Abrasion and blockage optimization method for coal ash conveying of ash conveying pipeline

Abstract

The invention provides an abrasion and blockage optimization method for coal ash conveying of an ash conveying pipeline, and the method comprises the steps: collecting the specific gravity data and airflow velocity data of coal ash in the ash conveying pipeline in real time through a sensor, and processing the data through a particle swarm optimization algorithm to simulate the stress distribution of the inner wall of the pipeline, thereby obtaining a quantitative evaluation value of the wear aggravation degree of the pipeline; according to the optimized sequence version, processing a historical coal ash conveying difficulty data set, classifying the validity of the sequence version by adopting a support vector machine model, and if a classification result shows that the validity is higher than a preset threshold value, integrating the sequence version into a control system, and determining a final wear resistance optimization strategy; and acquiring strategy parameters from the final wear resistance optimization strategy, inputting the strategy parameters into a field equipment interface to execute pressure precise cooperative adjustment, and judging whether the strategy parameters reduce the surge increase of the blockage frequency or not by continuously monitoring the adjusted airflow distribution data to obtain an updated version of the overall response mechanism of the system.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for optimizing wear and blockage in fly ash conveying pipelines. Background Technology

[0002] As a core pillar of energy supply, the power industry is crucial for ensuring regional economic development and industrialization. The ash conveying system of coal-fired power plants directly impacts the stable operation and safety of generating units. This system, using compressed air, transports fly ash from dust collectors and denitrification devices to ash silos, a critical link in power plant operation. However, with fluctuations in coal quality and high-load operation of units, existing ash conveying systems face severe challenges, revealing problems such as low operating efficiency and significant safety hazards, urgently requiring technological breakthroughs to improve system reliability. Current solutions for ash conveying systems mostly focus on improving single pipe materials or optimizing overall purging, but these methods have significant limitations under complex operating conditions. Traditional wear-resistant pipes struggle to adapt to high ash density and frequent load changes, leading to accelerated pipe wear and high maintenance costs. Meanwhile, while overall purging technology can temporarily alleviate pipe blockage, it wastes energy due to a lack of precise control and is unable to respond quickly to localized blockages. These limitations make the system unstable under high ash content and high load scenarios, with frequent pipe blockages and equipment failures becoming bottlenecks for safe power plant operation. The core technical challenge lies in the synergistic optimization of pipeline wear resistance and local pressure control. Pipeline materials need to maintain long-term wear resistance under high ash density conditions, but the interface between dissimilar materials is prone to delamination due to stress concentration, affecting pipeline lifespan. This problem further exacerbates the difficulty of local pressure control, as pipeline wear leads to uneven airflow distribution, and local blockages are difficult to clear quickly through conventional purging. For example, during ash conveying, when the fly ash specific gravity increases from the design value of 0.75 tons per cubic meter to 1.3 tons per cubic meter, the wear on the inner wall of the pipeline intensifies, local airflow resistance increases, and the pressure control device struggles to accurately detect blockage points, resulting in delayed purging actions. The frequency of pipeline blockages surges from 50 times per month to 80 times, severely impacting the continuous operation of the unit. Therefore, how to achieve synergistic optimization of pipeline wear resistance and precise local pressure control under high ash density conditions has become a key issue in improving the safety, reliability, and operational efficiency of the ash conveying system. Summary of the Invention

