Boiler maintenance prompting system and maintenance method based on boiler ash deposition condition

Abstract

The invention discloses a boiler maintenance prompting system and maintenance method based on boiler ash deposition conditions, and the method comprises the steps: monitoring the ash deposition conditions in a boiler in real time through a plurality of sensors, including the thickness, distribution and influence of the deposited ash, and generating real-time data; the data processing module combines historical operation data and ash deposition rule analysis, predicts the influence of ash deposition on the boiler efficiency, and generates maintenance suggestions for operators; when the ash deposition amount reaches a preset threshold value, the system prompts to clean or adjust boiler parameters; if the accumulated dust is too high, the automatic cleaning control module starts the dust cleaning device, and the cleaning process is adjusted according to the accumulated dust distribution; after cleaning is completed, the system feeds back a cleaning state and adjusts a maintenance strategy; according to the boiler maintenance prompting system based on the boiler ash deposition condition and the maintenance method, the boiler ash deposition condition is monitored in real time through an intelligent sensor, a data analysis algorithm and an automatic cleaning mechanism, suggestions are provided for operators through a predictive maintenance model, and the operation efficiency and safety of the boiler are improved.

Description

Technical Field

[0001] The present invention belongs to the field of boiler maintenance based on boiler ash accumulation conditions, and particularly relates to a boiler maintenance prompt system and a maintenance method based on boiler ash accumulation conditions. Background Art

[0002] A boiler maintenance reminder system based on boiler ash accumulation primarily monitors ash accumulation within the boiler to provide early warning of maintenance needs, thereby improving boiler efficiency and reducing malfunctions. The system uses sensors or detection equipment to monitor ash accumulation in real time. Common detection methods include temperature, pressure, vibration, and other sensors, as well as flue gas emission monitoring to indirectly determine the ash accumulation within the boiler. The collected ash accumulation data is transmitted to a data processing system for real-time analysis. The system can determine whether ash accumulation exceeds the specified threshold based on a set threshold or predict the growth trend of ash accumulation, thereby determining when cleaning or maintenance is necessary. When the ash accumulation level approaches or exceeds a safe value, the system alerts relevant personnel through alarms, text messages, emails, and other means to promptly perform cleaning or other maintenance measures to prevent excessive ash accumulation from leading to decreased efficiency, increased fuel consumption, and even malfunctions. In addition to real-time alerts, the system can also optimize the boiler's maintenance plan based on the changing trends of ash accumulation to ensure optimal operation. Regular maintenance can effectively extend the service life of the boiler and reduce downtime.

[0003] However, although the boiler maintenance reminder system based on boiler ash accumulation can help improve the operating efficiency of the boiler and reduce failures, it also has some potential defects or limitations. At present, during the operation of the boiler, due to long-term use and fuel combustion, a certain amount of ash will accumulate inside the boiler, resulting in a decrease in heat transfer efficiency and may even cause failures. Traditional boiler maintenance mainly relies on manual inspections and regular maintenance, which has problems such as slow response, frequent manual intervention, and long maintenance cycles. It is difficult to effectively prevent the problem of boiler ash accumulation, and it is impossible to accurately predict when cleaning is needed. Summary of the Invention

[0004] In response to the deficiencies in the prior art, the purpose of the present invention is to provide a boiler maintenance reminder system and maintenance method based on the boiler ash accumulation situation. By introducing intelligent sensors, data analysis algorithms and automatic cleaning mechanisms, the boiler ash accumulation situation is monitored in real time, and maintenance suggestions are provided to the operator through a predictive maintenance model, thereby improving the operating efficiency and safety of the boiler.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A boiler maintenance reminder system based on boiler ash accumulation conditions, comprising:

[0007] The ash accumulation monitoring sensor module uses multiple sensors to detect the ash accumulation inside the boiler in real time. The sensors work together to ensure the monitoring of ash accumulation.

[0008] The data acquisition and processing module is used to transmit the data collected by the sensor to the central processing unit. The data processing module uses intelligent data analysis technology to analyze the ash accumulation trend during the operation of the boiler through algorithms;

[0009] The maintenance suggestion generation module is used to generate maintenance suggestions based on the output of the data processing module, combined with the boiler's operating history data and ash accumulation patterns, and optimize the maintenance cycle and cleaning strategy through artificial intelligence algorithms;

[0010] Automatic cleaning control module, used to automatically issue a cleaning instruction when the ash accumulation reaches a preset threshold, triggering the boiler's automatic cleaning device. The module connects to the existing boiler automatic cleaning system and automatically starts the cleaning process;

[0011] The user interaction interface module is used to allow operators to view the boiler's ash accumulation, maintenance recommendations and system status in real time through a simple interface, and adjust relevant parameters. Users can remotely monitor and manage through mobile devices and computer platforms.

[0012] A boiler maintenance method based on boiler ash accumulation includes the following steps:

[0013] Multiple sensors monitor the ash accumulation inside the boiler in real time, including its thickness, distribution, and impact on boiler operation, generating real-time data.

[0014] The collected data is analyzed through the data processing module. Combined with the boiler's historical operating data and the development pattern of ash accumulation, the impact of ash accumulation on boiler operating efficiency is predicted and maintenance recommendations are generated for operators in advance.

[0015] Based on the analysis results, the maintenance suggestion generation module automatically generates cleaning suggestions for the operator. If the ash accumulation reaches the preset cleaning threshold, the system prompts the operator to clean the boiler or recommends adjusting the boiler operating parameters.

[0016] When the system detects that the amount of ash accumulation is too high, the automatic cleaning control module activates the boiler's cleaning device for automatic cleaning. The cleaning process is adjusted according to the distribution of ash accumulation.

[0017] After cleaning is completed, the system provides feedback on the boiler's cleaning status and adjusts the maintenance strategy based on the new monitoring data.

[0018] Preferably, multiple sensors are used to monitor the ash accumulation inside the boiler in real time, including the thickness and distribution of the ash accumulation and its impact on boiler operation. The method for generating real-time data is as follows:

[0019] Dust accumulation thickness H ash The measurement is performed using an optical sensor or a laser ranging sensor. The sensor infers the thickness of the dust layer by emitting a light beam and calculating the time or intensity of the reflected light. Assuming the propagation speed of the light wave emitted by the sensor is c and the measurement time is t, the dust thickness is calculated using the following formula:

[0020]

[0021] Among them, H ash is the dust accumulation thickness;

[0022] c is the speed of light;

[0023] t is the time it takes for the light wave to propagate;

[0024] The ash accumulation distribution is the distribution of ash accumulation in different areas inside the boiler. It is evaluated by measuring data from sensors at multiple locations. Multiple optical sensors and vibration sensors are installed inside the boiler. The output signal of each sensor reflects the degree of ash accumulation at that location. , dust accumulation distribution D i It is expressed by the following formula:

[0025] D i =f(I i , T i , V i )

[0026] Among them, D i For sensors Dust distribution at the measurement location;

[0027] I i For sensors Measured light intensity or infrared reflection intensity;

[0028] T i For sensors Measured temperature;

[0029] V i For sensors Measured vibration signal;

[0030] f() is a function based on sensor data;

