Method for improving pressure fluctuation control process of oxygen pipe network

Abstract

The invention relates to the technical field of steelmaking production optimization, and discloses a method for improving an oxygen pipe network pressure fluctuation control process, which comprises the following steps: step 1, actually measured data support: establishing an actually measured data acquisition system, and verifying and correcting a simulation result by using actually measured data as a basis for optimization design; step 2, real-time monitoring and control accuracy integration: a, monitoring equipment maintenance: regularly maintaining the monitoring equipment, and optimizing a monitoring algorithm; b, a control system is improved, and pipe network pressure fluctuation is prevented from being controlled; and step 3, integration of maintenance timeliness and risk management: a, establishing a regular maintenance plan to prevent the occurrence of problems. By establishing an actual measurement data acquisition system and verifying and correcting a simulation result, the accuracy and reliability of model establishment are ensured, and accurate actual measurement data support and real-time monitoring and control accuracy contribute to optimizing pipe network operation, improving operation efficiency and stability and reducing risks of energy waste and production interruption.

Description

Technical Field

[0001] The invention relates to the technical field of steelmaking production optimization, and in particular to a method for improving a pressure fluctuation control process of an oxygen pipeline network. Background Art

[0002] High-purity oxygen is injected during the steelmaking process to help reduce the carbon content of steel and remove impurities such as phosphorus, sulfur, and silicon. At the same time, the heat generated by oxidation maintains the temperature of the steelmaking process, effectively shortening the smelting time and improving the quality of steel. The design of the oxygen and nitrogen supply system is based on the average gas consumption of the steel process to make a static balance, but in actual production, the dynamic balance of oxygen supply and demand reflects the essence of the balance of the steel plant's oxygen supply system; steelmaking oxygen consumption varies with changes in production rhythm, intensity, and equipment conditions. When the smelting intensity is increased and the instantaneous production load is greater than the design value, the pipeline network pressure will drop and the oxygen supply will be insufficient for a short time. When the converter fails, is under maintenance, or the molten iron supply is insufficient, if the original air separation load is maintained, the main pipe pressure will rise, resulting in oxygen release.

[0003] In the existing technology, it is impossible to accurately control the pipeline pressure, resulting in an increased risk of fluctuations, which in turn causes damage to the pipeline or equipment, and even causes safety accidents such as leakage and explosion, endangering the safety of life and property. At the same time, frequent fluctuations in pipeline pressure will lead to increased wear of pipelines and equipment, increase maintenance and replacement costs, reduce the service life of equipment, and increase operating costs.

[0004] Therefore, those skilled in the art provide a method for improving the oxygen pipeline network pressure fluctuation control process to solve the problems raised in the above background technology. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for improving the oxygen pipeline network pressure fluctuation control process, so as to solve the problem that the pipeline network pressure cannot be accurately controlled, resulting in an increase in the risk of fluctuations, which in turn causes damage to the pipeline or equipment, and even causes safety accidents such as leakage and explosion, endangering the life safety of personnel and property safety. At the same time, frequent fluctuations in pipeline network pressure will lead to increased wear of pipelines and equipment, increase maintenance and replacement costs, reduce the service life of equipment, and increase operating costs.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for improving the pressure fluctuation control process of an oxygen pipeline network, comprising the following steps:

[0007] Step 1: Measured data support: Establish a measured data acquisition system and use the measured data as the basis for optimizing the design, verifying and correcting the simulation results;

[0008] Step 2: Real-time monitoring and control precision integration:

[0009] a. Monitoring equipment maintenance: Regularly maintain monitoring equipment and optimize monitoring algorithms;

[0010] b. Improve the control system to prevent the pressure fluctuation of the pipeline network from being controlled;

[0011] Step 3: Integrate maintenance timeliness and risk management:

[0012] a. Establish a regular maintenance plan to prevent problems from occurring;

[0013] b. Formulate emergency response plans and establish risk management mechanisms;

[0014] Step 4: Algorithm optimization and intelligent control integration:

[0015] a. Use measured data and historical data to conduct in-depth analysis and optimize model algorithms;

[0016] b. Real-time monitoring and intelligent control of pipeline network pressure fluctuations through real-time monitoring algorithms;

[0017] c. Design intelligent control strategies to automatically adjust control parameters based on real-time monitoring data and prediction models;

[0018] Step 5: Data mining and prediction algorithm integration:

[0019] a. Use data mining technology to discover the patterns and information hidden behind the data;

[0020] b. Use prediction algorithms to predict oxygen network pressure fluctuations and take control measures in advance;

[0021] Step 6: Control algorithm integration:

[0022] a. Combine PID control, fuzzy control and optimization control algorithms to optimize the pipe network pressure control strategy;

[0023] b. Design an adaptive control algorithm to adjust control parameters in real time according to the network operation status and environmental changes to meet the pressure control requirements under various working conditions;

[0024] Step 7: Data-driven optimization decision integration:

[0025] a. Establish a data-driven optimization decision-making model, combining real-time monitoring data and algorithm analysis results to support intelligent and precise decision-making for pipeline network operation;

[0026] b. Develop a real-time optimization decision-making system to monitor and optimize the pressure fluctuation control of the pipeline network in real time based on data-driven models and algorithms.

