Vulcanizing machine steam condensate drainage data integrated management system and management method

Abstract

The invention relates to the technical field of data analysis, in particular to a comprehensive management system and management method for steam condensate drainage data of a vulcanizing machine. According to the comprehensive management system and method, a generative adversarial network is adopted to calculate the steam flowing node correlation degree, analyze the condensate drainage valve opening and closing influence, construct a steam hydrophobic topology network and establish a flow distribution model, and temperature gradient changes are combined; the method comprises the following steps: acquiring a steam condensate drainage state mapping structure, enhancing the global analysis capability of a condensate drainage system, analyzing steam load balance by adopting a long-short-term memory network, predicting steam supply demands, forming a steam condensate drainage intelligent regulation and control strategy, calculating pressure abnormal deviation, analyzing a steam leakage rate, predicting a steam loss trend, and detecting an abnormal switch state of an electromagnetic valve. The system stability is improved, and the energy loss is reduced by extracting the blockage characteristics of the condensate drain valve, evaluating the pressure loss of the system, improving the fault prediction capability, adjusting the opening and closing states of the electromagnetic valve and correcting the control instruction of the condensate drain valve according to the steam condensate drain intelligent regulation and control strategy and the condensate drain abnormal characteristic analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and particularly to a comprehensive management system and method for steam drainage condensation data of a vulcanizer. Background Art

[0002] The technical field of data analysis aims to collect, process, analyze, and apply various types of data, discover the laws in the data, optimize the decision-making process, improve the operating efficiency of the system, and support intelligent management and control. By means of data-driven methods, it improves resource utilization rate, optimizes process control, predicts abnormal situations, reduces operating costs, and improves overall production and management efficiency.

[0003] The purpose of the comprehensive management system for steam drainage condensation data of a vulcanizer is to improve steam utilization rate, reduce energy consumption, optimize the drainage condensation process, reduce steam leakage and unnecessary emissions, improve the operating efficiency of equipment, reduce energy loss, improve production stability, and reduce maintenance costs.

[0004] The existing technology relies on the valve opening and closing method with a fixed cycle, lacking precise analysis of key parameters such as real-time steam flow rate, temperature fluctuation, and pressure distribution. This leads to problems such as steam leakage, energy loss, and unstable equipment operation during the drainage condensation process, and cannot accurately reflect the steam flow state, resulting in a lag in the opening and closing control of the drainage condensation valve, being unable to perform dynamic optimization for different working conditions, increasing maintenance costs, and affecting the stability of steam supply. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a comprehensive management system and method for steam drainage condensation data of a vulcanizer.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A comprehensive management system for steam drainage condensation data of a vulcanizer includes:

[0007] A steam drainage condensation control module: Extract the action signal of the drainage condensation valve, the signal of the steam pressure sensor, the state of the steam trap, and the solenoid valve duration through sensors, calculate the steam flow rate, match the drainage condensation cycle, detect temperature fluctuations, analyze the flow pressure, calculate the steam utilization rate, and generate a steam drainage condensation data set;

[0008] A state topology analysis module: Based on the steam drainage condensation data set, adopt a generative adversarial network, calculate the correlation degree of steam flow nodes, analyze the influence of the opening and closing of the drainage condensation valve, construct a steam drainage topology network, calculate the flow distribution model, analyze the temperature gradient change, map the solenoid valve state, and obtain a steam drainage condensation state mapping structure;

[0009] Intelligent condensate drainage optimization module: Based on the steam condensate drainage state mapping structure, calculate the adaptive switching period of the condensate drainage valve, correct the pressure distribution of the drain pipe, optimize the steam recovery utilization rate, dynamically adjust the opening and closing time of the valve, use long short-term memory network, analyze the steam load balance, predict the steam supply demand, and generate an intelligent control strategy for steam condensate drainage;

[0010] Abnormal evolution detection module: Based on the steam condensate drainage data set, analyze the steam leakage rate, calculate the abnormal pressure deviation, predict the steam consumption trend, detect the abnormal switching of the solenoid valve, extract the characteristics of the condensate drainage valve blockage, evaluate the pressure loss of the condensate drainage system, and obtain the analysis results of the condensate drainage abnormal characteristics;

[0011] Abnormal response control module: Based on the intelligent control strategy for steam condensate drainage and the analysis results of the condensate drainage abnormal characteristics, adjust the opening and closing state of the solenoid valve, correct the control instruction of the condensate drainage valve, balance the steam supply pressure, control the cleaning of the steam pipeline blockage, calculate the compensation of the drain system, correct the condensate drainage strategy, and establish an automatic control scheme for condensate drainage anomalies.

[0012] As a further solution of the present invention, the steam condensate drainage control module includes:

[0013] Signal extraction sub-module: Extract the action signal of the condensate drainage valve, the steam pressure sensor signal, the state of the drain valve and the solenoid valve duration through the sensor, synchronize the data, align the multi-source signals through the time stamp, and classify the signals. Divide the working condition characteristics by amplitude, duration and change trend, and establish a steam condensate drainage signal set;

[0014] Steam parameter analysis sub-module: Based on the steam condensate drainage signal set, calculate the steam flow rate, calculate the instantaneous flow rate using the flow rate change during the opening and closing period of the condensate drainage valve, and match the condensate drainage period. Match the condensate drainage period by matching the flow rate fluctuation period with the valve opening and closing state to obtain the steam condensate drainage parameter set;

[0015] Condensate drainage data calculation sub-module: Based on the steam condensate drainage parameter set, calculate the steam utilization rate, calculate the effective steam ratio using the flow accumulation value, and perform flow pattern analysis. Extract the condensate drainage working condition characteristics from the time change of the steam flow rate to generate a steam condensate drainage data set.

[0016] As a further solution of the present invention, the state topology analysis module includes:

[0017] Node correlation analysis sub-module: Based on the steam condensate drainage data set, use the generative adversarial network to calculate the correlation degree of steam flow nodes, analyze the flow rate trend using the flow rate change matrix, and perform analysis on the influence of valve opening and closing. Calculate the relationship between valve opening and closing and flow rate change to generate steam node correlation characteristics;

[0018] Topological structure construction sub-module: Based on the steam node association features, construct a steam drainage topological network, construct a topological structure using the pipeline connection relationship and the flow weight between nodes, and perform pressure distribution analysis. Calculate the steam flow trend through the pressure difference between nodes to generate a steam flow topological structure;

[0019] State mapping calculation sub-module: Based on the steam flow topological structure, perform flow distribution model calculation, summarize the flow state using the flow change trend, and perform solenoid valve state mapping. Calculate the corresponding state through the relationship between the valve signal and the flow rate to obtain a steam condensate drainage state mapping structure.

