Boiler four-tube service life evaluation system

Abstract

The invention relates to the technical field of control and regulation systems, in particular to a boiler four-tube life evaluation system, which acquires tube wall temperature gradient, water quality parameters and soot blowing operation data through a distributed sensor network, and performs noise reduction and timestamp alignment through a preprocessing unit to generate a multi-dimensional data matrix; the dynamic coupling analysis module integrates a thermal-hydraulic coupling model, an oxidation kinetic equation and an ash deposition model, calculates thermal stress distribution, an oxide skin growth rate and a heat transfer coefficient decrease range, generates a comprehensive damage degree evaluation value according to a preset weight, and pushes the comprehensive damage degree evaluation value to a DCS system for execution through an OPC protocol; and the decision output module generates a customized detection scheme and a causal analysis atlas, and optimizes sensor arrangement in combination with closed-loop verification data. According to the method, multi-dimensional data collaborative analysis, life prediction model dynamic correction and preventive maintenance strategy closed-loop optimization are realized, and the problems of parameter isolation, prediction deviation and high manual dependence in a traditional method are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of control and regulation systems, and particularly to a life assessment system for four tubes of a boiler. Background Art

[0002] As key pressure-bearing components of a thermal power plant boiler, the operating status of the four tubes of the boiler directly affects the safety and economy of the unit. The existing maintenance methods mainly rely on manual visual inspection after regular boiler shutdowns, lacking data-driven targeted inspection means and making it difficult to effectively predict the life cycle of the four tubes. During operation, there is a strong coupling relationship between the dynamic change characteristics of the tube wall temperature and water quality parameters of the four tubes. The conventional monitoring system fails to establish a multi-parameter collaborative analysis mechanism, resulting in a lag in abnormal state identification. When a four-tube leakage accident occurs, the existing technical system cannot provide preventive maintenance guidance, leading to an increase in the number of unplanned boiler shutdowns and affecting the unit availability. In addition, the method of relying on experience to adjust operation parameters by operators has subjective differences, making it difficult to accurately control the thermal stress distribution of the four tubes and accelerating the life loss of local pipe sections. The above technical defects indicate that it is urgent to construct a life assessment system integrating multi-dimensional operation data to achieve quantitative evaluation of the health status of the four tubes of the boiler and optimization of maintenance strategies. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a life assessment system for four tubes of a boiler, which is used to solve the problems that the existing monitoring methods for four tubes of a boiler lack a dynamic collaborative analysis mechanism of multi-dimensional operation parameters, resulting in insufficient life prediction accuracy, lag in abnormal state identification, and the operation mode relying on manual experience is difficult to achieve accurate regulation of thermal stress and preventive maintenance guidance.

[0004] To solve the above technical problems, the specific technical solutions of the present invention are as follows: A life assessment system for four tubes of a boiler provided by the present invention includes: A data acquisition module, which is used to collect the tube wall temperature gradient distribution data of key areas of the four tubes of the boiler through a distributed sensor network, and integrate water quality parameters, soot blower action frequency, and over-temperature event logs to generate a multi-dimensional operation parameter set; A dynamic coupling analysis module, which is used to input the temperature gradient distribution data in the multi-dimensional operation parameter set into a thermal-hydraulic coupling model to calculate the characteristics of the tube wall thermal stress distribution, and at the same time input the water quality parameters into an oxidation kinetics equation to calculate the oxidation scale growth rate, and construct a slag deposition model in combination with the soot blower action frequency, and output creep damage assessment parameters including thermal stress distribution, oxidation scale thickness, and slag deposition coefficient; A life assessment module is used to compare the creep damage assessment parameters with the high-temperature endurance strength curve in the material performance database, generate the remaining life value of each pipe segment through a life prediction model trained with an LSTM neural network structure, and map the prediction results to the boiler three-dimensional model to generate a visual map; An operation optimization module is used to generate combustion air damper opening adjustment parameters, soot blower priority action sequence, and dosing pump adjustment instructions for the corresponding area when the water quality deviates from the threshold value based on the remaining life value output by the life prediction model of the pipe section below the set threshold, and push the operation guidance strategy to the DCS operation station through the OPC protocol; A decision output module is used to integrate the remaining life value of the life prediction model, historical maintenance records, and the NDT inspection procedure knowledge base to generate an inspection report with the coordinates of high-risk pipe sections and a causal analysis map that associates operating parameter deviations with pipe degradation; Among them, the dynamic coupling analysis module receives the preprocessed data output by the data acquisition module, the life assessment module receives the creep damage assessment parameters of the dynamic coupling analysis module to perform life prediction, the operation optimization module dynamically adjusts the control instructions according to the prediction results of the life assessment module, and the decision output module transmits the maintenance measured data back to the life assessment model for parameter calibration, forming a closed-loop control link including data acquisition, damage assessment, life prediction, operation optimization and decision feedback.

[0005] Furthermore, in the boiler four-tube life assessment system provided by the present invention, the data acquisition module includes: a preprocessing unit for performing noise reduction processing and time stamp alignment on the raw sensor data, specifically including: Eliminate electromagnetic interference noise through wavelet transform and compensate for sensor drift error using Kalman filter algorithm; Perform interpolation processing on data streams with different sampling frequencies to generate a multi-dimensional data matrix with time stamps and store it in a real-time database; The multidimensional data matrix output by the preprocessing unit is transmitted to the dynamic coupling analysis module via industrial real-time Ethernet.

[0006] Furthermore, in the boiler four-tube life assessment system provided by the present invention, the dynamic coupling analysis module includes: a thermal stress calculation unit, configured to input the temperature field data output by the preprocessing unit into a finite element model to calculate the axial and circumferential thermal stress distribution of the pipe section; an oxidation damage assessment unit, configured to substitute the water quality parameters output by the pretreatment unit into an oxidation kinetics equation to solve for a scale thickness growth rate; an ash deposition correlation unit, configured to construct a nonlinear regression model based on the sootblowing frequency data output by the pre-processing unit, and calculate a decrease in the heat transfer coefficient; Among them, the output of the thermal stress calculation unit, the result of the oxidation damage assessment unit, and the reduction amplitude of the heat transfer coefficient of the slag deposition correlation unit are linearly superimposed according to preset weight coefficients to generate a comprehensive damage degree evaluation value, which is used as the input parameter of the life evaluation module.