[0027] The collection and analysis of measured data can provide the real operation status of the pipeline network, help verify and correct the simulation results, ensure the accuracy and reliability of the model, and the measured data can be used to optimize the design and improve the pipeline network pressure control strategy to make it more in line with the actual situation and improve the operation efficiency of the pipeline network; regular maintenance of monitoring equipment can ensure the accuracy and reliability of the monitoring system, improve the effectiveness of real-time monitoring, improve the control system to enhance the controllability of pipeline network pressure fluctuations, and adjust the control parameters in time to maintain stable operation of the pipeline network; the establishment of regular maintenance plans and emergency plans can effectively prevent the occurrence of problems, reduce pipeline network operation risks, and ensure the safe and stable operation of the system; use measured data and historical data for in-depth analysis, optimize model algorithms, improve the accuracy and efficiency of pipeline network pressure control, and design intelligent control strategies to automatically adjust control parameters according to real-time monitoring data and prediction models to achieve intelligent control and optimization of pipeline network pressure fluctuations; data mining technology can help to discover the laws and information hidden in the data, and provide guidance for the prediction and control of pipeline network pressure fluctuations. The application of prediction algorithms can predict the pressure fluctuation trend of the pipeline network in advance, which is helpful to take control measures in time and reduce risks. The combination of PID control, fuzzy control and optimization control algorithms can optimize the pipeline network pressure control strategy and improve the stability and efficiency of pipeline network operation. The design of adaptive control algorithms can adjust the control parameters in real time according to the operation status of the pipeline network and environmental changes, and improve the flexibility and adaptability of the system. The establishment of data-driven optimization decision-making models can combine real-time monitoring data and algorithm analysis results to support the intelligence and precision of pipeline network operation decisions, improve the scientificity and accuracy of decisions, and develop real-time optimization decision-making systems based on data-driven models and algorithms. Real-time monitoring and optimization of pipeline network pressure fluctuation control decisions can improve the efficiency and level of pipeline network operation management. In summary, the integration and optimization of steps will help improve the effect of oxygen pipeline network pressure fluctuation control process, improve the safety, stability and efficiency of pipeline network operation, reduce accident risks, optimize resource utilization, and improve the overall operation management level.

[0028] Preferably, in step 1, the specific implementation steps supported by the measured data are:

[0029] Establish a measured data collection system: build an oxygen pipeline network pressure monitoring system to collect pipeline network pressure data in real time;

[0030] Basis of measured data for design optimization: Collect measured data to establish a data storage and management system to facilitate subsequent model building and algorithm optimization;

[0031] Verify and correct simulation results: Compare and analyze the measured data with the results of the currently established simulation model to verify the accuracy of the simulation model.

[0032] Build an oxygen pipeline network pressure monitoring system and collect pipeline network pressure data in real time, which can realize real-time monitoring and recording of pipeline network operation status, and provide real and reliable data support for subsequent analysis and decision-making; collect measured data and establish a data storage and management system to provide a basis for subsequent model establishment and algorithm optimization, and ensure the accuracy and reliability of the model; compare and analyze the measured data with the simulation model results to verify the accuracy of the simulation model and ensure that the model can accurately reflect the actual operation of the pipeline network.

[0033] Preferably, in step 2, the monitoring algorithm is equivalent to a PID control algorithm, which is used to adjust the output of the system, and its basic formula is as follows:

[0034] Among them, u(t) is the controller output, that is, the control quantity applied to the system; e(t) is the difference between the set value and the actual value; K p is the proportional gain, which is used to adjust the influence of the current error on the control quantity; K i is the integral gain, which is used to eliminate the steady-state error of the system; K d is the differential gain, which is used to predict the changing trend of the error.

[0035] Preferably, in step three, the specific implementation steps of formulating the emergency plan are:

[0036] Risk Assessment and Identification:

[0037] Assess the risks and potential hazards that may cause pressure fluctuations in the pipeline network and identify emergency situations;

[0038] Emergency plan formulation:

[0039] Develop a detailed emergency plan and clarify the tasks and action plans of each responsible person;

[0040] Resource preparation:

[0041] Ensure that the emergency response team has the necessary resources and equipment to take prompt action in an emergency, and that communications equipment is available to facilitate real-time communication and coordination among team members;

[0042] Training and drills:

[0043] Train the emergency response team, familiarize them with emergency plans and response procedures, organize drills regularly, simulate emergency situations, and test the team's response capabilities and collaboration efficiency;

[0044] Monitoring and Alarm Systems:

[0045] Set alarm thresholds to facilitate timely action when network pressure fluctuations exceed normal ranges;

[0046] Coordination and Communication:

[0047] Establish a good communication mechanism to promptly inform relevant parties of the situation in an emergency and coordinate resources to effectively solve the problem.

