Generator water dissolved oxygen safety monitoring system

Abstract

The invention relates to the field of dissolved oxygen monitoring, and in particular to a generator water dissolved oxygen safety monitoring system. The system comprises an isotope analysis module, a comprehensive prediction module, a data-driven optimization module, a safety threshold setting module, a nonlinear dynamic analysis module, a real-time monitoring module, a preventive maintenance module, and a system optimization module. The monitoring accuracy of the system for dissolved oxygen levels and trace hydrogen leakage is significantly improved through isotope analysis and hydrogen leakage detection. The prediction and optimization functions are combined with a fluid dynamics model and a neural network prediction algorithm, as well as the use of a linear regression and a K-means clustering algorithm, so that the equipment operation strategy is more optimized and the overall efficiency is improved. The dynamic safety threshold setting, using a decision tree and a support vector machine algorithm, provides the system with a more flexible and accurate threshold adjustment mechanism. Through the application of a multi-objective optimization algorithm and a fault tree analysis, the performance of the system is fully optimized, and the monitoring and control efficiency is improved.

Description

Technical Field

[0001] The invention relates to the field of dissolved oxygen monitoring, and in particular to a generator water dissolved oxygen safety monitoring system. Background Art

[0002] Dissolved oxygen monitoring is a technical field that focuses on measuring and analyzing the dissolved oxygen content in water. It has important applications in environmental monitoring, water quality analysis, and industrial process control. Monitoring dissolved oxygen levels is essential for maintaining the balance of aquatic ecosystems, ensuring drinking water safety, and monitoring industrial processes (such as cooling systems in power plants). Technically, this field involves a variety of physical and chemical methods to measure oxygen content in water, including electrochemical sensors (such as polarography), optical methods (such as fluorescence), etc.

[0003] The generator water dissolved oxygen safety monitoring system is a system used in power stations. Its main purpose is to monitor and control the water quality in the generator cooling system, especially the dissolved oxygen content in the water. The dissolved oxygen level is crucial to preventing corrosion and maintaining the effective operation of the equipment. It usually includes sensors, data processing units and alarm mechanisms. The sensors continuously monitor the dissolved oxygen level in the water. The data processing unit analyzes this data and determines whether it is within a safe and effective operating range. If the dissolved oxygen level is too high or too low, the system will trigger an alarm to notify the operator to take necessary measures, thereby ensuring the long-term stable operation and safety of the equipment. In this way, the system helps prevent equipment corrosion, improve power generation efficiency and reduce maintenance costs.

[0004] Traditional dissolved oxygen monitoring systems have shown shortcomings in the following aspects: their monitoring range and accuracy are limited, and they can usually only measure dissolved oxygen levels. The lack of monitoring of parameters such as trace hydrogen leaks limits their ability to prevent corrosion and ensure equipment safety. The lack of prediction and optimization functions means that the system is usually not accurate or efficient in adjusting equipment efficiency and operating strategies. The safety thresholds of traditional systems are usually static and insufficient to cope with complex or rapidly changing operating conditions. The system also shows limitations in system-level performance optimization, resulting in inefficiency and higher maintenance costs. Summary of the invention

[0005] The present invention aims at the technical problems existing in the prior art and provides a generator water dissolved oxygen safety monitoring system.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: the generator water dissolved oxygen safety monitoring system includes an isotope analysis module, a comprehensive prediction module, a data-driven optimization module, a safety threshold setting module, a nonlinear dynamic analysis module, a real-time monitoring module, a preventive maintenance module, and a system optimization module;

[0007] The isotope analysis module uses mass spectrometry and isotope dilution method to detect and analyze hydrogen leakage based on trace hydrogen leakage, and generates a hydrogen isotope analysis report;

[0008] The comprehensive prediction module predicts the dissolved oxygen level based on the hydrogen isotope analysis report, using a fluid dynamics model and a neural network prediction algorithm to generate a dissolved oxygen level prediction report;

[0009] The data-driven optimization module optimizes the equipment operation strategy based on the dissolved oxygen level prediction report and generates an optimization strategy report using linear regression and K-means clustering algorithms;

[0010] The security threshold setting module uses decision tree and support vector machine algorithms based on the optimization strategy report to dynamically adjust the security threshold and generate a security threshold adjustment report;

[0011] The nonlinear dynamic analysis module uses chaos theory and dynamic system analysis methods based on the safety threshold adjustment report to perform leakage dynamic analysis and generate a leakage dynamic analysis report;

[0012] The real-time monitoring module uses real-time data monitoring and status assessment methods based on the leakage dynamic analysis report to perform real-time monitoring and generate a real-time monitoring data report;

[0013] The preventive maintenance module uses failure mode and effect analysis based on real-time monitoring data reports to perform preventive maintenance planning and generate maintenance effect verification reports;

[0014] The system optimization module uses a multi-objective optimization algorithm and fault tree analysis based on the maintenance effect verification report to perform system optimization and generate a system optimization plan.

[0015] As a further solution of the present invention, the hydrogen isotope analysis report is specifically a hydrogen isotope ratio and leakage estimation report, the dissolved oxygen level prediction report is specifically a dissolved oxygen level change prediction report based on models and data, the optimization strategy report is specifically an equipment operating parameter adjustment and maintenance recommendation report, the safety threshold adjustment report is specifically a threshold adjustment recommendation report based on data analysis, the leakage dynamic analysis report is specifically a nonlinear characteristic analysis report of the leakage process, the real-time monitoring data report is specifically a real-time monitoring data report of hydrogen leakage and dissolved oxygen level, and the maintenance effect verification report includes maintenance measures and operation recommendation reports.