[0020] As a further solution of the present invention, the generative adversarial network performs normalization processing based on the steam condensate drainage data set, constructs a generative adversarial network composed of a generator and a discriminator, generates virtual data samples, improves the feature learning ability of the model through adversarial training. After training, use the generator to generate flow change patterns, combine the discriminator to calculate the similarity of the node flow rate changes, and calculate the flow trend changes between different nodes by analyzing the flow rate change matrix.

[0021] As a further solution of the present invention, the intelligent condensate drainage optimization module includes:

[0022] Condensate drainage cycle optimization sub-module: Based on the steam condensate drainage state mapping structure, calculate the adaptation switch cycle of the condensate drainage valve, extract the flow change curve using the valve opening and closing timing data, and perform flow rate fluctuation analysis. Calculate the cycle by matching the flow rate sudden increase point with the valve signal, adjust the condensate drainage control timing, and obtain the optimized parameters for the opening and closing of the condensate drainage valve;

[0023] Pipeline pressure correction sub-module: Based on the optimized parameters for the opening and closing of the condensate drainage valve, correct the pressure distribution of the drainage pipeline, calculate the pressure gradient using the pipeline node pressure data, and perform flow rate balance analysis. Calculate the uneven area through the flow rate accumulation value and the pressure fluctuation, optimize the condensate drainage cycle to match the steam recovery rate, and obtain the steam flow correction parameters;

[0024] Steam demand prediction sub-module: Based on the steam flow correction parameters, use a long short-term memory network to perform steam load balance analysis, calculate the steam supply demand fluctuation using historical load data, and perform trend calculation. Predict the short-term demand through the steam supply pressure change rate and the historical flow rate, match the condensate drainage cycle to adjust the load fluctuation, and generate an intelligent control strategy for steam condensate drainage.

[0025] As a further solution of the present invention, for calculating the cycle by matching the flow surge point with the valve signal, the judgment criterion of the flow surge point is based on the calculation of the flow change rate, the statistical analysis of historical data, and the elimination of outliers. The flow time series data is collected, and the short-term fluctuations are smoothed by the moving average method. The flow change rate is calculated by the first-order difference, and the surge threshold is set based on the mean and standard deviation of historical data. When the flow change rate exceeds the threshold, the corresponding time point is marked as the surge point. At the same time, it is analyzed and judged whether the surge point appears within a short time after the valve is opened to ensure that the detection result can accurately reflect the actual change of the system state.

[0026] As a further solution of the present invention, for the long short-term memory network, the historical data of steam supply and demand extracted by the sensor is collected to construct a time series data set. The input data is normalized and trained, and the current real-time steam supply data is input into the trained model to output the short-term steam demand change trend.

[0027] As a further solution of the present invention, the abnormal evolution detection module includes:

[0028] Steam leakage analysis sub-module: Based on the steam drainage data set, the steam leakage rate is analyzed. The difference between steam input and output is calculated by using the pipeline flow data, and the leakage point is located. The leakage area is identified by comparing the flow loss point with the normal range, the emission data loss rate is calculated, and the steam leakage characteristic data is obtained.

[0029] Abnormal offset detection sub-module: Based on the steam leakage characteristic data, the abnormal pressure offset is calculated. The offset amplitude is calculated by comparing the pressure sensor data with the steady-state pressure, and the abnormal valve is detected. The abnormal switch situation is identified by comparing the signal mutation point of the solenoid valve with the flow abnormality, the abnormal rate is counted, and the abnormal parameters of the drainage system are obtained.

[0030] Drainage blockage identification sub-module: Based on the abnormal parameters of the drainage system, the drainage valve blockage characteristics are extracted. The switch lag degree is calculated by comparing the valve opening and closing response time with the normal working condition, and the pipeline pressure loss is evaluated. The blockage situation is calculated by the flow decrease rate, and the influence range of the blockage is located by matching the pressure mutation area, and the analysis result of the drainage abnormality characteristics is obtained.

[0031] As a further solution of the present invention, the abnormal response regulation module includes:

[0032] Valve dynamic adjustment sub-module: Based on the intelligent regulation strategy of steam drainage and the analysis result of drainage abnormality characteristics, the opening and closing of the solenoid valve are adjusted. The flow change rate is calculated by using the opening and closing time series data, and the response lag is analyzed. The control instruction is adjusted by matching the flow mutation point with the valve signal, and the valve adjustment parameters are obtained.

[0033] Steam supply pressure balance sub-module: Based on the valve adjustment parameters, calculate the steam supply pressure distribution, calculate the deviation by comparing the node pressure data with the steam flow rate change, perform pressure compensation, adjust the steam supply ratio through the flow stability interval, conduct load balance analysis, and obtain the steam supply pressure correction data;

[0034] Abnormal condensate drainage correction sub-module: Based on the steam supply pressure correction data, detect steam pipeline blockages, identify blocked areas using the pressure fluctuation curve, and perform drainage system compensation. Adjust the compensation ratio based on the valve opening and closing time and steam accumulation data, correct the condensate drainage cycle, and establish an automatic regulation plan for abnormal condensate drainage.

[0035] A comprehensive management method for vulcanizer steam condensate drainage data. The comprehensive management method for vulcanizer steam condensate drainage data is executed based on the above-mentioned comprehensive management system for vulcanizer steam condensate drainage data, and includes the following steps:

[0036] Step 1: Extract the condensate drain valve action signal, steam pressure sensor signal, drain valve status, and solenoid valve duration through sensors, calculate the steam flow rate, extract the steam flow direction, analyze the temperature fluctuation range within the condensate drainage cycle, calculate the pressure change value, match the valve opening and closing with the steam utilization situation, and obtain the steam condensate drainage data set;

[0037] Step 2: Based on the steam condensate drainage data set, calculate the steam flow rate change rate, analyze the influence of the condensate drain valve opening and closing, establish a flow change matrix, calculate the steam flow correlation between nodes, screen nodes with abnormal flow changes, calculate the relationship between the valve switch signal and the flow change, construct the steam flow pattern using a generative adversarial network, compare the similarity of node flow rate changes, analyze the pipeline connection relationship, calculate the flow weight, extract the steam flow direction data, and obtain the steam flow topology;

[0038] Step 3: Based on the steam flow topology, calculate the flow change trend, analyze the steam flow state, extract the load change characteristics, match the valve opening and closing time with the flow change, establish the steam condensate drainage state set, calculate the steam flow characteristics, calculate the condensate drain valve switch cycle, correct the pressure distribution of the drain pipeline, calculate the steam recovery rate, extract the historical load data, use a long short-term memory network to predict the short-term change trend of the steam supply demand, and analyze the impact of the condensate drainage cycle adjustment on the steam load to obtain the steam condensate drainage operation data;

[0039] Step 4: Based on the steam condensate discharge operation data, calculate the steam flow offset, analyze the steam leakage rate, extract the abnormal fluctuation characteristics of the valve signal, calculate the abnormal pressure offset, match the load change trend, analyze the clogging state of the condensate discharge valve, calculate the pressure loss value of the condensate drainage system, evaluate the steam supply pressure deviation, extract the abnormal opening and closing signals of the solenoid valve, calculate the adjustment amount of the solenoid valve opening and closing, correct the control instruction of the condensate discharge valve, calculate the compensation parameter of the condensate drainage system, adjust the steam flow in the pipeline, and obtain the automatic control scheme for condensate discharge abnormality.

