Boiler anti-abrasion and anti-explosion online early warning system based on sensor and big data

Abstract

The embodiment of the application discloses a boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data, relates to the technical field of boiler safety monitoring, and comprises a sensor module, which collects operation parameters in a boiler in real time; a data collection module, which collects various data collected by the sensor module; a data processing module, which processes, analyzes and trend forecasts real-time data collected through big data analysis technology; an early warning module, which evaluates the anti-abrasion and anti-explosion risks of the boiler according to the analysis results output by the data processing module and generates an alarm when the risk threshold is exceeded; and a display and interaction module, which shows the boiler operation state, the risk evaluation results and the early warning information to an operator. The application not only can realize intelligent monitoring and risk early warning of the boiler equipment, but also can improve the operation safety of the boiler, reduce the occurrence of safety accidents and optimize the maintenance strategy of the boiler through accurate data analysis and prediction.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of boiler safety monitoring, and particularly relates to a boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data. BACKGROUND

[0002] As an important heat energy equipment in industry, boilers are widely used in energy production, chemical industry, steel, cement, papermaking and other fields. Due to the high-temperature and high-pressure environment, various safety hazards, especially abrasion and explosion, are prone to occur during the operation of the boiler. These problems not only cause direct damage to the boiler equipment, but also may lead to serious safety accidents, resulting in personnel casualties and property losses. Therefore, it is of great practical significance to ensure the safety of the boiler operation, especially in preventing the abrasion and explosion of the boiler.

[0003] Traditional boiler safety monitoring usually relies on manual inspection and single monitoring means, which has poor real-time performance, limited monitoring accuracy and cannot timely discover potential risks. With the development of sensor technology and big data analysis technology, a boiler anti-abrasion and anti-explosion online early warning system based on sensor data and big data technology emerges as the times require. The system can monitor and analyze various operating parameters of the boiler in real time, timely predict possible faults or safety risks of the boiler, and greatly improve the safety and reliability of the boiler operation. At present, most boiler safety monitoring systems have not realized comprehensive prediction and early warning of boiler anti-abrasion and anti-explosion, and have limited data processing capacity. How to integrate various sensor information, use advanced data analysis and prediction algorithms, and realize efficient and intelligent risk prediction and early warning is an important technical problem to improve the safety of the boiler. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art, and discloses a boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data.

[0005] The present application provides a boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data, which comprises:

[0006] Sensor module: real-time acquisition of operating parameters in the boiler, the parameters including boiler temperature, pressure, flow, combustion efficiency and flue gas composition;

[0007] Data acquisition module: the data acquisition module is electrically connected with the sensor module, and the data acquisition module is used for collecting, storing and transmitting various data collected by the sensor module;

[0008] Data processing module: the data processing module is electrically connected with the data acquisition module, and the data processing module is used for processing, analyzing and trend forecasting of the collected real-time data through big data analysis technology;

[0009] Early warning module: The early warning module is electrically connected to the data processing module. The early warning module is used to assess the risk of boiler wear and explosion based on the analysis results output by the data processing module, and generate an alarm when the risk threshold is exceeded.

[0010] Display and Interaction Module: The display and interaction module is electrically connected to the early warning module. The display and interaction module is used to display the boiler operating status, risk assessment results and early warning information to the operator, and to provide operation guidance.

[0011] In some possible embodiments, the sensor module specifically includes:

[0012] Temperature sensor unit: Real-time monitoring of the temperature in different parts of the boiler, monitoring the boiler's combustion temperature, steam temperature and flue gas temperature;

[0013] Pressure sensor unit: monitors the real-time pressure at various pressure points in the boiler, including the pressure of steam and water inside the boiler;

[0014] Flow sensor unit: Collects flow data of boiler feed water, fuel and steam, and monitors water flow, steam flow and fuel flow in the boiler in real time;

[0015] Combustion efficiency sensor unit: Collects combustion efficiency data in the boiler combustion chamber, including oxygen concentration, carbon dioxide concentration, and composition of combustion gases;

[0016] Flue gas composition sensor unit: Real-time monitoring of gas composition in boiler exhaust gas;

[0017] Vibration sensor unit: monitors the vibration of the boiler and its auxiliary equipment;

[0018] Corrosion sensor unit: Monitors corrosion on the surface of materials inside the boiler and on critical pipes.

