Intelligent information acquisition method and system for steel pipe galvanizing production line

Abstract

The invention discloses an intelligent information acquisition method and system for a steel pipe galvanizing production line, and relates to the technical field of information acquisition optimization. An intelligent information acquisition system for a steel pipe galvanizing production line comprises a steel pipe galvanizing production information acquisition module and a steel pipe galvanizing production optimization module. According to the method, the metal vapor concentration, the iron-zinc alloy layer frequency spectrum signal and the zinc liquid temperature data are collected at high frequency, and the dynamic zinc explosion risk coefficient is quantitatively analyzed, so that real-time early warning of a potential zinc explosion event is realized; compared with a traditional method depending on macroscopic temperature or concentration observation, the zinc explosion risk can be recognized in advance, the occurrence probability of zinc explosion accidents is remarkably reduced, and production safety is improved; according to the system, galvanizing production parameters, galvanizing liquid components, production microscopic signals and steel pipe state data can be collected at the same time, full-process and multi-dimensional data coverage is achieved, information integrity is guaranteed, and a reliable basis is provided for production monitoring and analysis.

Description

Technical Field

[0001] This invention relates to the field of information acquisition and optimization technology, and in particular to an intelligent information acquisition method and system for a steel pipe galvanizing production line. Background Technology

[0002] In the hot-dip galvanizing production of steel pipes, the existing monitoring methods of galvanizing production lines mainly rely on the collection of macroscopic parameters such as temperature, ferrous ion concentration, and lifting speed. Although such data can reflect the overall trend of the galvanizing process, its timeliness and resolution are limited. It can only realize basic process monitoring and post-event adjustment functions, and it is difficult to capture the weak characteristic signals before zinc explosion in time, and it cannot realize the early quantitative identification of zinc explosion risk. At the same time, regarding the relationship between iron loss of galvanizing liquid, zinc activity and iron-zinc alloy layer growth state, the existing technology generally adopts single-point monitoring or experience judgment, lacking the ability to perceive the microscopic reaction mechanism of the coating growth process in real time.

[0003] Therefore, it is necessary to optimize the intelligent information collection method of steel pipe galvanizing production line, collect information from multiple perspectives of steel pipe galvanizing production, form a complete coating status evaluation index system, and thus optimize steel pipe coating production. Summary of the Invention

[0004] This invention aims to provide an intelligent information acquisition method and system for steel pipe galvanizing production lines. By acquiring and analyzing multi-dimensional real-time data of the steel pipe galvanizing process, it achieves early warning of zinc explosion risk, precise control of coating quality, reduction of zinc liquid loss, and intelligent optimization of production parameters, significantly improving production safety, product quality, and resource utilization efficiency.

[0005] A method for intelligent information collection in a steel pipe galvanizing production line includes the following steps: During the galvanizing process of steel pipes, the galvanizing production dataset is acquired simultaneously. This dataset includes galvanizing production parameter data, galvanizing solution composition parameter data, galvanizing production micro-data, and galvanized steel pipe condition data. Based on the collected galvanizing production dataset, real-time monitoring of galvanizing production is performed, and a dynamic zinc explosion risk coefficient is calculated. Based on the dynamic zinc explosion risk coefficient, safety control operations for steel pipe galvanizing are executed. Simultaneously, based on the galvanizing production dataset, galvanizing loss monitoring was conducted to obtain the zinc melt iron loss rate and zinc melt activity index; based on the galvanized steel pipe condition data, the steel pipe coating growth quality was evaluated to obtain steel pipe coating condition data; based on the dynamic zinc explosion risk coefficient, zinc melt iron loss rate, zinc melt activity index, and steel pipe coating condition data, galvanizing correlation prediction analysis was performed to obtain steel pipe galvanizing production control optimization parameters; and the current steel pipe galvanizing process was optimized based on the steel pipe galvanizing production control optimization parameters.

[0006] As a preferred technical solution of the present invention, in the galvanizing production dataset, the galvanizing production parameter data includes galvanizing liquid temperature data and internal blowing zinc equipment data; the galvanizing liquid composition parameter data includes ferrous ion concentration data and escaping metal vapor concentration data; the galvanizing production micro data includes iron-zinc alloy layer spectrum signal data and steel pipe temperature fluctuation signal data; the galvanized steel pipe iron-zinc alloy layer spectrum state data includes steel pipe spatial tilt angle data, axial deformation data, and steel pipe dynamic lifting speed.

[0007] As a preferred technical solution of the present invention, the specific steps for real-time monitoring of galvanizing production based on the collected galvanizing production dataset include: Based on the composition parameters of the galvanizing solution and the concentration of the escaping metal vapor, a time-series variation curve of the metal vapor concentration is constructed to obtain the abnormal fluctuation segment of the metal concentration; simultaneously, based on the spectral signal data of the iron-zinc alloy layer in the micro-data of galvanizing production, the cumulative value of acoustic emission energy in the preset frequency band is extracted to obtain the characteristics of the metal energy gradient change. The starting time points of abnormal fluctuations in metal concentration and changes in metal energy gradient are time-series aligned and analyzed. The time difference between the two is calculated to obtain the galvanizing risk time window. If the galvanizing risk time window is less than the time window threshold, it is determined to be a signal of synergistic enhancement of zinc explosion risk. The correlation between galvanizing liquid temperature data and the signal of synergistic enhancement of zinc explosion risk is analyzed to obtain the correlation oscillation characteristics of liquid temperature. The synergistic growth trend of the signal of synergistic enhancement of zinc explosion risk and the correlation oscillation characteristics of liquid temperature are quantitatively analyzed to obtain the dynamic zinc explosion risk coefficient.

[0008] As a preferred technical solution of the present invention, the specific steps for performing safety control operations for steel pipe galvanizing based on a dynamic zinc explosion risk coefficient include: The analysis process based on the dynamic zinc explosion risk coefficient identifies the main propagation path of zinc explosion risk, thus obtaining the zinc explosion risk propagation path; based on the zinc explosion risk propagation path, the corresponding galvanizing distribution control parameters are matched; simultaneously, a galvanizing feedforward control loop is constructed based on the galvanizing risk time window; a galvanizing feedback control loop is constructed based on the zinc liquid temperature-related oscillation characteristics; and the galvanizing distribution control parameters are adjusted based on the galvanizing feedforward control loop and the galvanizing feedback control loop. During the galvanizing process of steel pipes, if the dynamic zinc explosion risk coefficient meets the characteristics of continuous growth and the zinc liquid temperature-related oscillation characteristics meet the characteristics of divergence, the control of the galvanizing distribution control parameters by the galvanizing feedforward control loop and the galvanizing feedback control loop is eliminated, and the galvanizing progressive interlocking control strategy is activated.