[0040] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0041] 1. In the present invention, a generative adversarial network is used to calculate the correlation degree of steam flow nodes, analyze the influence of the opening and closing of the condensate discharge valve, construct a steam condensate drainage topological network, establish a flow distribution model, combine with the temperature gradient change, map the state of the solenoid valve, and obtain a steam condensate discharge state mapping structure, enhancing the global analysis ability of the condensate drainage system.

[0042] 2. In the present invention, a long short-term memory network is used to analyze the steam load balance, predict the steam supply demand, form an intelligent control strategy for steam condensate discharge, improve the steam supply precise matching ability, calculate the abnormal pressure offset, analyze the steam leakage rate, predict the steam consumption trend, detect the abnormal opening and closing state of the solenoid valve, extract the clogging characteristics of the condensate discharge valve, and evaluate the system pressure loss, improving the fault prediction ability.

[0043] 3. In the present invention, through the intelligent control strategy for steam condensate discharge and the analysis results of condensate discharge abnormality characteristics, the opening and closing state of the solenoid valve is adjusted, the control instruction of the condensate discharge valve is corrected, the steam supply pressure is balanced, the cleaning of the steam pipeline blockage is implemented, the compensation amount of the condensate drainage system is calculated, the condensate discharge strategy is corrected, an automatic control scheme for condensate discharge abnormality is formed, realizing the adaptive optimization adjustment of the steam condensate discharge process, reducing the steam leakage rate, improving the system stability, and reducing the energy consumption. Description of the Drawings

[0044] Figure 1 is the system flow chart of the present invention;

[0045] Figure 2 is the schematic diagram of the system framework of the present invention;

[0046] Figure 3 is the schematic diagram of the method steps of the present invention. Detailed Embodiment

[0047] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] Please refer to Figure 1, the present invention provides a technical solution: a comprehensive management system for steam condensate drainage data of a vulcanizer includes:

[0049] Steam condensate drainage control module: Extract the action signal of the condensate drainage valve, the signal of the steam pressure sensor, the status of the steam trap, and the duration of the solenoid valve through sensors, calculate the steam flow rate, match the condensate drainage cycle, detect temperature fluctuations, analyze flow and pressure, calculate the steam utilization rate, and generate a set of steam condensate drainage data;

[0050] Status topology analysis module: Based on the set of steam condensate drainage data, use a generative adversarial network to calculate the correlation degree of steam flow nodes, analyze the influence of the opening and closing of the condensate drainage valve, construct a steam trap topology network, calculate the flow distribution model, analyze the change of temperature gradient, map the status of the solenoid valve, and obtain the steam condensate drainage status mapping structure;

[0051] Intelligent condensate drainage optimization module: Based on the steam condensate drainage status mapping structure, calculate the adaptive switching cycle of the condensate drainage valve, correct the pressure distribution of the steam trap pipeline, optimize the steam recovery utilization rate, dynamically adjust the opening and closing time of the valve, use a long short-term memory network to analyze the steam load balance, predict the steam supply demand, and generate an intelligent control strategy for steam condensate drainage;

[0052] Abnormal evolution detection module: Based on the set of steam condensate drainage data, analyze the steam leakage rate, calculate the abnormal deviation of pressure, predict the trend of steam consumption, detect the abnormal opening and closing of the solenoid valve, extract the characteristics of the condensate drainage valve blockage, evaluate the pressure loss of the condensate drainage system, and obtain the analysis result of the condensate drainage abnormal characteristics;

[0053] Abnormal response regulation module: Based on the intelligent control strategy for steam condensate drainage and the analysis result of the condensate drainage abnormal characteristics, adjust the opening and closing status of the solenoid valve, correct the control instruction of the condensate drainage valve, balance the steam supply pressure, control the cleaning of the steam pipeline blockage, calculate the compensation of the steam trap system, correct the condensate drainage strategy, and establish an automatic regulation plan for condensate drainage anomalies.

[0054] Please refer to Figure 2 , the steam condensate drainage control module includes:

[0055] Signal extraction sub-module: Extract the action signal of the condensate drainage valve, the signal of the steam pressure sensor, the status of the steam trap, and the duration of the solenoid valve through sensors, synchronize the data, align multi-source signals through timestamps, and classify the signals. Divide the working condition characteristics by amplitude, duration, and change trend to establish a set of steam condensate drainage signals;

[0056] Steam parameter analysis sub-module: Based on the set of steam condensate drainage signals, calculate the steam flow rate, calculate the instantaneous flow rate using the flow change during the opening and closing period of the condensate drainage valve, and match the condensate drainage cycle. Match the appropriate condensate drainage cycle through the flow rate fluctuation cycle and the valve opening and closing status to obtain a set of steam condensate drainage parameters;

[0057] Drainage data calculation sub-module: Based on the steam drainage parameter set, calculate the steam utilization rate, calculate the effective steam ratio using the flow accumulation value, conduct a flow pattern analysis, extract the drainage condition characteristics through the time variation of the steam flow rate, and generate a steam drainage data set;

[0058] Signal extraction sub-module: Based on the drainage valve action signal, steam pressure sensor signal, steam trap status, and solenoid valve duration collected by the sensor, use the time series interpolation algorithm, specifically the Lagrange interpolation method, and adopt the third-order interpolation method. The interpolation points are set as the signal acquisition timestamps, and the known data points correspond to the sensor signal values respectively. Synchronize the collected signals and align the multi-source signal timestamps. Use the K-Means clustering algorithm, set the number of clustering centers to 3, calculate the similarity between samples using the Euclidean distance, set the number of iterations to 100 times, and set the convergence condition to an error change less than 0.001. Classify the signals. Based on the classification results, use wavelet transform, specifically select the Daubechies 4th-order wavelet, and use the Mallat algorithm for decomposition. Set the threshold to 0.05 to decompose the signals, extract the amplitude characteristics, duration characteristics, and change trend characteristics of the signals. Use the state transition matrix, set the matrix dimension to 3×3, and the state variables are high steam flow rate, medium steam flow rate, and low steam flow rate. Calculate the transition probability based on the state transformation frequency in the past 100 time points, divide the condition characteristics, and generate a steam drainage signal set;