[0048] By assessing the risks and potential risk factors of pipeline pressure fluctuations, potential emergency situations can be identified in advance, and emergency plans can be formulated in a targeted manner to reduce the impact of emergency events on pipeline network operations. Detailed emergency plans can clarify the tasks and action plans of each responsible person to ensure that problems can be responded to and handled quickly and effectively in emergency situations. Ensure that the emergency response team has the necessary resources and equipment to take prompt action in emergency situations to ensure the safety and stability of pipeline network operations, and regularly check and update resource lists to ensure the completeness and effectiveness of resources, while ensuring that communication equipment is unobstructed so that team members can communicate and coordinate in real time. Through training and drills, the emergency response team's emergency handling capabilities and collaboration efficiency can be improved to ensure that problems can be dealt with quickly and effectively in emergency situations. Setting alarm thresholds can issue alarms in a timely manner when pipeline pressure fluctuations exceed the normal range, helping the team to take quick action to avoid escalation. Establishing a good coordination and communication mechanism can achieve timely information transmission and effective coordination of resources in emergency situations, improve the efficiency of problem solving, and clarify the contact and communication methods of responsible personnel to ensure rapid response and coordination in emergency situations to minimize losses.

[0049] Preferably, in step 4, the optimization model algorithm involves a particle swarm optimization algorithm, and the particle swarm optimization algorithm includes a particle velocity update formula and a particle position update formula;

[0050] Particle velocity update formula:

[0051]

[0052] in, is the updated value of the velocity of particle i in dimension j, w is the inertia weight, c1 and c2 are acceleration factors, r1 and r2 are random numbers, and pbest ij is the individual's best historical position, gbest j is the historical optimal position of the group, is the position of the particle in dimension;

[0053] Particle position update formula:

[0054]

[0055] in, is the updated value of the position of particle i in dimension j.

[0056] Preferably, in the step 5, in the integration of data mining and prediction algorithms involved in the step 5, the prediction algorithms include regression analysis, neural networks and support vector machines;

[0057] The regression analysis formula is (y = b0 + b1x1 + b2x2 + ... + b n x n +∈), where b n is the regression coefficient, x n is the independent variable, ∈ is the error term;

[0058] Neural network models are used for nonlinear data modeling and prediction. A common neural network model is the multilayer perceptron (MLP). The formula of MLP is: Among them, w i is the weight, x i is the input, b is the bias, and f is the activation function;

[0059] Support vector machine, support vector machine is used for classification and regression tasks. In the regression problem, the basic formula of support vector machine is (y = w T x+b), where w is the weight vector, x is the input vector, and b is the bias.

[0060] Preferably, in step 6, the adaptive control algorithm generally involves real-time adjustment of parameters to adapt to the operation status of the pipe network and environmental changes. Among them, a common adaptive control algorithm is adaptive fuzzy control, the basic idea of ​​which is to dynamically adjust the parameters of the fuzzy controller according to the real-time status and performance requirements of the system;

[0061] Adaptive fuzzy control: [u(t) = f(e(t), Δe(t))],

[0062] Among them, u(t) is the output of the controller, that is, the required control quantity, e(t( is the difference between the target value and the actual value, and Δe(t) is the rate of change of the error, which is used to consider the trend of the error.

[0063] Preferably, in step seven, the implementation steps of the data-driven optimization decision integration are:

[0064] Data collection and preprocessing: collect real-time monitoring data and historical data, and preprocess the data;

[0065] Feature engineering: feature extraction and feature selection of data, by extracting useful features from the original data;

[0066] Establish data-driven decision models: Build decision models through machine learning algorithms or statistical methods, and use real-time monitoring data and algorithm analysis results as input features to predict pipeline pressure fluctuations or other key indicators;

[0067] Model training and optimization: Use historical data to train models and optimize model parameters based on real-time monitoring data;

[0068] Real-time decision support system: Develop a real-time decision support system, integrate the established data-driven decision model into the system, and use real-time monitoring data as input. The model outputs prediction results or recommended decisions to support operators in making intelligent decisions;

[0069] Decision execution and feedback: Based on the decision results output by the model, corresponding control measures are executed to adjust the operation status of the pipeline network.

[0070] The role of data collection and preprocessing is to collect real-time monitoring data and historical data and preprocess the data to provide a high-quality data foundation for subsequent feature engineering and modeling; the role of feature engineering is to extract and select features from the data, which can help improve the prediction performance and explanatory ability of the model by extracting useful features from the original data; the role of establishing a data-driven decision model is to provide decision support for operators by building a decision model and using machine learning algorithms or statistical methods to predict pipeline pressure fluctuations or other key indicators; the role of model training and optimization is to use historical data to train the model and optimize the model parameters based on real-time monitoring data to continuously improve the prediction performance and generalization ability of the model; the role of real-time decision support system is to develop a real-time decision support system, integrate the established data-driven decision model into the system, and provide intelligent decision support for operators in combination with real-time monitoring data; decision execution and feedback is to execute corresponding control measures to adjust the operation status of the pipeline network according to the decision results output by the model to ensure the effectiveness and stability of pipeline pressure fluctuation control, establish a feedback mechanism, monitor and evaluate the effect of decision execution, timely adjust and optimize the decision model and control strategy, and continuously improve the level of pipeline network operation management.