[0009] As a preferred technical solution of the present invention, the trend of iron-zinc vapor concentration change is identified based on ferrous ion concentration data and abnormal fluctuation range of metal concentration; and the iron loss rate of zinc liquid and zinc liquid activity index are obtained by analyzing the trend of iron-zinc vapor concentration change. Based on the temperature fluctuation signal data of the steel pipe, the pattern characteristics of the temperature fluctuation of the iron-zinc reaction are identified, and the frequency distribution and stable fluctuation amplitude variation law of the temperature fluctuation are obtained. Combined with the dynamic lifting speed variation trend of the steel pipe, the correlation characteristics between temperature fluctuation and steel pipe lifting speed are analyzed to obtain the coating surface distribution evaluation index. Based on the data of the internal zinc blowing equipment, the spatial inclination data of the steel pipe, and the axial deformation data, the coating structure quality evaluation index is obtained. The coating surface distribution evaluation index and the coating structure quality evaluation index are fused to obtain the coating state data of the steel pipe.

[0010] As a preferred technical solution of the present invention, the specific steps for performing galvanizing correlation prediction analysis include: Based on dynamic zinc explosion risk coefficient, zinc liquid iron loss rate, zinc liquid activity index and steel pipe coating status data, a galvanizing parameter correlation map is constructed, which includes production parameter nodes, status index nodes and quality index nodes. The leading-lag relationship of galvanizing production parameters is extracted from the galvanizing parameter correlation map to obtain the evolution law of galvanizing production parameters. A prediction model for the expected effect of steel pipe galvanizing was established based on the evolution law of galvanizing production parameters. The prediction model for the expected effect of steel pipe galvanizing was optimized by multi-objective optimization algorithm to obtain the optimized parameters for steel pipe galvanizing production control.

[0011] A smart information acquisition system for a steel pipe galvanizing production line includes: The steel pipe galvanizing production information acquisition module includes an information acquisition unit. The information acquisition unit is used to synchronously acquire the galvanizing production dataset during the steel pipe galvanizing process. The galvanizing production dataset includes galvanizing production parameter data, galvanizing solution composition parameter data, galvanizing production micro data, and galvanized steel pipe status data. The steel pipe galvanizing production optimization module includes a risk optimization unit and a quality optimization unit. The risk optimization unit performs real-time monitoring of galvanizing production based on collected galvanizing production datasets, analyzes and calculates a dynamic zinc explosion risk coefficient, and executes safety control operations for steel pipe galvanizing based on this coefficient. The quality optimization unit monitors galvanizing loss based on the galvanizing production datasets, obtaining the zinc melt iron loss rate and zinc melt activity index; evaluates the coating growth quality of the galvanized steel pipe based on the galvanized steel pipe condition data, obtaining the coating condition data; performs galvanizing correlation prediction analysis based on the dynamic zinc explosion risk coefficient, zinc melt iron loss rate, zinc melt activity index, and steel pipe coating condition data, obtaining optimized parameters for steel pipe galvanizing production control; and optimizes the current steel pipe galvanizing process based on these optimized parameters.

[0012] The present invention has the following advantages: 1. This invention acquires high-frequency data on metal vapor concentration, iron-zinc alloy layer spectral signals, and zinc bath temperature, and quantifies and analyzes the dynamic zinc explosion risk coefficient to achieve real-time early warning of potential zinc explosion events. Compared with traditional methods that rely on macroscopic temperature or concentration observations, this invention can identify zinc explosion risks in advance, significantly reduce the probability of zinc explosion accidents, and improve production safety. It can simultaneously acquire galvanizing production parameters, galvanizing bath composition, production micro-signals, and steel pipe status data, achieving full-process, multi-dimensional data coverage, ensuring information integrity, and providing a reliable foundation for production monitoring and analysis.

[0013] 2. This invention establishes evaluation indicators for coating surface distribution and structural quality by analyzing steel pipe temperature fluctuations, lifting speed, spatial tilt angle, and axial deformation. This enables real-time evaluation of coating thickness, uniformity, and mechanical integrity, providing a basis for production optimization. By analyzing the trend of iron-zinc vapor concentration changes, the invention calculates the zinc liquid iron loss rate and zinc liquid activity index, enabling quantitative monitoring of zinc liquid consumption and activity status. This helps reduce zinc liquid loss and extend the service life of the liquid. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the structure of an intelligent information acquisition system for a steel pipe galvanizing production line used in an embodiment of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention.

[0016] Example 1: A method for intelligent information collection in a steel pipe galvanizing production line, comprising the following steps: During the galvanizing process of steel pipes, the galvanizing production dataset is acquired simultaneously. This dataset includes galvanizing production parameter data, galvanizing solution composition parameter data, galvanizing production micro-data, and galvanized steel pipe condition data. In the galvanizing production dataset, the galvanizing production parameter data includes galvanizing bath temperature data and internal blowing zinc equipment data; the galvanizing bath composition parameter data includes ferrous ion concentration data and escaped metal vapor concentration data; the galvanizing production micro-data includes iron-zinc alloy layer spectrum signal data and steel pipe temperature fluctuation signal data; the galvanized steel pipe iron-zinc alloy layer spectrum state data includes steel pipe spatial tilt angle data, axial deformation data, and steel pipe dynamic lifting speed; The temperature data of the zinc bath is used to reflect the actual temperature state of different areas in the zinc pot. It is an important basic parameter for judging the reactivity of iron and zinc, grasping the risk of zinc explosion, and controlling the coating thickness. In this embodiment, the temperature data is collected in real time by high-temperature thermocouples placed at different depths and positions in the zinc pot. The sampling frequency is set to the second level or higher to ensure that rapid fluctuations in the temperature of the zinc bath can be captured. The collected temperature is stored in the form of a continuous time series, and the record includes timestamps and temperature values. It is transmitted to the data acquisition unit through an industrial bus for subsequent analysis of the time window of zinc explosion risk and the temperature-related oscillation characteristics.

[0017] Data from the internal zinc blowing equipment is used to characterize the gas purging conditions on the inner surface of the steel pipe during the zinc removal stage, including dynamic information such as air pressure, air flow rate, nozzle geometry, and purging time. This data reflects the flow state of the zinc liquid on the inner surface of the steel pipe and the zinc removal effect. In this embodiment, the pressure sensor, mass flow meter, and position encoder of the internal zinc blowing device output the operating status in real time. The data is stored in a structured equipment parameter recording method, including current pressure, instantaneous flow rate, nozzle angle, operation start and stop time, and duration. This data is used to evaluate the uniformity of the coating thickness distribution on the inner wall of the steel pipe and serves as an important input for evaluating the coating structure quality.

