Real-time monitoring and regular safety inspection method for boiler operation state

Abstract

The invention relates to the field of industrial automation and equipment maintenance, and discloses a boiler operation state real-time monitoring and regular safety inspection method, which comprises the following steps: acquiring key parameters of an industrial boiler operation state in real time through a plurality of sensors, including pressure, temperature, water level and combustion condition; and transmitting data collected by the sensor to a data processing platform, wherein the data processing platform integrates a big data analysis module, an artificial intelligence algorithm module and a historical data comparison module. Through a linkage mechanism of the data processing platform and the industrial boiler control system, operation parameters can be automatically adjusted; necessary safety measures are prompted to be taken through continuous on-line monitoring and alarming of boiler load and water quality. An artificial intelligence algorithm is introduced, dynamic maintenance cycle adjustment is realized, unnecessary maintenance is reduced, and the maintenance cost is reduced; the remote monitoring and control system supports real-time monitoring and remote control, and improves the flexibility of operation and maintenance and the fault response speed.

Description

Technical Field

[0001] The invention relates to the field of industrial automation and equipment maintenance, and in particular to a method for real-time monitoring and regular safety inspection of boiler operating status. Background Art

[0002] As a key equipment in industrial production, the safe and stable operation of industrial boilers directly affects production efficiency and safety. The operating status monitoring and regular safety inspection of industrial boilers are the core means to ensure the safety of equipment. By real-time monitoring of key parameters such as pressure, temperature, water level, combustion conditions, etc. of industrial boilers, abnormal phenomena in equipment operation can be discovered in time to ensure that industrial boilers operate in the best condition. In addition, regular safety inspections of industrial boilers, including visual inspections, functional tests, and pressure tests, can detect potential problems of industrial boilers, extend the service life of equipment, and reduce the occurrence of failures.

[0003] The existing industrial boiler operation status monitoring system mainly relies on sensors to collect real-time data, and judges the operation status of industrial boilers by monitoring parameters such as pressure, temperature, water level and boiler water quality. At the same time, regular safety inspections of industrial boilers are carried out manually or semi-automatically, including regular physical inspections and equipment testing. Generally speaking, these methods can ensure the safe operation of industrial boilers to a certain extent.

[0004] However, the existing technology still has some shortcomings. First, the existing monitoring system lacks an automatic linkage mechanism with the control system, which requires reliance on manual operation when an abnormality occurs, reducing the adaptability of the equipment. Secondly, the traditional maintenance cycle is a fixed time interval and fails to be optimized according to the actual operating conditions of the equipment, which may lead to increased maintenance costs and shortened equipment life. In addition, the existing system does not provide sufficient support for remote monitoring and control, and cannot effectively realize remote diagnosis and maintenance. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for real-time monitoring and regular safety inspection of the boiler operating status. Through the linkage mechanism between the data processing platform and the industrial boiler control system, the present invention can automatically adjust the operating parameters to ensure the safe and stable operation of the equipment. The artificial intelligence algorithm is introduced to realize dynamic maintenance cycle adjustment, reduce unnecessary maintenance, and reduce maintenance costs. The remote monitoring and control system supports real-time monitoring and remote control, improves the flexibility of operation and maintenance and the speed of fault response, and solves the problem of lack of automatic adjustment, adaptive maintenance and remote management capabilities in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for real-time monitoring and regular safety inspection of boiler operation status comprises the following steps:

[0007] Multiple sensors are used to collect key parameters of industrial boiler operation status in real time, including pressure, temperature, water level and combustion conditions;

[0008] Transmitting the data collected by the sensor to a data processing platform, wherein the data processing platform integrates a big data analysis module, an artificial intelligence algorithm module, and a historical data comparison module;

[0009] Use the big data analysis platform to process real-time data, analyze operating status and generate early warnings;

[0010] Regularly conduct safety inspections on industrial boilers under operating conditions, including visual inspections, functional tests of safety protection devices, and online calibration of the set values ​​of pressure, water level, and temperature measurement and control devices;

[0011] Based on the results of real-time monitoring and regular inspections, an industrial boiler operation status report is automatically generated, providing recommendations on key inspection items and maintenance items for regular inspections inside and outside the boiler.

[0012] Preferably, the plurality of sensors include:

[0013] Pressure sensors, used to monitor real-time pressure inside industrial boilers;

[0014] Temperature sensors are used to monitor industrial boiler media (steam boiler feed water and superheated steam temperature, hot water boiler supply and return water temperature, organic heat carrier boiler supply and return oil temperature) and exhaust gas temperature;

[0015] Water level sensors, used to monitor water levels in industrial boilers;

[0016] Flow sensors to monitor feedwater flow and boiler load changes;

[0017] Combustion sensor, used to monitor the combustion conditions in the combustion chamber;

[0018] Online continuous monitoring of boiler water conductivity, pH value, feed water hardness and change trends.

