AI intelligent early warning and multi-dimensional monitoring platform application of main auxiliary machine on boiler side

Abstract

The invention discloses application of a boiler side main auxiliary machine AI intelligent early warning and multi-dimensional monitoring platform. Belongs to the technical field of boiler side auxiliary machine monitoring, and in the boiler side auxiliary machine monitoring system, a model training sub-module can learn complex modes and relations from historical data by adopting advanced machine learning algorithms such as a support vector machine, so that accurate prediction of the equipment state is realized. The self-learning and adjusting capabilities of the model enable the model to adapt to changes in equipment operation, and the prediction accuracy is kept. In addition, the continuous optimization of the model training sub-module is beneficial to improving the operation efficiency and reliability of the equipment, and powerful support is provided for equipment maintenance and fault prevention. The model evaluation sub-module ensures that the performance of the model reaches an expected standard through evaluation technologies such as cross validation and the like, and prevents an over-fitting phenomenon from occurring. The evaluation not only improves the reliability of the model in practical application, but also ensures that the model has good generalization ability when facing unknown data.

Description

Technical Field

[0001] The present invention belongs to the technical field of boiler-side auxiliary machine monitoring, and specifically is an application of an AI intelligent early warning and multi-dimensional monitoring platform for main auxiliary machines on the boiler side. Background Art

[0002] The main auxiliary machines on the boiler side are key equipment for the normal operation of the boiler system. They each assume important responsibilities to ensure that the boiler works efficiently and safely. The following is a brief introduction to the main auxiliary machines on the boiler side: Blower: The blower provides the boiler with an appropriate amount of air to ensure that the fuel is fully burned in the furnace. It sends cold air into the furnace and mixes it with the fuel to improve the combustion efficiency. The performance of the blower directly affects the thermal efficiency and fuel consumption of the boiler. Induced draft fan: The task of the induced draft fan is to extract the flue gas generated by the combustion of the boiler, maintain the negative pressure in the furnace, prevent the flue gas from leaking into the workshop, and create conditions for the subsequent treatment of the flue gas. The selection of the induced draft fan should be determined according to the flow rate and resistance of the boiler flue gas. Feedwater pump: The feedwater pump is responsible for sending the softened water in the deaerator into the boiler to replenish the water consumed by the boiler during operation. The flow rate and head of the feedwater pump must meet the maximum evaporation requirements of the boiler and have a certain spare capacity. Deaerator: The deaerator is used to remove dissolved oxygen in the feedwater to prevent oxygen corrosion inside the boiler. It reduces the solubility of oxygen in the water through the combined action of heating and vacuum to ensure the water quality of the boiler. Softening water equipment: Softening water equipment removes calcium and magnesium ions in water through ion exchange and other technologies to prevent boiler scaling. These main auxiliary machines on the boiler side together constitute the auxiliary system of the boiler. Their effective operation is the premise for the stable, efficient and safe operation of the boiler. In daily maintenance and management, inspection, maintenance and troubleshooting of these auxiliary machines are important measures to ensure the normal operation of the boiler system.

[0003] However, the common main auxiliary machine monitoring system has problems with low data quality during use, such as data noise, outliers, etc., which will affect the training effect and prediction accuracy of the model. At the same time, the existing technology has the problem of insufficient generalization ability of the model, especially when processing data of different distributions or scales, the model may not be able to adapt well. Summary of the invention

[0004] The purpose of the present invention is to provide an AI intelligent early warning and multi-dimensional monitoring platform application for the main auxiliary equipment on the boiler side in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: an AI intelligent early warning and multi-dimensional monitoring platform application for the main auxiliary equipment on the boiler side, the platform application includes:

[0006] Data acquisition module, data transmission module, data processing and analysis module, AI intelligent early warning module, multi-dimensional monitoring module, alarm and emergency processing module and system management and maintenance module;

[0007] The AI ​​intelligent early warning module is internally provided with a data preprocessing submodule, a feature extraction submodule, a model training submodule, a model evaluation submodule, a real-time monitoring submodule and an early warning triggering submodule;

[0008] The data acquisition module is responsible for collecting the operating data of the boiler auxiliary equipment and sending the data through the data transmission module.

[0009] The data transmission module transmits the collected data to the data processing and analysis module in real time for further processing and analysis.

[0010] After preprocessing and analyzing the data, the data processing and analysis module passes the results to the AI ​​intelligent early warning module for building and updating the early warning model.