[0016] As a further solution of the present invention, the isotope analysis module includes an isotope identification submodule, a leakage estimation submodule, a gas composition comparison submodule, and an isotope ratio monitoring submodule;

[0017] The isotope identification submodule uses mass spectrometry analysis technology to identify hydrogen isotopes based on trace hydrogen leakage, and performs data comparison to generate isotope type data;

[0018] The leakage estimation submodule calculates the leakage based on the isotope type data using the isotope dilution quantitative method, and performs result analysis to generate leakage estimation data;

[0019] The gas composition comparison submodule uses gas chromatography analysis to compare gas compositions based on leakage estimation data, and performs ratio analysis to generate a gas composition comparison report;

[0020] The isotope ratio monitoring submodule uses a time series analysis method based on a gas composition comparison report to monitor hydrogen isotope ratios, track trends, and generate a hydrogen isotope analysis report.

[0021] As a further solution of the present invention, the comprehensive prediction module includes a fluid dynamic simulation submodule, an artificial intelligence prediction submodule, an environmental adaptability adjustment submodule, and a long-term trend analysis submodule;

[0022] The fluid dynamics simulation submodule performs fluid dynamics simulation based on the hydrogen isotope analysis report and adopts the Reynolds average equation model to generate preliminary dissolved oxygen prediction data;

[0023] The artificial intelligence prediction submodule uses a convolutional neural network to analyze and optimize data features based on preliminary dissolved oxygen prediction data to generate optimized dissolved oxygen prediction data;

[0024] The environmental adaptability adjustment submodule uses a genetic algorithm to adjust and optimize model parameters based on the optimized dissolved oxygen prediction data to generate adaptive environmental adjustment data;

[0025] The long-term trend analysis submodule uses an autoregressive integrated moving average model based on the adaptive environmental adjustment data to analyze and predict trends and generate a dissolved oxygen level prediction report.

[0026] As a further solution of the present invention, the data-driven optimization module includes a maintenance strategy optimization submodule, an operation mode optimization submodule, an efficiency evaluation submodule, and an operation specification submodule;

[0027] The maintenance strategy optimization submodule uses multiple linear regression analysis based on the dissolved oxygen level prediction report to perform maintenance demand analysis and generate maintenance strategy optimization suggestions;

[0028] The operation mode optimization submodule uses the K-means clustering algorithm to adjust the operation parameters and generate an operation mode optimization plan based on the maintenance strategy optimization suggestion;

[0029] The efficiency evaluation submodule uses a performance index evaluation method based on the operation mode optimization scheme to evaluate the improvement effect and generate an efficiency evaluation report;

[0030] The operation specification submodule formulates operation specifications and generates an optimization strategy report based on the efficiency evaluation report and adopts the standard operation procedure development method.

[0031] As a further solution of the present invention, the safety threshold setting module includes a dynamic threshold adjustment submodule, a threshold monitoring submodule, a risk warning submodule, and a response speed optimization submodule;

[0032] The dynamic threshold adjustment submodule uses a decision tree analysis method based on the optimization strategy report to evaluate security parameters and generate a preliminary security threshold adjustment plan;

[0033] The threshold monitoring submodule uses a dynamic threshold monitoring system based on a preliminary safety threshold adjustment scheme to perform real-time safety parameter tracking and generate threshold monitoring data;

[0034] The risk warning submodule uses a support vector machine model based on threshold monitoring data to identify potential indicators of security risks and generate a risk warning report;

[0035] The response speed optimization submodule is based on the risk warning report and adopts an optimization method based on a rapid response strategy to improve the system's response speed to risks and generate a safety threshold adjustment report.

[0036] As a further solution of the present invention, the nonlinear dynamic analysis module includes a leakage dynamic tracking submodule, a nonlinear characteristic analysis submodule, a leakage trend prediction submodule, and a response mode analysis submodule;

[0037] The leakage dynamic tracking submodule uses a time series analysis method to track leakage dynamics and generate leakage dynamic tracking data based on the safety threshold adjustment report;

[0038] The nonlinear characteristic analysis submodule uses Lyapunov exponent calculation based on leakage dynamic tracking data to reveal nonlinear characteristics and generate a nonlinear characteristic analysis report;

[0039] The leakage trend prediction submodule predicts the leakage trend based on the nonlinear characteristic analysis report and adopts a nonlinear dynamic prediction model to generate a leakage trend prediction report;

[0040] The response mode analysis submodule uses system dynamic response analysis based on the leakage trend prediction report to evaluate the response strategy and generate a leakage dynamic analysis report.

[0041] As a further solution of the present invention, the real-time monitoring module includes an operation status monitoring submodule, an operation abnormality identification submodule, a safety alarm activation submodule, and a monitoring response coordination submodule;

[0042] The operation status monitoring submodule uses real-time trend analysis technology based on the leakage dynamic analysis report to monitor the equipment operation status and generate operation status monitoring data;

[0043] The operation abnormality identification submodule uses an abnormal pattern recognition algorithm based on the operation status monitoring data to identify abnormal operation status and generate an operation abnormality identification report;

[0044] The safety alarm activation submodule activates the safety alarm and generates a safety alarm activation record based on the operation abnormality identification report by using an automatic alarm triggering mechanism;

[0045] The monitoring response coordination submodule adopts emergency response coordination strategies based on security alarm activation records, optimizes monitoring responses, and generates real-time monitoring data reports.