[0019] Preferably, the artificial intelligence algorithm module of the data processing platform is implemented by the following steps:

[0020] Clean and extract features from historical operating data of industrial boilers, including pressure fluctuations, temperature gradient changes, and combustion mode features;

[0021] Training an artificial intelligence model based on the cleaned and feature extracted data, the model being used to identify potential failure modes of industrial boilers;

[0022] The real-time monitoring data is analyzed through the artificial intelligence model to generate fault predictions and abnormal warnings.

[0023] Preferably, the big data analysis module is implemented by the following steps:

[0024] Acquire and clean sensor data in real time to remove outliers and erroneous data;

[0025] Calculate trends of key operating parameters through data aggregation and summarization;

[0026] Generate a deviation analysis report of the operating status based on the comparison between the baseline value of historical data and the current data.

[0027] Preferably, regular safety inspections include:

[0028] Visual inspection of the exterior and interior of industrial boilers to check for defects such as failure, leakage, corrosion, scaling and deformation of pressure-bearing parts and welded joints;

[0029] Conduct functional tests to test the working conditions of the safety alarms and interlocking protection devices of industrial boilers;

[0030] Carry out online calibration of the set values ​​of pressure, water level and temperature measurement and control devices.

[0031] Preferably, the industrial boiler operation status report includes the following contents:

[0032] Real-time monitoring of pressure, temperature and water level data;

[0033] Operational trends and risk predictions generated by big data analysis;

[0034] Regular inspection results and safety assessment reports.

[0035] Preferably, the generation of the industrial boiler operation status report is based on a preset threshold value. When a certain parameter exceeds the threshold value, a fault warning message is automatically generated and pushed to the maintenance personnel through the notification system.

[0036] Preferably, the operating status data of the industrial boiler is transmitted to a remote monitoring platform via wired or wireless means, and the monitoring platform allows an operator to remotely monitor the operating status of the industrial boiler and generate remote control instructions.

[0037] Preferably, the big data analysis module in the data processing platform is linked with the industrial boiler control system, and can automatically adjust the operating parameters of the industrial boiler to maintain safe operation when the data is abnormal.

[0038] Preferably, the frequency of regular safety inspections of industrial boilers is automatically adjusted by an artificial intelligence algorithm based on historical operating data and current equipment health to optimize maintenance cycles and reduce the number of unnecessary inspections.

[0039] The present invention provides a method for real-time monitoring and regular safety inspection of boiler operation status.

[0040] Beneficial effects:

[0041] 1. The present invention, through the linkage mechanism between the data processing platform and the industrial boiler control system, can automatically adjust the operating parameters of the industrial boiler when abnormal data is monitored, thereby ensuring the safety and stability of the industrial boiler during operation and reducing the occurrence of sudden failures. At the same time, the system can provide real-time feedback on the operating status, thereby improving the equipment's adaptability and operating efficiency.

[0042] 2. The present invention realizes dynamic maintenance cycle adjustment based on historical operation data and current equipment health status by introducing artificial intelligence algorithm. This method reduces unnecessary maintenance times, optimizes maintenance plans, extends equipment service life, and reduces maintenance costs of industrial boilers.

[0043] 3. The present invention uses a remote monitoring and control system to monitor the operating status of industrial boilers in real time by wired or wireless means, and remotely generate control instructions when necessary, thereby improving the flexibility of industrial boiler operation and maintenance, enhancing fault response speed and the intelligent level of equipment management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Please see attached Figure 1 The embodiment of the present invention provides a method for real-time monitoring and periodic safety inspection of boiler operation status, comprising the following steps:

[0047] Through the real-time collection of key parameters of the industrial boiler operating status, including pressure, temperature, water level and combustion conditions, multiple sensors are deployed to achieve all-round real-time monitoring of the industrial boiler operating status, thereby improving the accuracy and coverage of data collection. At the same time, potential problems in the operation of industrial boilers can be discovered in time to prevent accidents:

[0048] The data collected by the sensor is transmitted to the data processing platform, which integrates the big data analysis module, artificial intelligence algorithm module and historical data comparison module. By integrating the big data analysis and artificial intelligence algorithm modules, the platform can automatically identify the operation anomalies and potential failures of industrial boilers, thereby reducing the workload of operators and reducing the limitations of relying on manual experience;

[0049] The big data analysis platform is used to process real-time data, analyze the operating status and generate early warnings. The intelligent early warning system can promptly notify operators before problems occur, avoiding equipment downtime due to failures, and improving the safety and stability of industrial boiler systems. By combining big data analysis and artificial intelligence technology, the accuracy of early warnings has been greatly improved, reducing false alarms and missed alarms, and improving overall operating efficiency.