[0031] Ash accumulation affects the thermal efficiency of the boiler, mainly by hindering heat conduction and causing heat loss. The thermal conductivity coefficient λ of the boiler is affected by the thickness of the ash accumulation H. ash The influence of heat loss Q loss It is expressed in the following formula:

[0032]

[0033] Among them, Q loss is the heat loss due to dust accumulation;

[0034] λ is the heat transfer coefficient of the boiler surface;

[0035] A is the surface area of ​​the boiler;

[0036] T surface is the temperature of the boiler surface;

[0037] T fluid is the temperature of the fluid inside the boiler;

[0038] H ash is the thickness of dust accumulation;

[0039] Based on the thickness and distribution of ash deposits and their impact on boiler operation, the boiler efficiency change η is calculated using the following formula: eff , set the original thermal efficiency to η0:

[0040]

[0041] Among them, η eff is the boiler operating efficiency affected by ash accumulation;

[0042] η0 is the original thermal efficiency of the boiler;

[0043] k1, k2, k3 are weight coefficients;

[0044] H ash is the thickness of dust accumulation;

[0045] D i For the Dust accumulation distribution measured by sensors;

[0046] Q loss For heat loss.

[0047] Preferably, the collected data is analyzed by a data processing module, and combined with the historical operation data of the boiler and the development pattern of ash accumulation, the impact of ash accumulation on the operating efficiency of the boiler is predicted, and maintenance recommendations are generated for the operator in advance as follows:

[0048] The development of ash accumulation is a dynamic process, which is affected by boiler operating conditions. The growth process of ash accumulation follows linear, exponential or other model laws. The following is the prediction formula for ash accumulation growth;

[0049] Establishment of H ash (t) is the dust accumulation thickness at time t, H ash (t-1) is the dust accumulation thickness at the previous moment, r is the dust accumulation growth rate, and the predicted value of dust accumulation at the next moment is Hash (t) is expressed by the following formula:

[0050] H ash (t) = H ash (t-1)+r·Δt

[0051] H ash (t) is the predicted dust accumulation thickness;

[0052] H ash (t-1) is the dust accumulation thickness at the previous moment;

[0053] r is the dust accumulation growth rate;

[0054] Δt is the time interval;

[0055] The impact of ash accumulation on boiler operating efficiency is the increase in heat loss. A thermal efficiency attenuation model is established.

[0056] η(t)=η0-k1·H ash (t)-k2·Q loss (t)

[0057] Where η(t) is the boiler operating efficiency at the predicted time t;

[0058] η0 is the initial thermal efficiency of the boiler;

[0059] k1 and k2 are coefficients fitted based on historical data;

[0060] H ash (t) is the dust accumulation thickness at time t;

[0061] Q loss (t) is the heat loss at time t;

[0062] Heat loss Q loss (t) is calculated as follows:

[0063]

[0064] Where λ is the heat transfer coefficient of the boiler surface;

[0065] A is the surface area of ​​the boiler;

[0066] T surfacr (t) is the temperature of the boiler surface;

[0067] T fluid (t) is the temperature of the fluid inside the boiler;

[0068] Combined with the historical operation data of the boiler, the prediction model of ash accumulation growth and boiler efficiency is further optimized by machine learning methods. Regression analysis or training prediction model is performed based on historical data. Assume that the historical data of the boiler is {(t i , H ash (t i ), Q loss (t i ),η(t i ))}, optimized by the following regression model:

[0069] H ash (t) = f(H ash (t-1), Q loss (t-1), T surface (t-1), T fluid (t-1))

[0070] η(t)=f(H ash (t), Q loss (t), T surface (t), T fluid (t))

[0071] Among them, f() is the model obtained by training based on regression analysis and neural network methods;

[0072] After predicting the decline in boiler operating efficiency, maintenance recommendations are generated based on the ash accumulation prediction model and the efficiency prediction model. threshold When the system generates maintenance suggestions, the logic for generating maintenance suggestions is as follows:

[0073]

[0074] Among them, η threshold is the minimum threshold of boiler efficiency;

[0075] H threshold is the critical value of dust accumulation.

[0076] Preferably, based on the analysis results, the maintenance suggestion generation module automatically generates cleaning suggestions for the operator. If the ash accumulation reaches a preset cleaning threshold, the system prompts the operator to clean the boiler or recommends adjusting the boiler operating parameters as follows:

[0077] Set the dust accumulation cleaning threshold H cleaning_threshold When the dust accumulation exceeds this threshold, the system prompts to clean it up. The dust accumulation H ash The judgment formula of (t) is as follows:

[0078]

[0079] Among them, Hash (t) is the dust accumulation thickness at time t;

[0080] H cleaning_threshold The cleaning threshold is set based on historical data and experience. When the dust accumulation thickness reaches or exceeds this threshold, the system recommends cleaning.

[0081] If the ash accumulation does not reach the cleaning threshold and the boiler's efficiency decreases, the system recommends adjusting the boiler's operating parameters. Changes in boiler efficiency are affected by the thickness of the ash accumulation, and adjustment recommendations are generated based on the relationship between efficiency and ash accumulation thickness.

[0082] The calculation formula for boiler efficiency η(t) is as follows:

[0083] η(t)=η0-k1·H ash (t)-k2·Q loss (t)

[0084] If the boiler efficiency η(t) is lower than the preset minimum efficiency threshold η threshold , and the ash accumulation thickness does not reach the cleaning threshold, the system will recommend adjusting the boiler operating parameters. The adjustment suggestion generation formula is as follows:

[0085]

[0086] Where η(t) is the current efficiency of the boiler;

[0087] η thresshold is the minimum boiler efficiency threshold. When the boiler efficiency is lower than this value, the operating parameters are adjusted.

[0088] H ash (t) is the dust accumulation thickness at time t;

[0089] H cleaning_threshold The cleaning threshold for dust accumulation;

[0090] Based on the low efficiency of the boiler, if the ash thickness does not reach the cleaning threshold, but the boiler efficiency is lower than the set threshold, the system recommends adjusting the following operating parameters to improve efficiency:

[0091] Increasing the boiler load improves the overall thermal efficiency of the boiler;

[0092] Increasing boiler fluid temperature helps improve heat exchange efficiency;

[0093] Based on the above analysis, the system will output cleanup or adjustment suggestions based on the following situations:

[0094] When the dust accumulation thickness exceeds the cleaning threshold, the operator is prompted to clean it;

[0095] When the ash accumulation has not reached the cleaning threshold, but the boiler efficiency is lower than the minimum efficiency threshold, it is recommended to adjust the boiler operating parameters;

[0096] When the ash accumulation has not reached the cleaning threshold and the boiler efficiency meets the requirements, it is recommended that the operator conduct regular monitoring;

[0097] Summary formula:

[0098]

[0099] Preferably, when the system detects that the amount of ash accumulation is too high, the automatic cleaning control module activates the boiler's ash cleaning device to perform automatic cleaning. The cleaning process is adjusted according to the ash distribution as follows:

[0100] Dust distribution D ash (x, t) is obtained through sensor data or simulation analysis, where x represents the location of different areas of the boiler and t represents time. The distribution of ash accumulation affects the activation mode and cleaning intensity of the cleaning equipment. The calculation formula of the ash accumulation distribution model is expressed as:

[0101] D ash (x, t) = f(H ash (x,t),Q flow (x), T flue (x))

[0102] Among them, D ash (x, t) is the distribution of ash accumulation in each area of ​​the boiler;

[0103] H ash (x, t) is the dust accumulation thickness of area x at time t;

[0104] Q flow (x) is the flue gas flow rate in area x;

[0105] T flue (x) is the flue gas temperature in area x;

[0106] When the system detects that the dust accumulation in a certain area exceeds the preset threshold, the dust cleaning device is activated and the dust accumulation threshold of each area is set as H. threshold (x), when the dust accumulation H in a certain area ash (x) When the threshold is exceeded, the dust cleaning device of the corresponding area is activated;

[0107] The judgment formula for activating the dust cleaning device is:

[0108]

[0109] Among them, H threshld (x) is the dust cleaning threshold of area x;

[0110] H ash (x) is the dust accumulation thickness in area x;

[0111] Set cleaning intensity I cleaning (x, t) and dust accumulation thickness H ash (x, t) is proportional to the flue gas flow rate Q flow (x) and flue gas temperature T flue The influence of (x), the adjustment formula of cleaning intensity is expressed as:

[0112] I cleaning (x, t) = k1·H ash (x, t)+k2·Q flow (x)+k3·T flue (x)

[0113] Among them, I cleaning (x, t) is the cleaning intensity of region x at time t;

[0114] k1, k2, and k3 are adjustment coefficients, which are calibrated through experiments or historical data;

[0115] H ash (x, t) is the dust accumulation thickness in region x;

[0116] Q flow (x) is the flue gas flow rate in area x;

[0117] T flue (x) is the flue gas temperature in area x;

[0118] Based on the calculation results of the dust accumulation distribution and cleaning intensity, the specific activation and execution process of the dust cleaning device is as follows:

[0119] When the dust accumulation thickness exceeds the cleaning threshold, the system activates the dust cleaning device in the corresponding area;

[0120] The cleaning intensity will be adjusted according to the ash distribution, flue gas flow and temperature;

[0121] The formula is:

[0122] Clean Action(x)=Activate Cleaning(x)·I clcaning (x, t)

[0123] Where Clean Action (x) is the cleaning action intensity of region x;

[0124] I cleaning (x, t) is the cleaning intensity;

[0125] Activate Cleaning(x) is whether to activate the cleaning device.

[0126] As a preferred method, after the cleaning is completed, the system provides feedback on the cleaning status of the boiler and adjusts the maintenance strategy based on the new monitoring data as follows:

[0127] The system uses sensors or monitoring equipment to evaluate the boiler status after cleaning. The cleaning status is usually expressed in terms of ash thickness H. ash It is measured by the degree of reduction of (x, t) and the improvement of boiler operating efficiency;

[0128] Dust accumulation after cleaning The dust accumulation thickness before and after cleaning is evaluated. The dust accumulation thickness after cleaning is expressed by the following formula:

[0129]

[0130] in, is the dust accumulation thickness of area x at time t after cleaning;

[0131] is the dust accumulation thickness of area x at time t before cleaning;

[0132] ΔH ash (x, t) is the dust thickness reduced during the cleaning process;

[0133] The cleaning efficiency E is established by comparing the ash thickness before and after cleaning and the boiler operating parameters. clean The ratio of dust accumulation reduction after cleaning is as follows:

[0134]

[0135] Among them, E clean (x, t) is the cleaning efficiency of region x at time t;

[0136] is the dust thickness before cleaning;

[0137] It is the dust thickness after cleaning.

[0138] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements a boiler maintenance reminder system and maintenance method based on the boiler ash accumulation condition as described above.

[0139] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a boiler maintenance reminder system and a maintenance method based on the boiler ash accumulation condition.

[0140] The beneficial effects of the present invention are:

[0141] By using multiple sensors to monitor the ash accumulation inside the boiler in real time, the thickness and distribution of the ash accumulation, as well as its impact on boiler operation, can be detected in a timely manner, thus avoiding reduced boiler efficiency or energy waste caused by excessive ash accumulation. When the ash accumulation reaches a preset threshold, the system can automatically trigger the cleaning device for cleaning. Through real-time analysis of the ash accumulation, the system can issue cleaning or adjustment warnings to the operator in advance, avoiding boiler shutdown or failure due to excessive ash accumulation, thereby reducing the high repair costs caused by sudden failures. Excessive ash accumulation can cause boiler components to overheat, corrode or clog, thereby shortening the service life of the boiler. By analyzing real-time and historical data, the system provides scientific maintenance recommendations and cleaning timing, allowing operators to make more accurate decisions and avoid blind operations. By collecting the distribution of ash accumulation through sensors, the system can intelligently adjust the intensity and method of cleaning to ensure that boilers in different areas are properly cleaned, avoiding problems such as excessive cleaning and uneven cleaning. Automated cleaning and precise maintenance recommendations help reduce unnecessary cleaning and operations, improve resource utilization efficiency, and reduce the consumption of labor and equipment during the cleaning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0142] Figure 1 The figure is a flow chart of a boiler maintenance reminder system based on boiler ash accumulation conditions according to the present invention. DETAILED DESCRIPTION

[0143] The principles and features of the present invention are described below. The examples provided are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The following paragraphs describe the present invention in more detail by way of example. The advantages and features of the present invention will become more apparent from the following description and claims.

[0144] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0145] Example

[0146] The technical solution adopted by the present invention to solve its technical problem is:

[0147] A boiler maintenance reminder system based on boiler ash accumulation conditions, comprising:

[0148] The ash accumulation monitoring sensor module uses multiple sensors to detect the ash accumulation inside the boiler in real time. The sensors work together to ensure the monitoring of ash accumulation.

[0149] The data acquisition and processing module is used to transmit the data collected by the sensor to the central processing unit. The data processing module uses intelligent data analysis technology to analyze the ash accumulation trend during the operation of the boiler through algorithms;

[0150] The maintenance suggestion generation module is used to generate maintenance suggestions based on the output of the data processing module, combined with the boiler's operating history data and ash accumulation patterns, and optimize the maintenance cycle and cleaning strategy through artificial intelligence algorithms;

[0151] Automatic cleaning control module, used to automatically issue a cleaning instruction when the ash accumulation reaches a preset threshold, triggering the boiler's automatic cleaning device. The module connects to the existing boiler automatic cleaning system and automatically starts the cleaning process;

[0152] The user interaction interface module is used to allow operators to view the boiler's ash accumulation, maintenance recommendations and system status in real time through a simple interface, and adjust relevant parameters. Users can remotely monitor and manage through mobile devices and computer platforms.