[0059] Steam parameter analysis sub-module: Based on the steam drainage signal set, use the numerical differentiation method, based on the Lagrange finite difference method, calculate the instantaneous flow rate of the steam flow during the opening and closing periods of the drainage valve. Use the dynamic time warping algorithm, set the window length to 5, calculate the matching degree between samples using the Euclidean distance, set the cumulative error threshold to 0.05, match the flow rate fluctuation period. Based on the matching results, combined with the valve opening and closing status, use the double exponential smoothing algorithm, set the smoothing coefficient to 0.3, and set the initial trend term to the average slope of the past 50 time points to calculate the adapted drainage period and generate a steam drainage parameter set;

[0060] Drainage data calculation sub-module: Based on the steam drainage parameter set, using the integral numerical calculation method, based on the Simpson integral method, the integral interval is set to the drainage cycle length, the number of segmentation points is set to 100, calculate the steam utilization rate, using the Markov chain Monte Carlo method, using the Metropolis-Hastings algorithm, the initial state is set to a random steam flow rate, the acceptance rate is set to 0.25, the sampling step size is set to 0.01, analyze the steam flow pattern, using wavelet packet decomposition, using the Daubechies 6th-order wavelet, the decomposition layer is set to 3, the decomposed frequency band range is set to 0.1Hz - 50Hz, extract the time-varying information of the steam flow rate, combined with the hidden Markov model, the hidden state is set to three working conditions, the transition probability is calculated based on the training data set, the observation probability is fitted using the Gaussian mixture model, extract the drainage condition characteristics, and generate the steam drainage data set.

[0061] Please refer to Figure 2 , the state topology analysis module includes:

[0062] Node association analysis sub-module: Based on the steam drainage data set, using the generative adversarial network, calculate the steam flow node association degree, analyze the flow trend using the flow velocity change matrix, and conduct the valve opening and closing influence analysis, calculate the relationship between valve opening and closing and flow rate change, and generate the steam node association characteristics;

[0063] Topology structure construction sub-module: Based on the steam node association characteristics, construct the steam drainage topology network, construct the topology structure using the pipeline connection relationship and the flow weight between nodes, and conduct the pressure distribution analysis, calculate the steam flow trend through the pressure difference between nodes, and generate the steam flow topology structure;

[0064] State mapping calculation sub-module: Based on the steam flow topology structure, calculate the flow distribution model, summarize the flow state using the flow rate change trend, and conduct the solenoid valve state mapping, calculate the corresponding state through the relationship between the valve signal and the flow rate, and obtain the steam drainage state mapping structure;

[0065] Node Association Analysis Sub-module: Based on the steam drainage data set, a generative adversarial network is adopted. The generator uses a multi-layer perceptron structure. The dimension of the input layer is set to 256. The hidden layer uses a three-layer fully connected neural network. The number of neurons in the hidden layer is 512, 256, and 128 respectively. The activation function uses ReLU. The output layer uses a linear transformation. The discriminator uses a two-layer convolutional neural network. The size of the convolutional kernel is set to 3×3, and the stride is set to 1. The pooling layer uses max pooling. Finally, the discrimination probability of real samples and generated samples is output to calculate the association degree of steam flow nodes. A flow velocity change matrix is used, and the matrix dimension is set to n×m, where n is the number of nodes and m is the time step. Each element represents the flow velocity of a node at a certain time step. The principal component analysis method is used to reduce the dimension of the matrix, extract the main flow velocity change patterns, and perform flow trend analysis. A random forest regression model is adopted, the number of trees is set to 100, the maximum depth is set to 10, and the minimum number of split samples is set to 5. The flow velocity trend is fitted and predicted, and the influence of valve opening and closing is analyzed. The relationship between valve opening and closing and flow rate change is calculated using Granger causality analysis. The lag order is set to 3, and the test level is set to 0.05. The causal relationship between each valve opening and closing state and the flow velocity change is calculated to generate steam node association features;

[0066] Topological Structure Construction Sub-module: Based on the steam node association features, a steam drainage topological network is constructed. A graph neural network is adopted. The input layer uses a node feature vector, and the feature dimension is set to 64. The hidden layer uses a two-layer graph convolutional neural network. The adjacency matrix is constructed based on the node connection relationship and flow weight. The normalized Laplacian matrix is used for feature propagation. The output layer uses the Softmax activation function to predict the node category and perform pressure distribution analysis. The finite element analysis method is used to establish a pressure balance equation for each node in the topological structure. The element division uses four-node quadrilateral elements. The pressure boundary conditions are based on the set pressures at the inlet and outlet. The numerical solution uses the conjugate gradient method. The steam flow trend is calculated through the pressure difference between nodes. The Newton iteration method is used to calculate the pressure gradient between nodes in the topological network to generate a steam flow topological structure;

[0067] Status mapping calculation sub-module: Based on the steam flow topology, perform the calculation of the flow distribution model. Use a long short-term memory network. The dimension of the input layer is set to the length of the time series. The hidden layer uses a double-layer long short-term memory network, and the number of hidden units in each layer is set to 128. The output layer uses a fully connected neural network, and the activation function is the hyperbolic tangent. Induce the flow change trend, construct a flow state transition matrix, and perform solenoid valve state mapping. Use the support vector machine classification method, set the kernel function to the radial basis kernel, and set the penalty parameter to 1. Use the cross-validation method to evaluate the model performance, calculate the relationship between the valve signal and the flow rate, use the Bayesian probability inference method, calculate the flow rate probability distribution under different valve states based on historical data, and obtain the steam condensate discharge state mapping structure.

[0068] Generative adversarial network: Based on the steam condensate discharge data set, perform normalization processing. Construct a generative adversarial network composed of a generator and a discriminator to generate virtual data samples. Improve the feature learning ability of the model through adversarial training. After training, use the generator to generate flow change patterns, combine with the discriminator to calculate the similarity of the node flow rate changes, and calculate the flow trend changes between different nodes by analyzing the flow rate change matrix.