[0050] Regularly conduct safety inspections on industrial boilers under operating conditions. Safety inspections include visual inspections, functional tests of safety protection devices, and online calibration of the set values ​​of pressure, water level, and temperature measurement and control devices. Through regular safety inspections, potential physical damage and performance degradation of industrial boilers can be discovered in a timely manner, and maintenance and repairs can be carried out in advance to prevent serious accidents. This method reduces the possibility of human negligence through automated recording and data analysis, ensuring the long-term stable operation of industrial boilers.

[0051] Based on the results of real-time monitoring and regular inspections, an industrial boiler operation status report is automatically generated, providing suggestions on key inspection items and maintenance items for regular internal and external inspections of the boiler. The automatically generated operation status report not only helps operators understand the current status of the industrial boiler, but also provides fault prediction and maintenance suggestions through intelligent analysis to avoid downtime caused by sudden failures. At the same time, the automatic generation of maintenance reminders improves the initiative of operation and maintenance, reduces the number of passive maintenance, and helps to extend the service life of equipment and improve overall efficiency.

[0052] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the plurality of sensors include:

[0053] Pressure sensors are used to monitor the real-time pressure inside industrial boilers. Pressure sensors sense the pressure changes inside industrial boilers through internal strain gauges or piezoelectric elements. When the pressure inside the industrial boiler acts on the sensor's sensing diaphragm, the diaphragm will produce a slight deformation, which is manifested by the change of electrical signals. Pressure sensors can help operators take timely adjustment measures to avoid industrial boilers working under high pressure, thereby reducing safety risks such as explosions.

[0054] Temperature sensors are used to monitor the medium temperature of industrial boilers (steam boiler feed water and superheated steam temperature, hot water boiler supply and return water temperature, organic heat carrier boiler supply and return oil temperature) and exhaust gas temperature. Temperature sensors such as thermocouples work based on the thermoelectric effect. When the temperature changes, the electromotive force of two different metal contacts will change, thereby measuring the temperature. The deployment of temperature sensors can ensure that industrial boilers operate at an appropriate temperature, prevent safety accidents caused by excessive temperature or reduced thermal efficiency due to improper temperature control. Especially during the combustion process, accurate temperature monitoring helps to optimize combustion efficiency, reduce energy consumption and reduce emissions. At the same time, these sensors can also detect temperature anomalies in a timely manner to avoid safety accidents caused by medium overheating;

[0055] Water level sensor, used to monitor the water level of industrial boilers. Electrode-type water level sensors are based on the conductivity of water. When the water level reaches different electrodes, the electrodes will form a closed circuit. The system determines the water level by detecting the change in resistance. Its real-time monitoring capability enables operators to adjust the water level in a timely manner to ensure the safe and efficient operation of industrial boilers.

[0056] Flow sensors are used to monitor feedwater flow and boiler load changes. By installing flow sensors on the feedwater pipe, the system can record the feedwater flow of the boiler in real time, thereby accurately reflecting the load change trend of the boiler. Load changes are often closely related to the operating conditions and combustion efficiency of the boiler. When the load of the boiler increases or decreases sharply, it may cause problems such as unstable combustion, too high or too low exhaust temperature, etc. Through this continuous monitoring, it can help operators to understand the actual operating status of the boiler in a timely manner, ensure that the boiler load matches the operating requirements, and automatically adjust the load changes to prevent safety hazards caused by sudden changes in load to the boiler, such as unstable combustion, steam-water co-boiling, etc.; control and prevent long-term low-load operation from reducing the thermal efficiency of the boiler.

[0057] Combustion sensors are used to monitor the combustion conditions in the combustion chamber. Flame detection sensors determine the combustion status by sensing the spectral characteristics of the flame. Flame spectrum detection is a method of determining the combustion status by detecting spectra of different wavelengths in the flame. The following are the specific items that this method usually detects:

[0058] Flame color: The color of the flame is related to the fuel being burned and the combustion efficiency. By detecting the spectrum, it is possible to determine whether there is incomplete combustion, lack of oxygen or other combustion anomalies.

[0059] For example: A blue flame usually indicates complete combustion.

[0060] A yellow or orange flame may indicate incomplete combustion or contaminants.