[0153] Multiple sensors working together monitor ash accumulation inside the boiler in real time, enabling timely detection of changes in ash accumulation and preventing it from impacting the boiler's heat transfer efficiency. When the ash accumulation reaches a preset threshold, the system automatically triggers a cleaning device for cleaning, reducing the need for manual intervention and improving automation. The system provides maintenance recommendations based on ash accumulation, preventing damage or corrosion to boiler components due to excessive ash accumulation and extending equipment life. The maintenance recommendation generation module predicts maintenance needs in advance based on data analysis, avoiding sudden equipment failures and downtime. The automatic cleaning control module precisely adjusts the cleaning process based on ash distribution, avoiding unnecessary cleaning waste and maximizing cleaning efficiency. The user interface module provides a simple and clear interface, allowing operators to view boiler ash accumulation, maintenance recommendations, and system status in real time and respond promptly. The use of multiple sensors for ash monitoring comprehensively and accurately reflects the boiler's ash accumulation, enhancing the system's adaptability and data accuracy, and ensuring monitoring without blind spots. The user interface module adopts a simple and intuitive design, allowing operators to easily understand the boiler's operating status and ash accumulation, allowing them to make quick decisions.

[0154] A boiler maintenance method based on boiler ash accumulation includes the following steps:

[0155] Multiple sensors monitor the ash accumulation inside the boiler in real time, including its thickness, distribution, and impact on boiler operation, generating real-time data.

[0156] The collected data is analyzed through the data processing module. Combined with the boiler's historical operating data and the development pattern of ash accumulation, the impact of ash accumulation on boiler operating efficiency is predicted and maintenance recommendations are generated for operators in advance.

[0157] Based on the analysis results, the maintenance suggestion generation module automatically generates cleaning suggestions for the operator. If the ash accumulation reaches the preset cleaning threshold, the system prompts the operator to clean the boiler or recommends adjusting the boiler operating parameters.

[0158] When the system detects that the amount of ash accumulation is too high, the automatic cleaning control module activates the boiler's cleaning device for automatic cleaning. The cleaning process is adjusted according to the distribution of ash accumulation.

[0159] After cleaning is completed, the system provides feedback on the boiler's cleaning status and adjusts the maintenance strategy based on the new monitoring data.

[0160] By real-time monitoring of the ash accumulation inside the boiler by multiple sensors, the distribution and thickness of the ash accumulation and its impact on the operation of the boiler can be accurately understood; combined with the historical data of the boiler and the development law of ash accumulation, the system can predict the impact of ash accumulation on the boiler efficiency in advance, ensuring that the operator takes appropriate maintenance measures before ash accumulation affects the operation of the boiler, thereby improving the operating efficiency of the boiler; the system automatically generates cleaning suggestions based on the amount of ash accumulation, reducing the subjectivity of manual judgment and ensuring the scientific nature and accuracy of maintenance work; when the ash accumulation reaches the cleaning threshold, the system can automatically activate the boiler's cleaning device for cleaning, greatly improving the automation level of boiler maintenance and reducing dependence on manual operation; excessive ash accumulation may cause internal corrosion of the boiler and thermal efficiency After cleaning is completed, the system will feedback the cleaning status and adjust the maintenance strategy according to the new data to avoid excessive or insufficient cleaning, ensuring long-term stable operation of the equipment. The system predicts the maintenance needs of the boiler through ash accumulation analysis and generates cleaning suggestions in advance, avoiding sudden boiler failures and reducing high maintenance costs. The system not only provides real-time monitoring data, but also provides operators with data-based cleaning suggestions and operating parameter adjustment plans through historical data and analysis results, helping operators make more scientific and accurate decisions. Through real-time data monitoring and ash accumulation prediction analysis, the system can detect problems that may cause downtime in advance, avoid unexpected downtime events, and ensure boiler stability and production efficiency.

[0161] Multiple sensors are used to monitor the ash accumulation inside the boiler in real time, including its thickness, distribution, and impact on boiler operation. The method for generating real-time data is as follows:

[0162] Dust accumulation thickness H ashThe measurement is performed using an optical sensor or a laser ranging sensor. The sensor infers the thickness of the dust layer by emitting a light beam and calculating the time or intensity of the reflected light. Assuming the propagation speed of the light wave emitted by the sensor is c and the measurement time is t, the dust thickness is calculated using the following formula:

[0163]

[0164] Among them, H ash is the dust accumulation thickness;

[0165] c is the speed of light;

[0166] t is the time it takes for the light wave to propagate;

[0167] The ash accumulation distribution is the distribution of ash accumulation in different areas inside the boiler. It is evaluated by measuring data from sensors at multiple locations. Multiple optical sensors and vibration sensors are installed inside the boiler. The output signal of each sensor reflects the degree of ash accumulation at that location. , dust accumulation distribution D i It is expressed by the following formula:

[0168] D i =f(I i , T i , V i )

[0169] Among them, D i For sensors Dust distribution at the measurement location;

[0170] I i For sensors Measured light intensity or infrared reflection intensity;

[0171] T i For sensors Measured temperature;

[0172] V i For sensors Measured vibration signal;

[0173] f() is a function based on sensor data;

[0174] Ash accumulation affects the thermal efficiency of the boiler, mainly by hindering heat conduction and causing heat loss. The thermal conductivity coefficient λ of the boiler is affected by the thickness of the ash accumulation H. ash The influence of heat loss Q loss It is expressed in the following formula:

[0175]

[0176] Among them, Q lossis the heat loss due to dust accumulation;

[0177] λ is the heat transfer coefficient of the boiler surface;

[0178] A is the surface area of ​​the boiler;

[0179] T surface is the temperature of the boiler surface;

[0180] T fluid is the temperature of the fluid inside the boiler;

[0181] H ash is the thickness of dust accumulation;

[0182] Based on the thickness and distribution of ash deposits and their impact on boiler operation, the boiler efficiency change η is calculated using the following formula: eff , set the original thermal efficiency to η0:

[0183]

[0184] Among them, η eff is the boiler operating efficiency affected by ash accumulation;

[0185] η0 is the original thermal efficiency of the boiler;

[0186] k1, k2, k3 are weight coefficients;

[0187] H ash is the thickness of dust accumulation;

[0188] D i For the Dust accumulation distribution measured by sensors;

[0189] Q loss For heat loss.

[0190] This solution uses multiple sensors, including optical sensors, laser ranging sensors, vibration sensors, etc., to monitor the thickness and distribution of ash accumulation inside the boiler and its impact on boiler operation in real time. Through real-time monitoring of ash accumulation and analysis of its impact on boiler thermal efficiency, the system can calculate the heat loss caused by ash accumulation and optimize the boiler's operating efficiency based on this. The system provides automated maintenance recommendations based on real-time data and analysis results, and can automatically activate the ash cleaning device to clean ash accumulation, reducing the frequency of human intervention and improving the automation level of boiler maintenance. Ash accumulation has a serious impact on the long-term operation of the boiler, especially the decline in thermal efficiency caused by ash accumulation, which may accelerate boiler aging. and damage; ash accumulation affects heat conduction, resulting in a decrease in the thermal efficiency of the boiler, thereby wasting a large amount of energy. By monitoring and cleaning ash accumulation, this heat loss can be effectively avoided, the energy utilization efficiency of the boiler can be improved, and energy consumption can be reduced; by predicting the impact of ash accumulation on boiler efficiency in advance and cleaning it in time based on the analysis results, the high maintenance costs of boiler shutdown or failure caused by excessive ash accumulation can be avoided; this solution can dynamically adjust the maintenance strategy according to the actual operation of the boiler, and continuously optimize the operation and cleaning strategy based on real-time monitoring data. This adaptive management method can not only improve boiler efficiency, but also ensure the long-term stable operation of the boiler.