[0007] Furthermore, for the boiler four-tube life evaluation system provided by the present invention, the life evaluation module includes: A material database unit that stores the high-temperature creep rupture strength curve and creep fracture toughness parameters of the pipe material; A neural network prediction unit that uses an LSTM model to compare the comprehensive damage degree evaluation value with the high-temperature creep rupture strength curve of the material database unit to generate a remaining life value; A dynamic correction unit that is used to correct the activation energy parameter of the oxidation kinetics equation in the oxidation damage assessment unit according to the measured oxidation layer thickness data from previous inspections, and update the weight coefficient of the LSTM model based on the corrected activation energy parameter.

[0008] Furthermore, for the boiler four-tube life evaluation system provided by the present invention, the operation optimization module includes: A combustion control unit that is used to simulate and generate a damper opening adjustment plan for the forced draft fan based on the current thermal stress distribution output by the thermal stress calculation unit; A soot blowing priority unit that is used to generate an action sequence of the soot blower corresponding to the high-risk pipe section according to the reduction amplitude of the heat transfer coefficient output by the slag deposition correlation unit; A water quality linkage unit that is used to trigger the closed-loop regulation of the dosing pump frequency when the oxidation damage assessment unit detects that the water quality parameter deviates from the threshold; Among them, the output instructions of the combustion control unit and the soot blowing priority unit are pushed to the DCS operation station through the OPC protocol and are linked and displayed with the abnormal areas marked in the boiler three-dimensional model.

[0009] Furthermore, for the boiler four-tube life evaluation system provided by the present invention, the decision-making output module includes: A detection plan generation unit that is used to call the NDT detection procedure knowledge base to match a combination plan of ultrasonic thickness measurement, eddy current detection, or magnetic memory detection according to the remaining life value output by the life evaluation module; A causal analysis unit that is used to perform correlation analysis on the multi-dimensional operation parameter set of the data acquisition module and the comprehensive damage degree evaluation value of the dynamic coupling analysis module to generate a tree-shaped correlation map of the operation parameter deviation and the pipe material deterioration; A closed-loop verification unit that is used to perform deviation analysis on the inspection measured data and the remaining life prediction value of the life evaluation module, generate a sensor layout adjustment instruction, and send it to the data acquisition module.

[0010] Furthermore, the boiler four-tube life assessment system provided by the present invention further includes: A self-feedback optimization module, configured to feed the execution effect of the control instructions output by the operation optimization module back to the data acquisition module via sensor data, and perform a model accuracy self-check every 24 hours based on the prediction results of the life assessment module; When the prediction errors of the life assessment module exceed the tolerance value for three consecutive times, the expert diagnosis mode is triggered and the automatic control instructions of the operation optimization module are frozen, switching to the manual confirmation operation mode.

[0011] Beneficial effects of the present invention: The boiler four-tube life assessment system provided by the present invention significantly improves the accuracy of life prediction and the effectiveness of maintenance strategies for boiler four-tubes through multi-dimensional data collaborative analysis and closed-loop control mechanism. The distributed sensor network and pre-processing unit eliminate the noise interference and time asynchrony problems of multi-source heterogeneous data, generate high-quality input parameters with unified time series, and provide a reliable data basis for dynamic coupling analysis; the collaborative calculation of the thermal-hydraulic coupling model, the oxidation kinetic equation and the ash deposition model breaks through the limitations of single parameter evaluation, quantifies the composite damage effects of thermal stress, oxidation damage and ash deposition on pipes, and generates a comprehensive damage assessment value, so that the damage assessment is more in line with the actual working condition evolution law. The life prediction model constructed based on the LSTM neural network establishes a nonlinear mapping relationship between damage accumulation and remaining life by integrating the material performance database and historical maintenance data, and combines the activation energy parameter calibration and model weight iteration of the dynamic correction unit to achieve adaptive optimization of the prediction model, overcoming the defect of insufficient adaptability of existing empirical formulas to the evolution of material properties. The operation optimization module generates combustion air distribution, soot blowing priority and water quality adjustment instructions based on the prediction results, and executes precise control in conjunction with the DCS system through the OPC protocol to reduce the subjective deviation of human operation; the decision output module associates the detection scheme, causal analysis map and closed-loop verification data to form a traceable link from abnormality identification to maintenance decision. The self-feedback optimization module freezes automatic control and triggers manual review when the prediction error exceeds the limit through the periodic model verification and expert diagnosis mode switching mechanism, thereby ensuring the reliability of the system under complex working conditions. The synergistic effect of the above technical features effectively solves the problems of life prediction deviation, abnormality identification lag and strong manual dependence caused by isolated parameter analysis in existing methods, and realizes dynamic monitoring of the health status of the four boiler tubes and closed-loop optimization of preventive maintenance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0013] Figure 1 This is a system architecture diagram of a boiler four-tube life assessment system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0015] See also Figure 1 The present invention provides a boiler four-tube life assessment system, comprising: The data acquisition module is used to collect the wall temperature gradient distribution data of the key areas of the four boiler tubes through a distributed sensor network, and integrate water quality parameters, sootblower operation frequency and overtemperature event logs to generate a multi-dimensional operating parameter set; A dynamic coupling analysis module is used to input the temperature gradient distribution data of the multi-dimensional operating parameter set into a thermal and hydraulic coupling model to calculate the thermal stress distribution characteristics of the pipe wall, input the water quality parameters into the oxidation kinetic equation to calculate the oxide scale growth rate, and construct an ash deposition model based on the sootblower operation frequency to output creep damage assessment parameters including thermal stress distribution, oxide scale thickness, and ash deposition coefficient; A life assessment module is used to compare the creep damage assessment parameters with the high-temperature endurance strength curve in the material performance database, generate the remaining life value of each pipe segment through a life prediction model trained with an LSTM neural network structure, and map the prediction results to the boiler three-dimensional model to generate a visual map; An operation optimization module is used to generate combustion air damper opening adjustment parameters, soot blower priority action sequence, and dosing pump adjustment instructions for the corresponding area when the water quality deviates from the threshold value based on the remaining life value output by the life prediction model of the pipe section below the set threshold, and push the operation guidance strategy to the DCS operation station through the OPC protocol; A decision output module is used to integrate the remaining life value of the life prediction model, historical maintenance records, and the NDT inspection procedure knowledge base to generate an inspection report with the coordinates of high-risk pipe sections and a causal analysis map that associates operating parameter deviations with pipe degradation; Among them, the dynamic coupling analysis module receives the preprocessed data output by the data acquisition module, the life assessment module receives the creep damage assessment parameters of the dynamic coupling analysis module to perform life prediction, the operation optimization module dynamically adjusts the control instructions according to the prediction results of the life assessment module, and the decision output module transmits the maintenance measured data back to the life assessment model for parameter calibration, forming a closed-loop control link including data acquisition, damage assessment, life prediction, operation optimization and decision feedback.