[0061] Flame light intensity: Monitor combustion stability and combustion efficiency by detecting the light intensity in the flame spectrum. If the light intensity fluctuates greatly, it may mean that the combustion is unstable.

[0062] Chemical composition: By detecting the characteristic spectral lines emitted by specific elements, the composition of the combustion products can be identified. For example, when hydrocarbons burn, they produce spectral lines of carbon and oxygen, which can indicate the completeness of the combustion and the presence of impurities in the fuel.

[0063] Flame temperature: The different wavelengths in the flame spectrum can be used to infer the temperature of the flame. There is a clear difference between the ultraviolet spectrum produced by high-temperature combustion and the infrared spectrum produced by low-temperature combustion.

[0064] Spectral continuity of flame: By detecting the continuity of the flame spectrum, it can be determined whether the fuel is burning normally. If the spectrum is interrupted during the combustion process, it means that the air supply is insufficient or the fuel supply is unstable during combustion. These detection items can help optimize the combustion efficiency of the boiler, reduce pollutant emissions, and issue timely warnings when the combustion is abnormal.

[0065] Oxygen sensors detect the oxygen content in flue gas based on the electrochemical properties of zirconium oxide cells or oxide resistors. The deployment of furnace combustion sensors helps optimize the combustion efficiency of industrial boilers, reduce fuel consumption, and reduce pollutant emissions. By real-time monitoring of the furnace combustion conditions, the system can adjust the combustion parameters in time to prevent unstable combustion or incomplete combustion, thereby improving the safety and thermal efficiency of industrial boilers. The arrangement of oxygen sensors and temperature sensors in the flue can continuously monitor the oxygen content and exhaust temperature in the flue gas, and the current combustion condition of the boiler can be determined through analysis. When the flue gas oxygen content and exhaust temperature deviate from the preset reasonable range, the system can automatically issue instructions to adjust the air and fuel ratio to ensure that the boiler's excess air coefficient and thermal efficiency meet the standards, reduce energy consumption and thus reduce pollutant emissions;

[0066] The object of flame detection is the flame in the furnace. The detection items include the flame spectrum and furnace temperature, which are used to judge the combustion state and efficiency. The object of flue gas detection is the flue gas in the tail flue. The detection items include flue gas composition (such as oxygen, carbon dioxide, etc.) and exhaust gas temperature, which are used to analyze the combustion products and the emission performance of the boiler.

[0067] Online continuous monitoring of boiler water conductivity, pH value, feed water hardness and change trends, as well as continuous monitoring and recording of boiler load changes. Conductivity is an indicator of the concentration of dissolved salts and minerals in boiler water. By continuously monitoring conductivity, the concentration of dissolved solids in boiler water can be determined, and timely alarms and sewage measures can be issued to avoid steam-water co-evaporation, scaling and corrosion problems caused by the concentration effect. Regular or continuous monitoring of pH values ​​can ensure that the boiler water quality is in an appropriate alkaline range to prevent acid corrosion inside the boiler. Regular testing of feed water hardness can identify the risk of scaling on the water side of the boiler heating surface, and prompt the adoption of softening water treatment measures;

[0068] If the system frequently records rapid changes in load, multiple activations of the high water level alarm chain, or the boiler water conductivity exceeds the set range, it will usually cause steam-water co-evaporation caused by steam carrying water, and may cause leakage at the flange connection of the steam pipe due to water hammer vibration. This will prompt the operator to improve the operation and check the flange connection and bolts of the steam pipe to prevent further damage and leakage, thereby ensuring the safety and continuous and stable operation of the boiler.

[0069] If the system records that the boiler is started and stopped frequently, the focus should be on checking the pressure components and welded joints that may fail due to brittle fracture due to alternating stress and low-cycle fatigue and thermal fatigue loads caused by alternating hot and cold during the frequent start-stop process. At the same time, for steam boilers that are started and stopped frequently, the carbon dioxide dissolved in the steam condensate will cause serious carbon dioxide corrosion in the steam pipe. At this time, a large amount of recycled steam condensate will cause serious acid corrosion on the boiler heating surface. It is recommended to conduct relevant corrosion inspections on the water side of the steam pipe and the boiler heating surface to avoid system failures and safety accidents caused by corrosion failure of the steam pressure pipe and the boiler heating surface;

[0070] Continuous monitoring and recording of boiler load changes can be achieved through feed water flow or steam flow measurement. Rapid changes in boiler load have a direct impact on boiler safety and energy saving. Timely monitoring of these parameters helps control and prevent system instability or equipment damage caused by rapid load fluctuations. By monitoring the possible long-term low-load operation of the boiler, suggestions for effective measures to improve the thermal efficiency of the boiler can be provided.