[0071] Preferably, in step 7, the data includes pressure, temperature and flow rate, the pressure range is 1 bar to 10 bar, the temperature range is 10°C to 40°C, and the flow rate range is 10m 3 / h~100m 3 / h.

[0072] The present invention provides a method for improving the pressure fluctuation control process of an oxygen pipe network. It has the following beneficial effects:

[0073] 1. The present invention ensures the accuracy and reliability of model establishment by establishing a measured data acquisition system and verifying and correcting simulation results. Accurate measured data support and real-time monitoring and control accuracy will help optimize pipeline network operation, improve operational efficiency and stability, and reduce the risk of energy waste and production interruption. At the same time, through regular maintenance of monitoring equipment and improvement of control systems, the integration of maintenance timeliness and risk management and the integration of optimized control algorithms will help reduce the risk of accidents in pipeline network operation, improve safety, and ensure production and environmental safety.

[0074] 2. The present invention will make pipeline network management intelligent and automated through the integration of algorithm optimization and intelligent control and data-driven optimization decision-making, improve the intelligence level of pipeline network operation, reduce the need for human intervention, and establish regular maintenance plans and emergency plans to effectively prevent problems from occurring, and be able to respond quickly in emergency situations to reduce risks. At the same time, a data-driven optimization decision-making model will be established to support intelligent and precise pipeline network operation decisions, monitor and optimize decisions in real time, and improve pipeline network operation efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 It is a stereogram of the present invention. DETAILED DESCRIPTION

[0076] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments 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 creative work should fall within the scope of protection of the present invention.

[0077] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0078] The present invention is described in detail below in conjunction with the accompanying drawings:

[0079] Embodiment 1:

[0080] Please see attached Figure 1 The embodiment of the present invention provides a method for improving the oxygen pipe network pressure fluctuation control process, comprising the following steps:

[0081] Step 1: Measured data support:

[0082] Establish an oxygen network pressure monitoring system, including sensors and data acquisition equipment;

[0083] Collect pipeline network pressure data in real time and establish data storage and management system;

[0084] Step 2: Real-time monitoring and control precision integration:

[0085] Maintain monitoring equipment, regularly maintain monitoring equipment, and optimize monitoring algorithms;

[0086] Improve the control system to ensure that the pressure fluctuation of the pipeline network is under control;

[0087] Step 3: Integrate maintenance timeliness and risk management:

[0088] Establish a regular maintenance plan to prevent problems from happening;

[0089] Develop emergency response plans;

[0090] Step 4: Algorithm optimization and intelligent control integration:

[0091] Analyze measured data and historical data to optimize model algorithms;

[0092] Real-time monitoring algorithm to monitor and intelligently control pipeline network pressure fluctuations in real time;

[0093] Design intelligent control strategies and automatically adjust control parameters;

[0094] Step 5: Data mining and prediction algorithm integration:

[0095] Use data mining technology to discover hidden patterns and information;

[0096] Use prediction algorithms such as regression analysis, neural networks, and support vector machines to predict oxygen network pressure fluctuations;

[0097] Step 6: Control algorithm integration:

[0098] Combine PID control, fuzzy control and optimization control algorithms to optimize the pipe network pressure control strategy;

[0099] Design adaptive control algorithms to adjust control parameters in real time to meet pressure control requirements under various working conditions;

[0100] Step 7: Data-driven optimization decision integration:

[0101] Data collection and preprocessing;

[0102] Feature engineering, building data-driven decision models;

[0103] Model training and optimization;

[0104] Real-time decision support system to support operators in making intelligent decisions.

[0105] Embodiment 2:

[0106] Please see attached Figure 1 The embodiment of the present invention provides a method for improving the oxygen pipe network pressure fluctuation control process, comprising the following steps:

[0107] Step 1: Advanced measured data support:

[0108] Introducing advanced sensing technology;

[0109] Implementing a data quality management system;

[0110] Step 2: Advanced real-time monitoring and control integration:

[0111] Introducing advanced monitoring equipment;

[0112] Implementing machine learning-based control systems;

[0113] Step 3: Advanced maintenance timeliness and risk management integration:

[0114] Implement predictive maintenance technology;

[0115] Introducing intelligent risk management system for real-time risk monitoring;

[0116] Step 4: Advanced algorithm optimization and intelligent control integration:

[0117] Introduce deep learning technology to optimize model algorithms;

[0118] Implementing intelligent control based on reinforcement learning;

[0119] Step 5: Integration of advanced data mining and prediction algorithms:

[0120] Introducing reinforcement learning technology to optimize data mining and prediction algorithms;

[0121] Implement real-time forecasting system for rapid prediction and response;

[0122] Step 6: Advanced control algorithm integration:

[0123] Introducing the model predictive control (MPC) algorithm to optimize the pipe network pressure control strategy;

[0124] Implement intelligent adaptive control algorithms;

[0125] Step 7: Advanced data-driven optimization decision integration:

[0126] Introducing big data analysis technology;

[0127] Implement real-time big data decision support systems.

