The surface temperature distribution is collected in real time using a thermocouple array installed on the heat exchanger tube wall; for example, the tube wall temperature is monitored to be 350℃ in a certain area. The fluid flow characteristics are monitored in real time using a differential pressure sensor and a flow meter; for example, the flue gas side differential pressure is 500Pa and the flow rate is 150kg / s. The thermal resistance distribution law of the heat exchanger surface is analyzed. The analysis of the thermal resistance distribution law is achieved by comprehensively calculating the surface temperature, fluid flow characteristics, and heat exchanger structural parameters (such as tube diameter, wall thickness, and material thermal conductivity) to obtain the local thermal resistance values ​​of different areas. For example, according to Fourier's law of thermal conduction and Newton's law of cooling, for a region with a tube wall temperature of 350℃ and a flue gas side temperature of 1000℃, if the heat flux density is 150kW / m², the thermal resistance of this region is calculated as (1000-350) / 150000=0.0043K·m² / W. By interpolating and visualizing the local thermal resistance values ​​of the entire heat exchanger surface, a thermal resistance distribution map is obtained. This map can intuitively show which areas of the heat exchanger surface have high thermal resistance. For example, the thermal resistance distribution map shows that in the furnace outlet area of ​​the heat exchanger, the local thermal resistance value is generally higher than 0.005K·m² / W, indicating that there is ash accumulation or scaling in this area, which leads to an increase in thermal resistance.

[0023] The thermal resistance anomaly detection submodule analyzes the time overlap between the heat exchanger surface temperature and fluid flow characteristics based on the thermal resistance distribution map, identifies thermal resistance anomaly regions, and generates a list of thermal resistance anomaly regions. According to the thermal resistance distribution map, which clearly shows the thermal resistance values ​​of different regions of the heat exchanger, for example, the thermal resistance value is 0.006 K·m² / W in a specific region A and 0.003 K·m² / W in region B. The analysis of the time overlap between heat exchanger surface temperature and fluid flow characteristics involves comparing historical temperature and flow rate data with the current thermal resistance distribution map to identify regions with persistently high thermal resistance. For example, if region A has consistently monitored a surface temperature 10°C higher than the historical average and a flue gas velocity 5 m / s lower than the historical average over the past 24 hours, while the thermal resistance value remains above 0.005 K·m² / W, an abnormal thermal resistance region is identified. A threshold of 0.005 K·m² / W is set for abnormal thermal resistance. This threshold is based on extensive historical operating data and expert experience. Experimental verification shows that when the thermal resistance value exceeds 0.005 K·m² / W, the heat exchange efficiency decreases significantly, and there is a risk of ash accumulation or scaling. For example, in a six-month actual operation test, multiple coal-fired boilers were continuously monitored, and thermal resistance data under different operating conditions were recorded. This data was then analyzed in conjunction with the boiler's heat exchange efficiency. The results showed that when the thermal resistance was below 0.004 K·m² / W, the heat exchange efficiency remained above 90%; when the thermal resistance reached 0.005 K·m² / W, the heat exchange efficiency dropped to around 85%; and when the thermal resistance exceeded 0.006 K·m² / W, the heat exchange efficiency... The thermal resistance will drop to below 80%. Based on this experimental data, 0.005 K·m² / W is used as the threshold for judging abnormal thermal resistance. When the thermal resistance value of a certain region exceeds 0.005 K·m² / W, it is marked as an abnormal region. For example, if the thermal resistance of region A is 0.006 K·m² / W, which is higher than the threshold of 0.005 K·m² / W, then region A is identified as a thermal resistance abnormal region, and a list of thermal resistance abnormal regions is generated. For example, the final list is: {region A, region C}.