[0018] Ferrous ion concentration data is used to reflect the degree of accumulation of iron impurities in zinc bath, and its changes directly affect the growth rate of the iron-zinc alloy layer and the iron loss level of zinc bath. In this embodiment, the real-time detection of ferrous ion concentration is achieved by combining an online sampling device with a spectrometer or electrochemical sensor. The sampling interval can be adjusted to minutes or shorter depending on the working conditions. The concentration data is recorded in the form of numerical process parameters, which has high time resolution and can be used for trend analysis, iron loss rate estimation, and calculation of zinc plating activity index. It is an important source of basic data for the construction of subsequent prediction models.

[0019] The data on the concentration of escaping metal vapor is used to reflect the local metal vapor emission from the surface of the zinc pot and the steel pipe during the entry and exit of the tank. This parameter is an important precursor to the risk of zinc explosion. In this embodiment, a high-sensitivity gas analysis probe is placed on the top of the zinc pot surface and in the area where the steel pipe passes through. By performing real-time optical or infrared absorption detection on the escaping vapor, data on the change of vapor concentration over time is obtained. This data is presented in the form of a continuous time series. By identifying abnormal segments in its change curve, it can be used for time-series co-analysis with the spectral signal of the iron-zinc alloy layer to calculate the time window of zinc plating risk.

[0020] The spectral signal data of the iron-zinc alloy layer is used to reflect the acoustic emission characteristics generated by the microstructure growth, fracture, and phase transformation during the iron-zinc interaction. It is an important data source for capturing the micro-galvanizing reaction in this embodiment. In this embodiment, a high-frequency acoustic emission sensor is arranged in the reaction area between the steel pipe and the zinc liquid. The transient acoustic wave signal during the formation of the iron-zinc alloy layer is recorded by ultra-high frequency sampling technology. After filtering and frequency domain transformation, the signal is converted into a spectral energy distribution and stored in the form of a high-dimensional matrix or spectral sequence. This data can be used to determine the growth stability of the alloy layer, extract the energy gradient change characteristics, and finally participate in the calculation of the dynamic zinc explosion risk coefficient.

[0021] The temperature fluctuation signal data of the steel pipe is used to record the subtle dynamic changes in temperature of the steel pipe throughout the entire process from entering the zinc bath to exiting the zinc bath. This fluctuation can characterize the iron-zinc reaction rate, the zinc bath coating state, and the cooling behavior of the coating. In this embodiment, a fast-response infrared thermometer and an attached thermistor are used to perform high-frequency temperature measurement on the outer surface of the steel pipe, obtaining a continuous temperature fluctuation signal over time. The collected temperature fluctuation data is stored in time series form, and the frequency and steady-state amplitude variation patterns of temperature fluctuations can be further extracted for coating surface distribution assessment and coating growth process analysis.

[0022] The spatial tilt angle data of the steel pipe is used to characterize the attitude change of the steel pipe relative to the horizontal plane during lifting and movement. This parameter affects the distribution pattern of zinc liquid on the pipe surface, thus affecting the uniformity of coating thickness. In this embodiment, a triaxial attitude angle sensor is arranged on the lifting mechanism or steel pipe clamping device to form an attitude time series by recording the tilt angle changes of the steel pipe in three directions in real time. This data can be used to analyze the causes of uneven coating distribution and provide key parameters for coating structure quality assessment.

[0023] The spatial tilt angle data of the steel pipe is used to characterize the attitude change of the steel pipe relative to the horizontal plane during lifting and movement. This parameter affects the distribution pattern of zinc liquid on the pipe surface, thus affecting the uniformity of coating thickness. In this embodiment, a triaxial attitude angle sensor is arranged on the lifting mechanism or steel pipe clamping device to form an attitude time series by recording the tilt angle changes of the steel pipe in three directions in real time. This data can be used to analyze the causes of uneven coating distribution and provide key parameters for coating structure quality assessment.

[0024] Axial deformation data reflects the minute changes in the length of the steel pipe under thermal and mechanical effects, and can be used to determine the deformation of the coating structure caused by uneven heating or unstable stress of the steel pipe. In this embodiment, a laser displacement sensor arranged in the steel pipe conveying process is used to obtain the minute expansion and contraction of the steel pipe along the axial direction. The acquisition results are presented in the form of displacement time series. This data, together with temperature fluctuation and lifting speed, is used to determine the geometric stability of the steel pipe during the galvanizing process and to evaluate the quality of the coating structure.

[0025] Dynamic lifting speed data of the steel pipe is used to record the speed change of the steel pipe being extracted from the zinc bath. This is a crucial parameter affecting coating thickness, coating microstructure, and zinc bath discharge efficiency. In this embodiment, the lifting speed is recorded in real time using an encoder or laser velocimeter on the lifting mechanism, and output as a continuous speed curve. This data clearly reflects acceleration, deceleration, and steady-speed ranges, and can be correlated with temperature fluctuation data to assess the uniformity of coating surface distribution, providing a basis for production optimization.

[0026] Based on the collected galvanizing production dataset, real-time monitoring of galvanizing production is performed, and a dynamic zinc explosion risk coefficient is calculated. Based on the dynamic zinc explosion risk coefficient, safety control operations for steel pipe galvanizing are executed. The specific steps for real-time monitoring of galvanizing production based on the collected galvanizing production dataset include: Based on the composition parameters of the galvanizing solution and the concentration of the escaping metal vapor, a time-series variation curve of the metal vapor concentration is constructed to obtain the abnormal fluctuation segment of the metal concentration; simultaneously, based on the spectral signal data of the iron-zinc alloy layer in the micro-data of galvanizing production, the cumulative value of acoustic emission energy in the preset frequency band is extracted to obtain the characteristics of the metal energy gradient change. First, the concentration data of metal vapor escaping from the surface of the zinc plating solution is acquired in real time using an escaping metal vapor concentration sensor. Based on this data, a time-series curve of the metal vapor concentration is constructed. The time-series curve reflects the change law of metal vapor concentration over time and can reflect the dynamic changes of vapor escaping from the zinc plating solution during the zinc plating process. By monitoring these data in real time, abnormal fluctuations in vapor concentration can be detected, which usually indicates some potential problems in the zinc plating process, such as temperature runaway, excessive volatilization of zinc liquid, or abnormal alloy reaction.