[0011] The AI ​​intelligent early warning module makes early warning judgments based on the analysis results and transmits the early warning information to the multi-dimensional monitoring module for display on the monitoring interface.

[0012] After receiving the early warning information, the multi-dimensional monitoring module can trigger the actions of the alarm and emergency processing module, such as issuing an alarm signal and displaying emergency processing suggestions.

[0013] The operation records and status information of the alarm and emergency processing module can be monitored by the system management and maintenance module to perform system log management and maintenance.

[0014] The system management and maintenance module is responsible for the configuration, monitoring, maintenance and upgrading of the entire system, and is connected with all other modules to ensure the stable operation and security of the system.

[0015] In a preferred embodiment, the data acquisition module is the front-end part of the system, which is responsible for collecting various operating parameters from the boiler auxiliary equipment in real time. Its operation process first captures the real-time status of the equipment through sensors installed on different equipment (such as temperature sensors, pressure sensors, flow sensors, etc.). Subsequently, the sensor converts the analog signal into a digital signal and transmits it to the data acquisition system through a signal converter. The data acquisition system samples the data according to the preset sampling frequency and accuracy to ensure the integrity and accuracy of the data, and finally packages the collected data and prepares to send it to the data transmission module;

[0016] The data transmission module is responsible for safely and efficiently transmitting the collected data to the data processing and analysis module. Its operation process includes establishing a stable communication connection and transmitting the data packaged by the data acquisition module through a wired or wireless network. During the transmission process, encryption and verification mechanisms are used to ensure the security, integrity and reliability of the data. The data transmission module also needs to deal with network delays, data packet loss and other issues to ensure the real-time and continuity of the data.

[0017] In a preferred embodiment, the data preprocessing submodule removes these outliers by data cleaning. Then, the data is standardized using the z-score standardization method, and the calculation formula is: Where x is the original data, μ is the mean, and σ is the standard deviation. Finally, the data is scaled to the [0,1] interval through maximum and minimum normalization, and the calculation formula is:

[0018] where x min and x max are the minimum and maximum values ​​of the data respectively;

[0019] The feature extraction submodule uses principal component analysis (PCA) to reduce dimension and extract features, and finds the main components of the data by calculating the eigenvalues ​​and eigenvectors of the data covariance matrix. The calculation formula is: Where X is the data matrix and n is the number of samples. Several eigenvectors with the largest eigenvalues ​​are selected as the main components to extract the most important features.

[0020] In a preferred embodiment, the model training submodule uses a support vector machine (SVM) as an early warning model, and its goal is to find an optimal hyperplane to separate data of different categories. The training process of SVM is to solve an optimization problem, and its objective function is: subject toys i (w T φ(x i )+b)≥1, where w is the weight vector, b is the bias term, (xi) is the feature vector, and yi is the category label.

[0021] In a preferred embodiment, the model evaluation submodule uses a cross-validation method to evaluate the model, divides the data set into 5 subsets, trains the model with 4 subsets each time, and tests the model with the remaining 1 subset, and calculates the average accuracy. Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example.

[0022] In a preferred embodiment, the real-time monitoring submodule monitors the equipment operation data in real time and uses the trained model for prediction. The real-time monitoring submodule collects a set of equipment data in real time and inputs it into the trained SVM model. The model outputs a prediction result indicating whether the equipment is in a normal state;

[0023] The early warning trigger submodule triggers an early warning signal when it detects a potential failure risk. If the prediction result of the real-time monitoring submodule shows that the equipment has a failure risk, the early warning trigger submodule will trigger an early warning according to a preset threshold. The early warning signal may include sending an alarm message to the mobile phone of the operation and maintenance personnel, or displaying a red warning on the monitoring interface.

[0024] In a preferred embodiment, the multi-dimensional monitoring module is responsible for displaying the real-time operating status and warning information of boiler auxiliary equipment. Its operation process includes receiving warning information from the AI ​​intelligent warning module and real-time data obtained from the data processing and analysis module. The monitoring interface intuitively displays data from multiple dimensions in the form of charts, curves, etc., which is convenient for operation and maintenance personnel to fully understand the operating status of the equipment and promptly discover and deal with problems.

[0025] In a preferred embodiment, the alarm and emergency processing module responds quickly when a device failure or warning is detected. Its operation process includes receiving the warning signal from the AI ​​intelligent warning module, immediately starting the alarm system, and issuing an audible and visual alarm to alert the operation and maintenance personnel. At the same time, according to the type and severity of the fault, corresponding emergency processing suggestions are provided to guide the operation and maintenance personnel to conduct fault investigation and processing to ensure equipment safety.