[0191] The collected data is analyzed by the data processing module. Combined with the historical operation data of the boiler and the development pattern of ash accumulation, the impact of ash accumulation on boiler operation efficiency is predicted. Maintenance recommendations are generated for operators in advance as follows:

[0192] The development of ash accumulation is a dynamic process, which is affected by boiler operating conditions. The growth process of ash accumulation follows linear, exponential or other model laws. The following is the prediction formula for ash accumulation growth;

[0193] Establishment of H ash (t) is the dust accumulation thickness at time t, H ash (t-1) is the dust accumulation thickness at the previous moment, r is the dust accumulation growth rate, and the predicted value of dust accumulation at the next moment is H ash (t) is expressed by the following formula:

[0194] H ash (t) = H ash (t-1)+r·Δt

[0195] H ash (t) is the predicted dust accumulation thickness;

[0196] H ash (t-1) is the dust accumulation thickness at the previous moment;

[0197] r is the dust accumulation growth rate;

[0198] Δt is the time interval;

[0199] The influence of ash accumulation on boiler operating efficiency is based on the increase in heat loss. A thermal efficiency attenuation model is established to establish the boiler efficiency η(t) affected by the ash accumulation thickness H. ash (t), the boiler efficiency prediction model is:

[0200] η(t)=η0-k1·H ash (t)-k2·Q loss (t)

[0201] Where η(t) is the boiler operating efficiency at the predicted time t;

[0202] η0 is the initial thermal efficiency of the boiler;

[0203] k1 and k2 are coefficients fitted based on historical data;

[0204] H ash (t) is the dust accumulation thickness at time t;

[0205] Q loss (t) is the heat loss at time t;

[0206] Heat loss Q loss (t) is calculated as follows:

[0207]

[0208] Where λ is the heat transfer coefficient of the boiler surface;

[0209] A is the surface area of ​​the boiler;

[0210] T surface (t) is the temperature of the boiler surface;

[0211] T fluid (t) is the temperature of the fluid inside the boiler;

[0212] Combined with the historical operation data of the boiler, the prediction model of ash accumulation growth and boiler efficiency is further optimized by machine learning methods. Regression analysis or training prediction model is performed based on historical data. Assume that the historical data of the boiler is {(t i ,H ash (t i ), Q loss (t i ),η(t i ))}, optimized by the following regression model:

[0213] H ash (t) = f(H ash (t-1), Q loss (t-1), T surface(t-1),T fluid (t-1))

[0214] η(t)=f(H ash (t), Q loss (t), T surface (t), T fluid (t))

[0215] Among them, f() is the model obtained by training based on regression analysis and neural network methods;

[0216] After predicting the decline in boiler operating efficiency, maintenance recommendations are generated based on the ash accumulation prediction model and the efficiency prediction model. threshold When the system generates maintenance suggestions, the logic for generating maintenance suggestions is as follows:

[0217]

[0218] Among them, η threshold is the minimum threshold of boiler efficiency;

[0219] H threshold is the critical value of dust accumulation.

[0220] By combining historical data, ash accumulation patterns, and machine learning methods, the system can accurately predict future development trends in ash accumulation and analyze its impact on boiler operating efficiency. By predicting the moment when boiler efficiency drops and when ash accumulation reaches a critical value, the solution makes maintenance work more accurate and timely. By predicting the impact of ash accumulation on boiler efficiency in real time, the system can optimize the boiler's operating strategy, reduce the negative impact of ash accumulation on thermal efficiency, and thus reduce energy consumption. Combined with machine learning algorithms, the system can automatically learn and adjust the ash accumulation growth and boiler efficiency decay models to improve the accuracy and flexibility of predictions. By predicting and promptly cleaning ash accumulation, the boiler's thermal efficiency can be effectively maintained, reducing the risk of boiler surface aging, damage, and failure caused by ash accumulation. The system provides a minimum threshold for boiler efficiency and sets a critical value for ash accumulation. Operators can adjust these thresholds according to specific circumstances to optimize the boiler's operation and maintenance strategies. Predicting ash accumulation development and efficiency decay can reduce the high maintenance costs caused by excessive ash accumulation.

[0221] Based on the analysis results, the maintenance suggestion generation module automatically generates cleaning suggestions for the operator. If the ash accumulation reaches the preset cleaning threshold, the system prompts the operator to clean the boiler or recommends adjusting the boiler operating parameters as follows:

[0222] Set the dust accumulation cleaning threshold H clcaning_threshold When the dust accumulation exceeds this threshold, the system prompts to clean it up. The dust accumulation H ashThe judgment formula of (t) is as follows:

[0223]

[0224] Among them, H ash (t) is the dust accumulation thickness at time t;

[0225] H cleaning_threshold The cleaning threshold is set based on historical data and experience. When the dust accumulation thickness reaches or exceeds this threshold, the system recommends cleaning.

[0226] If the ash accumulation does not reach the cleaning threshold and the boiler's efficiency decreases, the system recommends adjusting the boiler's operating parameters. Changes in boiler efficiency are affected by the thickness of the ash accumulation, and adjustment recommendations are generated based on the relationship between efficiency and ash accumulation thickness.

[0227] The calculation formula for boiler efficiency η(t) is as follows:

[0228] η(t)=η0-k1·H ash (t)-k2·Q loss (t)

[0229] If the boiler efficiency η(t) is lower than the preset minimum efficiency threshold η threshold , and the ash accumulation thickness does not reach the cleaning threshold, the system will recommend adjusting the boiler operating parameters. The adjustment suggestion generation formula is as follows:

[0230]

[0231] Where η(t) is the current efficiency of the boiler;

[0232] η threshold is the minimum boiler efficiency threshold. When the boiler efficiency is lower than this value, the operating parameters are adjusted.

[0233] H ash (t) is the dust accumulation thickness at time t;

[0234] H cleaning_threshold The cleaning threshold for dust accumulation;

[0235] Based on the low efficiency of the boiler, if the ash thickness does not reach the cleaning threshold, but the boiler efficiency is lower than the set threshold, the system recommends adjusting the following operating parameters to improve efficiency:

[0236] Increasing the boiler load improves the overall thermal efficiency of the boiler;

[0237] Increasing boiler fluid temperature helps improve heat exchange efficiency;

[0238] Based on the above analysis, the system will output cleanup or adjustment suggestions based on the following situations:

[0239] When the dust accumulation thickness exceeds the cleaning threshold, the operator is prompted to clean it;

[0240] When the ash accumulation has not reached the cleaning threshold, but the boiler efficiency is lower than the minimum efficiency threshold, it is recommended to adjust the boiler operating parameters;

[0241] When the ash accumulation has not reached the cleaning threshold and the boiler efficiency meets the requirements, it is recommended that the operator conduct regular monitoring;

[0242] Summary formula:

[0243]