[0024] The heat exchange optimization submodule, based on a list of abnormal thermal resistance regions, optimizes the heat exchanger surface temperature distribution and flue gas emission path according to combustion conditions, heat exchanger structure, and fluid flow characteristics, and adjusts the fluid flow direction and velocity using the following formula: ; Calculate the heat exchange optimization value, adjust the heat exchange strategy based on the optimization value, and generate the optimized heat exchange adjustment strategy; in, This represents the heat exchange optimization value. This represents the surface temperature of the i-th heat exchanger segment. Represents the inlet and outlet temperatures of the fluid. This represents the thermal resistance of the i-th segment. Represents the flow velocity of the i-th segment of fluid. Represents the reference flow rate. This represents the total number of sections in a heat exchanger. Based on a list of regions with abnormal thermal resistance, such as region A and region C, the surface temperature distribution of the heat exchanger and the flue gas emission path are optimized according to the combustion state, heat exchanger structure, and fluid flow characteristics. For example, for region A, the abnormal thermal resistance is caused by ash accumulation. In this case, the burner nozzle angle is adjusted so that the flame center is slightly shifted towards this region, increasing the local temperature to promote ash removal. At the same time, the induced draft fan frequency is adjusted to slightly increase the flue gas velocity in this region, helping to remove the detached ash. The fluid flow direction and velocity are also adjusted, for example, by adjusting the guide vane angle or locally increasing the sootblower operating frequency, to change the flow direction and local velocity of the flue gas on the heat exchanger surface, thereby enhancing the convective heat transfer effect. The formula is: Calculate the optimal heat transfer value, where, This represents the heat exchange optimization value, indicating the degree of improvement in overall heat exchange performance after optimization. Representing the The surface temperature of a heat exchanger section specifically refers to the average temperature of the heated surface of that section of the heat exchanger. This represents the inlet and outlet temperatures of the fluid, specifically the average of the flue gas inlet temperature entering the heat exchanger and the flue gas outlet temperature exiting the heat exchanger. Representing the The thermal resistance of a section specifically refers to the resistance during heat transfer in that section of the heat exchanger, including convective thermal resistance and conductive thermal resistance. Representing the The flow velocity of the fluid in a section specifically refers to the average flow velocity of the flue gas flowing through that section of the heat exchanger. The reference velocity refers to the average velocity of flue gas flowing through the heat exchanger under ideal operating conditions, taken as the design value or the average value of historical best operating values. Represents the total number of sections in the heat exchanger, indicating the logical or physical number of sections into which the heat exchanger is divided.

[0025] The calculation logic of this formula is as follows: First, for each section of the heat exchanger... Calculate its heat transfer efficiency term This part represents the amount of heat transferred per unit time through this section of the heat exchanger, given a temperature difference and thermal resistance. The larger the value, the greater the heat transfer potential; thermal resistance The smaller the value, the smoother the heat transfer. Secondly, calculate the flow rate correction term. This part is used to quantify the impact of the actual flow rate deviating from the reference flow rate on the heat transfer effect. The closer to the reference flow rate When, absolute value The closer the value is to 0, the closer the correction term is to 1, indicating that the flow rate has a smaller impact on heat transfer. Finally, multiply the two terms together and apply the result to all... By summing the values ​​of each segment, the overall optimized heat transfer value is obtained. The purpose is to comprehensively evaluate the combined effects of temperature, thermal resistance, and flow rate on heat transfer performance, and to guide the adjustment of optimization strategies. The advantage of this formula is that by introducing a correction factor for flow rate deviation from the reference flow rate, the impact of fluid flow characteristics on heat transfer efficiency can be evaluated more precisely, thereby enabling more accurate adjustments to heat transfer strategies. Taking a heat exchanger divided into three sections as an example, the parameter settings are as follows: Reference flow rate Fluid inlet and outlet temperatures .

[0026] Table 1: Parameters of each section of the heat exchanger; As shown in Table 1, the surface temperature, thermal resistance, and flow rate data for each segment were obtained through real-time sensor monitoring and pre-calibration. For example, the surface temperature... It is the average temperature measured directly on the surface of each heat exchanger section by a thermocouple array; thermal resistance The flow rate is determined based on the calculation results of the thermal resistance analysis submodule, combined with the geometric dimensions and material properties of each segment; It is the average flow velocity measured by Pitot tubes and differential pressure sensors installed in each section of the flue; Substitute the parameters into the formula to calculate: For the first segment: ; ; Contribution value of paragraph 1 ; Regarding paragraph 2: ; ; Contribution value of paragraph 2 ; For paragraph 3: ; ; Contribution value of paragraph 3 ; Calculate the overall heat transfer optimization value. : ; The results indicate that the current total optimized heat exchanger value is 194772.16 W / m², representing the comprehensive heat exchange performance under the current operating conditions, taking into account factors such as temperature, thermal resistance, and flow rate. This value will be used to evaluate the effectiveness of the optimization strategy and compare it with the preset heat exchange performance target value (e.g., 200000 W / m²), guiding subsequent adjustments to the heat exchange strategy and generating an optimized heat exchange adjustment strategy. For example, based on the calculated optimized heat exchange value, specific operational instructions could be included, such as "increase the flue gas velocity by 5% in area A and adjust the soot blowing frequency to once every 2 hours in area C".