[0128] Embodiment three:

[0129] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for improving the oxygen pipe network pressure fluctuation control process, comprising the following steps:

[0130] Step 1: Efficient data processing and real-time monitoring:

[0131] Introducing high-speed data processing technology to improve data processing efficiency;

[0132] Implement a real-time monitoring system to ensure timely data updates;

[0133] Step 2: Fast algorithm optimization and control integration:

[0134] Use parallel computing technology to accelerate the algorithm optimization process;

[0135] Implement fast response control systems to reduce control delays;

[0136] Step 3: Intelligent risk management and plan formulation:

[0137] Introducing intelligent risk identification technology to improve risk management efficiency;

[0138] Formulate intelligent emergency plans to improve emergency response speed;

[0139] Step 4: Efficient data mining and prediction algorithm integration:

[0140] Use high-performance computing platforms to accelerate the data mining process;

[0141] Implement rapid prediction algorithms to apply control measures in advance;

[0142] Step 5. Parameters:

[0143] Data processing speed optimization parameters;

[0144] Algorithm optimization acceleration parameters;

[0145] Intelligent parameters for risk management;

[0146] Data mining prediction efficiency parameters.

[0147] Embodiment 4:

[0148] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for improving the oxygen pipe network pressure fluctuation control process, comprising the following steps:

[0149] Embodiment 4:

[0150] Step 1: Adaptive system optimization:

[0151] Introduce adaptive control algorithms to improve the system's ability to adapt to environmental changes;

[0152] Implement self-learning mechanism to improve the system's self-adjustment ability;

[0153] Step 2: Intelligent monitoring and control integration:

[0154] Use machine learning algorithms to realize intelligent monitoring systems;

[0155] Implement intelligent controllers to achieve intelligent control of the system;

[0156] Step 3: Advanced risk management and plan optimization:

[0157] Introducing advanced risk assessment technology to improve risk management accuracy;

[0158] Optimize emergency plans and improve emergency response efficiency;

[0159] Step 4: Integration of adaptive algorithm optimization and intelligent control:

[0160] Implement an adaptive algorithm optimization system to achieve real-time adjustment of algorithm parameters;

[0161] Introduce intelligent control strategies to improve the intelligence level of the system;

[0162] Step 5. Parameters:

[0163] Adaptive parameter optimization;

[0164] Intelligent monitoring and control parameters;

[0165] Advanced risk management parameters;

[0166] Intelligent parameters of adaptive algorithm.

[0167] Embodiment five:

[0168] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for improving the oxygen pipe network pressure fluctuation control process, comprising the following steps:

[0169] Step 1: Advanced Data Mining and Feature Engineering:

[0170] Use advanced data mining technology to discover the patterns and information hidden in the data;

[0171] Perform feature extraction and feature selection to improve the accuracy of data analysis;

[0172] Step 2: Integration of advanced prediction algorithms:

[0173] Introducing advanced prediction algorithms, such as deep learning models or reinforcement learning algorithms;

[0174] Implementing advanced predictive models to achieve more accurate predictions of oxygen network pressure fluctuations;

[0175] Step 3: Advanced parameters:

[0176] Data mining algorithm parameter optimization;

[0177] Prediction model parameter adjustment;

[0178] Feature Engineering Parameter Settings

[0179] Step 4: Model training and verification:

[0180] Divide the data set into training set and validation set for model training and evaluation;

[0181] Perform model training and adjust model parameters to best fit the data;

[0182] Use the validation set to evaluate model performance and adjust model structure and hyperparameters;

[0183] Step 5: Model integration and optimization:

[0184] Implement model integration technology, such as integrating multiple forecasting models to improve the accuracy of volatility forecasting; perform model optimization to further improve forecasting performance through model fusion, parameter adjustment, etc.;

[0185] Step 6: Real-time monitoring and feedback adjustment:

[0186] Establish a real-time monitoring system to continuously monitor the pressure fluctuations of the oxygen pipeline network;

[0187] Adjust prediction model parameters and feature engineering settings based on real-time data feedback to maintain model accuracy and timeliness;

[0188] Step 7: Continuous Improvement and Feedback Loop:

[0189] Establish a continuous improvement mechanism to regularly evaluate system performance and make improvement suggestions;

[0190] Based on user feedback and system monitoring results, we cyclically optimize data mining and prediction algorithms to continuously improve the overall efficiency of the system.

[0191] Summary:

[0192] Embodiment 1:

[0193] It emphasizes the steps of measured data support, real-time monitoring and control, maintenance timeliness and risk management, algorithm optimization and intelligent control; it focuses on establishing monitoring systems, maintaining equipment, optimizing algorithms, implementing control strategies and data mining predictions.

[0194] Embodiment 2:

[0195] Emphasis is placed on steps such as advanced measured data support, advanced real-time monitoring and control, advanced maintenance timeliness and risk management, advanced algorithm optimization and intelligent control; and the introduction of advanced technologies such as machine learning, deep learning, and reinforcement learning to improve the efficiency of pipeline network pressure fluctuation control.

[0196] Embodiment three:

[0197] Highlight steps such as efficient data processing and real-time monitoring, rapid algorithm optimization and control, intelligent risk management and plan formulation, efficient data mining and prediction algorithm integration; introduce high-speed processing technology, parallel computing, and intelligent risk identification to accelerate data mining and early prediction.