[0244] This solution dynamically monitors ash accumulation and boiler efficiency, enabling more accurate maintenance decisions. The system tracks ash accumulation in real time and, if ash accumulation reaches a preset threshold, immediately alerts the operator to clean the boiler. If ash accumulation does not reach the cleaning threshold but boiler efficiency declines, the system automatically recommends adjusting boiler operating parameters. By timely adjusting boiler operating parameters, the system effectively improves the boiler's overall thermal efficiency. Automated cleaning and adjustment recommendations prevent boiler inefficiencies caused by excessive ash accumulation, reducing energy waste. By predicting ash accumulation and generating corresponding maintenance recommendations based on changes in boiler efficiency, operators can perform preventive maintenance earlier, avoiding sudden boiler failures caused by excessive ash accumulation or declining efficiency, reducing equipment downtime and ensuring production continuity. The solution offers multiple maintenance paths. Based on different combinations of ash accumulation and boiler efficiency, the system flexibly provides maintenance recommendations, such as cleaning, adjusting operating parameters, or regular monitoring. Combining historical data with patterns of ash accumulation growth and efficiency changes, the system intelligently generates cleaning or adjustment recommendations, reducing manual intervention and enabling automated and intelligent boiler management. Through machine learning optimization models, the system continuously improves prediction accuracy and the relevance of recommendations.

[0245] When the system detects that the amount of ash accumulation is too high, the automatic cleaning control module activates the boiler's cleaning device for automatic cleaning. The cleaning process is adjusted according to the distribution of ash accumulation as follows:

[0246] Dust distribution D ash (x, t) is obtained through sensor data or simulation analysis, where x represents the location of different areas of the boiler and t represents time. The distribution of ash accumulation affects the activation mode and cleaning intensity of the cleaning equipment. The calculation formula of the ash accumulation distribution model is expressed as:

[0247] D ash (x, t) = f(H ash (x, t), Q flow (x), T flue (x))

[0248] Among them, Dash (x, t) is the distribution of ash accumulation in each area of ​​the boiler;

[0249] H ash (x, t) is the dust accumulation thickness of area x at time t;

[0250] Q flow (x) is the flue gas flow rate in area x;

[0251] T flue (x) is the flue gas temperature in area x;

[0252] When the system detects that the dust accumulation in a certain area exceeds the preset threshold, the dust cleaning device is activated and the dust accumulation threshold of each area is set as H. thrcshold (x), when the dust accumulation H in a certain area ash (x) When the threshold is exceeded, the dust cleaning device of the corresponding area is activated;

[0253] The judgment formula for activating the dust cleaning device is:

[0254]

[0255] Among them, H threshold (x) is the dust cleaning threshold of area x;

[0256] H ash (x) is the dust accumulation thickness in area x;

[0257] Set cleaning intensity I cleaning (x, t) and dust accumulation thickness H ash (x, t) is proportional to the flue gas flow rate Q flow (x) and flue gas temperature T flue The influence of (x), the adjustment formula of cleaning intensity is expressed as:

[0258] I cleaning (x, t) = k1·H ash (x, t)+k2·Q flow (x)+k3·T flue (x)

[0259] Among them, I cleaning (x, t) is the cleaning intensity of region x at time t;

[0260] k1, k2, and k3 are adjustment coefficients, which are calibrated through experiments or historical data;

[0261] H ash (x, t) is the dust accumulation thickness in region x;

[0262] Q flow (x) is the flue gas flow rate in area x;

[0263] T flue (x) is the flue gas temperature in area x;

[0264] Based on the calculation results of the dust accumulation distribution and cleaning intensity, the specific activation and execution process of the dust cleaning device is as follows:

[0265] When the dust accumulation thickness exceeds the cleaning threshold, the system activates the dust cleaning device in the corresponding area;

[0266] The cleaning intensity will be adjusted according to the ash distribution, flue gas flow and temperature;

[0267] The formula is:

[0268] Clean Action(x)=Activate Cleaning(x)·I cleaning (x, t)

[0269] Where Clean Action (x) is the cleaning action intensity of region x;

[0270] I cleaning (x, t) is the cleaning intensity;

[0271] Activate Cleaning(x) is whether to activate the cleaning device.

[0272] This solution ensures a more efficient and accurate boiler cleaning process by intelligently monitoring the distribution of ash deposits and adjusting the cleaning intensity in real time. By automatically adjusting the cleaning intensity, the efficiency of the cleaning device is optimized according to the influence of flue gas flow and temperature. This solution can flexibly adjust the cleaning intensity and cleaning method according to the specific conditions of regional ash deposits, and carry out targeted cleaning based on the distribution differences of ash deposits in different areas, avoiding the inefficiency of unified cleaning of the entire boiler. The combined adjustment of cleaning intensity with ash thickness, flue gas flow and temperature helps to achieve more accurate cleaning. The system calculates the ash distribution in real time based on sensor data and automatically starts the cleaning device without manual intervention. The cleaning intensity is adjusted by adjusting the coefficient (based on historical data and experimental results) to make the cleaning process more accurate, which not only ensures the cleaning effect but also reduces unnecessary energy consumption and equipment burden.

[0273] After cleaning is completed, the system provides feedback on the boiler's cleaning status and adjusts the maintenance strategy based on the new monitoring data as follows:

[0274] The system uses sensors or monitoring equipment to evaluate the boiler status after cleaning. The cleaning status is usually expressed in terms of ash thickness H. ash It is measured by the degree of reduction of (x, t) and the improvement of boiler operating efficiency;

[0275] Dust accumulation after cleaning The dust accumulation thickness before and after cleaning is evaluated. The dust accumulation thickness after cleaning is expressed by the following formula:

[0276]

[0277] in, is the dust accumulation thickness of area x at time t after cleaning;

[0278] is the dust accumulation thickness of area x at time t before cleaning;

[0279] ΔH ash (x, t) is the dust thickness reduced during the cleaning process;

[0280] The cleaning efficiency E is established by comparing the ash thickness before and after cleaning and the boiler operating parameters. clean The ratio of dust accumulation reduction after cleaning is as follows:

[0281]

[0282] Among them, E clean (x, t) is the cleaning efficiency of region x at time t;

[0283] is the dust thickness before cleaning;

[0284] It is the dust thickness after cleaning.

[0285] This solution can evaluate the effectiveness of boiler cleaning in real time. By providing feedback on the thickness of ash deposits after cleaning and the boiler's operating efficiency, the system can promptly understand the effectiveness of the cleaning work and avoid over- or under-cleaning. The reduced ash thickness after cleaning helps improve the boiler's thermal efficiency and prevents excessive ash from obstructing the boiler's heat exchanger. Based on the cleaning efficiency assessment, the system can adjust the frequency and intensity of cleaning in future operations, thereby reducing unnecessary cleaning operations and saving manpower, material, and energy resources. By automatically monitoring the cleaning status and based on the changes in ash deposit thickness and boiler operating efficiency before and after cleaning, the system can automatically provide feedback and adjust maintenance strategies, reducing manual intervention and achieving efficient and accurate maintenance management. By intelligently monitoring cleaning efficiency and the reduction in ash deposit thickness, the system can promptly determine whether the cleaning effect has met expectations. Cleaning efficiency, as real-time feedback, can help the system predict the boiler's operating status and future cleaning needs over a long period of time, making boiler operation more controllable and predictable. The real-time monitoring and assessment of ash deposit status can better provide data support for future maintenance plans.