[0019] In some possible embodiments, the data acquisition module specifically includes:

[0020] Data receiving unit: Receives data from various sensor modules and acquires boiler operating parameters in real time;

[0021] Data conversion unit: converts analog signals into digital data, ensuring that all sensor data can be processed uniformly;

[0022] Data aggregation unit: Based on the data aggregation formula, data from different sensors are aggregated into a unified dataset, where the aggregation formula is:

[0023]

[0024] where T is the arithmetic mean of the data, n is the number of data, i is an index variable, a i is the ith data point in the data set;

[0025] Data storage unit: store the aggregated data in the cloud database, and store the data in the time series database;

[0026] Data transmission unit: transmit the stored data to the data processing module through the network interface for further data analysis and trend prediction.

[0027] In some possible embodiments, the data processing module specifically includes:

[0028] Data analysis unit: analyze the correlation between different variables based on the Pearson correlation coefficient formula, wherein the Pearson correlation coefficient formula is:

[0029]

[0030] where r is the Pearson correlation coefficient, x i is the independent variable of the ith sample, y i is the dependent variable of the ith sample, is the mean of the independent variable x, is the mean of the dependent variable y, and n is the total number of samples in the data set;

[0031] Trend prediction and modeling unit: according to the real-time data analysis result, based on autoregression, a boiler combustion state prediction model is established to predict the trend of boiler combustion;

[0032] Abnormality detection and risk assessment unit: assess whether the boiler is likely to fail through abnormality detection, and use a standard deviation-based detection method. When a data point deviates from the mean by more than a set multiple of the standard deviation, it is determined to be abnormal;

[0033] Risk level analysis unit: assess the anti-wear and anti-explosion risks of the boiler, and use multivariate analysis to calculate the risk level.

[0034] In some possible embodiments, the boiler combustion state prediction model is established based on autoregression according to the real-time data analysis result, and the boiler combustion trend is predicted, specifically including:

[0035] wherein the boiler combustion state prediction model formula is:

[0036]

[0037] wherein, is the predicted value at time t+h, t is the current time point, and h is the prediction step, represents the influence of the observation value at the past p time points on the current prediction value, 、 is an autoregressive coefficient, represents the prediction value at time t+h-1, represents the prediction value at time t+h-2, represents the prediction value at time t+h-p, θ1 is a moving average coefficient, and θ q represents the error at the past q time points, is an error term at time t+h-1, is an error term at time t+h-q.

[0038] In some possible embodiments, the anomaly detection and risk assessment unit specifically comprises:

[0039] a data acquisition unit: acquiring time series data of the boiler, including pressure, temperature, and flow, acquiring the mean and standard deviation of the data, and acquiring the mean and standard deviation of the time window;

[0040] a threshold setting unit: setting a threshold, and calculating the deviation degree of each data point based on a deviation degree calculation formula, wherein the deviation degree calculation formula is:

[0041]

[0042] In the formula, Z t is the standardized deviation value of the tth data point, B t is the current observation value, μ is the mean, and σ is the standard deviation;

[0043] If the absolute value of the deviation degree Z t is greater than the set threshold, the data point is determined to be abnormal.

[0044] In some possible embodiments, the risk level analysis unit specifically comprises:

[0045] a risk factor and risk level definition unit: determining key factors affecting anti-wear and anti-explosion, including anti-wear risk and anti-explosion risk;

[0046] a risk level definition unit: the risk level includes low risk, medium risk, and high risk, and the weights and thresholds of various factors are set according to historical data;

[0047] a multivariate analysis unit: dimension reduction based on a principal component analysis formula, identification of key influencing factors, and extraction of main components;

[0048] a boiler risk prediction model establishment unit: establishing a boiler risk prediction model based on linear regression, and outputting the risk level according to the evaluation result.