[0003] This invention provides an optimization method for wear and blockage in fly ash conveying pipelines, addressing the problems of uneven stress distribution on the pipeline inner wall, severe wear, and difficulty in accurately predicting and controlling potential blockage risks under high ash density scenarios. The method includes: Real-time data on fly ash specific gravity and airflow velocity inside the ash conveying pipeline are collected by sensors. Particle swarm optimization algorithm is used to process these data to simulate the stress distribution on the inner wall of the pipeline and obtain a quantitative assessment value of the degree of pipeline wear aggravation. The quantitative assessment value of the degree of pipe wear is input into the support vector machine model, which is pre-trained on a dataset of historical high gray ratio increase scenarios. If the support vector machine model determines that the assessment value exceeds the preset threshold, the local pressure control module is activated to adjust the airflow distribution parameters and identify the risk points of potential blockage frequency surge. Risk point data obtained from the local pressure control module are used to deploy a virtual simulation environment to simulate the fly ash transportation problem at these points. By iteratively calculating the change in airflow resistance, it is determined whether the wear resistance parameters need to be optimized, and suggestions for composite pipe materials for the increased ash content are obtained. After obtaining the pipeline material composite scheme suggestion, it is input into the real-time monitoring system. The interface stress concentration index in the scheme suggestion is optimized again using the particle swarm optimization algorithm. If the interface stress concentration index is lower than the preset threshold, the initial configuration of pressure precision coordination is confirmed, and the purging sequence suitable for local blockage response is determined. The purging sequence is extracted from the initial configuration of precise pressure coordination, and dynamic simulation tests are conducted on the challenges of ash conveying pipelines. By comparing the surge in blockage frequency before and after the simulation, it is determined whether the purging sequence covers all risk points, and an optimized sequence version is obtained to adapt to the condition of accelerated pipeline wear. The optimized sequence version is used to process the historical fly ash conveying problem dataset. The effectiveness of the sequence version is classified using a support vector machine model. If the classification result shows that the effectiveness is higher than the preset threshold, the sequence version is integrated into the control system to determine the final wear resistance optimization strategy. The strategy parameters are obtained from the final wear resistance optimization strategy and input into the field equipment interface to perform precise and coordinated pressure adjustment. By continuously monitoring the adjusted airflow distribution data, it is determined whether the strategy parameters reduce the surge in clogging frequency, and an updated version of the overall system response mechanism is obtained.

[0004] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention collects real-time data on fly ash specific gravity and airflow velocity, employs a particle swarm optimization algorithm to simulate the stress distribution on the inner wall of the pipeline, quantifies the degree of wear aggravation, and inputs it into a pre-trained support vector machine model to determine whether a threshold is exceeded. This activates a local pressure control module to adjust the airflow distribution and identify risk points. For risk points, a virtual simulation environment is deployed to iteratively calculate airflow resistance, optimize wear resistance parameters, and generate a composite pipeline material scheme. The scheme is further refined using the particle swarm optimization algorithm to reduce interface stress concentration, confirm precise pressure coordination, and generate a purging sequence covering all risk points. After validating the effectiveness of the optimized sequence through support vector machine classification, it is integrated into the control system to perform pressure adjustments and continuously monitor the blockage frequency. This invention, through a combination of data-driven and intelligent optimization, achieves precise control of pipeline wear and blockage risks, significantly improving the operational stability and wear resistance of ash conveying pipelines. Detailed Implementation

[0005] The technical solutions described below in conjunction with the embodiments of the present invention will be clearly and thoroughly described. The described embodiments are merely a part of the embodiments of the present invention.

[0006] The optimization method for wear and blockage in fly ash conveying pipelines in this embodiment specifically includes the following steps: S101. Real-time data on fly ash specific gravity and airflow velocity in the ash conveying pipeline are collected by sensors. The data are processed by particle swarm optimization algorithm to simulate the stress distribution on the inner wall of the pipeline and obtain a quantitative assessment value of the degree of pipeline wear aggravation.

[0007] Specifically, real-time data on the specific gravity of fly ash and the airflow velocity inside the ash conveying pipeline are collected by sensors and stored in a database to obtain a structured dataset. Data preprocessing methods are used to denoise and normalize the structured dataset to obtain a standardized dataset. A particle swarm optimization algorithm is used to iteratively calculate the standardized dataset to simulate the stress distribution on the pipeline's inner wall, resulting in a stress distribution matrix. Based on the stress distribution matrix, the stress concentration coefficient of each region on the pipeline's inner wall is calculated to obtain the stress concentration distribution. If the stress concentration coefficient is greater than a preset threshold, the corresponding region is marked as a high-wear-risk area, resulting in a risk area distribution. Based on the risk area distribution and combined with real-time data on the specific gravity of fly ash and the airflow velocity, regression analysis is used to calculate the wear aggravation rate, obtaining a wear rate value. Based on the wear rate value, a weighted average method is used to quantify the overall wear degree of the pipeline, obtaining a wear assessment value.

[0008] S102. Input the quantitative assessment value of the degree of pipeline wear aggravation into the support vector machine model. The model is pre-trained on a dataset under historical high gray weight increase scenarios. If the support vector machine model determines that the assessment value exceeds the preset threshold, the local pressure control module is activated to adjust the airflow distribution parameters and determine the risk points of potential blockage frequency surge.