[0016] The four-tube boiler life assessment system provided by the present invention achieves closed-loop control through the collaboration of multiple modules. The data acquisition module arranges thermocouple arrays in key areas of the four boiler tubes to collect real-time tube wall temperature gradient distribution data. It also communicates with the power plant's distributed control system (DCS) via the Modbus protocol, integrates pH, dissolved oxygen content, and conductivity parameters collected by the online water quality monitor, records the sootblowing timing and pressure parameters in the sootblower PLC control signal, and synchronizes the over-temperature alarm events of the combustion control system. The collected raw data undergoes wavelet transformation for noise reduction to eliminate electromagnetic interference noise. A Kalman filter algorithm is used to compensate for sensor drift errors. Data streams with different sampling frequencies are interpolated to generate a timestamped multidimensional data matrix that is stored in a real-time database, providing standardized data input for subsequent analysis.

[0017] The dynamic coupling analysis module receives preprocessed multidimensional data and inputs the temperature gradient distribution data into the finite element thermal model to calculate the axial and circumferential thermal stress distribution characteristics of the pipe section. Simultaneously, water quality parameters are substituted into the oxidation kinetics equation to solve for the oxide scale thickness growth rate based on the activation energy parameter and the temperature change rate. Sootblower operation frequency data is used to construct a nonlinear regression model for ash deposition, inferring the decrease in heat transfer coefficient caused by ash deposition. The thermal stress distribution, oxide scale growth rate, and decrease in heat transfer coefficient are linearly superimposed according to preset weighting coefficients to generate a comprehensive damage assessment value, quantifying the extent of creep damage in the pipe section.

[0018] The life assessment module calls the high-temperature endurance strength curve and creep fracture toughness parameters of the pipe in the material performance database, and inputs the comprehensive damage assessment value into the life prediction model built based on the LSTM neural network. During the training phase, the model introduces the metallographic inspection data of previous inspections to establish a mapping relationship between operating parameters and oxide layer thickness and micro-crack propagation trends. The prediction results are mapped to the boiler model through a three-dimensional coordinate system to generate a visual map, marking the remaining life value of each pipe section and high-risk areas. After each inspection, the system dynamically corrects the activation energy parameter in the oxidation kinetics equation based on the deviation between the measured oxide layer thickness and the predicted value, and updates the weight coefficient of the LSTM model to improve the subsequent prediction accuracy.

[0019] The operation optimization module generates control instructions based on the life prediction results. When it is detected that the remaining life of a specific pipe section is lower than the safety threshold, the combustion control unit calculates the air blower damper opening adjustment plan based on the current thermal stress distribution simulation to balance the flue gas temperature distribution; the sootblowing priority unit generates the sootblower action sequence corresponding to the high-risk pipe section according to the decrease in the heat transfer coefficient output by the ash deposition model; the water quality linkage unit triggers the dosing pump frequency adjustment instruction when the water quality parameters deviate from the set threshold. The above control instructions are pushed to the DCS operation station through the OPC protocol, highlighting the abnormal area in the three-dimensional model of the boiler, and linking historical processing cases with the same working conditions for reference by the operating personnel.

[0020] The decision-making output module integrates the remaining life prediction value, historical maintenance records, and a knowledge base of nondestructive testing (NDT) procedures to generate customized inspection reports. The inspection plan generation unit matches the remaining life value with a combination of ultrasonic thickness measurement, eddy current testing, or magnetic memory testing. The causal analysis unit establishes a tree-like correlation map between operating parameter deviations and pipe degradation, tracing the causal relationship between overtemperature events, abnormal water quality, and oxidative damage. The closed-loop verification unit analyzes the deviation between the actual maintenance data and the predicted value. If the error exceeds a threshold, it generates sensor layout adjustment instructions and feeds them back to the data acquisition module to optimize the sensor network layout.

[0021] The system implements data exchange via industrial real-time Ethernet. The preprocessing module outputs standardized data packets and writes them to an in-memory database. The dynamic coupling model obtains real-time data via subscription. The results of control command execution are fed back to the data acquisition module via sensor data, and the model automatically performs self-checks of accuracy every 24 hours. If the prediction error exceeds the tolerance value three times in a row, the system triggers expert diagnosis mode, freezes the automatic control instructions, and switches to manual confirmation mode to ensure the reliability of the control strategy. The data flow between modules forms a closed-loop chain from data acquisition, damage assessment, life prediction, operation optimization, to decision feedback, enabling dynamic monitoring of the health status of the four boiler tubes and optimization of maintenance strategies.

[0022] Specifically, for the boiler four-tube life assessment system provided by the present invention, the data acquisition module includes: a preprocessing unit for performing noise reduction processing and timestamp alignment on the original sensor data, specifically including: Eliminating electromagnetic interference noise through wavelet transform and compensating for sensor drift error using the Kalman filter algorithm; Interpolating data streams with different sampling frequencies to generate a multi-dimensional data matrix with timestamps and storing it in the real-time database; The multi-dimensional data matrix output by the preprocessing unit is transmitted to the dynamic coupling analysis module through the industrial real-time Ethernet.

[0023] The preprocessing unit in the data acquisition module performs multi-level processing on the original sensor data. The data of the temperature gradient distribution on the tube wall collected by the thermocouple array is denoised through wavelet transform, filtering out the high-frequency electromagnetic interference components and retaining the effective signal characteristics of the temperature change. The denoised data stream is input into the Kalman filter algorithm, and a dynamic error model is established based on the historical state data of the sensor to compensate in real time for the zero drift and sensitivity attenuation of the temperature sensor caused by the long-term high-temperature environment. The temperature data after error compensation is time-aligned with the pH value and dissolved oxygen parameters collected by the on-line water quality monitor. For the temperature data stream with a sampling frequency of 1 Hz and the water quality parameter with a 5-minute average sampling period, the cubic spline interpolation algorithm is used to generate a synchronous time series, forming a multi-dimensional data matrix with a unified timestamp. The preprocessed data matrix is stored in the real-time database and transmitted to the dynamic coupling analysis module through the publish-subscribe mechanism of the industrial real-time Ethernet to ensure that the subsequent modules obtain standardized data input with time synchronization and noise suppression.