[0046] As a further solution of the present invention, the preventive maintenance module includes a maintenance strategy planning submodule, an operation risk assessment submodule, a maintenance measure implementation submodule, and a maintenance effect verification submodule;

[0047] The maintenance strategy planning submodule uses failure mode and effect analysis and fault tree analysis based on real-time monitoring data reports to perform risk assessment and priority determination and generate a maintenance strategy optimization report;

[0048] The operation risk assessment submodule uses Monte Carlo simulation and Bayesian network to perform probability analysis and impact assessment based on the maintenance strategy optimization report to generate an operation risk assessment report;

[0049] The maintenance measures implementation submodule uses performance indicator analysis and resource optimization algorithm based on the operation risk assessment report to perform maintenance demand analysis and resource allocation, and generate a maintenance measures implementation plan;

[0050] The maintenance effect verification submodule uses the Six Sigma method and statistical process control based on the maintenance measure implementation plan to perform improvement effect analysis and generate a maintenance effect verification report.

[0051] As a further solution of the present invention, the system optimization module includes a performance improvement solution submodule, an efficiency optimization solution submodule, a system feedback analysis submodule, and a strategy improvement submodule;

[0052] The performance improvement plan submodule uses linear programming and sensitivity analysis based on the maintenance effect verification report to evaluate the system performance status, formulate preliminary improvement measures, and generate a draft performance improvement plan;

[0053] The efficiency optimization program submodule generates a draft efficiency optimization program based on the draft performance improvement program by using cost-benefit analysis and operation research techniques to optimize resource allocation and energy utilization;

[0054] The system feedback analysis submodule uses time series analysis and control chart technology based on the efficiency optimization plan draft to monitor system response and analyze feedback data to generate a system feedback analysis report;

[0055] The strategy improvement submodule is based on the system feedback analysis report, adopts a multi-objective genetic algorithm and a fault tree analysis, integrates the analysis results and optimizes the decision-making strategy to generate a system optimization solution.

[0056] The beneficial effects of the present invention are as follows: through isotope analysis and hydrogen leakage detection, the monitoring accuracy of the system for dissolved oxygen levels and trace hydrogen leakage is significantly improved; the prediction and optimization functions are combined with fluid dynamics models and neural network prediction algorithms, as well as the use of linear regression and K-means clustering algorithms, so that the equipment operation strategy is more optimized and the overall efficiency is improved; dynamic safety threshold setting, using decision trees and support vector machine algorithms, provides the system with a more flexible and accurate threshold adjustment mechanism; through the application of multi-objective optimization algorithms and fault tree analysis, the performance of the system is comprehensively optimized, the monitoring and control efficiency is improved, and the maintenance cost is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a system flow chart of the present invention;

[0058] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0059] Figure 3 is a flow chart of the isotope analysis module of the present invention;

[0060] Figure 4 is a flow chart of the comprehensive prediction module of the present invention;

[0061] Figure 5 A flow chart of a data driven optimization module of the present invention;

[0062] Figure 6 This is a flow chart of the safety threshold setting module of the present invention;

[0063] Figure 7 It is a flow chart of the nonlinear dynamic analysis module of the present invention;

[0064] Figure 8 It is a flow chart of the real-time monitoring module of the present invention;

[0065] Fig. 9 is a flow chart of the preventive maintenance module of the present invention;

[0066] Fig.10 It is a flow chart of the system optimization module of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0068] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0069] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0070] Example 1

[0071] See also Figure 1-2 ,The safety monitoring system for dissolved oxygen in water of generator ,includes isotope analysis module, comprehensive prediction module, data-driven optimization module, safety threshold setting module, nonlinear dynamic analysis module,

[0072] The isotope analysis module uses mass spectrometry and isotope dilution methods to detect and analyze hydrogen leaks based on trace hydrogen leaks, and generates a hydrogen isotope analysis report;

[0073] The comprehensive prediction module uses a fluid dynamics model and a neural network prediction algorithm based on the hydrogen isotope analysis report to predict the dissolved oxygen level and generate a dissolved oxygen level prediction report;

[0074] The data-driven optimization module uses linear regression and K-means clustering algorithms based on the dissolved oxygen level prediction report to optimize the equipment operation strategy and generate an optimization strategy report;

[0075] The security threshold setting module uses decision tree and support vector machine algorithms based on the optimization strategy report to dynamically adjust the security threshold and generate a security threshold adjustment report;

[0076] The nonlinear dynamic analysis module uses chaos theory and dynamic system analysis methods based on the safety threshold adjustment report to perform leakage dynamic analysis and generate leakage dynamic analysis reports;

[0077] The real-time monitoring module uses real-time data monitoring and status assessment methods based on the leakage dynamic analysis report to conduct real-time monitoring and generate real-time monitoring data reports;

[0078] The preventive maintenance module uses failure mode and effect analysis based on real-time monitoring data reports to carry out preventive maintenance planning and generate maintenance effect verification reports;

[0079] The system optimization module uses multi-objective optimization algorithm and fault tree analysis based on the maintenance effect verification report to optimize the system and generate a system optimization plan.

[0080] The hydrogen isotope analysis report is specifically a hydrogen isotope ratio and leakage estimation report; the dissolved oxygen level prediction report is specifically a dissolved oxygen level change prediction report based on models and data; the optimization strategy report is specifically an equipment operating parameter adjustment and maintenance recommendation report; the safety threshold adjustment report is specifically a threshold adjustment recommendation report based on data analysis; the leakage dynamic analysis report is specifically a nonlinear characteristic analysis report of the leakage process; the real-time monitoring data report is specifically a real-time monitoring data report of hydrogen leakage and dissolved oxygen levels; the maintenance effect verification report includes maintenance measures and operation recommendation reports.