[0009] Specifically, pipeline wear assessment values ​​are collected in real time by sensors to obtain pipeline status data; a support vector machine model is used to classify the pipeline status data and determine whether it exceeds a preset threshold to obtain the classification result; if the classification result shows that the assessment value exceeds the preset threshold, the local pressure control module is activated to obtain a pressure adjustment command; the airflow distribution parameters are modified by the pressure adjustment command to obtain optimized airflow distribution data; based on the optimized airflow distribution data, the locations in the pipeline with a surge in potential blockage frequency are analyzed to determine the set of risk locations; for the set of risk locations, historical datasets under high gray ratio scenarios are obtained to obtain a blockage frequency prediction model; the blockage frequency prediction model is used to analyze the set of risk locations to determine the blockage probability of each location and obtain a list of high-risk locations.

[0010] S103. Risk point data obtained from the local pressure control module are used to deploy a virtual simulation environment to simulate the fly ash transportation problem at these points. By iteratively calculating the change in airflow resistance, it is determined whether the wear resistance parameters need to be optimized, and suggestions for composite pipe materials for the increased ash content are obtained.

[0011] Specifically, risk point data is obtained from the local pressure control module, and noise is removed using data cleaning techniques to obtain a standardized point dataset. Based on the standardized point dataset, a virtual simulation environment is deployed, and the fly ash conveying process is simulated using the discrete element method to obtain airflow distribution data during the conveying process. For the airflow distribution data, the finite element analysis method is used to iteratively calculate the airflow resistance change to obtain the resistance change trend. If the resistance change trend exceeds a preset threshold, the influence weight of wear resistance parameters is analyzed using the support vector machine algorithm to obtain a key parameter set. Based on the key parameter set, a pre-established material performance database is queried to match pipe material combinations that meet the requirements of increased ash specificity, resulting in a preliminary composite scheme. For the preliminary composite scheme, the Monte Carlo method is used to simulate long-term operating conditions, analyze the stability of material wear resistance performance, and obtain an optimized composite scheme. Based on the optimized composite scheme, the material parameters of the virtual simulation environment are updated, the simulation is rerun, and the airflow resistance change is verified to meet the preset threshold, resulting in the final composite scheme.

[0012] S104. After obtaining the pipeline material composite scheme suggestion, input it into the real-time monitoring system, and use the particle swarm optimization algorithm to optimize the interface stress concentration index in the scheme suggestion again. If the interface stress concentration index is lower than the preset threshold, confirm the initial configuration of pressure precision coordination and determine the purging sequence suitable for local blockage response.

[0013] Specifically, the process involves obtaining composite pipeline material schemes, extracting interface stress concentration indices, and generating a stress distribution dataset using data parsing techniques. A real-time monitoring system is then used to acquire this stress distribution dataset, inputting it into the monitoring data stream to generate a real-time stress state matrix. A particle swarm optimization algorithm is employed to iterate parameters on the real-time stress state matrix, resulting in an optimized stress parameter set. If the optimized stress parameter set is below a preset threshold, a pressure coordination initial configuration is generated, and a configuration parameter set is output. Based on the configuration parameter set, local blockage response characteristics are analyzed, generating an initial purging sequence dataset. Using the purging sequence dataset, sequence optimization techniques are employed to adjust the sequence execution order, resulting in the final purging sequence. Finally, pipeline operation control commands are generated for the final purging sequence, and a control command set is output.

[0014] S105. Extract the purging sequence from the initial configuration of precise pressure coordination, conduct dynamic simulation tests for the challenges of ash conveying pipelines, and determine whether the purging sequence covers all risk points by comparing the surge in blockage frequency before and after the simulation, and obtain an optimized sequence version to adapt to the increased pipeline wear conditions.

[0015] Specifically, the purging sequence is obtained from the pressure coordination configuration, and the pressure parameters and time intervals of each purging node in the sequence are analyzed to generate an initial sequence dataset. Using a dynamic simulation tool, the initial sequence dataset is loaded, and a blockage risk scenario is simulated for the ash conveying pipeline operation to obtain the blockage frequency distribution. A support vector machine algorithm is used to classify the blockage frequency distribution, determine the coverage degree of each risk point, and identify the set of uncovered points. Based on the set of uncovered points, the pressure parameters and time intervals of the purging sequence are adjusted to generate a candidate optimized sequence set. The candidate optimized sequence set is simulated again using the dynamic simulation tool to obtain the blockage frequency change trend of each sequence. If the frequency change trend is lower than a preset threshold, the sequence with the smallest frequency change is selected from the candidate optimized sequence set to obtain the optimized purging sequence. For the pipeline wear aggravation condition, the optimized purging sequence is loaded, the pressure parameters are adjusted to adapt to the wear degree, and the final sequence version is generated.