[0027] Please see Figure 4 The operating mode adjustment module includes: The runtime extraction submodule extracts the start and end times of boiler operation based on the optimized heat exchange adjustment strategy, organizes the corresponding time periods of boiler operation, and generates a boiler operation schedule. Based on the optimized heat exchange adjustment strategy, for example, the strategy suggests performing heat exchange optimization operations between 8:00 AM and 4:00 PM. The boiler operation start and end times are extracted, and the planned start and stop times of the boiler are read from the boiler control system. For example, if the boiler is planned to start at 7:00 AM and stop at 5:00 PM, the corresponding time periods for boiler operation are organized. For example, the above-mentioned heat exchange optimization operation time periods are cross-compared with the boiler operation start and end times to determine that the actual optimizable operation time period is from 8:00 AM to 4:00 PM. A boiler operation schedule is generated, which accurately lists the boiler's operation arrangements and corresponding optimization operation plans in different time periods.

[0028] The environmental impact assessment submodule, based on the boiler operation schedule and combined with real-time environmental conditions, filters environmental factors for the period in which the boiler is operating, assesses whether they exceed the operating range, marks the restricted time period, and obtains the environmental impact assessment results. Based on the boiler's operating schedule, for example, if the schedule shows the boiler will operate from 8:00 AM to 4:00 PM, and combined with real-time environmental conditions, real-time environmental information is obtained through external sensors and meteorological data interfaces, including outdoor temperature, humidity, wind speed, and atmospheric pressure. For example, if the real-time outdoor temperature is 35°C and the humidity is 80%, environmental factors during the boiler's operating period are screened and assessed to determine if they exceed the operating range. Higher ambient temperatures are defined as greater than 30°C, higher humidity as greater than 75%, and lower wind speed as less than 2 m / s. The ranges are set according to boiler design specifications and long-term operating experience to avoid operation under adverse environmental conditions. For example, if the outdoor temperature is higher than 30°C and the humidity is higher than 75%, the environmental conditions are considered to have a potential impact on boiler operation, and the restricted time period is marked. For example, if the real-time outdoor temperature is 35°C and the humidity is 80%, since this exceeds the preset normal operating environment range (e.g., temperature 0°C-30°C, humidity 30%-75%), then 8:00 AM to 4:00 PM is marked as the restricted time period, resulting in an environmental impact assessment.

[0029] The operation mode adjustment submodule assesses the degree of reduction in boiler thermal efficiency based on the environmental impact assessment results and the restricted operation period. It also rearranges the operation mode order based on the original boiler operation plan and the postponement range, identifies the adjusted boiler operation conditions, and sorts the modes to obtain the boiler operation mode adjustment table. Based on the environmental impact assessment results, for example, if the assessment results show that 8:00 AM to 4:00 PM is a restricted period during which the ambient temperature and humidity exceed the normal operating range, the degree of reduction in boiler thermal efficiency is assessed according to the restricted operating period. Under the conditions of an ambient temperature of 35°C and a humidity of 80%, through historical operating data analysis and model prediction, the boiler thermal efficiency will decrease by 1.5% compared to normal operating conditions. Combining the original boiler operating plan and the delay range, the boiler was originally scheduled to start full-load operation at 8:00 AM, with a maximum delay of 1 hour. The operating mode is rearranged, prioritizing operation during unrestricted periods. For example, if the environmental conditions are suitable from 7:00 AM to 8:00 AM and from 4:00 PM to 5:00 PM, the high-load operation mode originally planned for the restricted period is adjusted to be carried out during the unrestricted period. The adjusted boiler operating conditions are identified, for example, the new operating conditions are: low-load operation from 7:00 AM to 8:00 AM, reduced-load operation from 8:00 AM to 4:00 PM, and high-load operation from 4:00 PM to 5:00 PM. The modes are then sorted to obtain the boiler operating mode adjustment table.