[0071] Please see attached Figure 1 In a preferred embodiment of the present invention, the artificial intelligence algorithm module of the data processing platform is implemented by the following steps:

[0072] Clean and extract features from the historical operating data of industrial boilers, including pressure fluctuations, temperature gradient changes, and combustion mode features. By cleaning the historical data of industrial boilers, noise and abnormal data are removed to ensure the accuracy and integrity of the data. The feature extraction step can convert a large amount of raw data into more useful features, such as pressure fluctuations, temperature changes, and combustion modes. These features are the key to judging the operating status of industrial boilers and identifying faults. Data cleaning and feature extraction ensure that the data used for model training is high-quality and representative. This step improves the training effect of the artificial intelligence model and reduces false positives or negatives caused by data noise. At the same time, the feature extraction process can identify key modes in the operation of industrial boilers and provide basic data support for fault diagnosis;

[0073] Based on the cleaned and feature-extracted data, the artificial intelligence model is trained to identify the potential failure modes of industrial boilers. Through supervised learning, the AI ​​model uses the relationship between the features and failure modes in the historical data of industrial boilers for training, and gradually learns and identifies the typical characteristics of industrial boiler failures. Through supervised learning, the AI ​​model uses the relationship between the features and failure modes in the historical data of industrial boilers for training, and gradually learns and identifies the typical characteristics of industrial boiler failures. Different machine learning algorithms have different advantages: support vector machines are suitable for processing high-dimensional and nonlinear data; random forests can improve prediction stability through integrated learning; LSTM is suitable for processing time series data and can identify changes in failure modes over time, thereby improving the system's prediction accuracy for abnormal conditions of industrial boilers and reducing the defects of traditional reliance on manual experience, making fault diagnosis more intelligent and reliable;

[0074] The artificial intelligence model analyzes the real-time monitoring data to generate fault predictions and abnormal warnings. Through real-time data analysis and fault prediction, the system can identify potential faults of industrial boilers in advance and help operators take intervention measures before the fault expands. This intelligent fault prediction system greatly reduces the possibility of industrial boiler failure shutdown and improves the operating safety and thermal efficiency of industrial boilers. At the same time, the prediction and warning mechanism provides strong support for maintenance work, making maintenance work more targeted and timely.

[0075] Please see attached Figure 1 In a preferred embodiment of the present invention, the big data analysis module is implemented by the following steps:

[0076] Acquire and clean sensor data in real time to remove outliers and erroneous data. The data cleaning process includes the following steps:

[0077] Outlier detection, error data removal and time alignment. By acquiring and cleaning sensor data in real time, the system can ensure that the data entering the analysis process is accurate, complete and noise-free, thereby improving the accuracy of data analysis. At the same time, real-time processing enables the system to quickly respond to abnormal conditions in the operation of industrial boilers, ensuring the stability and safety of industrial boiler operation.

[0078] Through data aggregation and induction, the trends of key operating parameters are calculated. Data aggregation and induction are carried out through a big data analysis framework, and a distributed computing model is used to ensure the efficiency of calculations. Through the aggregation and trend analysis of key operating parameters, the system can identify signs of industrial boiler performance degradation in advance and avoid major accidents. For example, by continuously monitoring and recording boiler water quality, it can be predicted that there may be risks of corrosion, scaling, and steam-water co-evaporation on the water side of the heating surface of the industrial boiler, thereby prompting the adjustment of operating conditions, taking water treatment measures to make the water quality meet the standards, preventing further scaling and corrosion, and arranging the cleaning of scale and corrosion products in advance, improving the thermal efficiency of industrial boilers, and extending the service life of equipment;

[0079] Based on the comparison between the baseline value of historical data and the current data, a deviation analysis report of the operating status is generated. The deviation analysis is based on statistical difference detection algorithms and machine learning models, which can accurately identify anomalies in the operation of industrial boilers. By comparing real-time data with historical baseline values, the system can quickly detect abnormal changes in the operating status, and generate a deviation analysis report with guiding significance based on the size, duration and other information of the deviation. The deviation analysis report provides a complete view of the operating status of industrial boilers, which can help operation and maintenance personnel to detect anomalies in time and analyze potential problems in operation. By regularly generating deviation analysis reports, operation and maintenance personnel can effectively identify early signs of equipment performance degradation and potential failures, and take corresponding preventive maintenance measures, which not only improves the safety of industrial boilers, but also reduces the number of unplanned shutdowns and further optimizes maintenance plans.