[0016] S106. Process the historical fly ash conveying problem dataset according to the optimized sequence version, and use the support vector machine model to classify the effectiveness of the sequence version. If the classification result shows that the effectiveness is higher than the preset threshold, integrate the sequence version into the control system and determine the final wear resistance optimization strategy.

[0017] Specifically, a historical dataset of fly ash conveying challenges is acquired, and sequence version feature data is extracted from it. The sequence version feature data is then classified for validity using a support vector machine model to obtain classification results. If the classification results are higher than a preset threshold, the sequence version data is integrated into the control system to generate integrated sequence data. Based on the integrated sequence data, the wear resistance parameters in the control system are analyzed to determine the direction of parameter adjustment. The control system configuration is updated using the parameter adjustment direction to generate an optimized wear resistance configuration. A fly ash conveying simulation test is performed using the optimized wear resistance configuration to obtain test results. Based on the test results, the optimization strategy is adjusted to determine the final optimized wear resistance scheme.

[0018] S107. Obtain strategy parameters from the final wear resistance optimization strategy, input them into the field equipment interface to perform precise and coordinated pressure adjustment, and determine whether the strategy parameters reduce the surge in clogging frequency by continuously monitoring the adjusted airflow distribution data, thereby obtaining an updated version of the overall system response mechanism.

[0019] Specifically, the parameters in the optimization strategy are obtained, key control variables are extracted by parsing wear resistance performance data, and a parameter set is determined. The parameter set is input into the device interface to perform pressure coordination adjustment and generate the adjusted device operating status. Airflow distribution data is continuously monitored by sensors to collect the adjusted airflow distribution status and obtain a distribution data sequence. If the fluctuation amplitude of the distribution data sequence exceeds a preset threshold, the relationship between airflow distribution and blockage frequency is analyzed using a support vector machine algorithm to determine the trend of blockage frequency change. Based on the trend of blockage frequency change, the control logic of the response mechanism is adjusted to generate an updated response mechanism configuration. The updated response mechanism configuration is used to perform system parameter optimization to obtain an updated version of the overall system response mechanism. By comparing the blockage frequency before and after the update through data analysis, the optimization effect is verified, and the final system operating status is determined.

[0020] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for optimizing wear and blockage in fly ash conveying pipelines, characterized in that, The method includes: The specific gravity data of fly ash and airflow velocity data in the ash conveying pipeline are collected in real time by sensors. The data are processed by particle swarm optimization algorithm to simulate the stress distribution of the inner wall of the pipeline and obtain a quantitative assessment value of the degree of pipeline wear aggravation. The quantitative assessment value of the increased wear of the pipeline is input into the support vector machine model. The support vector machine model is pre-trained on a dataset under historical high gray ratio increase scenarios. If the support vector machine model determines that the assessment value exceeds a preset threshold, the local pressure control module is activated to adjust the airflow distribution parameters and identify the risk points of potential blockage frequency surge. The risk point data obtained from the local pressure control module are used to deploy a virtual simulation environment to simulate the fly ash transportation problem at these points. By iteratively calculating the change in airflow resistance, it is determined whether the wear resistance parameters need to be optimized, and suggestions for composite pipe materials for the increased ash content are obtained. After obtaining the proposed composite scheme for the pipeline material, it is input into the real-time monitoring system. The interface stress concentration index in the proposed scheme is further optimized using the particle swarm optimization algorithm. If the interface stress concentration index is lower than the preset threshold, the initial configuration of precise pressure coordination is confirmed, and a purging sequence suitable for local blockage response is determined. The purging sequence is extracted from the initial configuration of precise pressure coordination, and dynamic simulation test is conducted for the challenges of ash conveying pipelines. By comparing the surge index of blockage frequency before and after the simulation, it is determined whether the purging sequence covers all risk points, and an optimized sequence version is obtained to adapt to the condition of accelerated pipeline wear. The optimized sequence version is used to process the historical fly ash conveying problem dataset. The effectiveness of the sequence version is classified using a support vector machine model. If the classification result shows that the effectiveness is higher than a preset threshold, the sequence version is integrated into the control system to determine the final wear resistance optimization strategy. The strategy parameters are obtained from the final wear resistance optimization strategy and input into the field equipment interface to perform precise and coordinated pressure adjustment. By continuously monitoring the adjusted airflow distribution data, it is determined whether the strategy parameters reduce the surge in clogging frequency, and an updated version of the overall system response mechanism is obtained.