[0027] By collecting the spectral signals generated by the iron-zinc alloy layer during the reaction process, and recording the changes in these signals using an ultra-high frequency acoustic emission sensor, these signals mainly originate from the microscopic reactions, crystallization process, and mechanical changes of the iron-zinc alloy layer. By performing frequency domain analysis on these spectral signals, the cumulative acoustic emission energy value within the preset frequency band is extracted. This energy value represents the energy changes and dynamic characteristics of the metal reaction during the alloy layer growth process, and can effectively reveal the minute changes occurring in the iron-zinc alloy layer, especially when microcracks, thermal expansion, or phase transformation reactions begin to appear in the alloy layer. At this time, the changes in the signal can serve as an early warning signal for the potential risk of zinc explosion.

[0028] The starting time points of abnormal fluctuations in metal concentration and changes in metal energy gradient are time-series aligned and analyzed. The time difference between the two is calculated to obtain the galvanizing risk time window. If the galvanizing risk time window is less than the time window threshold, it is determined to be a synergistic enhancement signal of zinc explosion risk. The correlation between galvanizing liquid temperature data and synergistic enhancement signal of zinc explosion risk is analyzed to obtain the zinc liquid temperature correlation oscillation characteristics. The synergistic growth trend of synergistic enhancement signal of zinc explosion risk and zinc liquid temperature correlation oscillation characteristics is quantitatively analyzed to obtain the dynamic zinc explosion risk coefficient. A time-series alignment analysis is performed between the obtained abnormal fluctuations in metal vapor concentration and the starting time points of the energy gradient change characteristics in the extracted iron-zinc alloy layer spectral signal. By comparing the time-series data of these two data sets, it can be determined whether there are any interrelated dynamic characteristics. For example, when the metal vapor concentration suddenly increases, will it be accompanied by a change in the energy gradient of the iron-zinc alloy layer? The time-series alignment analysis can help accurately determine the time difference between the abnormal vapor concentration and the energy change of the alloy layer, thereby revealing the potential risk of zinc explosion. The above steps are key to identifying the risk time window of zinc plating. By calculating the time difference between the two, it is possible to assess whether there are any precursors to zinc explosion.

[0029] By quantifying the time difference obtained from the above time-series alignment analysis, the time window for zinc plating risk is calculated. If this time window is less than the preset time window threshold, it means that the time interval between the abnormal fluctuation of metal vapor concentration and the change of energy gradient in the iron-zinc alloy layer is too short. The synergistic effect of the two is very likely to indicate the risk of zinc explosion. At this time, it can be identified as a synergistic enhancement signal of zinc explosion risk, and the relevant safety warning mechanism will be triggered immediately. The appearance of the synergistic enhancement signal of zinc explosion risk represents the joint abnormality of two key indicators (vapor concentration and spectral energy), indicating that the safety hazards in the zinc plating process are relatively urgent and must be given high attention.

[0030] The method for setting the time window threshold is as follows: During a period of stable equipment operation without any abnormalities, a sufficient number of time differences between the start time of the abnormal metal concentration segment and the start time of the energy gradient change are continuously collected to form a normal time difference sequence. Then, these normal time differences are sorted from smallest to largest, and the 10th percentile or slightly higher percentile (e.g., the 15th percentile) is taken as the threshold, that is, the earliest small portion of time differences under normal conditions are taken as the safety lower limit. Subsequently, in real-time monitoring, once the observed time difference is less than this threshold, it is determined that there may be a signal of synergistic enhancement of zinc explosion risk. This method conforms to the general rule that abnormal synchronization in production must be earlier than normal synchronization, does not rely on scarce accident samples, is simple to calculate and easy for on-site personnel to understand, and can adapt to changes in operating conditions by re-statistically calculating the normal quantile weekly or monthly.

[0031] Further analysis was conducted to examine the correlation between galvanized zinc bath temperature data and the synergistic enhancement signal of zinc explosion risk. By comparing the temperature fluctuations of the zinc bath with the occurrence time of the zinc explosion risk signal, the potential impact of zinc bath temperature on the zinc explosion risk can be revealed. For example, when the zinc bath temperature rises abnormally, it may exacerbate the escape of metal vapor and accelerate the reaction changes of the iron-zinc alloy layer, thereby increasing the probability of zinc explosion. By quantitatively analyzing the correlation between the two, the correlation oscillation characteristics of zinc bath temperature can be obtained, thus providing a basis for subsequent calculation of the dynamic zinc explosion risk coefficient.

[0032] By quantitatively analyzing the synergistic growth trend of the zinc melt explosion risk signal and the correlation oscillation characteristics of zinc bath temperature, a dynamic zinc melt explosion risk coefficient can be obtained. This coefficient can reflect the risk level in the galvanizing process in real time, and galvanizing production can be adjusted in real time according to its changing trend. If the zinc melt explosion risk coefficient continues to increase and shows a divergent trend, it indicates that the risk of zinc melt explosion is increasing, and operators should immediately take safety control measures, such as adjusting the zinc bath temperature and optimizing the blowing conditions. Through this dynamic zinc melt explosion risk coefficient, preventive measures can be taken before a zinc melt explosion event occurs, thereby effectively reducing the possibility of accidents.

[0033] In the quantitative analysis of the synergistic growth trend between the synergistic enhancement signal of zinc explosion risk and the correlation oscillation characteristics of zinc liquid temperature, a synergistic growth index is typically constructed using normalized amplitude changes on a unified time axis. First, the intensity sequence of the synergistic enhancement signal of zinc explosion risk and the amplitude sequence of zinc liquid temperature oscillations are normalized separately, allowing direct comparison of the two types of data within the same amplitude range. Then, the growth rates of the two normalized sequences are calculated synchronously within the same time window, and the synergistic growth amount within that time period is obtained by calculating the hourly point-by-hour product of these two growth rate curves. Finally, the synergistic growth amount within a certain time window is accumulated through sliding and smoothed to form a dynamic zinc explosion risk coefficient. This coefficient reflects both short-term drastic changes and maintains sensitivity to long-term trends, thus serving as a quantitative indicator of real-time risk level.