[0026] In a preferred embodiment, the system management and maintenance module is responsible for the configuration, monitoring, maintenance and upgrade of the entire monitoring and early warning system. Its operation process includes user authority management, system parameter setting, data query, report generation, etc. In addition, the module is also responsible for monitoring the operating status of the system, timely alarming when abnormalities are found, and performing software upgrades and hardware maintenance as needed to ensure stable and efficient operation of the system.

[0027] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0028] 1. In the present invention, the beneficial effects of the data preprocessing submodule are summarized as follows: the data preprocessing submodule significantly improves the quality and availability of the data by cleaning, standardizing and normalizing the raw data. This processing not only eliminates the noise and outliers in the data, ensures that the input data of the model is accurate, but also enhances the generalization ability of the model through feature scaling, so that it can better adapt to different data distributions. Therefore, the data preprocessing submodule lays a solid foundation for subsequent feature extraction and model training, reduces the error in the model training process, and improves the prediction accuracy and robustness of the model. The feature extraction submodule uses techniques such as principal component analysis to effectively extract the most important features from high-dimensional data and realizes the dimensionality reduction of the data. This not only simplifies the complexity of the model, reduces the consumption of computing resources, but also improves the training and prediction efficiency of the model. In addition, by screening out features that are highly relevant to the prediction target, the feature extraction submodule enhances the interpretability of the model, making the model output more reliable and easy to understand. Therefore, the feature extraction submodule not only improves the performance of the model, but also provides convenience for the practical application of the model.

[0029] 2. In the present invention, the model training submodule can learn complex patterns and relationships from historical data by adopting advanced machine learning algorithms such as support vector machines, thereby achieving accurate prediction of equipment status. The self-learning and adjustment capabilities of the model enable it to adapt to changes in equipment operation and maintain the accuracy of prediction. In addition, the continuous optimization of the model training submodule helps to improve the operating efficiency and reliability of the equipment, and provides strong support for equipment maintenance and fault prevention. The model evaluation submodule ensures that the performance of the model meets the expected standards and prevents the occurrence of overfitting through evaluation techniques such as cross-validation. This evaluation not only improves the reliability of the model in practical applications, but also ensures that the model has good generalization ability when facing unknown data. Therefore, the model evaluation submodule is a key step in ensuring the effectiveness of the model, and it provides an important reference for the deployment and application of the model.

[0030] 3. In the present invention, the real-time monitoring submodule can detect potential problems and anomalies in the first place by continuously tracking the operating status of the equipment, and provide timely feedback to the operation and maintenance personnel. This real-time monitoring mechanism greatly shortens the fault response time, helps to take corrective measures in time, thereby avoiding possible equipment damage and production line interruption, and ensuring the continuity and safety of production. When the early warning trigger submodule detects equipment anomalies or potential failures, it can quickly trigger an early warning signal to notify relevant personnel to take action. This early warning mechanism significantly improves the initiative and predictability of equipment maintenance, effectively prevents the occurrence of equipment failures, reduces maintenance costs and downtime, and also ensures the safety of operators and equipment, bringing significant economic benefits and safety guarantees to the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is the overall system block diagram of the present invention;

[0032] Figure 2 This is a system block diagram of the AI ​​intelligent early warning module in the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0034] Reference Figure 1-2 ,

[0035] The AI ​​intelligent early warning and multi-dimensional monitoring platform for the main auxiliary equipment on the boiler side is applied. The platform applications include:

[0036] Data acquisition module, data transmission module, data processing and analysis module, AI intelligent early warning module, multi-dimensional monitoring module, alarm and emergency processing module and system management and maintenance module;

[0037] The AI ​​intelligent early warning module is internally configured with a data preprocessing submodule, a feature extraction submodule, a model training submodule, a model evaluation submodule, a real-time monitoring submodule, and an early warning triggering submodule;

[0038] The data acquisition module is responsible for collecting the operating data of the boiler auxiliary equipment and sending this data through the data transmission module.

[0039] The data transmission module transmits the collected data to the data processing and analysis module in real time for further processing and analysis.

[0040] After preprocessing and analyzing the data, the data processing and analysis module passes the results to the AI ​​intelligent early warning module for building and updating the early warning model.

[0041] The AI ​​intelligent early warning module makes early warning judgments based on the analysis results and passes the early warning information to the multi-dimensional monitoring module for display on the monitoring interface.