2. The optimization method according to claim 1, characterized in that, The process involves real-time acquisition of fly ash specific gravity and airflow velocity data within the ash conveying pipeline using sensors. These data are then processed using a particle swarm optimization algorithm to simulate the stress distribution on the pipeline's inner wall, yielding a quantitative assessment of the degree of pipeline wear aggravation. This includes: Real-time data on the specific gravity of fly ash and airflow velocity in the ash conveying pipeline are collected by sensors and stored in a database to obtain a structured dataset. The structured dataset is denoised and normalized using data preprocessing methods to obtain a standardized dataset; The stress distribution matrix of the pipe inner wall is obtained by iteratively calculating the standardized dataset using the particle swarm optimization algorithm. Based on the stress distribution matrix, the stress concentration factor of each region of the inner wall of the pipe is calculated to obtain the stress concentration distribution; If the stress concentration factor is greater than a preset threshold, the corresponding area is marked as a high wear risk area, and the risk area distribution is obtained; Based on the risk area distribution and combined with real-time data on fly ash specific gravity and airflow velocity, regression analysis was used to calculate the wear aggravation rate and obtain the wear rate value. Based on the wear rate value, a weighted average method is used to quantify the overall wear degree of the pipeline to obtain a wear assessment value.

3. The optimization method according to claim 1, characterized in that, The quantitative assessment value based on the degree of pipe wear is input into the support vector machine model, which is pre-trained on a dataset of historical high gray content increase scenarios. If the support vector machine model determines that the assessment value exceeds a preset threshold, the local pressure control module is activated to adjust the airflow distribution parameters and identify potential risk points of increased blockage frequency, including: Pipeline status data is obtained by collecting pipeline wear assessment values ​​in real time using sensors. The pipeline status data is classified using a support vector machine model to determine whether it exceeds a preset threshold, and the classification result is obtained. If the classification result indicates that the evaluation value exceeds the preset threshold, the local pressure control module is activated to obtain a pressure adjustment command. The airflow distribution parameters are modified by the pressure adjustment command to obtain optimized airflow distribution data; Based on the optimized airflow distribution data, analyze the locations where the frequency of potential blockages in the pipeline surges, and determine the set of risk points. For the set of risk points, historical datasets under high gray ratio scenarios are obtained to obtain a congestion frequency prediction model; The congestion frequency prediction model is used to analyze the set of risk locations, determine the congestion probability of each location, and obtain a list of high-risk locations.

4. The optimization method according to claim 1, characterized in that, The risk point data obtained from the local pressure control module is used to deploy a virtual simulation environment to simulate the fly ash transportation problem at these points. By iteratively calculating the changes in airflow resistance, it is determined whether the wear resistance parameters need to be optimized, and suggestions for composite pipe materials for increased ash content are obtained, including: Risk point data are obtained from the local pressure control module, and noise is removed using data cleaning techniques to obtain a standardized point dataset. Based on the standardized point dataset, a virtual simulation environment is deployed, and the fly ash conveying process is simulated using the discrete element method to obtain the airflow distribution data of the conveying process; For the airflow distribution data, the finite element analysis method is used to iteratively calculate the airflow resistance change and obtain the resistance change trend. If the trend of resistance change exceeds a preset threshold, the influence weight of wear resistance performance parameters is analyzed by support vector machine algorithm to obtain a set of key parameters; Based on the set of key parameters, a pre-established material performance database is queried to match pipe material combinations that meet the increased ash content, thus obtaining a preliminary composite scheme. For the initial composite scheme, the Monte Carlo method was used to simulate long-term operating conditions, analyze the stability of the material's wear resistance, and obtain the optimized composite scheme. By updating the material parameters of the virtual simulation environment using the optimized composite scheme, the simulation is rerun to verify whether the change in airflow resistance meets the preset threshold, thus obtaining the final composite scheme.