[0069] Please refer to Figure 2 , the intelligent condensate discharge optimization module includes:

[0070] Condensate discharge cycle optimization sub-module: Based on the steam condensate discharge state mapping structure, perform the calculation of the adaptation switch cycle of the condensate discharge valve. Extract the flow rate change curve using the valve opening and closing timing data, and perform flow rate fluctuation analysis. Calculate the cycle by matching the flow rate sudden increase point with the valve signal, adjust the condensate discharge control timing, and obtain the optimized parameters for the opening and closing of the condensate discharge valve;

[0071] Pipeline pressure correction sub-module: Based on the optimized parameters for the opening and closing of the condensate discharge valve, perform the correction of the pressure distribution of the drain pipeline. Calculate the pressure gradient using the pipeline node pressure data, and perform flow rate balance analysis. Calculate the uneven area through the flow rate accumulation value and the pressure fluctuation, optimize the condensate discharge cycle to match the steam recovery rate, and obtain the steam flow correction parameters;

[0072] Steam demand prediction sub-module: Based on the steam flow correction parameters, use a long short-term memory network to perform steam load balance analysis. Calculate the steam supply demand fluctuation using historical load data, and perform trend calculation. Predict the short-term demand through the steam supply pressure change rate and historical flow rate, match the condensate discharge cycle to adjust the load fluctuation, and generate an intelligent control strategy for steam condensate discharge;

[0073] Drainage cycle optimization sub-module: Based on the steam drainage state mapping structure, the dynamic time warping algorithm is adopted. The window length is set to five, and the Euclidean distance is used to calculate the matching degree between samples. The cumulative error threshold is set to five percent. The flow change curve is extracted from the timing data of the drainage valve opening and closing. The wavelet transform method is used, the fourth-order Daubechies wavelet is selected, and the Mallat algorithm is used for decomposition. The threshold is set to 0.05, and the frequency domain analysis of the flow signal is carried out to extract different frequency components. The fluctuation characteristics are decomposed, the discrete Fourier transform is used, the amplitude of each frequency component is calculated, the main flow fluctuation patterns are identified, and the flow velocity fluctuation analysis is carried out. The sliding window method is used, the window size is set to ten time steps, the flow velocity curve is segmented and calculated, and the flow rate sudden increase points are extracted. The Granger causality analysis method is used, the lag order is set to three, and the significance level is set to five percent. The causal relationship between the flow rate sudden increase points and the valve opening and closing signals is calculated, the drainage cycle is matched, and the particle swarm optimization algorithm is used. The population size is set to fifty, the maximum number of iterations is set to one hundred, and the inertia weight is set to 0.7. The drainage control timing is adjusted to obtain the optimized parameters for the drainage valve opening and closing;

[0074] Pipeline pressure correction sub-module: Based on the optimized parameters for the drainage valve opening and closing, the finite difference method is adopted to discretize the drain pipeline. The spatial step size is set to 0.05 m, and the time step size is set to 0.01 s. The pressure gradient is calculated. The least squares fitting method is used to perform a quadratic polynomial fitting on the pipeline node pressure data to solve the pressure change rate. The Monte Carlo simulation method is used, and the number of random samples is set to one thousand. The flow rate uneven region is calculated based on the pressure change, and the flow rate balance analysis is carried out. The mass flow conservation equation is used to calculate the pressure correction parameters for the uneven region based on the flow rate accumulation value. The gradient descent optimization method is used, the learning rate is set to 0.01, and the maximum number of iterations is set to fifty. The uneven region is adjusted, the influence of the drainage cycle on the steam recovery rate is calculated, and the adaptive control method is used. The control target is set to maximize the recovery rate, and the drainage cycle is optimized to obtain the steam flow correction parameters;

[0075] Steam demand forecasting sub-module: Based on the steam flow correction parameters, a long short-term memory network is used. The dimension of the input layer is set as the steam flow time series in the past twenty-four hours. The hidden layer adopts a double-layer structure, and the number of hidden units in each layer is set to one hundred and twenty-eight. The output layer adopts a fully connected neural network, and the activation function is the hyperbolic tangent. Analyze the steam load balance, use the time series decomposition method to decompose the historical load data into periodic and trend components, set the moving window size to seven days, calculate the steam supply demand fluctuation, and perform trend calculation. Use the exponential smoothing method with a smoothing coefficient set to 0.3 to smooth the steam supply pressure change rate, calculate the short-term demand, use the Bayesian regression method to fit the historical flow data, predict the short-term demand fluctuation, use the dynamic programming method, set the adaptive condensate discharge cycle adjustment strategy to match the load fluctuation, and generate an intelligent control strategy for steam condensate discharge.

[0076] Calculate the matching period between the flow surge point and the valve signal. The judgment standard for the flow surge point is based on the calculation of the flow change rate, statistical analysis of historical data, and outlier removal. Collect the flow time series data and smooth the short-term fluctuations by the moving average method. Calculate the flow change rate by the first-order difference, and set the surge threshold based on the mean and standard deviation of the historical data. When the flow change rate exceeds the threshold, mark the corresponding time point as the surge point. At the same time, analyze and judge whether the surge point appears within a short time after the valve is opened to ensure that the detection result can accurately reflect the actual change of the system state.

[0077] Long short-term memory network, collect the historical data of steam supply and demand extracted by sensors, construct a time series data set, normalize the input data and train it, input the current real-time steam supply data into the trained model, and output the short-term steam demand change trend.

[0078] Please refer to Figure 2 , the abnormal evolution detection module includes:

[0079] Steam leakage analysis sub-module: Based on the steam condensate discharge data set, analyze the steam leakage rate, calculate the difference between steam input and output using the pipeline flow data, and locate the leakage point. Identify the leakage area by comparing the flow loss point with the normal range, calculate the emission data loss rate, and obtain the steam leakage characteristic data;

[0080] Abnormal offset detection sub-module: Based on the steam leakage characteristic data, calculate the abnormal pressure offset, compare the pressure sensor data with the steady-state pressure to calculate the offset amplitude, and detect abnormal valves. Identify the abnormal switch situation by comparing the sudden change point of the solenoid valve signal with the flow anomaly, count the abnormal rate, and obtain the abnormal parameters of the condensate discharge system;

[0081] Drainage Blockage Identification Sub-module: Based on the abnormal parameters of the drainage system, extract the characteristics of the drainage valve blockage. Calculate the switch lag degree by comparing the valve opening and closing response time with the normal operating condition, and evaluate the pipeline pressure loss. Calculate the blockage situation through the flow rate decrease rate, match the pressure mutation area to locate the influence range of the blockage, and obtain the analysis results of the drainage abnormality characteristics;

[0082] Steam Leakage Analysis Sub-module: Based on the steam drainage data set, use the mass conservation equation calculation method to calculate the difference between the input and output of the pipeline flow rate data. Set the calculation step size to 0.05 seconds, and use the finite difference method to segment the steam flow rate in the time series. Calculate the change in the steam accumulation per unit time and locate the leakage point. Use the anomaly detection method based on Kalman filtering, set the state transition model to a first-order autoregressive process, and set the measurement noise covariance matrix to 0.01. Filter the flow rate sequence, extract the abnormal mutation points, compare the flow rate change patterns in the normal interval, calculate the flow rate loss points, use the density clustering algorithm, set the neighborhood radius to 0.05, and the minimum number of samples to 5. Identify the high-density steam loss area and calculate the discharge data loss rate. Use the linear regression model, set the input variable to the time step, the target variable to the leakage flow rate, and solve the regression coefficient using the least squares method to obtain the steam leakage characteristic data;