[0081] The isotope analysis module can detect hydrogen leaks with high precision, the comprehensive prediction module predicts the dissolved oxygen level, the data-driven optimization module optimizes the equipment operation strategy, the safety threshold setting module dynamically adjusts the safety threshold, the nonlinear dynamic analysis module analyzes the leakage process, the real-time monitoring module provides real-time monitoring data, and the preventive maintenance module plans maintenance measures, ensuring the safe monitoring and maintenance of dissolved oxygen in the generator water, reducing safety risks, improving efficiency, and extending equipment life to ensure the reliability and safety of the system and improve the efficiency and sustainability of the industrial production process.

[0082] See also Figure 3 ,The isotope analysis module includes an isotope identification submodule, a leakage estimation submodule, a gas composition comparison submodule, and an isotope ratio monitoring submodule;

[0083] The isotope identification submodule uses mass spectrometry technology to identify hydrogen isotopes based on trace hydrogen leakage, and performs data comparison to generate isotope type data;

[0084] The leakage estimation submodule calculates the leakage based on the isotope type data and adopts the isotope dilution quantitative method to analyze the results and generate leakage estimation data;

[0085] The gas composition comparison submodule uses gas chromatography analysis to compare gas compositions based on leakage estimation data, and performs ratio analysis to generate a gas composition comparison report.

[0086] The isotope ratio monitoring submodule uses time series analysis method to monitor hydrogen isotope ratios based on gas composition comparison reports, track trends, and generate hydrogen isotope analysis reports.

[0087] The isotope identification submodule collects trace hydrogen leakage samples, analyzes the samples using mass spectrometry technology, identifies the hydrogen isotope type, and records the data. It then compares the isotope type data to ensure the accuracy and consistency of the data. The leakage estimation submodule uses the isotope type data to calculate the leakage using the isotope dilution quantitative method, and carefully analyzes the results, taking into account possible errors and uncertainties, and ultimately generates leakage estimation data, including leakage and related statistical information. The gas composition comparison submodule uses gas chromatography analysis to analyze the gas composition in detail based on the leakage estimation data, compares the gas composition in the sample with the standard gas composition, and performs a ratio analysis of the gas composition. Finally, a gas composition comparison report is generated, including detailed information and proportion data of the gas composition. Based on the gas composition comparison report, the isotope ratio monitoring submodule uses time series analysis to monitor the hydrogen isotope ratio, regularly measures and records the hydrogen isotope ratio, uses trend tracking technology to detect the changing trend of the hydrogen isotope ratio, and generates a hydrogen isotope analysis report, including the monitoring results and trend analysis of the isotope ratio. The integration of these operating steps ensures the high accuracy and reliability of the isotope analysis module, provides the system with accurate hydrogen leakage information and dissolved oxygen level data, helps users identify potential problems in a timely manner, ensures the safety and reliability of the system, and provides important support for maintenance personnel.

[0088] See also Figure 4 ,The comprehensive prediction module includes a fluid dynamic simulation submodule, an artificial intelligence prediction submodule, an environmental adaptability adjustment submodule, and a long-term trend analysis submodule;

[0089] The fluid dynamics simulation submodule uses the Reynolds average equation model to perform fluid dynamics simulation based on the hydrogen isotope analysis report and generate preliminary dissolved oxygen prediction data;

[0090] The artificial intelligence prediction submodule uses convolutional neural networks to analyze and optimize data features based on preliminary dissolved oxygen prediction data to generate optimized dissolved oxygen prediction data;

[0091] The environmental adaptability adjustment submodule uses genetic algorithms to adjust and optimize model parameters based on the optimized dissolved oxygen prediction data to generate adaptive environmental adjustment data;

[0092] The long-term trend analysis submodule uses the autoregressive integrated moving average model based on the adaptive environmental adjustment data to analyze and predict trends and generate a dissolved oxygen level forecast report.

[0093] The fluid dynamics simulation submodule uses the Reynolds average equation model to simulate fluid dynamics based on the hydrogen isotope analysis report and generates preliminary dissolved oxygen prediction data to reflect the dissolved oxygen level in the current environment. The artificial intelligence prediction submodule uses convolutional neural networks and other technologies to analyze and optimize the data based on the preliminary prediction data, improve the prediction accuracy, and generate optimized dissolved oxygen prediction data. The environmental adaptability adjustment submodule uses genetic algorithms and other optimization methods based on the optimized prediction data to adjust and optimize the model parameters to adapt to different environmental conditions, generate adaptive environmental adjustment data, and ensure the robustness and stability of the model. The long-term trend analysis submodule uses the autoregressive integrated moving average model and other technologies based on the adaptive environmental adjustment data to analyze and predict the long-term dissolved oxygen level trend and generate a dissolved oxygen level prediction report, including trend analysis results and prediction information. The integration of these operation steps ensures the efficiency and reliability of the comprehensive prediction module, providing accurate dissolved oxygen level predictions based on the current environment and historical data. The system can identify potential problems in a timely manner and help users take necessary measures to ensure the safety and reliability of dissolved oxygen in generator water, providing engineers and operators with powerful tools to improve the efficiency and sustainability of industrial production processes.

[0094] See also Figure 5 ,The data-driven optimization module includes the maintenance strategy optimization submodule, the ,operation mode optimization submodule, the efficiency evaluation submodule, and the ,operation specification submodule;

[0095] The maintenance strategy optimization submodule uses multiple linear regression analysis based on the dissolved oxygen level prediction report to perform maintenance demand analysis and generate maintenance strategy optimization suggestions;

[0096] The operation mode optimization submodule uses the K-means clustering algorithm to adjust the operation parameters and generate an operation mode optimization plan based on the maintenance strategy optimization suggestions;

[0097] The efficiency evaluation submodule uses the performance index evaluation method based on the operation mode optimization plan to evaluate the improvement effect and generate an efficiency evaluation report;

[0098] The operating specification sub-module is based on the efficiency evaluation report and adopts the standard operating procedure development method to formulate operating specifications and generate optimization strategy reports.