[0024] Specifically, for the boiler four-tube life assessment system provided by the present invention, the dynamic coupling analysis module includes: A thermal stress calculation unit for inputting the temperature field data output by the preprocessing unit into a finite element model to calculate the axial and circumferential thermal stress distributions of the pipe section; An oxidation damage assessment unit for substituting the water quality parameters output by the preprocessing unit into the oxidation kinetics equation to solve the growth rate of the oxide scale thickness; An ash deposit correlation unit for constructing a non-linear regression model based on the sootblowing frequency data output by the preprocessing unit to calculate the decrease in the heat transfer coefficient; Among them, the output of the thermal stress calculation unit, the result of the oxidation damage assessment unit, and the decrease in the heat transfer coefficient of the ash deposit correlation unit are linearly superimposed according to the preset weight coefficients to generate a comprehensive damage degree assessment value, which is used as the input parameter of the life assessment module.

[0025] The dynamic coupling analysis module realizes damage assessment through multi-unit collaborative processing. The thermal stress calculation unit receives the preprocessed temperature field data, divides the grids of the four tubes of the boiler based on the finite element model, combines the thermal expansion coefficient of the pipe material and the characteristics of the temperature gradient distribution, calculates the axial and circumferential thermal stress distributions of the pipe section, and identifies the stress concentration phenomenon in the heat load concentration area. The oxidation damage assessment unit substitutes the preprocessed water quality parameters into the oxidation kinetics equation, and dynamically calculates the growth rate of the oxide scale thickness according to the dissolved oxygen content, pH value, and the change rate of the pipe wall temperature, and quantifies the degree of wall thickness reduction caused by high-temperature oxidation.

[0026] The ash deposit correlation unit constructs a non-linear regression model of the ash deposit amount and the sootblowing operation based on the sootblowing operation frequency data, and combines the heat transfer formula to inversely deduce the correlation between the ash deposit layer thickness and the reduction amplitude of the heat transfer coefficient, and evaluates the influence of ash deposit on the heat transfer efficiency of the pipe wall. The thermal stress distribution data, the oxide scale growth rate, and the reduction amplitude of the heat transfer coefficient are respectively mapped to a unified coordinate system and linearly superimposed according to the preset weight coefficient. The weight coefficient is determined according to the contribution degree of different damage factors in historical leakage accidents. The superimposed comprehensive damage degree evaluation value characterizes the cumulative effect of the creep damage of the pipe section and is used as the input parameter of the life assessment module for subsequent remaining life prediction. The calculation results of each unit are transmitted to the memory database through the industrial real-time Ethernet to ensure the timeliness and consistency of data calling.

[0027] The multi-dimensional data matrix output by the preprocessing unit is synchronously pushed to the thermal stress calculation unit, the oxidation damage assessment unit, and the ash deposit correlation unit through the data distribution mechanism to ensure that each unit performs calculations based on the input parameters with the same time reference. The output data packet of the dynamic coupling analysis module contains a timestamp, a pipe section position code, and damage assessment parameters, which match the data interface protocol of the life assessment module to form a standardized data transmission link.

[0028] Specifically, for the boiler four-tube life assessment system provided by the present invention, the life assessment module includes: The material database unit stores the high-temperature creep rupture strength curve and creep fracture toughness parameters of the pipe material; The neural network prediction unit uses the LSTM model to compare the comprehensive damage degree evaluation value with the high-temperature creep rupture strength curve of the material database unit to generate the remaining life value; The dynamic correction unit is used to correct the activation energy parameter of the oxidation kinetics equation in the oxidation damage assessment unit according to the measured oxide layer thickness data of previous inspections, and update the weight coefficient of the LSTM model based on the corrected activation energy parameter.

[0029] The Life Assessment Module achieves life prediction and model optimization through the collaboration of multiple units. The material database unit stores high-temperature endurance strength curves for common materials used in boiler four-tube systems. These curves are generated based on creep test data according to the ASTM E139 standard and include creep fracture toughness parameters for different temperature ranges. Database data is categorized by pipe grade, service life, and heat treatment process, supporting rapid retrieval based on pipe segment location codes, providing a material performance benchmark for life prediction.

[0030] The neural network prediction unit receives the comprehensive damage assessment value output by the dynamic coupling analysis module and performs feature matching on this value against the high-temperature endurance strength curve in the material database. A trained LSTM neural network model is then used to establish a mapping between damage accumulation and remaining life. The input layer of the LSTM model integrates the time series characteristics of the damage assessment value and the gradient parameters of the material strength curve. The hidden layer captures the nonlinear evolution of creep damage in high-temperature environments. The output layer generates a predicted remaining life value for each pipe segment. The predicted results are correlated with the spatial coordinates of the three-dimensional boiler model to generate a visual map and annotate the coordinates of high-risk areas.

[0031] The dynamic correction unit calibrates the oxidation kinetics equations used in the oxidation damage assessment unit based on measured oxide layer thickness data from previous inspections. By comparing the deviation between the predicted oxide layer thickness and the actual metallographic measurements, the least squares method is used to correct the activation energy parameter in the oxidation kinetics equations, eliminating model calculation errors. This corrected activation energy parameter is then fed into the LSTM model, and the hidden layer weights of the neural network are adjusted using a backpropagation algorithm to ensure convergence between the life prediction results and the measured data. The updated model parameters after each inspection are stored in the material database unit, forming a self-optimizing mechanism for prediction accuracy.

[0032] The data update trigger mechanism of the material database unit is linked to the dynamic correction unit. When a new pipe grade or process change is detected, the high-temperature endurance strength curve dataset is automatically expanded. After the LSTM model's weight coefficients are updated, the neural network prediction unit reloads the model parameters, ensuring that the life assessment module adapts to changes in pipe performance and fluctuations in operating conditions. Data exchange between units is accomplished through an in-memory database, using timestamps and pipe segment codes as data association identifiers to ensure consistency in both temporal and spatial dimensions of multi-source data.

[0033] Specifically, the boiler four-tube life assessment system provided by the present invention, the operation optimization module includes: A combustion control unit, configured to simulate and generate a blower damper opening adjustment plan based on the current thermal stress distribution output by the thermal stress calculation unit; a sootblowing priority unit, configured to generate a sootblower action sequence corresponding to a high-risk pipe section according to the heat transfer coefficient decrease amplitude output by the ash deposition correlation unit; A water quality linkage unit, configured to trigger closed-loop frequency regulation of a dosing pump when the oxidative damage assessment unit detects that a water quality parameter deviates from a threshold value; The output instructions of the combustion control unit and the sootblowing priority unit are pushed to the DCS operation station through the OPC protocol and displayed in conjunction with the abnormal areas marked in the three-dimensional model of the boiler.