[0080] Please see attached Figure 1 In a preferred embodiment of the present invention, the periodic safety inspection includes:

[0081] Visual inspection of the exterior and interior of industrial boilers, inspection of cracks, leakage, corrosion, scaling and deformation of pressure-bearing parts, welded joints and flange connections. Visual inspection can quickly find visible defects on the outer surface of industrial boilers by visually observing the exterior and interior of industrial boilers or using video equipment. Endoscope equipment can penetrate into the hard-to-reach areas inside industrial boilers to inspect the surface of welded joints and corrosion and scaling on the water side. Through regular visual inspections, damage, corrosion, scaling and other problems on the exterior and interior of industrial boilers can be discovered and handled at an early stage to avoid serious structural damage;

[0082] Conduct functional tests to test the working conditions of the safety alarm and interlocking protection devices of industrial boilers. The alarm system and automatic control system are the last line of defense for the safe operation of industrial boilers. Regular testing of these devices can ensure that they can operate normally under actual working conditions and prevent industrial boilers from losing control under abnormal conditions. Functional tests can timely discover potential faults in industrial boiler safety equipment and control systems, ensure that industrial boilers are always in a controlled state during operation, and avoid safety accidents caused by equipment damage. Regular functional tests can also verify the sensitivity and reliability of the equipment and extend the service life of industrial boilers.

[0083] Online calibration of the set values ​​of pressure, water level and temperature measurement control devices is performed. By applying water pressure exceeding the normal operating pressure to the industrial boiler, the pressure-bearing state of the industrial boiler under extreme working conditions can be simulated to detect whether the industrial boiler has leakage or damage caused by material aging, weld failure, etc. The pressure test provides a guarantee for the long-term operation safety of the industrial boiler and extends the safe service life of the industrial boiler.

[0084] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the industrial boiler operation status report includes the following contents:

[0085] Real-time monitoring of pressure, temperature and water level data. The operating data of industrial boilers are collected in real time through pressure, temperature and water level sensors. These data can reflect the real-time operation status of industrial boilers. The system uses the data transmission module to ensure that the data can be transmitted to the central data processing platform in a timely and accurate manner for recording and display after collection. Real-time monitoring data can help operators quickly understand the current operating status of industrial boilers. By analyzing the fluctuation of real-time data, abnormalities of industrial boilers can be discovered early to avoid accidents caused by overpressure, overtemperature or abnormal water level of industrial boilers.

[0086] Operation trend and risk prediction generated by big data analysis. Trend prediction generated by big data analysis can help operators identify signs of industrial boiler performance degradation in advance and avoid accidents. Through risk prediction, the system can provide fault warnings, allowing operators to perform maintenance in advance to avoid sudden equipment shutdowns and major losses. Overall, this part of the content improves the foresight and scientific nature of industrial boiler operation management;

[0087] Regular inspection results and safety assessment reports, regular inspections through visual inspections, functional tests and pressure tests of industrial boilers can evaluate the operating conditions of industrial boilers and the working conditions of safety accessories. Through these inspections, the system can ensure that all parameters of industrial boilers are within a safe range and detect potential equipment problems in a timely manner. Through regular safety inspection reports, operators can understand the overall safety status of industrial boilers and make plans for future maintenance work. The results of visual inspections, functional tests and pressure tests can help determine whether the equipment meets the operating requirements and give corresponding maintenance and overhaul recommendations based on the health status of the equipment.

[0088] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the generation of the industrial boiler operation status report is based on a preset threshold value. When a certain parameter exceeds the threshold value, a fault warning message is automatically generated and pushed to the maintenance personnel through the notification system. The threshold value is set based on the design parameters and safety requirements of the equipment. Appropriate warning lines are set through historical data and industry standards. During the operation of the industrial boiler, the system compares the parameters collected in real time by the sensor with the set threshold value. If any parameter exceeds the threshold value, the system automatically triggers a fault warning. By setting reasonable threshold values, the system can accurately monitor the key parameters of the industrial boiler and avoid abnormal conditions during operation. The threshold adjustment function allows the system to adapt to different operating conditions and maintenance requirements, which improves the flexibility of monitoring. In this way, a warning can be issued before the problem worsens, thereby reducing the risk of downtime and ensuring safe operation.