[0099] The maintenance strategy optimization submodule identifies potential equipment maintenance needs and opportunities based on the dissolved oxygen level prediction report through methods such as multivariate linear regression analysis, and generates corresponding maintenance strategy optimization suggestions, including maintenance measures, schedules, and required maintenance materials. The operation mode optimization submodule uses technologies such as the K-means clustering algorithm based on the maintenance strategy optimization suggestions to adjust the operating parameters to minimize the impact of maintenance on system operation and generate an operation mode optimization plan, including parameter configuration and operation suggestions. The efficiency evaluation submodule uses performance indicator evaluation methods and other methods based on the operation mode optimization plan to comprehensively evaluate the improvement effect of the system, including quantitative analysis of energy utilization efficiency, equipment life, etc., and generates an efficiency evaluation report. The operation specification submodule uses tools such as the standard operating procedure development method based on the efficiency evaluation report to develop detailed operation specifications, including equipment operation steps, maintenance procedures, emergency treatment, etc., to ensure that the system achieves optimal performance in actual operation. The integration of these operation steps ensures the efficiency and reliability of the data-driven optimization module. Through maintenance and operation optimization suggestions, efficiency evaluation, and the formulation of operation specifications, the system can improve maintainability, operation efficiency, and equipment life, reduce downtime, and provide higher efficiency and sustainability for industrial production.

[0100] See also Figure 6 ,The security threshold setting module includes a dynamic threshold adjustment submodule, a threshold monitoring submodule, a risk warning submodule, and a response speed optimization submodule;

[0101] The dynamic threshold adjustment submodule uses decision tree analysis to evaluate security parameters and generate a preliminary security threshold adjustment plan based on the optimization strategy report;

[0102] The threshold monitoring submodule uses a dynamic threshold monitoring system based on the preliminary security threshold adjustment scheme to track security parameters in real time and generate threshold monitoring data;

[0103] The risk warning submodule uses the support vector machine model based on threshold monitoring data to identify potential indicators of security risks and generate risk warning reports;

[0104] The response speed optimization submodule is based on the risk warning report and adopts an optimization method based on a rapid response strategy to improve the system's response speed to risks and generate a safety threshold adjustment report.

[0105] The dynamic threshold adjustment submodule is started according to the optimization strategy report, and the decision tree analysis method is used to evaluate the security parameters. Taking into account the equipment status and environmental conditions, a preliminary security threshold adjustment plan is generated to ensure that the system strikes a balance between security and performance. The threshold monitoring submodule deploys a dynamic threshold monitoring system based on the preliminary security threshold adjustment plan, tracks the changes in security parameters in real time, and collects threshold monitoring data, including the difference between actual data and set thresholds, for timely monitoring. The risk warning submodule is started based on the threshold monitoring data, and uses support vector machine models and other technologies to analyze the monitoring data, identify possible security risk indicators, and generate a risk warning report, providing which parameters can be used. The response speed optimization submodule is activated according to the risk warning report, and the optimization method based on the rapid response strategy is used to formulate a rapid response strategy for the system to deal with risks, including automatic shutdown, alarm notification, maintenance plan, etc., to reduce potential risks. The integration of these operating steps ensures the efficiency and reliability of the safety threshold setting module. The system can adjust the safety threshold in time according to real-time data and risk warning reports to ensure the safety and reliability of the system. Through dynamic threshold adjustment, real-time monitoring, risk identification and optimization of rapid response strategies, the system can better respond to potential risks, reduce the risk of safety accidents, and improve the safety and stability of industrial production processes.

[0106] See also Figure 7 ,The nonlinear dynamic analysis module includes a leakage dynamic tracking submodule, a nonlinear characteristic analysis submodule, a leakage trend prediction submodule, and a response mode analysis submodule;

[0107] The leakage dynamic tracking submodule uses time series analysis method to track leakage dynamics and generate leakage dynamic tracking data based on the safety threshold adjustment report;

[0108] The nonlinear characteristic analysis submodule uses Lyapunov exponent calculation based on leakage dynamic tracking data to reveal nonlinear characteristics and generate nonlinear characteristic analysis reports;

[0109] The leakage trend prediction submodule uses a nonlinear dynamic prediction model based on the nonlinear characteristic analysis report to predict the leakage trend and generate a leakage trend prediction report;

[0110] The response mode analysis submodule uses system dynamic response analysis based on the leakage trend prediction report, evaluates the response strategy, and generates a leakage dynamic analysis report.

[0111] The leakage dynamic tracking submodule is started according to the safety threshold adjustment report, and the time series analysis method is used to monitor the leakage dynamics in real time, track the leakage situation, and generate leakage dynamic tracking data to understand the real-time status of the leakage. The nonlinear feature analysis submodule is started based on the leakage dynamic tracking data, and the Lyapunov exponent calculation and other technologies are used to reveal the nonlinear characteristics in the leakage dynamics and generate a nonlinear feature analysis report, which helps to identify potential nonlinear behaviors. The leakage trend prediction submodule is started based on the nonlinear feature analysis report, and a nonlinear dynamic prediction model is used to predict the leakage trend and generate a leakage trend prediction report, providing possible development trends of future leakage so that corresponding measures can be taken. The response mode analysis submodule is started based on the leakage trend prediction report, and the system dynamic response analysis method is used to evaluate the effects of different response strategies, and a leakage dynamic analysis report is generated to help formulate effective response strategies. The integration of these operation steps ensures that the nonlinear dynamic analysis module is fully functional. The system can monitor the leakage situation in real time, identify nonlinear characteristics, predict trends and evaluate the effectiveness of response strategies, which helps to improve the system's response capabilities to potential leakage risks, improve the safety and reliability of industrial production processes, and thus reduce potential safety accident risks.