[0198] Embodiment 4:

[0199] Focus on steps such as adaptive system optimization, intelligent monitoring and control, advanced risk management and plan optimization, adaptive algorithm optimization and intelligent control; introduce adaptive control, machine learning, and intelligent controllers to improve system adaptability and intelligence.

[0200] Embodiment five:

[0201] Focusing on advanced data mining and feature engineering, advanced prediction algorithm integration, advanced parameters, model training and verification, model integration and optimization, real-time monitoring and feedback adjustment, continuous improvement and feedback loop, etc., by integrating advanced data mining technology and prediction algorithms, and continuously optimizing models and systems, the accuracy and efficiency of oxygen pipeline network pressure fluctuation prediction can be improved.

[0202] Experimental design:

[0203] Comparative experiment 1: algorithm performance comparison

[0204] Experimental purpose: To compare the performance of various algorithms in predicting pressure fluctuations in oxygen pipeline networks;

[0205] Experimental setup: Comparison of neural network (NN), support vector machine (SVM), and random forest (RF);

[0206] Parameter settings:

[0207] Comparison neural network: number of hidden layer neurons = 100, learning rate = 0.001, number of iterations = 1000;

[0208] Support vector machine: kernel function = radial basis function, penalty parameter C = 1.0, kernel function parameter = 0.1;

[0209] Random Forest: number of trees = 100, maximum number of features = total number of features, maximum depth = 10;

[0210] Steps:

[0211] Step 1: Data preparation:

[0212] Feature extraction:

[0213] By extracting features from the oxygen network pressure fluctuation data;

[0214] Consider using methods such as sliding windows to capture the dynamic characteristics of time series data;

[0215] Data cleaning and processing:

[0216] Perform data cleaning, process missing values ​​and outliers, and ensure data quality;

[0217] Use interpolation methods to fill missing values ​​and employ anomaly detection techniques to handle abnormal data;

[0218] Step 2: Feature Engineering:

[0219] Feature Selection:

[0220] Perform feature selection, use correlation analysis and feature importance ranking methods to select features that affect the target variable;

[0221] Use PCA (principal component analysis) dimensionality reduction technology to reduce the dimension of feature space;

[0222] Feature transformation:

[0223] Transform the features to improve their distribution;

[0224] Try to generate new features to enrich feature representation capabilities;

[0225] Step 3: Model selection:

[0226] Expand the comparison range:

[0227] Introducing ensemble learning methods and regression models;

[0228] Try deep learning models to capture long-term dependencies in time series data;

[0229] Step 4: Cross-validation:

[0230] Model Evaluation:

[0231] Use cross-validation to evaluate the generalization ability of the model and prevent the model from overfitting;

[0232] Adjust the model's hyperparameters to improve model performance.

[0233] Experimental data table:

[0234] algorithm Accuracy (%) F1 score Comparing Neural Networks 85.3 0.842 Support Vector Machine 81.6 0.802 Random Forest 89.2 0.895

[0235] Comparative Experiment 2: Comparison of Data Processing Methods Experimental Purpose: To compare the impact of feature engineering on the performance of the prediction model;

[0236] Experimental setup: Comparison of raw data, standardized data, and data processing methods after feature selection; Parameter settings:

[0237] Standardization using mean and variance;

[0238] Use the variance threshold method to select features with variance > threshold;

[0239] Steps:

[0240] Step 1: Data preparation:

[0241] Feature extraction:

[0242] By extracting features from the oxygen network pressure fluctuation data;

[0243] Consider introducing frequency domain features or time-frequency features to capture the periodicity and spectrum information of the data;

[0244] Data cleaning and processing:

[0245] Perform data cleaning, handle missing values ​​and outliers, and use interpolation methods to fill missing values;

[0246] Consider using anomaly detection algorithms to identify and handle anomalous data points;

[0247] Step 2: Feature Engineering:

[0248] Feature Selection:

[0249] Perform feature selection, combining business knowledge and feature importance ranking methods to select the most relevant features;

[0250] Try model-based feature selection methods, such as feature importance ranking based on tree models;

[0251] Feature transformation:

[0252] Consider using nonlinear transformations to improve the distribution of features;

[0253] Try to use methods such as autoencoders for feature learning to extract abstract feature representations from the data;

[0254] Step 3: Model selection:

[0255] Data processing method extension:

[0256] Introduce data enhancement technology to deal with data imbalance problems;

[0257] Try to use model fusion techniques to combine the prediction results of the models;

[0258] Step 4: Cross-validation:

[0259] Model Evaluation:

[0260] During the cross-validation process, the impact of each data processing method on the model performance is compared to evaluate the generalization ability of the model;

[0261] The phase difference evaluation metric is used to comprehensively evaluate the model performance.