[0034] The specific steps for implementing safety control operations for steel pipe galvanizing based on the dynamic zinc explosion risk coefficient include: The analysis process based on the dynamic zinc explosion risk coefficient identifies the main propagation path of zinc explosion risk, thus obtaining the zinc explosion risk propagation path; based on the zinc explosion risk propagation path, the corresponding galvanizing distribution control parameters are matched; simultaneously, a galvanizing feedforward control loop is constructed based on the galvanizing risk time window; a galvanizing feedback control loop is constructed based on the zinc liquid temperature-related oscillation characteristics; and the galvanizing distribution control parameters are adjusted based on the galvanizing feedforward control loop and the galvanizing feedback control loop. When identifying the main propagation paths of zinc explosion risk based on the analysis process of dynamic zinc explosion risk coefficient, the spatial and temporal distribution of the risk coefficient is first correlated with various sensor data. Through causal tracing and sensitivity analysis, typical transmission links from metal vapor emission and alloy layer energy release to zinc liquid temperature fluctuations and coating distribution changes are determined. This process uses the lag / lead relationship of time series to determine the causal order, and also uses the parameter sensitivity matrix to determine the contribution of different control nodes (such as internal airflow, lifting speed, and local heating) on ​​the risk coefficient in each path, thereby outputting several zinc explosion risk propagation paths ranked by influence for subsequent control mapping.

[0035] When matching the corresponding galvanizing distribution control parameters based on the risk propagation path of zinc explosion, the key nodes on each propagation path are mapped to adjustable production variables. For example, the steam accumulation-local temperature rise path is mapped to the injection pressure and nozzle angle, the alloy layer abrupt change-surface unevenness path is mapped to the lifting speed and the time to exit the tank, and the temperature oscillation-intensified iron loss path is mapped to the heating power and stirring intensity. Then, these control parameters are assigned different control weights according to the priority of the node's contribution to the risk coefficient, so that the feedforward and feedback loops can focus on the parameters with the greatest impact on the risk to achieve rapid intervention when adjusting.

[0036] When constructing a galvanizing feedforward control loop based on the galvanizing risk time window, a set of preset mitigation action sequences are activated in advance using the risk time window as the trigger condition. These feedforward actions are generated from historical operating conditions and simulation models and ordered by priority. For example, the lifting speed is reduced in advance within the risk window, the internal zinc blowing airflow is finely adjusted, or local exhaust is temporarily added to change the flow field and heat transfer conditions before the zinc explosion event may occur. The execution of the feedforward loop adopts a timer and event-driven mechanism to ensure that the action is completed before the expected danger point is reached, and the effect of the action is recorded for subsequent self-learning optimization.

[0037] When constructing a galvanizing feedback control loop based on the temperature-related oscillation characteristics of molten zinc, the high-frequency sampling results of the temperature sensor are used as the feedback quantity. The closed-loop regulation is driven by real-time calculation of the temperature oscillation amplitude, frequency, and stability index. For example, cascaded control or proportional-integral-derivative control logic is used to adjust the heating power, stirring rate, or gas purging intensity in real time to suppress amplified temperature oscillations and reduce the excitation of the reaction rate of the alloy layer. The feedback loop also includes fault suppression logic. When the controller action fails to bring the temperature amplitude back to the allowable range, it automatically increases the control authority or enters a stronger protection mode.

[0038] When adjusting the galvanizing distribution control parameters based on the galvanizing feedforward control loop and the galvanizing feedback control loop, a weighted fusion strategy is adopted to coordinate the outputs of the two controls. The feedforward loop provides an open-loop pre-adjustment based on the risk time window, while the feedback loop performs closed-loop correction on the real-time deviation. The two are fused through priority and smoother to avoid control conflicts and reduce the impact on production cycle. In actual execution, the fused control quantity is first verified by the safety limiter and the rate limiter, and then sent to the actuator (such as the hoisting motor, internal air source, and local heating device), and the execution results are recorded simultaneously for recursive optimization and manual review.

[0039] During the galvanizing process of steel pipes, if the dynamic zinc explosion risk coefficient meets the characteristics of continuous growth and the zinc liquid temperature-related oscillation characteristics meet the characteristics of divergence, the control of the galvanizing distribution control parameters by the galvanizing feedforward control loop and the galvanizing feedback control loop is eliminated, and the galvanizing progressive interlocking control strategy is activated.

[0040] During the galvanizing process of steel pipes, if the dynamic zinc explosion risk coefficient exhibits a continuously increasing characteristic and the zinc liquid temperature-related oscillation characteristic exhibits a divergent trend, the control authority of the feedforward and feedback routine fine-tuning over the galvanizing distribution control parameters will be cancelled. Instead, a progressive interlocking control strategy for galvanizing will be activated. This strategy first reduces the intensity of key processes in a graded and progressive manner (e.g., gradually slowing down the lifting speed, reducing the internal blowing air pressure in stages, and reducing the heating power in sections), while triggering clear manual alarms and records. A short observation period is set between each level of interlocking action to confirm the risk response. If the risk continues to worsen, it will further enter a more stringent interlocking level until a safe shutdown or on-site cooling is achieved, provided that safety rules are met. The entire interlocking process requires clear authority and confirmation mechanisms between actuators, monitoring, and manual operation to ensure that production continuity is protected as much as possible while effectively preventing the spread of zinc explosion accidents.

[0041] Specifically, the continuous growth characteristic indicates that the risk coefficient shows a stable upward trend within a continuous monitoring period, and its growth rate exceeds the normal fluctuation limit automatically established by the system based on historical operating conditions. This is manifested in the risk coefficient's slope always being positive, the increase per unit time reaching or exceeding the set minimum growth threshold, and the failure to effectively decline in multiple consecutive sampling periods. This indicates that factors such as microscopic energy release, metal vapor escape, and temperature anomalies are continuously accumulating and are difficult to recover on their own. The divergence trend characteristic indicates that the fluctuation range of zinc liquid temperature is constantly expanding and cannot be stably suppressed by the feedback regulation system. This expansion is cumulative and accelerating. Its typical manifestation is that the peak-to-valley difference of temperature fluctuations is continuously increasing, the fluctuation frequency tends to accelerate, and the oscillation amplitude breaks through the upper limit of the historical stable range. At the same time, the oscillation envelope curve shows an outward expansion shape, that is, the fluctuation energy is not only higher than the normal noise level, but also shows a state of increasing intensity with each oscillation. This indicates that the zinc liquid is driven into the nonlinear amplification range by alloy reaction and vapor disturbance, and is approaching the critical point of inducing zinc explosion.