[0112] See also Figure 8 ,The real-time monitoring module includes an operation status monitoring submodule, an operation anomaly identification submodule, a safety alarm activation submodule, and a monitoring response coordination submodule;

[0113] The operation status monitoring submodule uses real-time trend analysis technology based on the leakage dynamic analysis report to monitor the equipment operation status and generate operation status monitoring data;

[0114] The operation abnormality identification submodule uses an abnormal pattern recognition algorithm based on the operation status monitoring data to identify abnormal operation status and generate an operation abnormality identification report;

[0115] The safety alarm activation submodule uses an automatic alarm triggering mechanism based on the operation abnormality identification report to activate the safety alarm and generate a safety alarm activation record;

[0116] The monitoring response coordination submodule adopts emergency response coordination strategies based on security alarm activation records, optimizes monitoring responses, and generates real-time monitoring data reports.

[0117] The operation status monitoring submodule is started according to the leakage dynamic analysis report, and uses real-time trend analysis technology to monitor the operation status of the equipment, including hydrogen leakage and dissolved oxygen level, etc., to generate operation status monitoring data to timely understand the actual operation status of the equipment. The operation abnormality identification submodule is started based on the operation status monitoring data, and uses the abnormal pattern recognition algorithm to analyze the monitoring data, identify abnormal operation status, and generate an operation abnormality identification report, which helps to timely discover possible problems and faults. The safety alarm activation submodule is started according to the operation abnormality identification report, and activates the safety alarm through the automatic alarm trigger mechanism, and records the safety alarm activation event to notify relevant personnel and system operators of potential safety issues. The monitoring response coordination submodule is started based on the security alarm activation record, adopts the emergency response coordination strategy, coordinates the monitoring response, takes necessary measures to handle the activated security alarm, and generates a real-time monitoring data report, including alarm information, response strategies and processing results. The integration of these operating steps ensures the complete functionality of the real-time monitoring module. The system can play an important role in real-time monitoring of equipment operating status, identifying abnormal situations, triggering security alarms and taking emergency response measures. Through rapid monitoring and response, the system can effectively reduce potential safety risks, improve the safety and reliability of industrial production processes, and ensure timely handling of possible problems and failures, thereby ensuring the stability and safety of the production environment.

[0118] See also Fig. 9 ,The preventive maintenance module includes the maintenance strategy planning submodule, the ,operation risk assessment submodule, the maintenance measure implementation submodule, and the ,maintenance effect verification submodule;

[0119] The maintenance strategy planning submodule uses failure mode and effect analysis and fault tree analysis based on real-time monitoring data reports to conduct risk assessment and priority determination and generate maintenance strategy optimization reports;

[0120] The operation risk assessment submodule uses Monte Carlo simulation and Bayesian network to perform probability analysis and impact assessment based on the maintenance strategy optimization report to generate an operation risk assessment report;

[0121] The maintenance measures implementation submodule uses performance indicator analysis and resource optimization algorithms based on the operation risk assessment report to perform maintenance demand analysis and resource allocation, and generate a maintenance measures implementation plan;

[0122] The maintenance effect verification submodule uses the Six Sigma method and statistical process control based on the maintenance measure implementation plan to conduct improvement effect analysis and generate a maintenance effect verification report.

[0123] The maintenance strategy planning submodule is started based on the real-time monitoring data report, and uses technologies such as failure mode and effect analysis and fault tree analysis to conduct risk assessment and priority determination, generate a maintenance strategy optimization report, and clarify which equipment needs maintenance and its priority. The operation risk assessment submodule is started based on the maintenance strategy optimization report, and uses methods such as Monte Carlo simulation and Bayesian network to conduct probability analysis and impact assessment, and generate an operation risk assessment report to determine the necessity and possible impact of maintenance. The maintenance measure implementation submodule is started based on the operation risk assessment report, and uses performance indicator analysis and resource optimization algorithms to conduct maintenance demand analysis and resource allocation, generate a maintenance measure implementation plan, and clarify specific maintenance The maintenance effect verification submodule is started based on the maintenance measure implementation plan, and uses Six Sigma methods and statistical process control and other technologies to conduct improvement effect analysis and generate a maintenance effect verification report to evaluate the effectiveness and effectiveness of maintenance measures. The integration of these operating steps ensures the comprehensiveness and operability of the preventive maintenance module. The system can identify potential risks and maintenance needs in a timely manner based on real-time monitoring data, formulate priority strategies, resource allocation plans, and monitor maintenance effects. Through this process, the system can minimize the risk of equipment failure, improve the reliability and performance of production equipment, ensure the continuity and stability of the production process, and thus ensure the smooth operation of industrial production.

[0124] See also Fig.10 ,The system optimization module includes the performance improvement scheme submodule, the efficiency optimization scheme submodule, the system feedback analysis submodule, and the strategy improvement submodule;

[0125] The performance improvement plan submodule uses linear programming and sensitivity analysis based on the maintenance effect verification report to evaluate the system performance status, formulate preliminary improvement measures, and generate a draft performance improvement plan;

[0126] The efficiency optimization plan submodule generates a draft efficiency optimization plan based on the draft performance improvement plan, using cost-benefit analysis and operations research techniques to optimize resource allocation and energy utilization;

[0127] The system feedback analysis submodule uses time series analysis and control chart technology based on the efficiency optimization plan draft to monitor system response and analyze feedback data to generate a system feedback analysis report;

[0128] The strategy improvement submodule uses multi-objective genetic algorithm and fault tree analysis based on the system feedback analysis report to integrate the analysis results and optimize the decision-making strategy to generate a system optimization plan.