[0049] In some possible embodiments, the dimension reduction based on the principal component analysis formula, identification of key influencing factors and extraction of main components specifically include:

[0050] wherein the principal component analysis formula is:

[0051]

[0052] In the formula, C is a covariance matrix, n is the total number of samples, D i is the observation value of the i th sample, is the mean vector of the sample data, (D i - ) represents the difference between the observation value of the i th sample and the mean vector of the sample data, (D i - T is the transpose of the bias vector.

[0053] In some possible embodiments, the boiler risk prediction model is established based on linear regression, and the risk level is output according to the evaluation result, specifically including:

[0054] wherein the boiler risk prediction model formula is:

[0055]

[0056] In the formula, E is the risk level, F1 and F2 are influencing factors, F n is the n th influencing factor, β0 is the intercept term, β1, β2 and β n are regression coefficients, is an error term.

[0057] In some possible embodiments, the early warning module specifically includes:

[0058] An alarm generation unit: when the analysis result shows that the anti-wear or anti-explosion risk of the boiler reaches or exceeds a set threshold value, the early warning module triggers an alarm;

[0059] A record and report unit: after the alarm is triggered, the early warning module records relevant risk data, threshold values and alarm time information into a log, generates a report, and the report content includes an analysis process of risk assessment, a reason for triggering the alarm, relevant data and early warning response information.

[0060] Compared with the prior art, the embodiment of the present application has the following advantages:

[0061] ​The boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data provided by the embodiments of the present application can not only realize intelligent monitoring and risk early warning of the boiler equipment, but also can improve the operation safety, economy and reliability of the boiler through accurate data analysis and prediction, reduce the occurrence of safety accidents, optimize the maintenance strategy of the boiler, and provide strong technical support for the operation management of the boiler. BRIEF DESCRIPTION OF DRAWINGS

[0062] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent upon reading of the following detailed description, taken in conjunction with the accompanying drawings, in which like references refer to like elements. Throughout the drawings, like or similar elements are referenced using the same element numbers. It is to be understood that the drawings are schematic, and elements and features are not necessarily drawn to scale.

[0063] Figure 1 The system framework diagram of the boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data of the embodiments of the present application. DETAILED DESCRIPTION

[0064] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in detail below in conjunction with the drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0065] Unless otherwise specifically indicated, the technical terms or scientific terms used in the embodiments of the present application should be understood as the usual meanings understood by those skilled in the art in the field to which the present application belongs. The use of "including" or "containing" and the like in the embodiments of the present application neither limits the mentioned shapes, numbers, steps, actions, operations, components, elements and / or their groups, nor excludes the presence or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements and / or their groups, or addition of these. In addition, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number and order of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0066] The relative arrangement of parts and steps, numerical expressions, and values set forth in the examples are not intended to limit the scope of the application unless otherwise specifically stated. It is to be understood that the drawings are not necessarily drawn to scale and that the specific techniques, methods, and apparatuses known to those of ordinary skill in the art can not be discussed in detail, but should be considered part of the disclosure where appropriate. In all examples shown and discussed herein, any specific other examples can have different values. It should be noted that like symbols and letters in the drawings represent like items, and thus, once an item is defined in one drawing, it need not be discussed further in subsequent drawings.

[0067] In the description of embodiments of the present application, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the embodiments of the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the embodiments of the present application and the features of the different embodiments or examples without contradiction.