[0042] Simultaneously, based on the galvanizing production dataset, galvanizing loss monitoring was conducted to obtain the zinc melt iron loss rate and zinc melt activity index; based on the galvanized steel pipe condition data, the steel pipe coating growth quality was evaluated to obtain steel pipe coating condition data; based on the dynamic zinc explosion risk coefficient, zinc melt iron loss rate, zinc melt activity index, and steel pipe coating condition data, galvanizing correlation prediction analysis was performed to obtain steel pipe galvanizing production control optimization parameters; and the current steel pipe galvanizing process was optimized based on the steel pipe galvanizing production control optimization parameters. The trend of iron and zinc vapor concentration variation was identified based on ferrous ion concentration data and abnormal fluctuations in metal concentration; the iron loss rate and zinc activity index of the zinc liquid were obtained based on the analysis of the iron and zinc vapor concentration variation trend. When monitoring galvanizing losses based on galvanizing production datasets, the consumption of iron in the zinc bath is first analyzed using ferrous ion concentration data and abnormal fluctuations in metal concentration. By comparing the real-time ferrous ion concentration changes over time with abnormal vapor emission periods, the rate of iron transfer to the zinc bath during the iron-zinc reaction can be identified, and the iron loss rate of the zinc bath can be calculated accordingly. Simultaneously, by combining the concentration level of active metals in the zinc bath and the reaction rate, a zinc bath activity index is generated to assess the activity and availability of the zinc bath in coating growth, providing a quantitative basis for loss control.

[0043] Based on the temperature fluctuation signal data of the steel pipe, the pattern characteristics of the temperature fluctuation of the iron-zinc reaction are identified, and the frequency distribution and stable fluctuation amplitude variation law of the temperature fluctuation are obtained. Combined with the dynamic lifting speed variation trend of the steel pipe, the correlation characteristics between temperature fluctuation and steel pipe lifting speed are analyzed to obtain the coating surface distribution evaluation index. Based on the data of the internal zinc blowing equipment, the spatial inclination data of the steel pipe, and the axial deformation data, the coating structure quality evaluation index is obtained. The coating surface distribution evaluation index and the coating structure quality evaluation index are fused to obtain the coating state data of the steel pipe.

[0044] When evaluating the coating growth quality of galvanized steel pipes based on their condition data, the following steps are taken: First, the temperature fluctuation signal of the steel pipe is analyzed to identify the pattern characteristics of the iron-zinc reaction temperature fluctuation, including the frequency distribution of temperature fluctuations and the variation law of steady-state fluctuation amplitude, thereby judging the uniformity and stability of the thermal reaction during the coating growth process. Next, the correlation between temperature fluctuations and lifting speed is analyzed in conjunction with the dynamic lifting speed trend of the steel pipe to assess the flow and coverage of zinc liquid on the pipe surface, generating a coating surface distribution evaluation index. Simultaneously, data from the internal zinc blowing equipment, the spatial inclination angle of the steel pipe, and axial deformation data are used to evaluate the continuity, thickness uniformity, and mechanical integrity of the coating structure, obtaining a coating structure quality evaluation index. Finally, the surface distribution evaluation index and the structural quality evaluation index are fused to obtain complete steel pipe coating condition data, providing input for subsequent production optimization.

[0045] The specific steps for conducting correlation prediction analysis of galvanized steel plating include: Based on dynamic zinc explosion risk coefficient, zinc liquid iron loss rate, zinc liquid activity index and steel pipe coating status data, a galvanizing parameter correlation map is constructed, which includes production parameter nodes, status index nodes and quality index nodes. The leading-lag relationship of galvanizing production parameters is extracted from the galvanizing parameter correlation map to obtain the evolution law of galvanizing production parameters. By integrating dynamic zinc explosion risk coefficient, zinc liquid iron loss rate, zinc liquid activity index, and steel pipe coating status data, and combining them with other collectable galvanizing production parameters such as zinc liquid temperature, internal blowing zinc equipment status, and steel pipe lifting speed, a galvanizing parameter correlation map is constructed, including production parameter nodes, status index nodes, and quality index nodes. This map describes the causal and correlation relationships between parameters through the connections between nodes, such as the degree of influence of a change in a certain production parameter on the risk coefficient or coating quality index. This maps complex multi-source data into an analyzable network structure, providing a foundation for subsequent pattern extraction and prediction model establishment.

[0046] When extracting the lead-lag relationship of galvanizing production parameters from the correlation graph of galvanizing parameters, by analyzing the change sequence of each parameter node over time, we can identify which production parameters change before other state or quality indicators, and the response delay time of these changes to subsequent indicators. Through this time series analysis, we can summarize the evolution law of galvanizing production parameters, that is, the influence path and intensity of different production parameters on risk, iron loss and coating quality at different stages, thereby revealing the direct and indirect effects of production adjustments on the final coating effect, and providing causal logic support for the prediction model.

[0047] A prediction model for the expected effect of steel pipe galvanizing was established based on the evolution law of galvanizing production parameters. The prediction model for the expected effect of steel pipe galvanizing was optimized by multi-objective optimization algorithm to obtain the optimized parameters for steel pipe galvanizing production control.

[0048] Based on the evolutionary patterns of extracted galvanizing production parameters, a predictive model for the expected effects of steel pipe galvanizing is established. This model maps the historical changes of each production parameter to the response relationships of coating quality and risk indicators. By establishing a functional relationship between input and output through mathematical models or machine learning methods, it is possible to predict the thickness distribution, structural quality, zinc liquid loss, and zinc explosion risk level of the steel pipe coating over a future period, given the current production status and parameters. This model enables quantitative prediction of the galvanizing process, providing a scientific basis for optimizing production decisions.

[0049] When establishing a predictive model for the expected effect of steel pipe galvanizing, the following steps are taken: First, historical galvanizing production data, real-time collected dynamic zinc explosion risk coefficient, zinc liquid iron loss rate, zinc liquid activity index, and coating state indicators are extracted and preprocessed. Then, a mapping relationship between production parameters and coating quality, zinc liquid loss, and risk level is established using regression models, support vector machines, or deep neural networks. This allows for the prediction of future coating effects and risk indicators under given current production conditions. Based on this predictive model, a multi-objective optimization algorithm is used to optimize the production parameters. The optimization objectives include coating uniformity, structural integrity, minimization of zinc liquid loss, and control of zinc explosion risk. The constraints include adjustable ranges for lifting speed, internal blowing air flow rate, zinc liquid temperature, and heating power. The optimization algorithm can employ iterative search methods such as genetic algorithms, particle swarm optimization, or gradient descent. By continuously evaluating the performance of the predictive model output on each objective, the optimal combination of production parameters under multi-objective trade-offs is found and used as the control optimization parameters for the steel pipe galvanizing process. This achieves a comprehensive optimization effect of improved coating quality, reduced zinc liquid consumption, and minimized production safety risks.