[0129] The performance improvement program submodule is initiated based on the maintenance effect verification report. It uses linear programming and sensitivity analysis methods to evaluate the system performance status, formulate preliminary improvement measures, and generate a draft performance improvement program to identify system performance bottlenecks and improvement opportunities. The efficiency optimization program submodule is initiated based on the draft performance improvement program. It uses cost-benefit analysis and operation research techniques to optimize resource allocation and energy utilization, and generates a draft efficiency optimization program to improve system operation efficiency and reduce costs. The system feedback analysis submodule is initiated based on the draft efficiency optimization program. It uses time series analysis and control chart technology to monitor system response and analyze feedback data, and generates a system feedback analysis report to ensure the implementation of the improvement plan. In order to meet the expected goals, the strategy improvement submodule is started based on the system feedback analysis report. It adopts methods such as multi-objective genetic algorithm and fault tree analysis to integrate the analysis results and optimize the decision-making strategy to generate a system optimization plan to achieve comprehensive optimization of system performance and efficiency. The integration of these operation steps ensures the comprehensiveness and operability of the system optimization module. The system can identify performance bottlenecks, improvement opportunities and resource allocation optimization plans based on maintenance effect verification reports and feedback data. Through this process, the system can continuously improve its own performance, efficiency and stability to ensure the smooth operation of the generator water dissolved oxygen safety monitoring system, while reducing costs and resource waste, thereby improving the sustainability of industrial production.

[0130] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0132] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0136] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. The generator water dissolved oxygen safety monitoring system is characterized by: The system includes an isotope analysis module, a comprehensive prediction module, a data-driven optimization module, a safety threshold setting module, a nonlinear dynamic analysis module, a real-time monitoring module, a preventive maintenance module and a system optimization module; The isotope analysis module uses mass spectrometry and isotope dilution method to detect and analyze hydrogen leakage based on trace hydrogen leakage, and generates a hydrogen isotope analysis report; the hydrogen isotope analysis report is specifically a hydrogen isotope ratio and leakage estimation report; The comprehensive prediction module predicts the dissolved oxygen level based on the hydrogen isotope analysis report using a fluid dynamics model and a neural network prediction algorithm to generate a dissolved oxygen level prediction report; The data-driven optimization module optimizes the equipment operation strategy based on the dissolved oxygen level prediction report and generates an optimization strategy report using linear regression and K-means clustering algorithms; The security threshold setting module uses decision tree and support vector machine algorithms based on the optimization strategy report to dynamically adjust the security threshold and generate a security threshold adjustment report; The nonlinear dynamic analysis module uses chaos theory and dynamic system analysis methods based on the safety threshold adjustment report to perform leakage dynamic analysis and generate a leakage dynamic analysis report; The real-time monitoring module uses real-time data monitoring and status assessment methods based on the leakage dynamic analysis report to perform real-time monitoring and generate a real-time monitoring data report; The preventive maintenance module uses failure mode and effect analysis based on real-time monitoring data reports to perform preventive maintenance planning and generate maintenance effect verification reports; The system optimization module uses a multi-objective optimization algorithm and fault tree analysis based on the maintenance effect verification report to perform system optimization and generate a system optimization plan.

2. The generator water dissolved oxygen safety monitoring system according to claim 1 is characterized by: The dissolved oxygen level prediction report is specifically a dissolved oxygen level change prediction report based on models and data, the optimization strategy report is specifically an equipment operating parameter adjustment and maintenance recommendation report, the safety threshold adjustment report is specifically a threshold adjustment recommendation report based on data analysis, the leakage dynamic analysis report is specifically a nonlinear characteristic analysis report of the leakage process, the real-time monitoring data report is specifically a real-time monitoring data report of hydrogen leakage and dissolved oxygen level, and the maintenance effect verification report includes a maintenance measure and operation recommendation report.

3. The generator water dissolved oxygen safety monitoring system according to claim 2 is characterized by: The isotope analysis module includes an isotope identification submodule, a leakage estimation submodule, a gas composition comparison submodule and an isotope ratio monitoring submodule; The isotope identification submodule uses mass spectrometry analysis technology to identify hydrogen isotopes based on trace hydrogen leakage, and performs data comparison to generate isotope type data; The leakage estimation submodule calculates the leakage based on the isotope type data and adopts the isotope dilution quantitative method, and performs result analysis to generate leakage estimation data; The gas composition comparison submodule uses gas chromatography analysis to compare gas compositions based on leakage estimation data, and performs ratio analysis to generate a gas composition comparison report; The isotope ratio monitoring submodule uses a time series analysis method based on a gas composition comparison report to monitor hydrogen isotope ratios, track trends, and generate a hydrogen isotope analysis report.

4. The generator water dissolved oxygen safety monitoring system according to claim 3 is characterized by: The comprehensive prediction module includes a fluid dynamic simulation submodule, an artificial intelligence prediction submodule, an environmental adaptability adjustment submodule and a long-term trend analysis submodule; The fluid dynamics simulation submodule performs fluid dynamics simulation based on the hydrogen isotope analysis report and adopts the Reynolds average equation model to generate preliminary dissolved oxygen prediction data; The artificial intelligence prediction submodule uses a convolutional neural network to analyze and optimize data features based on preliminary dissolved oxygen prediction data to generate optimized dissolved oxygen prediction data; The environmental adaptability adjustment submodule uses a genetic algorithm to adjust and optimize model parameters based on the optimized dissolved oxygen prediction data to generate adaptive environmental adjustment data; The long-term trend analysis submodule uses an autoregressive integrated moving average model based on the adaptive environmental adjustment data to analyze and predict trends and generate a dissolved oxygen level prediction report.