[0089] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the operating status data of the industrial boiler is transmitted to the remote monitoring platform by wired or wireless means. The monitoring platform allows the operator to remotely monitor the operating status of the industrial boiler and generate remote control instructions. The wired method realizes high-speed and stable data transmission through optical fiber or Ethernet, while the wireless method relies on cellular network or Wi-Fi technology to transmit data to the remote monitoring platform through a wireless base station. Each method has its own specific communication protocol to ensure that the data is not lost or delayed during the transmission process. By making the optimal configuration according to the on-site conditions, the real-time and stability of data transmission are guaranteed, and the needs of different application scenarios are met. The remote monitoring platform can provide the real-time status, historical data analysis and abnormal alarm functions of the industrial boiler through data visualization tools and big data analysis modules. The remote monitoring platform enables operators to view the operating status of the industrial boiler anytime and anywhere, reduces the frequency of on-site inspections, and can respond quickly when problems occur. The remote control instruction function enables operators to intervene in abnormal situations of industrial boilers at the first time without waiting for the intervention of on-site personnel, thereby improving the flexibility and safety of industrial boiler operation.

[0090] Please refer to the attached Figure 1 In a preferred embodiment of the present invention, the big data analysis module in the data processing platform is linked with the industrial boiler control system, and can automatically adjust the operating parameters of the industrial boiler to maintain safe operation when the data is abnormal. The big data analysis module uses time series analysis and machine learning algorithms to accurately identify abnormal situations in the operation of the industrial boiler. By comparing with historical benchmark data and preset thresholds in real time, the system can quickly detect potential faults and abnormal trends, and feed back the analysis results to the control system through a linkage mechanism. Through real-time monitoring and feedback mechanisms, the system can respond quickly when an abnormality occurs, effectively preventing sudden failures or major safety hazards in the operation of industrial boilers. At the same time, automated abnormality detection can reduce the burden on operators and reduce delays and errors in human judgment, thereby improving the safety and efficiency of industrial boiler operation. Through the PID controller in the industrial boiler control system, the system can adjust the operating parameters in real time according to the abnormal information provided by the big data analysis module. Automatic adjustment of the operating parameters of the industrial boiler can quickly respond to abnormal situations and prevent accidents from worsening or spreading. The safe operation guarantee mechanism realizes multi-level security protection through the coordinated work of the linkage control system and the big data analysis module. The multi-level security guarantee mechanism provides comprehensive protection for the operation of industrial boilers.

[0091] Please refer to the attached Figure 1 ,In a preferred embodiment of the present invention, the frequency of regular safety inspection of ,industrial boilers is automatically adjusted by an artificial intelligence algorithm based on ,historical operation data and the current equipment health status to optimize the ,maintenance cycle and reduce the number of unnecessary inspections. ,By collecting key data during the operation of industrial boilers over a long ,time, the system can accurately grasp the performance ,change trend of the equipment. The feature extraction process can use machine learning algorithms, such as principal component analysis (PCA) or deep learning networks, to automatically extract key factors that affect equipment health from complex data. Through the extracted feature data, the system can not only provide a basis for maintenance plans, but also identify long-term change trends of industrial boilers, further improving the accuracy of operation management. Artificial intelligence algorithms build prediction models by learning historical data and health characteristics of industrial boiler operations. They can predict the probability of equipment failure and health change trends in the future. Dynamic adjustment of maintenance frequency not only improves the maintenance efficiency of industrial boilers, but also ensures that the equipment operates in the best condition and reduces the probability of sudden failures. Dynamic adjustment of maintenance cycles reduces the waste of resources caused by traditional fixed-cycle maintenance and optimizes the operation and maintenance costs of industrial boilers. At the same time, the strategy of early or delayed maintenance can ensure that industrial boilers are maintained at the most appropriate time, reduce unplanned downtime due to unexpected failures, and thus improve the service life and operational efficiency of equipment.

[0092] Through the detection and analysis of this system, the following suggestions can be provided to the regular inspection personnel of the boiler:

[0093] Suggestions on key inspection areas and items for internal inspection during shutdown:

[0094] Based on the system's monitoring data and historical records, the system can prompt key inspections of boiler areas prone to corrosion or scaling.

[0095] Inspection recommendations include specific inspection items such as leakage, weld failure, corrosion inside the boiler and pipelines, and deposits to ensure that the boiler's pressure-bearing parts are in a safe state.

[0096] Automatic verification and recording of external inspections in operation:

[0097] The system can automatically trigger and record the verification of high and low water level alarms and interlocking functions when the boiler is running, and verify whether the water level interlocking function is operating normally.

[0098] Automatically detect and record the status of overpressure alarm interlocking and overtemperature alarm interlocking functions to ensure that these protection measures take effect in time during operation and prevent the boiler pressure and temperature from exceeding the limit, causing damage to the pressure components and safety accidents.