[0068] As shown in Figure 1 Embodiments of the present application relate to a boiler anti-abrasion and anti-explosion online early warning system based on sensors and big data, comprising:

[0069] Sensor module: real-time acquisition of operating parameters in the boiler, the parameters including boiler temperature, pressure, flow, combustion efficiency and flue gas composition;

[0070] Data acquisition module: the data acquisition module is electrically connected with the sensor module, and the data acquisition module is used for collecting, storing and transmitting various data collected by the sensor module;

[0071] Data processing module: the data processing module is electrically connected with the data acquisition module, and the data processing module is used for processing, analyzing and trend forecasting of the collected real-time data through big data analysis technology;

[0072] Early warning module: the early warning module is electrically connected with the data processing module, and the early warning module is used for evaluating the anti-abrasion and anti-explosion risk of the boiler according to the analysis results output by the data processing module, and generating an alarm when the risk threshold is exceeded;

[0073] The display and interaction module is electrically connected with the early warning module, and is used for showing the boiler operation state, risk assessment result and early warning information to an operator and providing operation guidance.

[0074] In some embodiments, the sensor module specifically comprises:

[0075] The temperature sensor unit is used for monitoring the temperature of different parts in the boiler, the combustion temperature, the steam temperature and the flue gas temperature of the boiler;

[0076] The pressure sensor unit is used for monitoring the real-time pressure of each pressure point of the boiler, including the pressure of steam and water in the boiler;

[0077] The flow sensor unit is used for collecting the flow data of water, fuel and steam of the boiler, and monitoring the water flow, steam flow and fuel flow in the boiler in real time;

[0078] The combustion efficiency sensor unit is used for collecting the combustion efficiency data in the combustion chamber of the boiler, including the oxygen concentration, the carbon dioxide concentration and the composition of combustion gas;

[0079] The flue gas composition sensor unit is used for monitoring the gas composition in the flue gas discharged by the boiler in real time;

[0080] The vibration sensor unit is used for monitoring the vibration of the boiler and its matched equipment;

[0081] The corrosion sensor unit is used for monitoring the corrosion of the surface of internal materials and key pipelines of the boiler.

[0082] In some embodiments, the data collection module specifically comprises:

[0083] The data receiving unit is used for receiving the data from each sensor module and acquiring the operation parameters of the boiler in real time;

[0084] The data conversion unit is used for converting the analog signal into digital data, so as to ensure that all sensor data can be uniformly processed;

[0085] The data summary unit is used for summarizing the data of different sensors into a unified data set based on a data summary formula, wherein the summary formula is:

[0086]

[0087] In the formula, T is the arithmetic mean of the data, n is the number of data, i is an index variable, a i is the i-th data point in the data set;

[0088] The data storage unit is used for storing the summarized data in a cloud database and storing the data in a time series database;

[0089] Data transmission unit: transmit the stored data to the data processing module through the network interface for further data analysis and trend prediction.

[0090] In some embodiments, the data processing module specifically comprises:

[0091] Data analysis unit: analyze the correlation between different variables based on the Pearson correlation coefficient formula, wherein the Pearson correlation coefficient formula is:

[0092]

[0093] In the formula, r is the Pearson correlation coefficient, x i is the independent variable of the ith sample, y i is the dependent variable of the ith sample, is the mean of the independent variable x, is the mean of the dependent variable y, and n is the total number of samples in the data set;

[0094] Trend prediction and modeling unit: according to the real-time data analysis result, based on autoregression, a boiler combustion state prediction model is established to predict the trend of boiler combustion;

[0095] Abnormality detection and risk assessment unit: assess whether the boiler is likely to fail through abnormality detection, and use a standard deviation-based detection method. When a data point deviates from the mean by more than a set multiple of the standard deviation, it is determined to be abnormal.

[0096] Risk level analysis unit: assesses the anti-wear and anti-explosion risks of the boiler, and uses multivariate analysis to calculate the risk level.

[0097] In some embodiments, the boiler combustion state prediction model is established based on autoregression according to the real-time data analysis result, and the boiler combustion trend prediction specifically comprises:

[0098] In the formula, the boiler combustion state prediction model is:

[0099]

[0100] In the formula, is the predicted value at time t+h, t is the current time point, h is the prediction step, represents the influence of the observed value at the past p time points on the current predicted value, 、 is the autoregressive coefficient, represents the predicted value at time t+h-1, represents the predicted value at time t+h-2, represents the predicted value at time t+h-p, and θ1 is the moving average coefficient.q error at time t+h-1, error at time t+h-1, error at time t+h-q.