[0030] Please see Figure 5 The security performance protection module includes: The heat-resistant layer inspection submodule monitors the operating status of the heat-resistant layer on the inner wall of the boiler based on the boiler operation mode adjustment table, checks whether the temperature rise rate and pressure fluctuation of the heat-resistant layer meet the safety standards, records parameter changes, and obtains the heat-resistant layer operating parameter record. Based on the boiler operation mode adjustment table, for example, if the table indicates that the boiler will operate at reduced load between 8:00 AM and 4:00 PM, the operating status of the heat-resistant layer on the inner wall of the boiler is monitored. This is achieved through a heat-resistant thermocouple array and pressure sensors installed on the inner wall of the boiler furnace, which monitor the temperature and pressure at various points on the heat-resistant layer in real time. For example, if the real-time temperature of the heat-resistant layer at a certain monitoring point is 850℃ and the pressure is 1.2MPa, the temperature rise rate and pressure fluctuation of the heat-resistant layer are checked to see if they meet safety standards. Safety standards stipulate that the temperature rise rate of the heat-resistant layer should not exceed 5℃ / minute, and the pressure fluctuation should not exceed 0.1MPa. For example, if the temperature rise rate at a certain monitoring point is 6℃ / minute and the pressure fluctuation is 0.15MPa, it is determined that it does not meet safety standards. Parameter changes are recorded; for example, the temperature rise rate and pressure fluctuation data of all monitoring points are recorded as a time series to obtain the operating parameters of the heat-resistant layer.

[0031] The risk list generation submodule identifies key points where the temperature rise rate or pressure fluctuation exceeds the safe range based on the recorded operating parameters of the heat-resistant layer, and marks these as areas with safety risks, using the following formula: ; Calculate the safety qualification rate of the heat-resistant layer and generate a list of safe boiler operations; in, This represents the safety compliance rate of the heat-resistant layer during operation. Representing the Pressure fluctuations at each record point Representing the The maximum safe pressure value for each record point. Representing the The temperature rise rate at each recording point Representing the The maximum safe temperature rise rate at each recording point Represents the total number of recorded points; Based on the recorded operating parameters of the heat-resistant layer, such as records showing that the temperature rise rate and pressure fluctuation at multiple monitoring points exceed the safe range, key points where the temperature rise rate or pressure fluctuation exceeds the safe range are identified. An excessively high temperature rise rate is defined as exceeding 5°C / minute, and an excessively high pressure fluctuation is defined as exceeding 0.1 MPa. These thresholds are set according to the heat-resistant material's tolerance limit and boiler safety operation specifications. Through long-term experimental testing and operational data analysis, it has been determined that when the temperature rise rate exceeds 5°C / minute, the internal stress of the heat-resistant layer material will increase sharply, leading to cracking; when the pressure fluctuation exceeds 0.1 MPa, it will cause the heat-resistant layer to detach or structural damage, and these areas are marked as having a safety risk. For example, if a monitoring point has a temperature rise rate consistently higher than 5°C / minute or a pressure fluctuation consistently higher than 0.1 MPa for a period of time, then that area is marked as having a safety risk, using the formula: Calculate the operational safety qualification rate of the heat-resistant layer, among which, This represents the safety compliance rate of the heat-resistant layer, indicating the overall safe operation status of the boiler's heat-resistant layer during the monitoring period. Representing the Each recording point refers to a specific location or time point for monitoring the operating status of the heat-resistant layer. Representing the Pressure fluctuation at a recording point specifically refers to the difference between the maximum and minimum pressure at that recording point within a certain time period. Representing the The maximum safe pressure value for a recording point refers specifically to the highest pressure that the recording point is allowed to withstand. The threshold is set at 1.5 MPa, which is determined based on the boiler's design pressure and safety margin. Through multiple overpressure tests and simulations, it has been confirmed that the heat-resistant layer can still maintain structural integrity under this pressure. For example, if the design pressure of a boiler is 1.3 MPa and the safety margin is set at 1.15, then the maximum safe pressure value is 1.3 MPa * 1.15 = 1.495 MPa, rounded to 1.5 MPa. Representing the The temperature rise rate at each recording point specifically refers to the amount of temperature change at that recording point per unit time. Representing the The maximum safe temperature rise rate for a recording point refers specifically to the highest permissible temperature rise rate for that recording point. The threshold was set at 5°C / minute. This threshold was determined based on the thermal shock properties and long-term stress test results of the heat-resistant material. For example, in laboratory conditions, rapid heating experiments on heat-resistant materials revealed that when the temperature rise rate exceeded 5°C / minute, the probability of microcracks appearing on the material surface increased significantly. The total number of recorded points indicates the number of monitoring points counted when calculating the safety compliance rate.