[0083] Abnormal Deviation Detection Sub-module: Based on the steam leakage characteristic data, use the time series difference analysis method to calculate the pressure abnormal deviation. Set the calculation window size to 20 time steps, use the moving average method to calculate the steady-state pressure reference value, compare the sensor data to calculate the pressure deviation amplitude, and perform abnormal valve detection. Use the mutation point detection algorithm, calculate the solenoid valve signal mutation point based on the double cumulative sum test method, set the mutation detection threshold to three times the standard deviation of the average signal change rate, compare the abnormal fluctuation of the steam flow rate, calculate the abnormal switch situation, use the Poisson distribution fitting method, set the reference frequency to the switch event occurrence rate in the normal opening and closing state, count the number of abnormal switches and calculate the abnormal rate to obtain the abnormal parameters of the drainage system;

[0084] Drainage blockage identification sub-module: Based on the abnormal parameters of the drainage system, the dynamic time warping method is used to calculate the characteristics of the drainage valve blockage. The reference sequence is set as the opening and closing response time sequence under normal operating conditions. The Euclidean distance is used to calculate the matching path between the test sequence and the reference sequence, and the degree of switch lag is calculated and compared. The pipeline pressure loss is evaluated. The finite element method is used to establish the pressure flow equation, and the conjugate gradient numerical solution method is used to solve the pressure distribution. The pressure gradient change under the normal operating state is compared. The flow rate decrease rate calculation method is used. Based on the sliding window difference, the flow rate change rate is calculated to identify the flow rate decrease trend. The piecewise regression method based on Bayesian inference is used, and the number of breakpoints is set to three. The flow rate trend changes in different pressure regions are compared to match the pressure mutation region. The minimum spanning tree method is used, and the edge weight is set as the pressure loss value to calculate the influence range of the blocked area, and the analysis result of the drainage abnormality characteristics is obtained.

[0085] Please refer to Figure 2 , the abnormal response regulation module includes:

[0086] Valve dynamic adjustment sub-module: Based on the intelligent regulation strategy of steam drainage and the analysis result of drainage abnormality characteristics, the opening and closing of the solenoid valve are adjusted. The flow rate change rate is calculated using the opening and closing timing data, and the response lag analysis is carried out. The control command is adjusted by matching the valve signal at the flow rate mutation point to obtain the valve adjustment parameters;

[0087] Steam supply pressure balance sub-module: Based on the valve adjustment parameters, the steam supply pressure distribution is calculated. The deviation is calculated by comparing the steam flow rate change with the node pressure data, and pressure compensation is carried out. The steam supply ratio is adjusted through the flow rate stable interval, and the load balance analysis is carried out to obtain the steam supply pressure correction data;

[0088] Abnormal drainage correction sub-module: Based on the steam supply pressure correction data, the steam pipeline blockage is detected. The blocked area is identified using the pressure fluctuation curve, and the drainage system compensation is carried out. The compensation ratio is adjusted through the valve opening and closing time and the steam accumulation data, and the drainage cycle is corrected to establish an automatic regulation plan for abnormal drainage;

[0089] Valve Dynamic Regulation Sub-module: Based on the intelligent regulation strategy of steam condensate drainage and the analysis results of condensate drainage anomaly characteristics, the dynamic time warping method is adopted. The window length is set to ten time steps to calculate the adaptive matching path of the solenoid valve opening and closing timing data. The flow rate change rate at different time points is compared. The differential sliding window method is used, with the window size set to five, to calculate the flow rate difference between adjacent time steps and perform response lag analysis. The autoregressive integrated moving average model is used, with the order set to two, to perform time series modeling on the valve opening and closing states. The time deviation between the current state and the predicted state is calculated. The Granger causality analysis method is used, with the lag order set to three, to calculate the correlation between the flow rate mutation point and the valve opening and closing signal and adjust the control instruction. The particle swarm optimization method is used, with the population size set to fifty, the maximum number of iterations set to one hundred, and the inertia weight set to 0.7, to optimize the control instruction adjustment strategy and obtain the valve adjustment parameters;

[0090] Steam Supply Pressure Balance Sub-module: Based on the valve adjustment parameters, the finite difference method is used to calculate the steam supply pressure distribution. The spatial step size is set to 0.05 meters, and the time step size is set to 0.01 seconds to calculate the change trend of the steam supply pressure at the pipeline nodes. The least squares fitting method is used to perform quadratic polynomial fitting on the steam flow rate change, calculate the flow rate change deviation, and perform pressure compensation. The Monte Carlo simulation method is used, with the number of random samples set to one thousand, to calculate the steam supply ratio adjustment parameters based on the flow rate stable interval. The mass flow conservation equation is used to calculate the load balance deviation based on the flow rate accumulation value. The gradient descent optimization method is used, with the learning rate set to 0.01 and the maximum number of iterations set to fifty, to adjust the load balance parameters and obtain the steam supply pressure correction data;

[0091] Abnormal Condensate Drainage Correction Sub-module: Based on the steam supply pressure correction data, the time series pattern recognition method is used to calculate the steam pipeline blockage situation. The reference sequence is set to the pressure fluctuation curve under normal conditions, and the dynamic time warping method is used to calculate the adaptive matching path between the current curve and the reference curve. The blockage area identification is compared. The mutation point detection method based on the double cumulative sum test is used, with the mutation detection threshold set to three times the standard deviation of the average pressure change rate, to compare the steam pressure fluctuation situation, calculate the influence range of the pipeline blockage, and perform hydrophobic system compensation. The Bayesian inference method is used, with the prior distribution set to the normal distribution, to calculate the steam accumulation situation under different valve states based on historical data. The multi-objective optimization method is used, with the objective function set to minimize the system pressure loss and maximize the steam discharge balance degree, to adjust the valve opening and closing time and perform condensate drainage cycle correction. The reinforcement learning method is used, with the intelligent optimization algorithm based on policy gradient, and the reward function is set to reduce the system pressure fluctuation, to adjust the control strategy and establish an automatic regulation scheme for abnormal condensate drainage.