[0101] In some embodiments, the anomaly detection and risk assessment unit specifically comprises:

[0102] a data acquisition unit: acquiring time series data of the boiler, including pressure, temperature and flow, acquiring mean and standard deviation of the data, acquiring mean and standard deviation of the time window;

[0103] a threshold setting unit: setting a threshold, calculating the deviation degree of each data point based on the deviation degree calculation formula, wherein the deviation degree calculation formula is:

[0104]

[0105] wherein, Z t is the standardized deviation value of the tth data point, B t is the current observation value, μ is the mean, and σ is the standard deviation;

[0106] If the absolute value of the deviation degree Z t is greater than the set threshold, the data point is determined to be abnormal.

[0107] In some embodiments, the risk level analysis unit specifically comprises:

[0108] a risk factor and risk level definition unit: determining key factors affecting anti-wear and anti-explosion, including anti-wear risk and anti-explosion risk;

[0109] a risk level definition unit: the risk level includes low risk, medium risk and high risk, and the weights and thresholds of each factor are set according to historical data;

[0110] a multivariate analysis unit: dimension reduction based on principal component analysis formula, identifying key influencing factors and extracting main components;

[0111] a boiler risk prediction model establishment unit: establishing a boiler risk prediction model based on linear regression, and outputting the risk level according to the evaluation result.

[0112] In some embodiments, the dimension reduction based on the principal component analysis formula, the identification of key influencing factors and the extraction of main components specifically comprise:

[0113] wherein the principal component analysis formula is:

[0114]

[0115] wherein C is the covariance matrix, n is the total number of samples, and Di is the observation value of the i-th sample, is the mean vector of the sample data, (D i - represents the difference between the observation value of the i-th sample and the mean vector of the sample data, (D i - ) T is the transpose of the bias vector.

[0116] In some embodiments, the boiler risk prediction model is established based on linear regression, and the risk level is output according to the evaluation result, specifically comprising:

[0117] wherein the boiler risk prediction model formula is:

[0118]

[0119] In the formula, E is the risk level, F1 and F2 are influence factors, F n is the n-th influence factor, β0 is the intercept term, β1, β2, β n are regression coefficients, is the error term.

[0120] In some embodiments, the early warning module specifically comprises:

[0121] an alarm generation unit: when the analysis result shows that the anti-wear or anti-explosion risk of the boiler reaches or exceeds the set threshold value, the early warning module triggers an alarm;

[0122] a record and report unit: after the alarm is triggered, the early warning module records the relevant risk data, threshold value and alarm time information into a log, generates a report, and the report content includes the analysis process of risk assessment, the reason for triggering the alarm, relevant data and early warning response information.

[0123] In summary, the advantages of the boiler anti-wear and anti-explosion online early warning system based on sensors and big data in the embodiments of the present application are:

[0124] The system can comprehensively monitor the running state of the boiler by collecting real-time multi-dimensional data such as temperature, pressure, flow, combustion efficiency, flue gas composition, vibration and corrosion of the boiler, and can generate an early warning in time to remind the operator to take appropriate measures to reduce the risk of failure or safety accidents once the boiler parameter is detected to be abnormal;

[0125] The system can analyze a large amount of real-time data in depth by combining big data analysis technology, find potential problems in the operation of the boiler, and predict the combustion state and trend of the boiler through advanced trend prediction methods such as autoregressive model, so as to early warn the possible anti-wear or anti-explosion risk, help the management personnel to take precautions in advance, and avoid accidents.