[0050] In this embodiment, before the steel pipe enters the galvanizing bath, the information acquisition system is activated to simultaneously acquire a complete dataset of the steel pipe galvanizing process, including galvanizing production parameters, galvanizing solution composition parameters, galvanizing production micro-data, and steel pipe coating status data. The galvanizing production parameters include zinc solution temperature, internal blowing zinc equipment operating status, and blowing air pressure, with a real-time sampling frequency of once per second. The galvanizing solution composition parameters include ferrous ion concentration and escaping metal vapor concentration, sampled at the second level using high-sensitivity electrochemical or optical sensors. The galvanizing production micro-data includes the spectral signal of the iron-zinc alloy layer and the steel pipe surface temperature fluctuation signal, acquired through an ultra-high frequency acoustic emission sensor and an infrared thermal imaging sensor. The galvanized steel pipe status data includes the steel pipe's spatial tilt angle, axial deformation, and dynamic lifting speed, acquired through a three-dimensional tilt sensor and a photoelectric encoder.

[0051] During data acquisition, the system first constructs a time-series curve of metal vapor concentration and extracts abnormal fluctuation segments. Simultaneously, it performs energy accumulation calculations on the spectral signal of the iron-zinc alloy layer in a preset frequency band to obtain the characteristics of metal energy gradient changes. The system then calculates the time difference between the two through time-series alignment analysis to obtain the galvanizing risk time window. When the time window is less than a set threshold (e.g., 3 seconds calculated based on the 10th percentile of normal operating conditions), it is determined to be a signal of synergistic enhancement of zinc explosion risk. At the same time, the system performs correlation analysis between the real-time fluctuation of zinc liquid temperature and this synergistic enhancement signal to extract temperature-related oscillation characteristics and quantifies the synergistic growth trend of the two to obtain a dynamic zinc explosion risk coefficient for subsequent safety control.

[0052] Based on a dynamic zinc explosion risk coefficient, the system identifies the risk propagation path and maps it to galvanizing distribution control parameters, such as lifting speed, internal air flow rate, and local heating power. Simultaneously, it constructs a feedforward control loop (adjusting the production process in advance based on the risk time window) and a feedback control loop (correcting the production process in real time based on temperature oscillations), integrating the outputs of both to adjust the galvanizing distribution parameters. When the risk coefficient continues to increase and temperature oscillations show a divergent trend, the system automatically activates a progressive interlocking control strategy, gradually reducing the intensity of key production processes and triggering manual alarms to proactively control the zinc explosion risk.

[0053] Meanwhile, the system analyzes the changing trends of iron and zinc vapor concentrations based on the abnormal fluctuations in ferrous ion concentration and metal concentration, calculates the iron loss rate of zinc liquid and the zinc liquid activity index; combined with the temperature fluctuations of the steel pipe and the dynamic lifting speed, it generates coating surface distribution evaluation indicators, and calculates coating structure quality evaluation indicators through the status of the internal blowing zinc equipment, the spatial inclination angle of the steel pipe and the axial deformation, and finally integrates the surface distribution and structural quality indicators to obtain the coating status data of the steel pipe.

[0054] In the galvanizing correlation prediction analysis, the system constructs a correlation map of galvanizing parameters using dynamic zinc explosion risk coefficient, zinc liquid iron loss rate, zinc liquid activity index, and steel pipe coating status data. By analyzing the lead-lag relationship of production parameters, the system extracts evolutionary patterns and establishes a prediction model for the expected galvanizing effect of steel pipes. Based on the prediction model, a multi-objective optimization algorithm (such as genetic algorithm or particle swarm optimization) is used to optimize the production parameters, achieving a comprehensive optimum in terms of coating thickness uniformity, structural integrity, zinc liquid loss, and zinc explosion risk. The optimization results are then used as the basis for adjusting various control parameters in actual production.

[0055] For example, in actual production, the system can collect real-time data such as zinc liquid temperature (460 to 480 degrees Celsius), ferrous ion concentration (0.02 to 0.05 mol / L), internal zinc airflow (2 to 3 m / s), and steel pipe lifting speed (0.5 to 1 m / min). Through the above information collection and analysis methods, the system obtains a dynamic zinc explosion risk coefficient of 0.7 (out of 1), a zinc liquid iron loss rate of 0.003 kg / min, and a coating thickness distribution standard deviation of 0.05 mm. It then generates corresponding optimization control commands, such as fine-tuning the lifting speed to 0.6 m / min and reducing the local heating power by 5%, to ensure stable coating quality and safe production.

[0056] Example 2: An intelligent information acquisition system for a steel pipe galvanizing production line, see [link / reference] Figure 1 As shown, it includes: The steel pipe galvanizing production information acquisition module includes an information acquisition unit. The information acquisition unit is used to synchronously acquire the galvanizing production dataset during the steel pipe galvanizing process. The galvanizing production dataset includes galvanizing production parameter data, galvanizing solution composition parameter data, galvanizing production micro data, and galvanized steel pipe status data. The steel pipe galvanizing production optimization module includes a risk optimization unit and a quality optimization unit. The risk optimization unit performs real-time monitoring of galvanizing production based on collected galvanizing production datasets, analyzes and calculates a dynamic zinc explosion risk coefficient, and executes safety control operations for steel pipe galvanizing based on this coefficient. The quality optimization unit monitors galvanizing loss based on the galvanizing production datasets, obtaining the zinc melt iron loss rate and zinc melt activity index; evaluates the coating growth quality of the galvanized steel pipe based on the galvanized steel pipe condition data, obtaining the coating condition data; performs galvanizing correlation prediction analysis based on the dynamic zinc explosion risk coefficient, zinc melt iron loss rate, zinc melt activity index, and steel pipe coating condition data, obtaining optimized parameters for steel pipe galvanizing production control; and optimizes the current steel pipe galvanizing process based on these optimized parameters.

[0057] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A steel pipe galvanizing production line intelligent information acquisition method, characterized in that, The method comprises the following steps: In the process of galvanizing steel pipes, the galvanizing production data set in the process of galvanizing steel pipes is synchronously acquired; wherein the galvanizing production data set comprises galvanizing production parameter data, galvanizing liquid component parameter data, galvanizing production micro data and galvanizing steel pipe state data; Based on the collected galvanizing production data set, real-time monitoring of galvanizing production is performed, and a dynamic zinc explosion risk coefficient is obtained through analysis and calculation; based on the dynamic zinc explosion risk coefficient, steel pipe galvanizing safety control operation is performed; Meanwhile, based on the galvanizing production data set, galvanizing loss monitoring is performed to obtain the zinc liquid iron loss rate and the zinc liquid activity index; based on the galvanizing steel pipe state data, the galvanizing layer growth quality of the steel pipe is evaluated to obtain the steel pipe galvanizing layer state data; based on the dynamic zinc explosion risk coefficient, the zinc liquid iron loss rate, the zinc liquid activity index and the steel pipe galvanizing layer state data, galvanizing correlation prediction analysis is performed to obtain steel pipe galvanizing production control optimization parameters; based on the steel pipe galvanizing production control optimization parameters, the current steel pipe galvanizing process is optimized.