[0286] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a boiler maintenance reminder system and maintenance method based on boiler ash accumulation as described above are implemented.

[0287] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the boiler maintenance reminder system and maintenance method based on the boiler ash accumulation condition as described above are implemented.

[0288] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0289] Those skilled in the art will clearly understand that for the sake of convenience and brevity in description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0290] The above embodiments of the present invention are not intended to limit the scope of protection of the present invention, and the implementation methods of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in this field, without departing from the above basic technical ideas of the present invention, should fall within the scope of protection of the present invention.

Claims

1. A boiler maintenance reminder system based on boiler ash accumulation, characterized in that: Includes: The ash accumulation monitoring sensor module uses multiple sensors to detect the ash accumulation inside the boiler in real time. The sensors work together to ensure the monitoring of ash accumulation. The data acquisition and processing module is used to transmit the data collected by the sensor to the central processing unit. The data processing module uses intelligent data analysis technology to analyze the ash accumulation trend during the operation of the boiler through algorithms; The maintenance suggestion generation module is used to generate maintenance suggestions based on the output of the data processing module, combined with the boiler's operating history data and ash accumulation patterns, and optimize the maintenance cycle and cleaning strategy through artificial intelligence algorithms; Automatic cleaning control module, used to automatically issue a cleaning instruction when the ash accumulation reaches a preset threshold, triggering the boiler's automatic cleaning device. The module connects to the existing boiler automatic cleaning system and automatically starts the cleaning process; The user interaction interface module is used to allow operators to view the boiler's ash accumulation, maintenance recommendations and system status in real time through a simple interface, and adjust relevant parameters. Users can remotely monitor and manage through mobile devices and computer platforms.

2. A boiler maintenance method based on boiler ash accumulation, characterized in that: The following steps are involved: Multiple sensors monitor the ash accumulation inside the boiler in real time, including its thickness, distribution, and impact on boiler operation, generating real-time data. The collected data is analyzed through the data processing module. Combined with the boiler's historical operating data and the development pattern of ash accumulation, the impact of ash accumulation on boiler operating efficiency is predicted and maintenance recommendations are generated for operators in advance. Based on the analysis results, the maintenance suggestion generation module automatically generates cleaning suggestions for the operator. If the ash accumulation reaches the preset cleaning threshold, the system prompts the operator to clean the boiler or recommends adjusting the boiler operating parameters. When the system detects that the amount of ash accumulation is too high, the automatic cleaning control module activates the boiler's cleaning device for automatic cleaning. The cleaning process is adjusted according to the distribution of ash accumulation. After cleaning is completed, the system provides feedback on the boiler's cleaning status and adjusts the maintenance strategy based on the new monitoring data.

3. The boiler maintenance method based on boiler ash accumulation according to claim 2, characterized in that: Multiple sensors are used to monitor the ash accumulation inside the boiler in real time, including its thickness, distribution, and impact on boiler operation. The method for generating real-time data is as follows: Dust accumulation thickness H ash The measurement is performed using an optical sensor or a laser ranging sensor. The sensor infers the thickness of the dust layer by emitting a light beam and calculating the time or intensity of the reflected light. Assuming the propagation speed of the light wave emitted by the sensor is c and the measurement time is t, the dust thickness is calculated using the following formula: Among them, H ash is the dust accumulation thickness; c is the speed of light; t is the time it takes for the light wave to propagate; The ash accumulation distribution is the distribution of ash accumulation in different areas inside the boiler. It is evaluated by measuring data from sensors at multiple locations. Multiple optical sensors and vibration sensors are installed inside the boiler. The output signal of each sensor reflects the degree of ash accumulation at that location. For each sensor i, the ash accumulation distribution D i It is expressed by the following formula: D i =f(I i ,T i ,V i ) Among them, D i The dust accumulation distribution at the measurement position of sensor i; I i is the light intensity or infrared reflection intensity measured by sensor i; T i is the temperature measured by sensor i; V i is the vibration signal measured by sensor i; f() is a function based on sensor data; Ash accumulation affects the thermal efficiency of the boiler, mainly by hindering heat conduction and causing heat loss. The thermal conductivity coefficient λ of the boiler is affected by the thickness of the ash accumulation H. ash The influence of heat loss Q loss It is expressed in the following formula: Among them, Q loss is the heat loss due to dust accumulation; λ is the heat transfer coefficient of the boiler surface; A is the surface area of ​​the boiler; T surface is the temperature of the boiler surface; T fluid is the temperature of the fluid inside the boiler; H ash is the thickness of dust accumulation; Based on the thickness and distribution of ash deposits and their impact on boiler operation, the boiler efficiency change η is calculated using the following formula: eff , set the original thermal efficiency to η0: Among them, η eff is the boiler operating efficiency affected by ash accumulation; η0 is the original thermal efficiency of the boiler; k1, k2, k3 are weight coefficients; H ash is the thickness of dust accumulation; D i is the dust accumulation distribution measured by the i-th sensor; Q loss For heat loss.

4. The boiler maintenance method based on boiler ash accumulation according to claim 3, characterized in that: The collected data is analyzed by the data processing module. Combined with the historical operation data of the boiler and the development pattern of ash accumulation, the impact of ash accumulation on boiler operation efficiency is predicted. Maintenance recommendations are generated for operators in advance as follows: The development of ash accumulation is a dynamic process, which is affected by boiler operating conditions. The growth process of ash accumulation follows linear, exponential or other model laws. The following is the prediction formula for ash accumulation growth; Establishment of H ash (t) is the dust accumulation thickness at time t, H ash (t-1) is the dust accumulation thickness at the previous moment, r is the dust accumulation growth rate, and the predicted value of dust accumulation at the next moment is H ash (t) is expressed by the following formula: H ash (t)=H ash (t-1)+r·Δt H ash (t) is the predicted dust accumulation thickness; H ash (t-1) is the dust accumulation thickness at the previous moment; r is the dust accumulation growth rate; Δt is the time interval; The influence of ash accumulation on boiler operating efficiency is based on the increase in heat loss. A thermal efficiency attenuation model is established to establish the boiler efficiency η(t) affected by the ash accumulation thickness H. ash (t), the boiler efficiency prediction model is: η(t)=η0-k1·H ash (t)-k2·Q loss (t) Where η(t) is the boiler operating efficiency at the predicted time t; η0 is the initial thermal efficiency of the boiler; k1 and k2 are coefficients fitted based on historical data; H ash (t) is the dust accumulation thickness at time t; Q loss (t) is the heat loss at time t; Heat loss Q loss (t) is calculated as follows: Where λ is the heat transfer coefficient of the boiler surface; A is the surface area of ​​the boiler; T surface (t) is the temperature of the boiler surface; T fluid (t) is the temperature of the fluid inside the boiler; Combined with the historical operation data of the boiler, the prediction model of ash accumulation growth and boiler efficiency is further optimized by machine learning methods. Regression analysis or training prediction model is performed based on historical data. Assume that the historical data of the boiler is {(t i , H ash (t i ), Q loss (t i ),η(t i ))}, optimized by the following regression model: H ash (t)=f(H ash (t-1),Q loss (t-1),T surface (t-1),T fluid (t-1)) η(t)=f(H ash (t),Q loss (t),T surface (t),T fluid (t)) Among them, f() is the model obtained by training based on regression analysis and neural network methods; After predicting the decline in boiler operating efficiency, maintenance recommendations are generated based on the ash accumulation prediction model and the efficiency prediction model. threshold When the system generates maintenance suggestions, the logic for generating maintenance suggestions is as follows: Among them, η threshold is the minimum threshold of boiler efficiency; H threshold is the critical value of dust accumulation.