[0042] After receiving the early warning information, the multi-dimensional monitoring module can trigger the actions of the alarm and emergency processing module, such as issuing an alarm signal and displaying emergency processing suggestions.

[0043] The operation records and status information of the alarm and emergency handling module can be monitored by the system management and maintenance module to facilitate system log management and maintenance.

[0044] The system management and maintenance module is responsible for the configuration, monitoring, maintenance and upgrade of the entire system, and is connected with all other modules to ensure the stable operation and security of the system.

[0045] The data acquisition module is the front-end part of the system, responsible for collecting various operating parameters from the boiler auxiliary equipment in real time. Its operation process first captures the real-time status of the equipment through sensors installed on different equipment (such as temperature sensors, pressure sensors, flow sensors, etc.). Subsequently, the sensor converts the analog signal into a digital signal and transmits it to the data acquisition system through a signal converter. The data acquisition system samples data according to the preset sampling frequency and accuracy to ensure the integrity and accuracy of the data, and finally packages the collected data and prepares to send it to the data transmission module;

[0046] The data transmission module is responsible for safely and efficiently transmitting the collected data to the data processing and analysis module. Its operation process includes establishing a stable communication connection and transmitting the data packaged by the data acquisition module through a wired or wireless network. During the transmission process, encryption and verification mechanisms are used to ensure the security, integrity and reliability of the data. The data transmission module also needs to deal with network delays, data packet loss and other issues to ensure the real-time and continuity of the data.

[0047] The data processing and analysis module is the core processing unit of the system, responsible for processing and analyzing the collected data. Its operation process first preprocesses the data, including denoising, filtering, normalization and other operations to improve data quality. Then, the preprocessed data is stored in the database for subsequent analysis and query. Next, statistical analysis, trend analysis and other methods are used to conduct in-depth analysis of the data, extract useful information, and provide data support for the AI ​​intelligent early warning module.

[0048] The data preprocessing submodule removes these outliers by cleaning the data. Then, the data is standardized using the z-score standardization method, and the calculation formula is: Where x is the original data, μ is the mean, and σ is the standard deviation. Finally, the data is scaled to the [0,1] interval through maximum and minimum normalization, and the calculation formula is:

[0049] where x min and x max are the minimum and maximum values ​​of the data respectively;

[0050] The feature extraction submodule uses principal component analysis (PCA) to reduce dimension and extract features. The main components of the data are found by calculating the eigenvalues ​​and eigenvectors of the data covariance matrix. The calculation formula is: Where X is the data matrix and n is the number of samples. Several eigenvectors with the largest eigenvalues ​​are selected as the main components to extract the most important features.

[0051] The model training submodule uses support vector machine (SVM) as the early warning model, and its goal is to find an optimal hyperplane to separate data of different categories. The training process of SVM is to solve an optimization problem, and its objective function is: subject toys i (w T φ(x i )+b)≥1, where w is the weight vector, b is the bias term, (xi) is the feature vector, and yi is the category label.

[0052] The model evaluation submodule uses the cross-validation method to evaluate the model. The data set is divided into 5 subsets, 4 subsets are used to train the model each time, and the remaining 1 subset is used to test the model, and the average accuracy is calculated. Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example.

[0053] The real-time monitoring submodule monitors the equipment operation data in real time and uses the trained model for prediction. The real-time monitoring submodule collects a set of equipment data in real time and inputs it into the trained SVM model. The model will output a prediction result indicating whether the equipment is in a normal state;

[0054] The early warning trigger submodule triggers an early warning signal when it detects a potential failure risk. If the prediction results of the real-time monitoring submodule show that the equipment has a failure risk, the early warning trigger submodule will trigger an early warning based on the preset threshold. The early warning signal may include sending an alarm message to the operation and maintenance personnel's mobile phone, or displaying a red warning on the monitoring interface.

[0055] The multi-dimensional monitoring module is responsible for displaying the real-time operating status and warning information of boiler auxiliary equipment. Its operation process includes receiving warning information from the AI ​​intelligent warning module and obtaining real-time data from the data processing and analysis module. The monitoring interface displays data from multiple dimensions in the form of charts, curves, etc., which is convenient for operation and maintenance personnel to fully understand the operating status of the equipment and discover and deal with problems in a timely manner.