[0092] Please refer to Figure 3, a comprehensive management method for steam condensate discharge data of vulcanizers. The comprehensive management method for steam condensate discharge data of vulcanizers is executed based on the above-mentioned comprehensive management system for steam condensate discharge data of vulcanizers, and includes the following steps:

[0093] Step 1: Extract the action signal of the condensate drain valve, the signal of the steam pressure sensor, the status of the steam trap, and the solenoid valve duration through sensors, calculate the steam flow rate, extract the steam flow direction, analyze the temperature fluctuation range within the condensate discharge period, calculate the pressure change value, match the opening and closing of the valve with the steam utilization situation, and obtain the steam condensate discharge data set;

[0094] Step 2: Based on the steam condensate discharge data set, calculate the steam flow rate change rate, analyze the influence of the opening and closing of the condensate drain valve, establish a flow change matrix, calculate the steam flow correlation degree between nodes, screen the nodes with abnormal flow changes, calculate the relationship between the valve switch signal and the flow change, use a generative adversarial network to construct the steam flow pattern, compare the similarity of the flow rate changes of the nodes, analyze the pipeline connection relationship, calculate the flow weight, extract the steam flow direction data, and obtain the steam flow topology structure;

[0095] Step 3: Based on the steam flow topology structure, calculate the flow change trend, analyze the steam flow state, extract the load change characteristics, match the valve opening and closing time with the flow change, establish a steam condensate discharge state set, calculate the steam flow characteristics, calculate the condensate drain valve switch cycle, correct the pressure distribution of the drain pipeline, calculate the steam recovery rate, extract the historical load data, use a long short-term memory network to predict the short-term change trend of the steam supply demand, analyze the influence of the condensate discharge period adjustment on the steam load, and obtain the steam condensate discharge operation data;

[0096] Step 4: Based on the steam condensate discharge operation data, calculate the steam flow deviation, analyze the steam leakage rate, extract the abnormal fluctuation characteristics of the valve signal, calculate the abnormal pressure offset, match the load change trend, analyze the clogging state of the condensate drain valve, calculate the pressure loss value of the steam trap system, evaluate the steam supply pressure deviation, extract the abnormal solenoid valve opening and closing signals, calculate the solenoid valve opening and closing adjustment amount, correct the condensate drain valve control instruction, calculate the compensation parameter of the steam trap system, adjust the steam flow in the pipeline, and obtain the automatic regulation scheme for condensate discharge anomalies.

[0097] The above is only the preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A comprehensive management system for steam drainage condensation data of a vulcanizer, characterized in that, The system includes: Steam condensate drainage control module: Extract the action signal of the condensate drain valve, the signal of the steam pressure sensor, the status of the steam trap, and the solenoid valve duration through sensors, calculate the steam flow rate, match the condensate drainage cycle, detect temperature fluctuations, analyze the flow pressure, calculate the steam utilization rate, and generate a steam condensate drainage data set; Status topology analysis module: Based on the steam condensate drainage data set, adopt a generative adversarial network, calculate the correlation degree of steam flow nodes, analyze the influence of the opening and closing of the condensate drain valve, construct a steam trap topology network, calculate the flow distribution model, analyze the change of temperature gradient, map the solenoid valve status, and obtain the steam condensate drainage status mapping structure; Intelligent condensate drainage optimization module: Based on the steam condensate drainage status mapping structure, calculate the appropriate switch cycle of the condensate drain valve, correct the pressure distribution of the condensate drain pipe, optimize the steam recovery utilization rate, dynamically adjust the opening and closing time of the valve, adopt a long short-term memory network, analyze the steam load balance, predict the steam supply demand, and generate a steam condensate drainage intelligent control strategy; Abnormal evolution detection module: Based on the steam condensate drainage data set, analyze the steam leakage rate, calculate the abnormal pressure deviation, predict the steam consumption trend, detect the abnormal opening and closing of the solenoid valve, extract the characteristics of the condensate drain valve blockage, evaluate the pressure loss of the condensate drainage system, and obtain the analysis result of the condensate drainage abnormal characteristics; Abnormal response control module: Based on the steam condensate drainage intelligent control strategy and the analysis result of the condensate drainage abnormal characteristics, adjust the opening and closing state of the solenoid valve, correct the control instruction of the condensate drain valve, balance the steam supply pressure, control the cleaning of the steam pipeline blockage, calculate the compensation of the steam trap system, correct the condensate drainage strategy, and establish an automatic control plan for condensate drainage abnormalities.

2. The integrated management system for vulcanizer steam drain data according to claim 1, wherein The steam condensate drainage control module includes: Signal extraction sub-module: Extract the action signal of the condensate drain valve, the signal of the steam pressure sensor, the status of the steam trap, and the solenoid valve duration through sensors, perform data synchronization, align multi-source signals through time stamps, and perform signal classification. Divide the working condition characteristics by amplitude, duration, and change trend to establish a steam condensate drainage signal set; Steam parameter analysis sub-module: Based on the steam condensate drainage signal set, calculate the steam flow rate, calculate the instantaneous flow rate using the flow rate change within the opening and closing period of the condensate drain valve, and match the condensate drainage cycle. Match the appropriate condensate drainage cycle through the flow rate fluctuation cycle and the valve opening and closing state to obtain a steam condensate drainage parameter set; Condensate drainage data calculation sub-module: Based on the steam condensate drainage parameter set, calculate the steam utilization rate, calculate the effective steam ratio using the flow accumulation value, and perform flow pattern analysis. Extract the condensate drainage working condition characteristics through the time change of the steam flow rate to generate a steam condensate drainage data set.

3. The integrated management system for vulcanizer steam condensate discharge data according to claim 1, characterized in that The status topology analysis module includes: Node correlation analysis sub-module: Based on the steam condensate drainage data set, adopt a generative adversarial network, calculate the correlation degree of steam flow nodes, analyze the flow trend using the flow rate change matrix, and analyze the influence of valve opening and closing. Calculate the relationship between valve opening and closing and flow rate change to generate steam node correlation characteristics; Topological Structure Construction Sub-module: Based on the steam node association features, construct a steam drainage topological network. Use the pipeline connection relationship and the flow weight between nodes to construct a topological structure, and conduct pressure distribution analysis. Calculate the steam flow trend through the pressure difference between nodes to generate a steam flow topological structure; State Mapping Calculation Sub-module: Based on the steam flow topological structure, calculate the flow distribution model. Use the flow change trend to summarize the flow state, and conduct solenoid valve state mapping. Calculate the corresponding state through the relationship between the valve signal and the flow rate to obtain the steam condensate drainage state mapping structure.

4. The integrated management system for vulcanizer steam drain data according to claim 3, characterized in that, The above includes the generative adversarial network. Based on the steam condensate drainage data set, conduct normalization processing, construct a generative adversarial network composed of a generator and a discriminator, generate virtual data samples, improve the feature learning ability of the model through adversarial training. After training, use the generator to generate flow change patterns, combine with the discriminator to calculate the similarity of the node flow rate changes, and calculate the flow trend changes between different nodes by analyzing the flow rate change matrix.