[0032] The calculation logic of this formula is as follows: for each monitoring and recording point... First, calculate its pressure fluctuation term. This part quantifies the degree of deviation of pressure fluctuation from the maximum safe pressure; the smaller the pressure fluctuation, the closer this value is to 1. Simultaneously, the temperature rise rate term is calculated. This part quantifies the deviation of the temperature rise rate from the maximum safe temperature rise rate; the smaller the temperature rise rate, the closer this value is to 1. Multiplying these two terms yields the safety factor for that record point, and then applying this factor to all... Sum the security factors of each record point and divide by the total number of record points. The average safety pass rate was obtained. The purpose is to comprehensively evaluate the overall safe operation of the heat-resistant layer under the dual influence of pressure fluctuation and temperature rise rate, and to provide quantitative safety assessment indicators. The advantage of the formula is that by multiplying and quantifying the two key safety indicators, pressure fluctuation and temperature rise rate, it can more comprehensively and rigorously assess the safety status of the heat-resistant layer, avoid the limitations of judging by a single indicator, and thus ensure the safe operation of the boiler. Taking monitoring three recording points as an example, the parameter settings are as follows: maximum safe pressure value Maximum safe temperature rise rate ; Table 2: Parameter Table of Monitoring Points for Heat-Resistant Layer; As shown in Table 2, the pressure fluctuation and temperature rise rate data at each recording point were obtained in real time through pressure sensors and thermocouple arrays on the boiler inner wall. For example, pressure fluctuation... It involves statistically analyzing pressure data from monitoring points over a specific time period to calculate the difference between the maximum and minimum values; the rate of temperature rise. It is obtained by differential calculation of the temperature change at the monitoring point per unit time; Substitute the parameters into the formula to calculate: For record point 1: pressure fluctuation term ; Temperature rise rate term ; Safety factor at record point 1 ; For record point 2: pressure fluctuation term ; Temperature rise rate term ; Safety factor at record point 2 ; For recording point 3: pressure fluctuation item ; Temperature rise rate term ; Safety Factor at Record Point 3 ; Calculate the operational safety pass rate of the heat-resistant layer : ; The results indicate that the current safe operation qualification rate of the heat-resistant layer is 0.13067. This value represents the overall safe operation status of the boiler heat-resistant layer during the monitoring period. Since the safety qualification rate is much lower than the expected value (e.g., the expected value is higher than 0.8), this indicates that there is a significant safety risk in the heat-resistant layer. In particular, the safety factor of recording point 2 is negative because the temperature rise rate exceeds the maximum safe temperature rise rate. A boiler safe operation list will be generated, which will list in detail the areas with safety risks, such as {recording point 2, temperature rise rate exceeds the limit}, and recommend corresponding maintenance or repair.

[0033] Please see Figure 6 The operation optimization module includes: The regional screening submodule extracts areas with risks based on the boiler safety operation checklist, filters associated heat exchangers and burners, extracts corresponding operation information for each area, analyzes the causes of regional risks, and generates a list of areas to be optimized. Based on the boiler safety operation checklist, for example, if the checklist lists "risk of excessive temperature rise rate in the left-side heat-resistant layer area of ​​the furnace," the area with risk is extracted, identifying the left-side heat-resistant layer area as the risk area to be addressed. Related heat exchangers and burners are then screened. For the left-side heat-resistant layer area, its spatial location and the layout of the boiler's internal equipment are analyzed to determine the heat exchangers (e.g., furnace water-cooled walls) and burners (e.g., coal-fired nozzles near the left side) adjacent to or directly affecting its operation. Corresponding operating information is extracted for each area; for example, the furnace water-cooled wall information is extracted. Historical heat exchange efficiency data of the cold wall (e.g., recent average heat exchange efficiency of 85%) and operating parameters such as fuel supply, primary air volume, secondary air volume, and flame shape near the left-side coal-fired nozzle are used to analyze the causes of regional risks. Combined with the operating parameters of the heat-resistant layer, heat exchange efficiency data, and burner operating parameters, the specific reasons for the excessive temperature rise rate are analyzed. For example, the analysis results show that the excessive temperature rise rate in the heat-resistant layer area on the left side of the furnace is due to the abnormal injection angle of the coal-fired nozzle near this area, which causes the flame to deviate towards the heat-resistant layer, resulting in local overheating. A list of areas to be optimized is generated.