[0056] The alarm and emergency handling module responds quickly when it detects equipment failure or warning. Its operation process includes receiving the warning signal from the AI ​​intelligent warning module, immediately starting the alarm system, and issuing an audible and visual alarm to alert the operation and maintenance personnel. At the same time, according to the type and severity of the fault, it provides corresponding emergency handling suggestions to guide the operation and maintenance personnel to conduct fault investigation and processing to ensure equipment safety.

[0057] The system management and maintenance module is responsible for the configuration, monitoring, maintenance and upgrade of the entire monitoring and early warning system. Its operation process includes user authority management, system parameter setting, data query, report generation, etc. In addition, this module is also responsible for monitoring the operating status of the system, timely alarming when abnormalities are found, and performing software upgrades and hardware maintenance as needed to ensure stable and efficient operation of the system.

[0058] In the present invention, the beneficial effects of the data preprocessing submodule are summarized as follows: the data preprocessing submodule significantly improves the quality and availability of the data by cleaning, standardizing and normalizing the raw data. This processing not only eliminates the noise and outliers in the data, ensures that the input data of the model is accurate, but also enhances the generalization ability of the model through feature scaling, so that it can better adapt to different data distributions. Therefore, the data preprocessing submodule lays a solid foundation for subsequent feature extraction and model training, reduces the error in the model training process, and improves the prediction accuracy and robustness of the model. The feature extraction submodule uses techniques such as principal component analysis to effectively extract the most important features from high-dimensional data and realizes data dimensionality reduction. This not only simplifies the complexity of the model, reduces the consumption of computing resources, but also improves the training and prediction efficiency of the model. In addition, by screening out features that are highly relevant to the prediction target, the feature extraction submodule enhances the interpretability of the model, making the model output more reliable and easy to understand. Therefore, the feature extraction submodule not only improves the performance of the model, but also provides convenience for the practical application of the model.

[0059] In the present invention, the model training submodule can learn complex patterns and relationships from historical data by adopting advanced machine learning algorithms such as support vector machines, thereby achieving accurate prediction of equipment status. The self-learning and adjustment capabilities of the model enable it to adapt to changes in equipment operation and maintain the accuracy of prediction. In addition, the continuous optimization of the model training submodule helps to improve the operating efficiency and reliability of the equipment, and provides strong support for equipment maintenance and fault prevention. The model evaluation submodule ensures that the performance of the model meets the expected standards and prevents the occurrence of overfitting through evaluation techniques such as cross-validation. This evaluation not only improves the reliability of the model in practical applications, but also ensures that the model has good generalization ability when facing unknown data. Therefore, the model evaluation submodule is a key step in ensuring the effectiveness of the model, and it provides an important reference for the deployment and application of the model.

[0060] In the present invention, the real-time monitoring submodule can detect potential problems and anomalies in the first place by continuously tracking the operating status of the equipment, and provide timely feedback to the operation and maintenance personnel. This real-time monitoring mechanism greatly shortens the fault response time, helps to take corrective measures in time, thereby avoiding possible equipment damage and production line interruption, and ensuring the continuity and safety of production. When the early warning trigger submodule detects equipment anomalies or potential failures, it can quickly trigger an early warning signal to notify relevant personnel to take action. This early warning mechanism significantly improves the initiative and predictability of equipment maintenance, effectively prevents the occurrence of equipment failures, reduces maintenance costs and downtime, and also ensures the safety of operators and equipment, bringing significant economic benefits and safety guarantees to enterprises.

[0061] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The AI ​​intelligent early warning and multi-dimensional monitoring platform for main auxiliary equipment on the boiler side is applied, which is characterized by: The platform applications include: Data acquisition module, data transmission module, data processing and analysis module, AI intelligent early warning module, multi-dimensional monitoring module, alarm and emergency processing module and system management and maintenance module; The AI ​​intelligent early warning module is internally provided with a data preprocessing submodule, a feature extraction submodule, a model training submodule, a model evaluation submodule, a real-time monitoring submodule and an early warning triggering submodule; The data acquisition module is responsible for collecting the operating data of the boiler auxiliary equipment and sending these data through the data transmission module; The data transmission module transmits the collected data to the data processing and analysis module in real time for further processing and analysis; After the data processing and analysis module preprocesses and analyzes the data, the results are passed to the AI ​​intelligent early warning module for building and updating the early warning model; The AI ​​intelligent early warning module makes early warning judgments based on the analysis results and transmits the early warning information to the multi-dimensional monitoring module for display on the monitoring interface; After receiving the warning information, the multi-dimensional monitoring module can trigger the actions of the alarm and emergency processing module, such as issuing an alarm signal and displaying emergency processing suggestions; The operation records and status information of the alarm and emergency processing module can be monitored by the system management and maintenance module to facilitate system log management and maintenance; The system management and maintenance module is responsible for the configuration, monitoring, maintenance and upgrading of the entire system, and is connected with all other modules to ensure the stable operation and security of the system.

2. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The data acquisition module is the front-end part of the system, responsible for collecting various operating parameters from the boiler auxiliary equipment in real time; its operation process first captures the real-time status of the equipment through sensors installed on different equipment; then, the sensor converts the analog signal into a digital signal and transmits it to the data acquisition system through a signal converter; The data acquisition system samples data according to the preset sampling frequency and accuracy to ensure the integrity and accuracy of the data, and finally packages the collected data and prepares to send it to the data transmission module; The data transmission module is responsible for transmitting the collected data to the data processing and analysis module safely and efficiently; Its operation process includes establishing a stable communication connection and transmitting the data packaged by the data acquisition module through a wired or wireless network; during the transmission process, encryption and verification mechanisms are used to ensure the security, integrity and reliability of the data; the data transmission module also needs to deal with network delays, data packet loss and other issues to ensure the real-time and continuity of the data.

3. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The data processing and analysis module is the core processing unit of the system, responsible for processing and analyzing the collected data; the data processing and analysis module first pre-processes the data, including denoising, filtering, normalization and other operations to improve data quality; then, the pre-processed data is stored in the database for subsequent analysis and query; next, statistical analysis, trend analysis and other methods are used to conduct in-depth analysis of the data, extract useful information, and provide data support for the AI ​​intelligent early warning module.

4. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The data preprocessing submodule removes these outliers by data cleaning; then, the data is standardized using the z-score standardization method, and the calculation formula is: Where x is the original data, μ is the mean, and σ is the standard deviation; finally, the data is scaled to the [0,1] interval through maximum and minimum normalization, and the calculation formula is: where x min and x max are the minimum and maximum values ​​of the data respectively; The feature extraction submodule uses principal component analysis (PCA) to reduce dimension and extract features, and finds the main components of the data by calculating the eigenvalues ​​and eigenvectors of the data covariance matrix. The calculation formula is: Where X is the data matrix and n is the number of samples; several eigenvectors with the largest eigenvalues ​​are selected as the main components to extract the most important features.

5. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The model training submodule uses a support vector machine (SVM) as an early warning model, and its goal is to find an optimal hyperplane to separate data of different categories; the training process of SVM is to solve an optimization problem, and its objective function is: subject to you i (w T φ(x i )+b)≥1, where w is the weight vector, b is the bias term, (xi) is the feature vector, and yi is the category label.

6. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The model evaluation submodule uses a cross-validation method to evaluate the model. The data set is divided into 5 subsets, 4 subsets are used to train the model each time, and the remaining 1 subset is used to test the model, and the average accuracy is calculated. Among them, TP is a true positive example, TN is a true negative example, FP is a false positive example, and FN is a false negative example.

7. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The real-time monitoring submodule monitors the equipment operation data in real time and uses the trained model for prediction; The real-time monitoring submodule collects a set of equipment data in real time and inputs it into the trained SVM model, which then outputs a prediction result indicating whether the equipment is in a normal state. The early warning trigger submodule triggers an early warning signal when a potential failure risk is detected; If the prediction result of the real-time monitoring submodule shows that the equipment has a risk of failure, the early warning triggering submodule will trigger an early warning according to a preset threshold.

8. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The multi-dimensional monitoring module is responsible for displaying the real-time operating status and early warning information of boiler auxiliary equipment; the multi-dimensional monitoring module receives early warning information from the AI ​​intelligent early warning module, as well as real-time data obtained from the data processing and analysis module; the monitoring interface intuitively displays data of multiple dimensions in the form of charts, curves, etc.

9. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The alarm and emergency processing module responds quickly when equipment failure or warning is detected; its operation process includes receiving the warning signal from the AI ​​intelligent warning module, immediately starting the alarm system, issuing an audible and visual alarm, and alerting the operation and maintenance personnel.

10. The AI ​​intelligent early warning and multi-dimensional monitoring platform application of the main auxiliary equipment on the boiler side as claimed in claim 1 is characterized by: The system management and maintenance module is responsible for the configuration, monitoring, maintenance and upgrading of the entire monitoring and early warning system; Its operation process includes user authority management, system parameter setting, data query, and report generation.

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