[0034] The Operation Optimization Module generates control commands and performs closed-loop regulation through the collaborative efforts of multiple units. The Combustion Control Unit receives the current thermal stress distribution data from the Dynamic Coupling Analysis Module. Based on the thermal stress simulation results of the finite element model and the real-time operating parameters of the boiler combustion control system, it calculates a draft fan damper opening adjustment plan to balance the heat load distribution in high-temperature areas. The adjustment plan includes the damper opening increment and adjustment sequence. Its effectiveness is verified using the combustion dynamics model, and then executable instructions are generated.

[0035] The sootblowing priority unit receives the heat transfer coefficient drop data output by the ash deposition correlation unit and identifies the spatial locations of high-risk pipe sections based on the deviation between the drop and a preset threshold. Based on historical data training results from the ash deposition model, a sootblower action sequence is generated for these high-risk pipe sections, including the sootblowing medium pressure, duration, and purge path. These action sequences are prioritized based on a comprehensive assessment of the heat transfer coefficient drop rate and the predicted remaining life, optimizing sootblowing resource allocation.

[0036] The water quality linkage unit monitors the water quality parameters output by the oxidation damage assessment unit in real time. If the pH, dissolved oxygen content, or conductivity deviates from the set threshold, it triggers a frequency adjustment command for the dosing pump. This adjustment command is generated based on a PID control algorithm and dynamically adjusts the phosphate or ammonia dosage through a closed-loop feedback mechanism to maintain water chemistry parameters within the permitted range. The water quality adjustment process is time-coordinated with the operating instructions of the combustion control unit and the sootblowing priority unit to avoid system disturbances caused by multiple adjustments.

[0037] Instructions generated by the combustion control unit and sootblowing priority unit are pushed to the power plant's distributed control system (DCS) operator station via the OPC protocol. These instructions contain the execution time, target device code, and operating parameters. Upon receiving the instructions, the DCS operator station maps the operating parameters to the corresponding areas in the boiler's 3D model, highlights the locations of abnormal pipe sections, and displays historical treatment records and operational performance analysis data under the same operating conditions. Dynamic updates to the 3D model are synchronized with real-time sensor data, providing operators with visual operational guidance.

[0038] After the control command is executed, the pipe wall temperature, water quality parameters, and sootblowing pressure data collected by the sensor network are fed back to the dynamic coupling analysis module via the data bus to verify the control effect. If the thermal stress distribution does not reach the expected equilibrium state or the heat transfer coefficient does not significantly recover, the system automatically triggers the command parameter review process and regenerates the optimization plan based on the latest prediction results from the life assessment module. Data exchange between units is associated with timestamps and pipe segment codes to ensure data consistency throughout the entire chain of command generation, execution, and feedback.

[0039] Specifically, the boiler four-tube life assessment system provided by the present invention, the decision output module includes: a detection scheme generating unit, configured to call an NDT detection procedure knowledge base to match a combination scheme of ultrasonic thickness measurement, eddy current testing, or magnetic memory testing according to the remaining life value output by the life assessment module; a causal analysis unit, configured to perform correlation analysis on the multi-dimensional operating parameter set of the data acquisition module and the comprehensive damage assessment value of the dynamic coupling analysis module, and generate a tree-like correlation map of operating parameter deviation and pipe degradation; The closed-loop verification unit is used to perform deviation analysis between the maintenance measured data and the remaining life prediction value of the life assessment module, generate a sensor layout adjustment instruction and send it to the data acquisition module.

[0040] The decision output module realizes the generation of detection plans and data closed-loop verification through the collaboration of multiple units. The detection plan generation unit receives the remaining life value output by the life assessment module, divides the pipe sections into different risk levels according to the preset threshold range, and calls the detection strategy in the NDT detection procedure knowledge base. The knowledge base stores the detection method combination rules corresponding to different risk levels. For example, the pipe sections with a remaining life value of less than 30% are matched with a combination of ultrasonic thickness measurement and magnetic memory detection, and the pipe sections with a remaining life value between 30% and 60% are matched with a combination of eddy current detection and macroscopic inspection. The detection plan includes the detection location coordinates, detection equipment type and execution priority, and is automatically associated with the coordinates of high-risk areas in the three-dimensional model of the boiler to generate a customized detection work order.

[0041] The causal analysis unit integrates the multi-dimensional operating parameter set of the data acquisition module with the comprehensive damage assessment value of the dynamic coupling analysis module to establish a correlation model between parameter deviation and damage evolution. Time series analysis extracts the temporal correlation between overtemperature events, water quality parameter fluctuations, and oxidation damage rates. Combined with the heat transfer coefficient drop data output by the ash deposition model, a tree-like correlation map is constructed. The root node of the map is the pipe degradation type, the branch nodes associate operating parameter deviation values, damage assessment values, and historical operation records, and the leaf nodes annotate specific maintenance measures, forming a traceable damage cause analysis path.

[0042] The closed-loop verification unit receives the actual values of oxide layer thickness and crack length data obtained during the maintenance process and calculates the deviation with the predicted values of the life assessment module. When the relative error between the predicted oxide layer thickness and the measured value exceeds 15%, the sensor layout optimization process is triggered, and an adjustment instruction is generated and sent to the data acquisition module. The adjustment instruction includes the coordinates of the new sensor location, sampling frequency optimization suggestions, and sensor type replacement plans, such as adding a high-frequency thermocouple array in the area of thermal stress concentration. The optimized sensor network data is re-input into the dynamic coupling analysis module through the preprocessing unit to update the damage assessment parameters, forming a closed-loop feedback link from data acquisition, damage assessment to detection and verification.

[0043] After executing the inspection work order, the measured data is transmitted back to the life assessment module via the data bus, driving the dynamic correction unit to update the oxidation kinetics equations and LSTM model parameters. The tree diagram generated by the causal analysis unit is associated with the deviation analysis results of the closed-loop verification unit and stored in the NDT inspection procedure knowledge base, optimizing the logic for subsequent inspection plan generation. Data exchange between these units is indexed by pipe segment code and time-synchronized via industrial real-time Ethernet, ensuring data consistency across the inspection plan, causal analysis, and closed-loop verification.