2. The intelligent information collection method for a steel pipe galvanizing production line according to claim 1, characterized in that, In the galvanizing production data set, the galvanizing production parameter data comprises galvanizing zinc liquid temperature data and internal zinc blowing equipment data; the galvanizing liquid component parameter data comprises ferrous ion concentration data and escaping metal vapor concentration data; the galvanizing production micro data comprises iron-zinc alloy layer spectrum signal data and steel pipe temperature fluctuation signal data; the galvanizing steel pipe iron-zinc alloy layer spectrum state data comprises steel pipe spatial inclination data, axial deformation variable data and steel pipe dynamic lifting speed.

3. The intelligent information collection method for a steel pipe galvanizing production line according to claim 2, characterized in that, The specific steps of performing real-time monitoring of galvanizing production based on the collected galvanizing production data set comprise: Based on the escaping metal vapor concentration data of the galvanizing liquid component parameter data, a time sequence change curve of the metal vapor concentration is constructed to obtain a metal concentration abnormal fluctuation section; based on the iron-zinc alloy layer spectrum signal data in the galvanizing production micro data, an acoustic emission energy accumulation value in a preset frequency band is extracted to obtain a metal energy gradient change characteristic; The starting time points of the metal concentration abnormal fluctuation section and the metal energy gradient change characteristic are time sequence aligned and analyzed, the time difference between the two is calculated to obtain a galvanizing risk time window; if the galvanizing risk time window is less than a time window threshold, it is determined that a zinc explosion risk synergistic enhancement signal is present; the correlation between the galvanizing zinc liquid temperature data and the zinc explosion risk synergistic enhancement signal is analyzed to obtain a zinc liquid temperature correlation oscillation characteristic; the synergistic growth trend of the zinc explosion risk synergistic enhancement signal and the zinc liquid temperature correlation oscillation characteristic is quantitatively analyzed to obtain a dynamic zinc explosion risk coefficient.

4. The intelligent information collection method for a steel pipe galvanizing production line according to claim 3, characterized in that, The specific steps of performing steel pipe galvanizing safety control operation based on the dynamic zinc explosion risk coefficient comprise: Based on the analysis process of the dynamic zinc explosion risk coefficient, the main propagation path of the zinc explosion risk is identified to obtain a zinc explosion risk propagation path; based on the zinc explosion risk propagation path, corresponding galvanizing distribution control parameters are matched; meanwhile, based on the galvanizing risk time window, a galvanizing feedforward control loop is constructed; based on the zinc liquid temperature correlation oscillation characteristic, a galvanizing feedback control loop is constructed; based on the galvanizing feedforward control loop and the galvanizing feedback control loop, the galvanizing distribution control parameters are adjusted; In the process of galvanizing steel pipe, if the dynamic zinc explosion risk coefficient meets the characteristics of continuous growth and the zinc liquid temperature associated oscillation characteristics meet the characteristics of divergent trend, the control of the galvanizing feedforward control loop and the galvanizing feedback control loop on the galvanizing distribution control parameters is eliminated, and the galvanizing gradual interlocking control strategy is started.

5. The intelligent information collection method for a steel pipe galvanizing production line according to claim 4, characterized in that, According to the ferrous ion concentration data and the abnormal fluctuation section of metal concentration, the change trend of iron and zinc vapor concentration is identified; based on the change trend of iron and zinc vapor concentration, the zinc liquid iron loss rate and the zinc liquid activity index are obtained; Based on the steel pipe temperature fluctuation signal data, the mode characteristics of iron and zinc reaction temperature fluctuation are identified, the temperature fluctuation frequency distribution and the stable fluctuation amplitude variation law are obtained; combined with the change trend of the dynamic lifting speed of the steel pipe, the correlation characteristics of temperature fluctuation and steel pipe lifting speed are analyzed, and the surface distribution evaluation index of the coating is obtained; according to the data of the internal zinc blowing equipment, the spatial inclination data of the steel pipe and the axial deformation data, the structure quality evaluation index of the coating is obtained; the surface distribution evaluation index of the coating and the structure quality evaluation index of the coating are fused, and the steel pipe coating state data is obtained.

6. The intelligent information collection method for a steel pipe galvanizing production line according to claim 5, characterized in that, The specific steps of the galvanizing correlation prediction analysis include: Based on the dynamic zinc explosion risk coefficient, the zinc liquid iron loss rate, the zinc liquid activity index and the steel pipe coating state data, a galvanizing parameter correlation graph containing production parameter nodes, state index nodes and quality index nodes is constructed; the lead-lag relationship of the galvanizing production parameters is extracted in the galvanizing parameter correlation graph, and the evolution law of the galvanizing production parameters is obtained; According to the evolution law of the galvanizing production parameters, a steel pipe galvanizing expected effect prediction model is established; the steel pipe galvanizing expected effect prediction model is optimized by using an optimization algorithm, and the steel pipe galvanizing production control optimization parameters are obtained.

7. An intelligent information acquisition system for a steel pipe galvanizing production line, characterized by, The system applies the steel pipe galvanizing production line intelligent information acquisition method of any one of the above claims 1-6, including: The steel pipe galvanizing production information acquisition module includes an information acquisition unit; the information acquisition unit is used to synchronously acquire the galvanizing production data set in the process of steel pipe galvanizing; wherein the galvanizing production data set includes galvanizing production parameter data, galvanizing liquid composition parameter data, galvanizing production micro data and galvanizing steel pipe state data; The steel pipe galvanizing production optimization module includes a risk optimization unit and a quality optimization unit; the risk optimization unit is used for real-time monitoring of galvanizing production based on the acquired galvanizing production data set, and the dynamic zinc explosion risk coefficient is calculated; the steel pipe galvanizing safety control operation is performed based on the dynamic zinc explosion risk coefficient; the quality optimization unit is used for galvanizing loss monitoring based on the galvanizing production data set, and the zinc liquid iron loss rate and the zinc liquid activity index are obtained; the steel pipe coating growth quality is evaluated based on the galvanizing steel pipe state data, and the steel pipe coating state data is obtained; the dynamic zinc explosion risk coefficient, the zinc liquid iron loss rate, the zinc liquid activity index and the steel pipe coating state data are used for galvanizing correlation prediction analysis, and the steel pipe galvanizing production control optimization parameters are obtained; the current steel pipe galvanizing process is optimized according to the steel pipe galvanizing production control optimization parameters.

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