5. The optimization method according to claim 1, characterized in that, After obtaining the proposed composite pipeline material scheme, it is input into the real-time monitoring system. The interface stress concentration index in the scheme proposal is further optimized using a particle swarm optimization algorithm. If the interface stress concentration index is lower than a preset threshold, the initial configuration for precise pressure coordination is confirmed, and a purging sequence suitable for localized blockage response is determined, including: Obtain composite pipe material schemes, extract interface stress concentration indices from them, and generate stress distribution datasets using data parsing techniques; The stress distribution dataset is obtained through a real-time monitoring system, and the monitoring data stream is input to generate a real-time stress state matrix. The particle swarm optimization algorithm is used to iterate the parameters of the real-time stress state matrix to obtain the optimized stress parameter set. If the optimized stress parameter set is lower than a preset threshold, a pressure coordination initial configuration is generated and the configuration parameter set is output. Based on the configuration parameter set, analyze the local blockage response characteristics and generate an initial purge sequence dataset; Using the purge sequence dataset, sequence optimization techniques are employed to adjust the sequence execution order, resulting in the final purge sequence. For the final purging sequence, pipeline operation control commands are generated and a control command set is output.

6. The optimization method according to claim 1, characterized in that, The process involves extracting a purging sequence from the initial configuration of precise pressure coordination, conducting dynamic simulation tests on ash conveying pipelines, and comparing the surge in blockage frequency before and after the simulation to determine whether the purging sequence covers all risk points. An optimized sequence version is then obtained to adapt to conditions of increased pipeline wear, including: The purge sequence is obtained from the pressure coordination configuration, and the pressure parameters and time intervals of each purge node in the sequence are parsed to generate the initial sequence dataset; By loading the initial sequence dataset using a dynamic simulation tool, a blockage risk scenario is simulated for the operating conditions of the ash conveying pipeline, and the blockage frequency distribution is obtained. The support vector machine algorithm is used to classify the congestion frequency distribution, determine the coverage of each risk point, and identify the set of uncovered points. Based on the set of uncovered locations, the pressure parameters and time intervals of the purging sequence are adjusted to generate a set of candidate optimized sequences; The candidate optimization sequence set was simulated again using a dynamic simulation tool to obtain the blocking frequency change trend of each sequence. If the frequency change trend is lower than a preset threshold, the sequence with the smallest frequency change is selected from the candidate optimized sequence set to obtain the optimized purge sequence; To address the issue of accelerated pipeline wear, the optimized purging sequence is loaded, and the pressure parameters are adjusted to adapt to the degree of wear, generating the final sequence version.

7. The optimization method according to claim 1, characterized in that, The process involves processing the historical fly ash conveying problem dataset based on optimized sequence versions, using a support vector machine model to classify the effectiveness of the sequence versions. If the classification results show that the effectiveness is higher than a preset threshold, the sequence versions are integrated into the control system to determine the final wear resistance optimization strategy, including: Obtain a historical dataset of fly ash transportation problems and extract sequence version feature data from it; The sequence version feature data is classified for validity using a support vector machine model to obtain the classification results; If the classification result is higher than a preset threshold, the sequence version data will be integrated into the control system to generate integrated sequence data. Based on the integrated sequence data, analyze the wear resistance parameters in the control system and determine the direction of parameter adjustment. The parameters are used to adjust the direction and update the control system configuration, generating an optimized configuration for wear resistance. By optimizing the wear resistance configuration, a fly ash conveying simulation test was performed to obtain the test results; Based on the test results, the optimization strategy was adjusted to determine the final wear resistance optimization scheme.

8. The optimization method according to claim 1, characterized in that, The process involves obtaining strategy parameters from the final wear resistance optimization strategy, inputting them into the field equipment interface to perform precise and coordinated pressure adjustments, and continuously monitoring the adjusted airflow distribution data to determine whether the strategy parameters reduce the surge in clogging frequency. This results in an updated version of the overall system response mechanism, including: Obtain the parameters in the optimization strategy, extract key control variables by parsing wear resistance data, and determine the parameter set; The parameter set is input into the device interface to perform pressure coordination adjustment and generate the adjusted device operating status. By continuously monitoring airflow distribution data with sensors, the adjusted airflow distribution state is collected to obtain a distribution data sequence; If the fluctuation range of the distributed data sequence exceeds a preset threshold, the relationship between airflow distribution and congestion frequency is analyzed by the support vector machine algorithm to determine the trend of congestion frequency change. Based on the trend of the congestion frequency change, adjust the control logic of the response mechanism to generate an updated response mechanism configuration; By using the updated response mechanism configuration, system parameter optimization is performed to obtain an updated version of the overall system response mechanism; By comparing the congestion frequency before and after the update through data analysis, the optimization effect is verified and the final system operating status is determined.