[0262] Experimental data table:

[0263] Data processing methods Accuracy (%) F1 score Raw data 82.5 0.815 Standardizing Data 86.7 0.869 Feature selection data 88.1 0.882

[0264] Comparative experiment 3: Comparison of model integration effects

[0265] Experimental purpose: To compare the effect of model integration on prediction performance;

[0266] Experimental setup: Compare the prediction accuracy of single model and ensemble model;

[0267] Parameter settings:

[0268] Bagging: Use decision trees as base learners, ensemble size = 50;

[0269] Boosting: base learner is decision tree, iteration number = 100;

[0270] Steps:

[0271] Step 1: Data preparation:

[0272] Feature extraction:

[0273] By extracting features from the oxygen network pressure fluctuation data;

[0274] Consider introducing frequency domain features or time-frequency features to capture the periodicity and spectrum information of the data;

[0275] Data cleaning and processing:

[0276] Perform data cleaning, handle missing values ​​and outliers, and use interpolation methods to fill missing values;

[0277] Consider using anomaly detection algorithms to identify and handle anomalous data points;

[0278] Step 2: Feature Engineering:

[0279] Feature Selection:

[0280] Perform feature selection, combining business knowledge and feature importance ranking methods to select the most relevant features;

[0281] Try model-based feature selection methods, such as feature importance ranking based on tree models;

[0282] Feature transformation:

[0283] Consider using nonlinear transformations to improve the distribution of features;

[0284] Use methods such as autoencoders to learn features and extract abstract feature representations from data;

[0285] Step 3: Model selection:

[0286] Data processing method extension:

[0287] Introduce data enhancement technology to deal with data imbalance problems;

[0288] Try to use model fusion technology to combine the prediction results of each model;

[0289] Step 4: Cross-validation:

[0290] Model Evaluation:

[0291] During the cross-validation process, the impact of each data processing method on the model performance is compared to evaluate the generalization ability of the model;

[0292] Use evaluation metrics to fully assess model performance.

[0293] Experimental data table:

[0294] Model Accuracy (%) F1 score Single Model 85.6 0.852 Bagging 88.3 0.884 Boosting 89.7 0.897

[0295] Summary of comparative experiments:

[0296] Experiment 1:

[0297] Data processing: basic data processing methods, including feature extraction, data cleaning and processing;

[0298] Features: Focuses on basic data processing workflows and is used for simple data sets and models.

[0299] Experiment 2:

[0300] Data processing: Expand feature engineering steps, including feature selection, PCA dimensionality reduction, and feature transformation;

[0301] Model selection: involves ensemble learning methods and regression models, expanding the scope of model comparison;

[0302] Features: Improved data processing and feature engineering, suitable for medium-complexity data sets and models.

[0303] Experiment 3:

[0304] Data processing: Expand the data processing steps, including the introduction of frequency domain features, anomaly detection processing, nonlinear transformation, etc.;

[0305] Feature engineering: involves more feature engineering techniques, such as data enhancement and model fusion, to improve feature expression and model performance;

[0306] Model selection: Try deep learning models to capture long-term dependencies in time series data and improve model performance;

[0307] Features: Great improvements in data processing, feature engineering, and model selection, and can be used for complex data sets and models.

[0308] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for improving the pressure fluctuation control process of an oxygen pipeline network, characterized in that: The following steps are involved: Step 1: Measured data support: Establish a measured data acquisition system and use the measured data as the basis for optimizing the design, verifying and correcting the simulation results; Step 2: Real-time monitoring and control precision integration: a. Monitoring equipment maintenance: regularly maintain monitoring equipment and optimize monitoring algorithms; b. Improve the control system to prevent the pressure fluctuation of the pipeline network from being controlled; Step 3: Integrate maintenance timeliness and risk management: a. Establish a regular maintenance plan to prevent problems from occurring; b. Formulate emergency response plans and establish risk management mechanisms; Step 4: Algorithm optimization and intelligent control integration: a. Use measured data and historical data to conduct in-depth analysis and optimize model algorithms; b. Real-time monitoring and intelligent control of pipeline network pressure fluctuations through real-time monitoring algorithms; c. Design intelligent control strategies to automatically adjust control parameters based on real-time monitoring data and prediction models; Step 5: Data mining and prediction algorithm integration: a. Use data mining technology to discover the patterns and information hidden behind the data; b. Use prediction algorithms to predict oxygen network pressure fluctuations and take control measures in advance; Step 6: Control algorithm integration: a. Combine PID control, fuzzy control and optimization control algorithms to optimize the pipe network pressure control strategy; b. Design an adaptive control algorithm to adjust control parameters in real time according to the network operation status and environmental changes to meet the pressure control requirements under various working conditions; Step 7: Data-driven optimization decision integration: a. Establish a data-driven optimization decision-making model, combining real-time monitoring data and algorithm analysis results to support intelligent and precise decision-making for pipeline network operation; b. Develop a real-time optimization decision-making system to monitor and optimize the pressure fluctuation control of the pipeline network in real time based on data-driven models and algorithms.

2. A method for improving the oxygen pipeline network pressure fluctuation control process according to claim 1, characterized in that: In step 1, the specific implementation steps supported by the measured data are: Establish a measured data collection system: build an oxygen pipeline network pressure monitoring system to collect pipeline network pressure data in real time; Basis of measured data for design optimization: Collect measured data to establish a data storage and management system to facilitate subsequent model building and algorithm optimization; Verify and correct simulation results: Compare and analyze the measured data with the results of the currently established simulation model to verify the accuracy of the simulation model.