[0044] Specifically, the boiler four-tube life assessment system provided by the present invention also includes: A self-feedback optimization module, configured to feed the execution effect of the control instructions output by the operation optimization module back to the data acquisition module via sensor data, and perform a model accuracy self-check every 24 hours based on the prediction results of the life assessment module; When the prediction errors of the life assessment module exceed the tolerance value for three consecutive times, the expert diagnosis mode is triggered and the automatic control instructions of the operation optimization module are frozen, switching to the manual confirmation operation mode.

[0045] The self-feedback optimization module achieves system reliability control through periodic verification and mode switching mechanisms. After the control instructions output by the operation optimization module are executed, the associated sensor network collects real-time data on pipe wall temperature gradient distribution, water quality parameters, and sootblowing pressure. This data is then fed back to the data acquisition module's real-time database via the data bus. This data packet contains the instruction execution timestamp, the target pipe segment location code, and the actual adjustment effect parameters. These data are then compared with the target parameters of the original control instruction to generate a record of instruction execution deviations.

[0046] Automatically trigger the model accuracy self-check process every 24 hours. Based on the remaining life prediction value of the life assessment module on the current day, conduct a trend comparison with the historical maintenance measured data in the same period (including the oxide layer thickness and crack propagation length). When the relative error between the predicted value and the measured value exceeds the tolerance limit of 15% in three consecutive self-checks, the system determines that there is a risk of model inaccuracy and triggers the expert diagnosis mode. After triggering, the self-feedback optimization module sends an instruction freeze signal to the operation optimization module, interrupts the automatic control instruction generation process, and packages and pushes the predicted data, operating parameters, and damage assessment values of the abnormal pipe section to the expert diagnosis interface.

[0047] In the expert diagnosis mode, the operator reviews the abnormal data packet through the human-machine interface, and manually retrieves the historical processing records and damage correlation maps marked with the coordinates of high-risk areas in the boiler three-dimensional model. After review and confirmation, manually input the corrected activation energy parameter of the oxidation kinetics equation or adjust the weight coefficient of the LSTM model to update the creep rupture strength curve data in the material database. After the parameter correction result is verified twice, the instruction freeze state is lifted, and the automatic control mode is restored. The data change records generated during the correction process are synchronously stored in the NDT inspection procedure knowledge base for optimizing the logic of subsequent inspection plan generation.

[0048] The instruction freeze and restoration signals of the self-feedback optimization module are linked with the DCS operation station through the OPC protocol. During the freeze period, the automatic control instruction queue pauses execution, but the sensor data acquisition and preprocessing process continues to run. The abnormal data packet displayed on the expert diagnosis interface contains a time series analysis chart, marking the corresponding relationship between the predicted error peak point and the associated operating parameter fluctuation range to assist manual judgment of the root cause of model inaccuracy. The closed-loop feedback mechanism drives the continuous improvement of the system prediction accuracy and control effectiveness through the iteration of data return, periodic verification, and parameter correction.

[0049] Explanation of the technical feature terms of the present invention: Distributed sensor network: Refers to multiple sensor arrays arranged in key areas of the four pipes of the boiler (such as elbows, welds, and heat load concentration areas), including thermocouples, pressure sensors, and water quality monitoring probes. The sensor network communicates with the power plant DCS system through the Modbus protocol to collect data such as temperature gradients, water quality parameters (pH value, dissolved oxygen, conductivity), and soot blower operation frequency in real time, forming the ability to collect multi-source heterogeneous data.

[0050] Preprocessing unit: A module used to perform standardization processing on the original sensor data, including: Wavelet transform: Filter out high-frequency electromagnetic interference noise through frequency domain analysis and retain the effective components of the temperature signal; Kalman filter: Dynamically compensate for the zero drift error based on the historical state data of the sensor; Timestamp Alignment: For data with different sampling frequencies (such as temperature at 1 Hz and 5-minute average water quality), cubic spline interpolation is used to generate a synchronous time series data matrix.

[0051] Thermal-Hydraulic Coupling Model: A physical model based on the finite element method. After inputting the preprocessed temperature field data, combined with the thermal expansion coefficient of the pipe material and hydrodynamic parameters, it calculates the axial and circumferential thermal stress distributions of the four pipes of the boiler, and identifies the potential creep damage risks in the stress concentration areas.

[0052] Oxidation Kinetics Equation: Used to quantify the loss rate of the pipe wall thickness due to high-temperature oxidation. The input parameters include water quality parameters (dissolved oxygen, pH value) and the rate of change of the pipe wall temperature, and the output is the growth rate of the oxide scale thickness, reflecting the impact of oxidation damage on the service life of the pipe material.

[0053] Ash Deposition Model: Based on the correlation between the operation frequency of soot blowers and the heat transfer efficiency, a non-linear regression model is constructed. By inputting the soot blowing pressure, duration, and path data, it inversely deduces the corresponding relationship between the ash deposition thickness and the decrease in the heat transfer coefficient, and evaluates the impact of ash on the heat load of the pipe material.

[0054] Comprehensive Damage Degree Evaluation Value: A quantitative index generated by linearly superimposing the thermal stress distribution, oxidation damage rate, and the decrease in the heat transfer coefficient through preset weight coefficients, used to characterize the cumulative effect of creep damage of the pipe section and serve as an input parameter for the life prediction model.

[0055] LSTM Neural Network Model: A machine learning model based on long short-term memory networks. By inputting the comprehensive damage degree evaluation value and the high-temperature creep strength curve of the material, a non-linear mapping relationship between damage accumulation and remaining life is established through training historical data, and the predicted remaining life values of each pipe section are output.

[0056] Dynamic Correction Unit: Based on the measured data of the oxide layer thickness from previous inspections, it reversely calibrates the activation energy parameter in the oxidation kinetics equation and updates the weight coefficients of the LSTM model to eliminate the prediction deviation caused by material property evolution or operating condition fluctuations, realizing self-optimization of the model.

[0057] OPC Protocol and DCS Operator Station: A standard communication protocol for instruction transmission in industrial control systems. It pushes control instructions such as combustion air distribution adjustment and soot blower action sequences to the distributed control system (DCS), highlights the abnormal areas in the 3D model of the boiler, and links with the historical operation case library to guide the operators to execute operations.

[0058] Closed-loop Verification Unit: By comparing the deviation between the measured data from inspections (such as oxide layer thickness, crack length) and the predicted life values, it generates optimization instructions for sensor layout (such as the position of newly added high-frequency thermocouples), and feeds them back to the data acquisition module to improve the subsequent data quality and model accuracy.