5. The boiler maintenance method based on boiler ash accumulation according to claim 4, characterized in that: Based on the analysis results, the maintenance suggestion generation module automatically generates cleaning suggestions for the operator. If the ash accumulation reaches the preset cleaning threshold, the system prompts the operator to clean the boiler or recommends adjusting the boiler operating parameters as follows: Set the dust accumulation cleaning threshold H cleaning_thrcshuth When the dust accumulation exceeds this threshold, the system prompts to clean it up. The dust accumulation H ash The judgment formula of (t) is as follows: Among them, H ash (t) is the dust accumulation thickness at time t; H clcaning_thrshold The cleaning threshold is set based on historical data and experience. When the dust accumulation thickness reaches or exceeds this threshold, the system recommends cleaning. If the ash accumulation does not reach the cleaning threshold and the boiler's efficiency decreases, the system recommends adjusting the boiler's operating parameters. Changes in boiler efficiency are affected by the thickness of the ash accumulation, and adjustment recommendations are generated based on the relationship between efficiency and ash accumulation thickness. The calculation formula for boiler efficiency η(t) is as follows: η(t)=η0-k1·H ash (t)-k2·Q loss (t) If the boiler efficiency η(t) is lower than the preset minimum efficiency threshold η threshold , and the ash accumulation thickness does not reach the cleaning threshold, the system will recommend adjusting the boiler operating parameters. The adjustment suggestion generation formula is as follows: Where η(t) is the current efficiency of the boiler; η threshold is the minimum boiler efficiency threshold. When the boiler efficiency is lower than this value, the operating parameters are adjusted. H ash (t) is the dust accumulation thickness at time t; H cleaningthreshold The cleaning threshold for dust accumulation; Based on the low efficiency of the boiler, if the ash thickness does not reach the cleaning threshold, but the boiler efficiency is lower than the set threshold, the system recommends adjusting the following operating parameters to improve efficiency: Increasing the boiler load improves the overall thermal efficiency of the boiler; Increasing boiler fluid temperature helps improve heat exchange efficiency; Based on the above analysis, the system will output cleanup or adjustment suggestions based on the following situations: When the dust accumulation thickness exceeds the cleaning threshold, the operator is prompted to clean it; When the ash accumulation has not reached the cleaning threshold, but the boiler efficiency is lower than the minimum efficiency threshold, it is recommended to adjust the boiler operating parameters; When the ash accumulation has not reached the cleaning threshold and the boiler efficiency meets the requirements, it is recommended that the operator conduct regular monitoring; Summary formula:

6. The boiler maintenance method based on boiler ash accumulation according to claim 5, characterized in that: When the system detects that the amount of ash accumulation is too high, the automatic cleaning control module activates the boiler's cleaning device for automatic cleaning. The cleaning process is adjusted according to the distribution of ash accumulation as follows: Dust distribution D dsf (x, t) is obtained through sensor data or simulation analysis, where x represents the location of different areas of the boiler and t represents time. The distribution of ash accumulation affects the activation mode and cleaning intensity of the cleaning equipment. The calculation formula of the ash accumulation distribution model is expressed as: D ash (x,t)=f(H ash (x,t),Q flow (x),T flue (x)) Among them, D ash (x, t) is the distribution of ash accumulation in each area of ​​the boiler; H ash (x, t) is the dust accumulation thickness of region x at time t; Q flow (x) is the flue gas flow rate in area x; T flue (x) is the flue gas temperature in area x; When the system detects that the dust accumulation in a certain area exceeds the preset threshold, the dust cleaning device is activated and the dust accumulation threshold of each area is set as H. threshold (x), when the dust accumulation H in a certain area ash (x) When the threshold is exceeded, the dust cleaning device of the corresponding area is activated; The judgment formula for activating the dust cleaning device is: Among them, H threshold (x) is the dust cleaning threshold of area x; H ash (x) is the dust accumulation thickness in area x; Set cleaning intensity I clcaning (x, t) and dust accumulation thickness H ash (x, t) is proportional to the flue gas flow rate Q flow (x) and flue gas temperature T fluc The influence of (x), the adjustment formula of cleaning intensity is expressed as: I cleaning (x,t)=k1·H ash (x,t)+k2·Q flow (x)+k3·T fluc (x) Among them, I cleaning (x, t) is the cleaning intensity of region x at time t; k1, k2, and k3 are adjustment coefficients, which are calibrated through experiments or historical data; H ash (x, t) is the dust accumulation thickness in region x; Q flow (x) is the flue gas flow rate in area x; T flue (x) is the flue gas temperature in area x; Based on the calculation results of the dust accumulation distribution and cleaning intensity, the specific activation and execution process of the dust cleaning device is as follows: When the dust accumulation thickness exceeds the cleaning threshold, the system activates the dust cleaning device in the corresponding area; The cleaning intensity will be adjusted according to the ash distribution, flue gas flow and temperature; The formula is: Clean Action(x)=Activate Cleaning(x)·I clening (x,t) Where Clean Action (x) is the cleaning action intensity of region x; I cleaning (x, t) is the cleaning intensity; Activate Cleaning(x) is whether to activate the cleaning device.

7. The boiler maintenance method based on boiler ash accumulation according to claim 6, characterized in that: After cleaning is completed, the system provides feedback on the boiler's cleaning status and adjusts the maintenance strategy based on the new monitoring data as follows: The system uses sensors or monitoring equipment to evaluate the boiler status after cleaning. The cleaning status is usually expressed in terms of ash thickness H. ash It is measured by the degree of reduction of (x, t) and the improvement of boiler operating efficiency; Dust accumulation after cleaning The dust accumulation thickness before and after cleaning is evaluated. The dust accumulation thickness after cleaning is expressed by the following formula: in, is the dust accumulation thickness of area x at time y after cleaning; is the dust accumulation thickness of area x at time t before cleaning; ΔH ash (x, t) is the dust thickness reduced during the cleaning process; The cleaning efficiency E is established by comparing the ash thickness before and after cleaning and the boiler operating parameters. clean The ratio of dust accumulation reduction after cleaning is as follows: Among them, E clean (x, t) is the cleaning efficiency of region x at time t; is the dust thickness before cleaning; It is the dust thickness after cleaning.

8. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, a boiler maintenance method based on boiler ash accumulation as claimed in any one of claims 2 to 7 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a boiler maintenance method based on boiler ash accumulation conditions as described in any one of claims 2 to 7 is implemented.

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