[0126] By comprehensive analysis of multiple operating parameters of the boiler, the system can evaluate the risk level of boiler anti-wear and explosion prevention, identify the key factors affecting the safety of the boiler by using multivariate analysis, principal component analysis and other methods, and manage different levels of risk, take different measures according to the evaluation results, so as to improve the safety and stability of the boiler operation;

[0127] The system uses an abnormality detection method based on standard deviation to continuously monitor the real-time data of the boiler. Once the deviation of some parameters exceeds the preset threshold, the system will trigger an alarm to indicate that the boiler may have a fault or accident, avoiding losses caused by not discovering the problem in time;

[0128] Through the display and interaction module, the system can intuitively display real-time monitoring results, risk assessment reports, early warning information and other content to the boiler operator, helping them quickly understand the operating conditions and potential risks of the boiler. At the same time, the system also provides operation guidance to guide the operator to effectively intervene and adjust the operating parameters of the boiler to optimize the operation of the boiler;

[0129] The system can store various types of data and alarm information collected in real time in the cloud database for later query and analysis. By generating detailed risk assessment reports, the operator can review and analyze the historical operation of the boiler, providing a basis for subsequent maintenance, repair, optimization, etc;

[0130] Through real-time monitoring and predictive analysis of the boiler combustion efficiency, the system can help the boiler operator optimize the combustion process, improve energy efficiency, reduce unnecessary energy consumption, reduce operating costs, and improve the overall economy of the boiler;

[0131] Through automatic data collection, analysis and early warning, the system can effectively reduce human errors and avoid safety hazards that may be missed during traditional manual inspection. Through intelligent anti-wear and explosion prediction, the system can reduce the risk of accidents caused by equipment failure or wear and tear, and improve the long-term reliability and service life of the boiler;

[0132] Through continuous monitoring and risk warning, the system can detect potential faults in the boiler equipment in advance and perform maintenance or replace parts in advance, thereby avoiding production losses caused by sudden boiler shutdown. Through data-driven predictive maintenance strategies, the system can effectively reduce maintenance costs, reduce boiler downtime and improve production efficiency.

[0133] It is understood that the above embodiments are only exemplary for illustrating the principles of the present application, and the present application is not limited thereto. Various modifications and improvements can be made by those skilled in the art without departing from the spirit and scope of the present application, and these modifications and improvements are also considered as the protection scope of the present application.

Claims

1. A boiler wear and explosion prevention online early warning system based on sensors and big data, characterized in that, include: The sensor module includes a temperature sensor unit, a pressure sensor unit, a flow sensor unit, a combustion efficiency sensor unit, a flue gas composition sensor unit, a vibration sensor unit, and a corrosion sensor unit. The temperature sensor unit monitors the temperature at different locations within the boiler in real time, monitoring the combustion temperature, steam temperature, and flue gas temperature. The pressure sensor unit monitors the real-time pressure at various pressure points within the boiler, including the pressure of steam and water. The flow sensor unit collects flow data of boiler feedwater, fuel, and steam, monitoring the water flow, steam flow, and fuel flow within the boiler in real time. The combustion efficiency sensor unit collects combustion efficiency data within the boiler combustion chamber, including oxygen concentration, carbon dioxide concentration, and the composition of the combustion gases. The flue gas composition sensor unit monitors the gas composition in the boiler exhaust gas in real time. The vibration sensor unit monitors the vibration of the boiler and its auxiliary equipment. The corrosion sensor unit monitors the corrosion of internal materials and critical pipes within the boiler. The data acquisition module includes a data receiving unit, a data conversion unit, a data aggregation unit, a data storage unit, and a data transmission unit. The data receiving unit is used to receive data from various sensor units and acquire the boiler's operating parameters in real time. The data conversion unit is used to convert analog signals into digital data to ensure that all sensor data can be processed uniformly. The data aggregation unit is used to aggregate data from different sensors into a unified dataset based on a data aggregation formula, where the aggregation formula is: In the formula, T is the arithmetic mean of the data, n is the number of data points, i is the index variable, and a i This represents the i-th data point in the dataset; the data storage unit stores the aggregated data in a cloud database and then stores the data in a time-series database; the data transmission unit transmits the stored data to the data processing module via a network interface for further data analysis and trend prediction. Data processing module: The data processing module is electrically connected to the data acquisition module. The data processing module is used to process, analyze and predict trends of the acquired real-time data through big data analysis technology. Early warning module: The early warning module is electrically connected to the data processing module. The early warning module is used to assess the risk of boiler wear and explosion based on the analysis results output by the data processing module, and generate an alarm when the risk threshold is exceeded. Display and Interaction Module: The display and interaction module is electrically connected to the early warning module. The display and interaction module is used to display the boiler operating status, risk assessment results and early warning information to the operator, and to provide operation guidance. The data processing module includes a risk level analysis unit, which specifically includes: a risk factor and risk level definition unit: identifying key factors affecting wear and explosion prevention, including wear risk and explosion risk; a risk level definition unit: risk levels include low risk, medium risk, and high risk; a multivariate analysis unit: using principal component analysis formula to reduce dimensionality, identify key influencing factors, and extract main components; and a boiler risk prediction model establishment unit: establishing a boiler risk prediction model based on linear regression and outputting the risk level based on the assessment results.