5. The generator water dissolved oxygen safety monitoring system according to claim 4 is characterized by: The data-driven optimization module includes a maintenance strategy optimization submodule, an operation mode optimization submodule, an efficiency evaluation submodule and an operation specification submodule; The maintenance strategy optimization submodule uses multiple linear regression analysis based on the dissolved oxygen level prediction report to perform maintenance demand analysis and generate maintenance strategy optimization suggestions; The operation mode optimization submodule uses the K-means clustering algorithm to adjust the operation parameters and generate an operation mode optimization plan based on the maintenance strategy optimization suggestion; The efficiency evaluation submodule uses a performance index evaluation method based on the operation mode optimization scheme to evaluate the improvement effect and generate an efficiency evaluation report; The operation specification submodule formulates operation specifications and generates an optimization strategy report based on the efficiency evaluation report and adopts the standard operation procedure development method.

6. The generator water dissolved oxygen safety monitoring system according to claim 5, characterized in that: The safety threshold setting module includes a dynamic threshold adjustment submodule, a threshold monitoring submodule, a risk warning submodule and a response speed optimization submodule; The dynamic threshold adjustment submodule uses a decision tree analysis method based on the optimization strategy report to evaluate security parameters and generate a preliminary security threshold adjustment plan; The threshold monitoring submodule uses a dynamic threshold monitoring system based on a preliminary safety threshold adjustment scheme to perform real-time safety parameter tracking and generate threshold monitoring data; The risk warning submodule uses a support vector machine model based on threshold monitoring data to identify potential indicators of security risks and generate a risk warning report; The response speed optimization submodule is based on the risk warning report and adopts an optimization method based on a rapid response strategy to improve the system's response speed to risks and generate a safety threshold adjustment report.

7. The generator water dissolved oxygen safety monitoring system according to claim 6, characterized in that: The nonlinear dynamic analysis module includes a leakage dynamic tracking submodule, a nonlinear characteristic analysis submodule, a leakage trend prediction submodule and a response mode analysis submodule; The leakage dynamic tracking submodule uses a time series analysis method to track leakage dynamics and generate leakage dynamic tracking data based on the safety threshold adjustment report; The nonlinear characteristic analysis submodule uses Lyapunov exponent calculation based on leakage dynamic tracking data to reveal nonlinear characteristics and generate a nonlinear characteristic analysis report; The leakage trend prediction submodule predicts the leakage trend based on the nonlinear characteristic analysis report and adopts a nonlinear dynamic prediction model to generate a leakage trend prediction report; The response mode analysis submodule uses system dynamic response analysis based on the leakage trend prediction report to evaluate the response strategy and generate a leakage dynamic analysis report.

8. The generator water dissolved oxygen safety monitoring system according to claim 7, characterized in that: The real-time monitoring module includes an operation status monitoring submodule, an operation abnormality identification submodule, a safety alarm activation submodule and a monitoring response coordination submodule; The operation status monitoring submodule uses real-time trend analysis technology based on the leakage dynamic analysis report to monitor the equipment operation status and generate operation status monitoring data; The operation abnormality identification submodule uses an abnormal pattern recognition algorithm based on the operation status monitoring data to identify abnormal operation status and generate an operation abnormality identification report; The safety alarm activation submodule activates the safety alarm and generates a safety alarm activation record based on the operation abnormality identification report by using an automatic alarm triggering mechanism; The monitoring response coordination submodule adopts emergency response coordination strategies based on security alarm activation records, optimizes monitoring responses, and generates real-time monitoring data reports.

9. The generator water dissolved oxygen safety monitoring system according to claim 8, characterized in that: The preventive maintenance module includes a maintenance strategy planning submodule, an operation risk assessment submodule, a maintenance measure implementation submodule and a maintenance effect verification submodule; The maintenance strategy planning submodule uses failure mode and effect analysis and fault tree analysis based on real-time monitoring data reports to perform risk assessment and priority determination and generate a maintenance strategy optimization report; The operation risk assessment submodule uses Monte Carlo simulation and Bayesian network to perform probability analysis and impact assessment based on the maintenance strategy optimization report to generate an operation risk assessment report; The maintenance measures implementation submodule uses performance indicator analysis and resource optimization algorithm based on the operation risk assessment report to perform maintenance demand analysis and resource allocation, and generate a maintenance measures implementation plan; The maintenance effect verification submodule uses the Six Sigma method and statistical process control based on the maintenance measure implementation plan to perform improvement effect analysis and generate a maintenance effect verification report.

10. The generator water dissolved oxygen safety monitoring system according to claim 9, characterized in that: The system optimization module includes a performance improvement solution submodule, an efficiency optimization solution submodule, a system feedback analysis submodule and a strategy improvement submodule; The performance improvement plan submodule uses linear programming and sensitivity analysis based on the maintenance effect verification report to evaluate the system performance status, formulate preliminary improvement measures, and generate a draft performance improvement plan; The efficiency optimization program submodule generates a draft efficiency optimization program based on the draft performance improvement program by using cost-benefit analysis and operation research techniques to optimize resource allocation and energy utilization; The system feedback analysis submodule uses time series analysis and control chart technology based on the efficiency optimization plan draft to monitor system response and analyze feedback data to generate a system feedback analysis report; The strategy improvement submodule is based on the system feedback analysis report, adopts a multi-objective genetic algorithm and a fault tree analysis, integrates the analysis results and optimizes the decision-making strategy to generate a system optimization solution.

Images