[0059] Self-feedback optimization module: Periodically (every 24 hours) verify the prediction accuracy of the model. When the prediction error exceeds 15% for three consecutive times, freeze the automatic control instruction and switch to the expert diagnosis mode. After manual review, correct the model parameters to restore the system reliability.

[0060] Description of the synergistic effect of technical features: The above features synergistically solve technical problems through the following logic: Multi-dimensional data collaboration: The distributed sensor network and the preprocessing unit eliminate data islands, providing standardized inputs for dynamic coupling analysis; Physical model and data-driven fusion: The thermal-hydraulic coupling model and the oxidation kinetic equation provide physical law constraints, and the LSTM model captures the non-linear damage accumulation effect, improving the accuracy of life prediction; Closed-loop control and feedback optimization: The execution effect of the operation optimization instruction is verified by the sensor return data, driving the correction of model parameters and the iteration of the detection scheme, forming a closed-loop link from data acquisition to maintenance decision-making.

[0061] Based on the above explanations, those skilled in the art can clearly define the functional boundaries and synergistic logic of each technical feature, and can implement the technical solution of the present invention without relying on creative labor.

[0062] The specific implementation of the present invention realizes the life assessment and maintenance optimization of the four boiler tubes through multi-module collaboration. A distributed thermocouple array is arranged in the key areas of the four boiler tubes to collect the wall temperature gradient distribution data in real time. At the same time, the pH value, dissolved oxygen, and conductivity parameters collected by the online water quality monitor are integrated through the Modbus protocol, and the soot blowing timing and pressure parameters in the soot blower PLC control signal are recorded, and the over-temperature event log of the combustion control system is synchronized. The original sensor data is denoised by wavelet transform and compensated for Kalman filter error through the preprocessing unit, and the data streams with different sampling frequencies are processed by cubic spline interpolation to generate a multi-dimensional data matrix with a unified time stamp, which is stored in the real-time database and transmitted to the dynamic coupling analysis module through the industrial Ethernet.

[0063] The dynamic coupling analysis module inputs the preprocessed temperature field data into the finite element thermal model to calculate the axial and circumferential thermal stress distributions of the pipe section and identify stress concentration areas; substitutes the water quality parameters into the oxidation kinetic equation, and solves the oxidation scale thickness growth rate based on the activation energy parameter and the temperature change rate; constructs a non-linear regression model of the ash deposition amount and the reduction amplitude of the heat transfer coefficient from the soot blowing frequency data. The thermal stress distribution, oxidation damage rate, and reduction amplitude of the heat transfer coefficient are linearly superimposed according to the preset weight coefficients to generate a comprehensive damage degree evaluation value, which is used as the input of the life assessment module. The life assessment module calls the high-temperature creep strength curve in the material database, maps the correlation between the damage accumulation effect and the remaining life through the LSTM neural network model, and maps the prediction results to the boiler three-dimensional model to generate a visualization atlas, marking the positions of high-risk pipe sections and the remaining life values. The measured oxidation layer thickness data from previous inspections are used to correct the activation energy parameter of the oxidation kinetic equation and update the weight coefficients of the LSTM model to achieve the adaptive optimization of the prediction model.

[0064] The operation optimization module generates control instructions based on the remaining life prediction value: the combustion air distribution baffle opening adjustment plan equalizes the flue gas temperature based on the thermal stress distribution simulation results; the soot blower action sequence determines the priority according to the reduction amplitude of the heat transfer coefficient output by the ash deposition model; when the water quality parameter deviates from the threshold, a closed-loop regulation instruction for the chemical dosing pump is triggered, and the above instructions are pushed to the DCS operation station for execution through the OPC protocol. The decision output module integrates the remaining life value, historical inspection records, and NDT inspection procedures to generate a customized inspection report, correlates the causal analysis atlas of the operation parameter deviation and the pipe material deterioration, and returns and optimizes the sensor layout plan based on the measured inspection data. The self-feedback optimization module periodically checks the model accuracy. When the prediction error exceeds 15% for three consecutive times, the automatic control instruction is frozen, and the expert diagnosis mode is switched to. After manually reviewing and correcting the model parameters, the closed-loop control is restored to form an operation and maintenance system that integrates data-driven and manual intervention.

[0065] The present invention constructs a multi-dimensional data collaborative analysis mechanism to solve the problem of life prediction deviation caused by parameter isolation in existing monitoring methods. The data acquisition module integrates a distributed sensor network to synchronously collect the wall temperature gradient, water quality parameters, and soot blowing operation data, and performs noise reduction processing and timestamp alignment through the preprocessing unit to generate a multi-dimensional data matrix with a unified time series. The dynamic coupling analysis module inputs the temperature field data into the thermal-hydraulic coupling model to calculate the thermal stress distribution, combines the oxidation kinetic equation and the ash deposition model to quantify the synergistic damage effect of oxidation damage and heat transfer efficiency reduction on the pipe material. The dynamic correlation analysis of multi-source data breaks through the limitation of single-parameter evaluation and realizes the comprehensive quantification of damage factors.

[0066] To address the deficiency in the accuracy of life prediction, the system introduces a machine learning optimization mechanism and a closed-loop feedback correction strategy. The life assessment module uses an LSTM neural network model to perform feature matching between the comprehensive damage degree assessment value and the high-temperature creep strength curve in the material database, and establishes a non-linear mapping relationship between damage accumulation and remaining life. The dynamic correction unit reversely calibrates the activation energy parameter of the oxidation kinetics equation based on the measured data of the oxide layer thickness during previous inspections, and updates the neural network weight coefficients, enabling the prediction model to adaptively optimize with the evolution of material properties. The closed-loop verification unit feeds back the deviation between the measured data during inspection and the predicted value to the data acquisition module, driving the optimization of sensor layout and model iteration, and forming a self-improving link for prediction accuracy.

[0067] To eliminate the dependence on manual experience and achieve precise control, the operation optimization module and the decision output module cooperate to generate preventive maintenance strategies. The heat stress distribution data drives the adjustment of the combustion air distribution baffle opening, the ash deposition model outputs the blowing priority sequence for high-risk pipe sections, and the closed-loop regulation of the chemical dosing pump is triggered when the water quality parameter deviates from the threshold. The above instructions are pushed to the DCS system for execution through the OPC protocol. The decision output module correlates the remaining life value, historical inspection records, and NDT inspection procedures to generate customized inspection work orders and causal analysis diagrams, marking the inspection plans for high-risk areas and the traceability path of damage causes. The self-feedback optimization module periodically checks the model accuracy. In case of anomalies, it switches to the expert diagnosis mode to freeze the automatic control, and restores the system reliability through manual review and parameter correction, forming a decision-making mechanism that integrates data-driven and manual intervention.