[0099] The automatic verification and recording functions provided by the system will effectively reduce possible negligence during manual inspections and ensure that the boiler's key alarm and interlocking systems are always in an effective state.

[0100] Through these functions, the system can provide detailed inspection suggestions and automatic function verification when the boiler is running and shut down, further improving the safety and operating efficiency of the equipment;

[0101] According to the superheated steam over-temperature record and water shortage record in the water level monitoring record provided by the system, when the boiler is shut down for internal inspection, it can prompt metallographic inspection of the pressure-bearing components that may be overheated or overburned to evaluate the degree of material damage and potential safety hazards, so as to take corresponding repair or replacement measures. Through the over-temperature record of the heat transfer oil in the organic heat carrier boiler, it can prompt the heat transfer oil medium sampling and inspection to prevent carbon deposition on the furnace tube. It can effectively prevent the occurrence of serious accidents and ensure the long-term safe operation of the boiler.

[0102] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for real-time monitoring and periodic safety inspection of boiler operation status, characterized in that: The following steps are involved: Multiple sensors are used to collect key parameters of industrial boiler operation status in real time, including pressure, temperature, water level and combustion conditions; Transmitting the data collected by the sensor to a data processing platform, wherein the data processing platform integrates a big data analysis module, an artificial intelligence algorithm module, and a historical data comparison module; Use the big data analysis platform to process real-time data, analyze operating status and generate early warnings; Regularly conduct safety inspections on industrial boilers under operating conditions, including visual inspections, functional tests of safety protection devices, and online calibration of the set values ​​of pressure, water level, and temperature measurement and control devices; Based on the results of real-time monitoring and regular inspections, an industrial boiler operation status report is automatically generated, providing recommendations on key inspection items and maintenance items for regular inspections inside and outside the boiler.

2. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1 is characterized in that: Multiple sensors include: Pressure sensors, used to monitor real-time pressure inside industrial boilers; Temperature sensors are used to monitor industrial boiler media (steam boiler feed water and superheated steam temperature, hot water boiler supply and return water temperature, organic heat carrier boiler supply and return oil temperature) and exhaust gas temperature; Water level sensors, used to monitor water levels in industrial boilers; Flow sensors to monitor feedwater flow and boiler load changes; Combustion sensor, used to monitor the combustion conditions in the combustion chamber; Online continuous monitoring of boiler water conductivity, pH value, feed water hardness and change trends.

3. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1 is characterized in that: The artificial intelligence algorithm module of the data processing platform is implemented through the following steps: Clean and extract features from historical operating data of industrial boilers, including pressure fluctuations, temperature gradient changes, and combustion mode features; Training an artificial intelligence model based on the cleaned and feature extracted data, the model being used to identify potential failure modes of industrial boilers; The real-time monitoring data is analyzed through the artificial intelligence model to generate fault predictions and abnormal warnings.

4. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: The big data analysis module is implemented through the following steps: Acquire and clean sensor data in real time to remove outliers and erroneous data; Calculate trends of key operating parameters through data aggregation and summarization; Generate a deviation analysis report of the operating status based on the comparison between the baseline value of historical data and the current data.

5. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: Regular safety inspections include: Visual inspection of the exterior and interior of industrial boilers to check for defects such as failure, leakage, corrosion, scaling and deformation of pressure-bearing parts and welded joints; Conduct functional tests to test the working conditions of the safety alarms and interlocking protection devices of industrial boilers; Carry out online calibration of the set values ​​of pressure, water level and temperature measurement and control devices.

6. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: Industrial boiler operation status report includes the following: Real-time monitoring of pressure, temperature and water level data; Operational trends and risk predictions generated by big data analysis; Regular inspection results and safety assessment reports.

7. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: The generation of industrial boiler operation status reports is based on preset thresholds. When a parameter exceeds the threshold, a fault warning message is automatically generated and pushed to maintenance personnel through the notification system.

8. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: The operating status data of the industrial boiler is transmitted to the remote monitoring platform via wired or wireless means. The monitoring platform allows operators to remotely monitor the operating status of the industrial boiler and generate remote control instructions.

9. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: The big data analysis module in the data processing platform is linked to the industrial boiler control system, and can automatically adjust the operating parameters of the industrial boiler to maintain safe operation when the data is abnormal.

10. The method for real-time monitoring and periodic safety inspection of boiler operation status according to claim 1, characterized in that: The frequency of regular safety inspections of industrial boilers is automatically adjusted by artificial intelligence algorithms based on historical operating data and current equipment health to optimize maintenance cycles and reduce unnecessary inspections.

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