5. The comprehensive management system for vulcanizer steam drain data according to claim 1, wherein The intelligent condensate drainage optimization module includes: Condensate Drainage Cycle Optimization Sub-module: Based on the steam condensate drainage state mapping structure, calculate the adaptation switch cycle of the condensate drain valve. Extract the flow change curve using the valve opening and closing timing data, and conduct flow rate fluctuation analysis. Calculate the cycle by matching the flow rate sudden increase point with the valve signal, adjust the condensate drainage control timing to obtain the optimized parameters for the opening and closing of the condensate drain valve; Pipeline Pressure Correction Sub-module: Based on the optimized parameters for the opening and closing of the condensate drain valve, correct the pressure distribution of the drainage pipeline. Calculate the pressure gradient using the pipeline node pressure data, and conduct flow balance analysis. Calculate the uneven area through the flow accumulation value and the pressure fluctuation, optimize the condensate drainage cycle to match the steam recovery rate, and obtain the steam flow correction parameters; Steam Demand Prediction Sub-module: Based on the steam flow correction parameters, use a long short-term memory network to conduct steam load balance analysis. Calculate the steam supply demand fluctuation using historical load data, and conduct trend calculation. Predict the short-term demand through the steam supply pressure change rate and the historical flow rate, match the condensate drainage cycle to adjust the load fluctuation, and generate an intelligent control strategy for steam condensate drainage.

6. The integrated management system for the steam drainage data of the vulcanizer according to claim 5, characterized in that, The above includes calculating the cycle by matching the flow rate sudden increase point with the valve signal. The judgment standard for the flow rate sudden increase point is based on the calculation of the flow rate change rate, historical data statistical analysis, and outlier removal. Collect the flow time series data, and smooth the short-term fluctuations through the moving average method. Calculate the flow rate change rate using the first-order difference, and set the sudden increase threshold based on the mean and standard deviation of the historical data. When the flow rate change rate exceeds the threshold, mark the corresponding time point as a sudden increase point. At the same time, analyze and judge whether the sudden increase point appears within a short time after the valve is opened to ensure that the detection result can accurately reflect the actual change of the system state.

7. The integrated management system for vulcanizer steam drain data according to claim 5, characterized in that For the long short-term memory network, collect the historical steam supply and demand data extracted by the sensor, construct a time series data set, conduct normalization processing on the input data and train it. Input the current real-time steam supply data into the trained model to output the short-term steam demand change trend.

8. The integrated management system for the steam drainage data of the vulcanizer according to claim 1, wherein, The abnormal evolution detection module includes: Steam Leakage Analysis Sub-module: Based on the steam condensate data set, conduct steam leakage rate analysis, calculate the difference between steam input and output using pipeline flow data, and locate the leakage point. Identify the leakage area by comparing the flow loss point with the normal range, calculate the loss rate of discharge data, and obtain steam leakage characteristic data; Abnormal Deviation Detection Sub-module: Based on the steam leakage characteristic data, conduct abnormal pressure deviation calculation, calculate the deviation amplitude by comparing the pressure sensor data with the steady-state pressure, and conduct abnormal valve detection. Identify the abnormal switch situation by comparing the sudden change point of the solenoid valve signal with the flow abnormality, count the abnormality rate, and obtain the abnormal parameters of the condensate drainage system; Condensate Drainage Blockage Identification Sub-module: Based on the abnormal parameters of the condensate drainage system, extract the condensate drainage valve blockage characteristics, calculate the switch lag degree by comparing the valve opening and closing response time with the normal working condition, and conduct pipeline pressure loss assessment. Calculate the blockage situation by the flow rate decrease rate, match the pressure mutation area to locate the influence range of the blockage, and obtain the analysis result of condensate drainage abnormal characteristics; 9. The integrated management system for vulcanizer steam drainage data according to claim 1, characterized in that The abnormal response regulation module includes: Valve Dynamic Regulation Sub-module: Based on the steam condensate intelligent regulation strategy and the analysis result of condensate drainage abnormal characteristics, conduct the opening and closing adjustment of the solenoid valve, calculate the flow rate change rate using the opening and closing timing data, and conduct response lag analysis. Adjust the control command by matching the flow rate mutation point with the valve signal to obtain the valve adjustment parameters; Steam Supply Pressure Balance Sub-module: Based on the valve adjustment parameters, calculate the steam supply pressure distribution, calculate the deviation by comparing the node pressure data with the steam flow rate change, and conduct pressure compensation. Adjust the steam supply ratio in the flow rate stable interval, conduct load balance analysis, and obtain the steam supply pressure correction data; Abnormal Condensate Drainage Correction Sub-module: Based on the steam supply pressure correction data, conduct steam pipeline blockage detection, identify the blockage area using the pressure fluctuation curve, and conduct condensate drainage system compensation. Adjust the compensation ratio by the valve opening and closing time and the steam accumulation data, conduct condensate drainage cycle correction, and establish an automatic regulation plan for condensate drainage abnormality; 10. A comprehensive management method for steam drainage condensation data of a vulcanizer, characterized in that, Execute according to the vulcanizer steam condensate data comprehensive management system described in any one of claims 1-9, including the following steps: Step 1: Extract the action signal of the condensate drainage valve, the steam pressure sensor signal, the status of the steam trap, and the solenoid valve duration through sensors, calculate the steam flow rate, extract the steam flow direction, analyze the temperature fluctuation range during the condensate drainage cycle, calculate the pressure change value, match the valve opening and closing with the steam utilization situation, and obtain the steam condensate data set; Step 2: Based on the steam condensate data set, calculate the steam flow rate change rate, analyze the influence of the condensate drainage valve opening and closing, establish a flow rate change matrix, calculate the steam flow correlation degree between nodes, screen the nodes with abnormal flow rate changes, calculate the relationship between the valve switch signal and the flow rate change, construct the steam flow pattern using the generative adversarial network, compare the similarity of the node flow rate changes, analyze the pipeline connection relationship, calculate the flow rate weight, extract the steam flow direction data, and obtain the steam flow topology structure; Step 3: Based on the steam flow topology structure, calculate the flow change trend, analyze the steam flow state, extract the load change characteristics, match the valve opening and closing time with the flow change, establish a steam condensate drainage state set, calculate the steam flow characteristics, calculate the condensate drainage valve switching period, correct the pressure distribution of the drain pipe, calculate the steam recovery rate, extract historical load data, use a long short-term memory network to predict the short-term change trend of the steam supply demand, analyze the impact of the condensate drainage period adjustment on the steam load, and obtain the steam condensate drainage operation data; Step 4: Based on the steam condensate drainage operation data, calculate the steam flow deviation, analyze the steam leakage rate, extract the abnormal fluctuation characteristics of the valve signal, calculate the abnormal pressure offset, match the load change trend, analyze the clogging state of the condensate drainage valve, calculate the pressure loss value of the drain system, evaluate the steam supply pressure deviation, extract the abnormal solenoid valve opening and closing signals, calculate the solenoid valve opening and closing adjustment amount, correct the condensate drainage valve control instruction, calculate the compensation parameter of the drain system, adjust the steam flow in the pipeline, and obtain the automatic regulation scheme for condensate drainage anomalies.

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