Claims

1. A boiler four-tube life assessment system, characterized in that Including: A data acquisition module, which is used to collect the wall temperature gradient distribution data of the key areas of the four pipes of the boiler, and integrate water quality parameters, soot blower operation frequency and over-temperature event logs to generate a multi-dimensional operation parameter set; A dynamic coupling analysis module, which is used to input the temperature gradient distribution data in the multi-dimensional operation parameter set into a thermal-hydraulic coupling model to calculate the wall thermal stress distribution characteristics, and at the same time input the water quality parameters into the oxidation kinetics equation to calculate the oxidation scale growth rate, and construct a slag deposition model in combination with the soot blower operation frequency, and output creep damage evaluation parameters including thermal stress distribution, oxidation scale thickness and slag deposition coefficient; A life assessment module, which is used to compare the creep damage evaluation parameters with the high-temperature creep rupture strength curve in the material property database, generate the remaining life values of each pipe section through a life prediction model trained with an LSTM neural network structure, and map the prediction results to a 3D model of the boiler to generate a visualization map; An operation optimization module, which is used to generate adjustment parameters for the opening of the combustion air distribution baffle in the corresponding area, the priority action sequence of the soot blower, and the dosing pump adjustment instruction when the water quality deviates from the threshold according to the pipe section position where the remaining life value output by the life prediction model is lower than the set threshold, and push the operation guidance strategy to the DCS operation station through the OPC protocol; A decision output module, which is used to integrate the remaining life value of the life prediction model, historical maintenance records and the NDT inspection procedure knowledge base, generate an inspection report marked with the coordinates of high-risk pipe sections, and associate the causal analysis map of the operation parameter deviation and the pipe material deterioration; Among them, the dynamic coupling analysis module receives the preprocessed data output by the data acquisition module, the life assessment module receives the creep damage evaluation parameters of the dynamic coupling analysis module for life prediction, the operation optimization module dynamically adjusts the control instruction according to the prediction result of the life assessment module, and the decision output module returns the inspection measured data to the life assessment model for parameter calibration.

2. The boiler four-tube life assessment system according to claim 1, characterized in that, The data acquisition module includes: a preprocessing unit, which is used to perform noise reduction processing and timestamp alignment on the original sensor data, specifically including: Eliminating electromagnetic interference noise through wavelet transform and compensating for sensor drift error using the Kalman filter algorithm; Performing interpolation processing on data streams with different sampling frequencies to generate a multi-dimensional data matrix with timestamps and storing it in a real-time database; The multi-dimensional data matrix output by the preprocessing unit is transmitted to the dynamic coupling analysis module through an industrial real-time Ethernet.

3. The boiler four-tube life assessment system according to claim 2, characterized in that, The dynamic coupling analysis module includes: A thermal stress calculation unit, which is used to input the temperature field data output by the preprocessing unit into a finite element model to calculate the axial and circumferential thermal stress distributions of the pipe section; An oxidation damage assessment unit, which is used to substitute the water quality parameters output by the preprocessing unit into the oxidation kinetics equation to solve the oxidation scale thickness growth rate; A slag deposition correlation unit, which is used to construct a non-linear regression model based on the soot blowing frequency data output by the preprocessing unit to calculate the reduction amplitude of the heat transfer coefficient; Among them, the output of the thermal stress calculation unit, the result of the oxidation damage assessment unit, and the reduction amplitude of the heat transfer coefficient of the ash deposition correlation unit are linearly superimposed according to preset weight coefficients to generate a comprehensive damage degree evaluation value, which is used as the input parameter of the life evaluation module.

4. The boiler four-tube life assessment system according to claim 3, wherein, The life evaluation module includes: A material database unit that stores the high-temperature creep rupture strength curve and creep fracture toughness parameters of the pipe material; A neural network prediction unit that uses an LSTM model to compare the comprehensive damage degree evaluation value with the high-temperature creep rupture strength curve of the material database unit to generate a remaining life value; A dynamic correction unit that is used to correct the activation energy parameter of the oxidation kinetics equation in the oxidation damage assessment unit according to the measured data of the oxidation layer thickness during previous inspections, and update the weight coefficient of the LSTM model based on the corrected activation energy parameter.

5. The boiler four-tube life assessment system according to claim 4, characterized in that, The operation optimization module includes: A combustion control unit that is used to simulate and generate a damper opening adjustment plan for the forced draft fan based on the current thermal stress distribution output by the thermal stress calculation unit; A soot blowing priority unit that is used to generate a soot blower action sequence corresponding to high-risk pipe sections according to the reduction amplitude of the heat transfer coefficient output by the ash deposition correlation unit; A water quality linkage unit that is used to trigger a closed-loop adjustment of the dosing pump frequency when the oxidation damage assessment unit detects that the water quality parameters deviate from the threshold value; Among them, the output instructions of the combustion control unit and the soot blowing priority unit are pushed to the DCS operation station through the OPC protocol and are linked and displayed with the abnormal areas marked in the boiler three-dimensional model.

6. The boiler four-tube life assessment system according to claim 5, characterized in that, The decision-making output module includes: A detection plan generation unit that is used to call the NDT detection procedure knowledge base to match a combined plan of ultrasonic thickness measurement, eddy current detection, or magnetic memory detection according to the remaining life value output by the life evaluation module; A causal analysis unit that is used to perform correlation analysis on the multi-dimensional operation parameter set of the data acquisition module and the comprehensive damage degree evaluation value of the dynamic coupling analysis module to generate a tree-shaped correlation map of the operation parameter deviation and the pipe material deterioration; A closed-loop verification unit that is used to perform deviation analysis on the measured data during maintenance and the remaining life prediction value of the life evaluation module, generate a sensor layout adjustment instruction, and send it to the data acquisition module.

7. The boiler four-tube life assessment system according to claim 6, wherein It also includes: A self-feedback optimization module that is used to return the execution effect of the control instruction output by the operation optimization module to the data acquisition module through sensor data, and perform a self-check on the model accuracy based on the prediction result of the life evaluation module every 24 hours; When the continuous three prediction errors of the life evaluation module exceed the tolerance limit, trigger the expert diagnosis mode, freeze the automatic control instruction of the operation optimization module, and switch to the manual confirmation operation mode.

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