2. The online early warning system for boiler wear prevention and explosion prevention based on sensors and big data as described in claim 1, characterized in that, The data processing module specifically includes: Data Analysis Unit: Analyzes the correlation between different variables based on the Pearson correlation coefficient formula, where the Pearson correlation coefficient formula is: In the formula, r is the Pearson correlation coefficient, and x i Let y be the independent variable for the i-th sample. i Let be the dependent variable for the i-th sample. Let x be the mean of the independent variable. Let y be the mean of the dependent variable, and n be the total number of samples in the dataset; Trend Prediction and Modeling Unit: Based on real-time data analysis results and autoregression, a boiler combustion state prediction model is established to predict boiler combustion trends. Anomaly Detection and Risk Assessment Unit: This unit assesses the potential for boiler malfunctions through anomaly detection. It employs a standard deviation-based detection method, identifying anomalies when a data point deviates from the mean by more than a set multiple of the standard deviation.

3. The online early warning system for boiler wear prevention and explosion prevention based on sensors and big data as described in claim 2, characterized in that, The anomaly detection and risk assessment unit specifically includes: Data acquisition unit: Acquires time series data of the boiler, including pressure, temperature and flow rate, and obtains the mean and standard deviation of the data, as well as the mean and standard deviation of the time window; Threshold setting unit: Sets a threshold and calculates the deviation of each data point based on the deviation calculation formula, whereby the deviation calculation formula is: In the formula, Z t B is the standardized deviation of the t-th data point. t Here, μ is the current observed value, σ is the mean, and σ is the standard deviation. If the degree of deviation Z t If the absolute value of a data point is greater than a set threshold, the data point is considered abnormal.

4. The online early warning system for boiler wear and explosion prevention based on sensors and big data as described in claim 1, characterized in that, The method involves dimensionality reduction based on principal component analysis formulas, identification of key influencing factors, and extraction of specific principal components. include: The principal component analysis formula is as follows: In the formula, C is the covariance matrix, n is the total number of samples, and D... i For the observation value of the i-th sample, Let D be the mean vector of the sample data. i - (D) represents the difference between the observed value of the i-th sample and the mean vector of the sample data. i - ) T This is the transpose of the deviation vector.

5. The online early warning system for boiler wear prevention and explosion prevention based on sensors and big data according to claim 4, characterized in that, The boiler risk prediction model, based on linear regression, outputs a specific risk level according to the assessment results. include: The boiler risk prediction model formula is as follows: In the formula, E represents the risk level, F1 and F2 are influencing factors, and F n It is the nth influencing factor, β0 is the intercept term, β1, β2, β n For regression coefficients, This is the error term.

6. The online early warning system for boiler wear prevention and explosion prevention based on sensors and big data as described in claim 5, characterized in that, The early warning module specifically includes: Alarm generation unit: When the analysis results show that the boiler's wear or explosion risk reaches or exceeds the set threshold, the early warning module triggers an alarm; Recording and Reporting Unit: After an alarm is triggered, the early warning module records the relevant risk data, thresholds, and alarm time information in the log and generates a report. The report includes the risk assessment analysis process, the reason for triggering the alarm, relevant data, and early warning response information.

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