[0034] The equipment scheduling submodule rearranges associated heat exchangers and burners based on the list of areas to be optimized, schedules currently available equipment resources, analyzes equipment performance and operating progress, and generates an equipment scheduling plan. Based on the list of areas to be optimized, for example, if the list indicates that the heat-resistant layer area on the left side of the furnace needs optimization, the associated heat exchangers and burners are rearranged. According to the specific location and cause of the risk area, the operating parameters of the equipment related to that area are replanned. For example, to address the overheating problem in the heat-resistant layer area on the left side of the furnace, the injection angle of the coal-fired nozzles corresponding to that area is adjusted from -5° to -2°. Simultaneously, the operating conditions of the water-cooled walls near that area are fine-tuned. Currently available equipment resources are scheduled, and the status of available and standby equipment in the boiler control system is queried. For example, if it is determined that there are 3 coal-fired nozzles on the left side of the boiler, of which 2 are operating normally and 1 is in standby, the equipment performance and operating progress are analyzed. For example, the wear condition and historical operating data of the operating coal-fired nozzles are analyzed, and their response speed and stability after angle adjustment are evaluated. Considering the fuel consumption progress under the current load, an equipment scheduling plan is generated.

[0035] The operation optimization submodule, based on the equipment scheduling plan and combined with the current real-time progress and environmental conditions, assesses potential operational hazards, optimizes the operation sequence and time allocation, and obtains the boiler optimal operation table. Based on the equipment scheduling plan, for example, if the plan requires adjusting the injection angle of the coal-fired nozzles, and considering the current real-time progress and environmental conditions, the flame shape and furnace temperature distribution after the adjustment of the coal-fired nozzles are monitored in real time. In conjunction with the latest outdoor temperature and humidity data, potential operational hazards are assessed. For example, it is assessed whether adjusting the injection angle will cause excessively high flue gas temperature at the furnace outlet or abnormal boiler vibration. The operation sequence and time allocation are optimized. Based on the assessment results, the execution sequence and duration of the optimization operations are fine-tuned. For example, if it is found that the furnace flue gas temperature is increasing after the adjustment, the duration of this adjustment will be appropriately shortened or the adjustment range will be reduced. A boiler optimization operation table is obtained, which details the optimized boiler operating parameters, operation sequence, and expected effects.

[0036] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An energy efficiency improvement system for industrial hot water boilers based on artificial intelligence, characterized in that, The system includes: The combustion state optimization module is based on the real-time operating characteristics of the boiler combustion process, including fuel supply, air ratio and flame temperature distribution. It monitors the matching relationship between combustion efficiency and heat transfer, and generates combustion state deviation values ​​by comparing real-time combustion parameters with the theoretical optimal combustion curve. The heat exchange efficiency improvement module analyzes the surface temperature distribution and fluid flow characteristics of the heat exchanger inside the boiler based on the combustion state deviation value, collects heat conduction data and flue gas emission path of key areas of the heat exchanger, identifies abnormal areas of thermal resistance on the surface of the heat exchanger, and generates an optimized heat exchange adjustment strategy. The operation mode adjustment module extracts the boiler operating period and external environmental conditions according to the optimized heat exchange adjustment strategy, evaluates the impact of ambient temperature on boiler thermal efficiency, analyzes the priority of operation mode switching, adjusts the boiler operating conditions, and obtains the boiler operation mode adjustment table. The safety performance assurance module calls the boiler operation mode adjustment table to identify key aspects of boiler operation, detect the temperature rise rate and pressure fluctuation of the boiler inner wall heat-resistant layer, mark key points that exceed the safety range, and generate a boiler safe operation list.

2. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 1, characterized in that, The combustion state deviation values ​​include combustion efficiency differences, heat transfer matching degree, and the degree of deviation between real-time and theoretical combustion curves. The optimized heat exchange adjustment strategy includes heat exchanger surface thermal resistance optimization points, flue gas emission path improvement schemes, and fluid flow characteristic optimization directions. The boiler operation mode adjustment table includes adjusted operating conditions, operating time arrangements, mode switching priorities, and environmental impact analysis results. The boiler safety operation checklist includes key points exceeding standards, abnormal temperature rise areas of the heat-resistant layer, and pressure fluctuation risk points.

3. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 1, characterized in that, The combustion state optimization module includes: The combustion parameter capture submodule is based on the real-time operating characteristics of the boiler combustion process, including changes in fuel supply, dynamic adjustment of air ratio, and acquisition of flame temperature distribution, and converts them into a combustion node operation sequence to obtain a synchronous operation sequence of combustion nodes. The combustion difference calculation submodule matches the optimal combustion curve with real-time combustion data based on the synchronous operation sequence of the combustion nodes, extracts the difference sequence, and obtains the combustion node operation difference dataset. The combustion deviation value acquisition submodule calls the combustion node running difference dataset, combines the fuel utilization rate and heat transfer efficiency under the node, and superimposes the change in air ratio and flame temperature fluctuation to obtain the combustion state deviation value.

4. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 3, characterized in that, The heat exchange efficiency improvement module includes: The thermal resistance analysis submodule collects the surface temperature distribution and fluid flow characteristics of the heat exchanger based on the combustion state deviation value, analyzes the thermal resistance distribution law of the heat exchanger surface, and obtains the thermal resistance distribution map. The thermal resistance anomaly detection submodule analyzes the time overlap between the heat exchanger surface temperature and fluid flow characteristics based on the thermal resistance distribution map, identifies thermal resistance anomaly regions, and generates a list of thermal resistance anomaly regions. The heat exchange optimization submodule, based on the list of abnormal thermal resistance regions, optimizes the surface temperature distribution of the heat exchanger and the flue gas emission path according to the combustion state, heat exchanger structure and fluid flow characteristics, adjusts the fluid flow direction and velocity, calculates the heat exchange optimization value, and adjusts the heat exchange strategy based on the optimization value to generate an optimized heat exchange adjustment strategy.

5. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 4, characterized in that, The operating mode adjustment module includes: The runtime extraction submodule extracts the start and end times of boiler operation according to the optimized heat exchange adjustment strategy, organizes the corresponding time periods of boiler operation, and generates a boiler operation schedule. The environmental impact assessment submodule, based on the boiler operation schedule and in conjunction with real-time environmental conditions, filters environmental factors for the period in which the boiler is operating, assesses whether they exceed the operating range, marks the restricted time period, and obtains the environmental impact assessment results. Based on the environmental impact assessment results, the operation mode adjustment submodule assesses the degree of reduction in boiler thermal efficiency according to the restricted operation period, and, in conjunction with the original boiler operation plan and the postponement range, rearranges the operation mode order, identifies the adjusted boiler operation conditions, and sorts the modes to obtain the boiler operation mode adjustment table.

6. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 5, characterized in that, The security performance assurance module includes: The heat-resistant layer inspection submodule monitors the operating status of the heat-resistant layer on the inner wall of the boiler based on the boiler operation mode adjustment table, checks whether the temperature rise rate and pressure fluctuation of the heat-resistant layer meet the safety standards, records parameter changes, and obtains the heat-resistant layer operating parameter record. The risk list generation submodule identifies key points where the temperature rise rate or pressure fluctuation exceeds the safe range based on the recorded operating parameters of the heat-resistant layer, marks them as areas with safety risks, calculates the safe operation qualification rate of the heat-resistant layer, and generates a boiler safe operation list.

7. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 1, characterized in that, The system also includes a performance optimization module: The operation optimization module extracts areas with risks based on the boiler safety operation list, rearranges associated heat exchangers and burners, assesses potential operational hazards, and optimizes boiler operating time based on real-time conditions to obtain a boiler optimal operation table. The boiler optimization operation schedule includes optimized operating time, rearranged heat exchangers and burners, and assessment results of potential operational hazards.

8. The energy efficiency improvement system for industrial hot water boilers based on artificial intelligence according to claim 7, characterized in that, The operation optimization module includes: The regional screening submodule extracts areas with risks based on the boiler safe operation list, filters associated heat exchangers and burners, extracts corresponding operation information for each area, analyzes the causes of regional risks, and generates a list of areas to be optimized. Based on the list of areas to be optimized, the equipment scheduling submodule rearranges associated heat exchangers and burners, schedules currently available equipment resources, analyzes equipment performance and operating progress, and generates an equipment scheduling plan. The operation optimization submodule, based on the equipment scheduling plan and considering the current real-time progress and environmental conditions, assesses potential operational risks, optimizes the operation sequence and time allocation, and obtains the boiler optimal operation table.

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