3. A method for improving the oxygen pipe network pressure fluctuation control process according to claim 1, characterized in that: In step 2, the monitoring algorithm is equivalent to the PID control algorithm, which is used to adjust the output of the system. Its basic formula is as follows: Among them, u(t) is the controller output, that is, the control quantity applied to the system; e(t) is the difference between the set value and the actual value; K p is the proportional gain, which is used to adjust the influence of the current error on the control quantity; K i is the integral gain, which is used to eliminate the steady-state error of the system; K d is the differential gain, which is used to predict the changing trend of the error.

4. A method for improving the oxygen pipeline network pressure fluctuation control process according to claim 1, characterized in that: In step 3, the specific implementation steps of formulating the emergency plan are: Risk Assessment and Identification: Assess the risks and potential hazards that may cause pressure fluctuations in the pipeline network and identify emergency situations; Emergency plan formulation: Develop a detailed emergency plan and clarify the tasks and action plans of each responsible person; Resource preparation: Ensure that the emergency response team has the necessary resources and equipment to take prompt action in an emergency, and that communications equipment is available to facilitate real-time communication and coordination among team members; Training and drills: Train the emergency response team, familiarize them with emergency plans and response procedures, organize drills regularly, simulate emergency situations, and test the team's response capabilities and collaboration efficiency; Monitoring and Alarm Systems: Set alarm thresholds to facilitate timely action when network pressure fluctuations exceed normal ranges; Coordination and Communication: Establish a good communication mechanism to promptly inform relevant parties of the situation in an emergency and coordinate resources to effectively solve the problem.

5. A method for improving the oxygen pipe network pressure fluctuation control process according to claim 1, characterized in that: In the step 4, the optimization model algorithm involves a particle swarm optimization algorithm, and the particle swarm optimization algorithm includes a particle velocity update formula and a particle position update formula; Particle velocity update formula: in, is the updated value of the velocity of particle i in dimension j, w is the inertia weight, c1 and c2 are acceleration factors, r1 and r2 are random numbers, and pbest ij is the individual's best historical position, gbest j is the historical optimal position of the group, is the position of the particle in dimension; Particle position update formula: in, is the updated value of the position of particle i in dimension j.

6. A method for improving the oxygen pipe network pressure fluctuation control process according to claim 1, characterized in that: In the step 5, in the data mining and prediction algorithm integration involved in the step 5, the prediction algorithm includes regression analysis, neural network and support vector machine; The regression analysis formula is (y = b0 + b1x1 + b2x2 + ... + b n x n +∈), where b n is the regression coefficient, x n is the independent variable, ∈ is the error term; Neural network models are used for nonlinear data modeling and prediction. A common neural network model is the multilayer perceptron (MLP). The formula of MLP is: Among them, w i is the weight, x i is the input, b is the bias, and f is the activation function; Support vector machine, support vector machine is used for classification and regression tasks. In the regression problem, the basic formula of support vector machine is (y = w T x+b), where w is the weight vector, x is the input vector, and b is the bias.

7. A method for improving the oxygen pipe network pressure fluctuation control process according to claim 1, characterized in that: In step 6, the adaptive control algorithm usually involves real-time adjustment of parameters to adapt to the operation status of the pipeline network and environmental changes. Among them, a common adaptive control algorithm is adaptive fuzzy control, the basic idea of ​​which is to dynamically adjust the parameters of the fuzzy controller according to the real-time status and performance requirements of the system; Adaptive fuzzy control: [u(t) = f(e(t), Δe(t))], Among them, u(t) is the output of the controller, that is, the required control quantity, e(t) is the difference between the target value and the actual value, and Δe(t) is the rate of change of the error, which is used to consider the trend of the error.

8. The method for improving the oxygen pipe network pressure fluctuation control process according to claim 1, characterized in that: In step seven, the implementation steps of the data-driven optimization decision integration are: Data collection and preprocessing: collect real-time monitoring data and historical data, and preprocess the data; Feature engineering: feature extraction and feature selection of data, by extracting useful features from the original data; Establish data-driven decision models: Build decision models through machine learning algorithms or statistical methods, and use real-time monitoring data and algorithm analysis results as input features to predict pipeline pressure fluctuations or other key indicators; Model training and optimization: Use historical data to train models and optimize model parameters based on real-time monitoring data; Real-time decision support system: Develop a real-time decision support system, integrate the established data-driven decision model into the system, and use real-time monitoring data as input. The model outputs prediction results or recommended decisions to support operators in making intelligent decisions; Decision execution and feedback: Based on the decision results output by the model, corresponding control measures are executed to adjust the operation status of the pipeline network.

9. A method for improving the oxygen pipe network pressure fluctuation control process according to claim 8, characterized in that: In step 7, the data includes pressure, temperature and flow rate, the pressure range is 1 bar to 10 bar, the temperature range is 10 ° C to 40 ° C, and the flow rate range is 10m 3 / h~100m 3 / h.

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