Safe steam discharge control method

Abstract

The invention discloses a safe steam discharge control method, and particularly relates to the technical field of steam discharge, and the method specifically comprises the steps: collecting operation monitoring data in a safe steam discharge process, determining potential risks in the safe steam discharge process, fitting steam flow data through a GARCH model, and determining the safety of the steam discharge process. The method comprises the following steps: determining the fluctuation change of steam flow data in a safe steam discharge process, determining the stability of the safe steam discharge process, simulating different pressure relief scenes by using a numerical simulation tool, collecting theoretical pressure relief data in the safe steam discharge process, and comparing the theoretical pressure relief data with the performance of actual data on a frequency domain, so as to determine the safety of the safe steam discharge process. Theoretical pressure relief data in the safe steam discharge process are collected, and the operation monitoring data and the theoretical pressure relief data in the safe steam discharge process are comprehensively analyzed. The instability in the steam pressure relief process can be effectively monitored, early warning can be given out in time, and a guarantee is provided for safe operation of a steam system.

Description

Technical Field

[0001] The present invention relates to the technical field of steam emission, and more particularly to a method for controlling safe steam emission. Background Art

[0002] Steam pressure relief systems are widely used in the industrial field, especially in high-temperature and high-pressure steam pipelines and equipment, for pressure regulation and safety release. When the pressure in the steam system exceeds the set safety range, the pressure relief device will start to discharge the steam into the external environment to avoid safety accidents such as equipment damage or explosion caused by excessive system pressure;

[0003] However, in actual operation, fluctuations in steam flow, changes in temperature and pressure often have a significant impact on the stability and safety of the pressure relief process. Traditional steam pressure relief process monitoring methods mostly rely on simple threshold alarms, which cannot fully consider the complex fluctuation characteristics of the pressure relief process, and fail to provide a comprehensive safety assessment, especially in a changing operating environment and emergency situations. It is difficult to accurately predict potential safety risks.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a steam safety emission control method to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A steam safety emission control method comprises the following steps:

[0008] S1: Collect the operation monitoring data during the steam safety discharge process, and determine the potential risks in the steam safety discharge process by integrating the real-time collected data;

[0009] S2: By using the GARCH model to fit the steam flow data, the fluctuation of the steam flow data during the steam safety discharge process is determined, and the stability of the steam safety discharge process is determined;

[0010] S3: Use numerical simulation tools to simulate different pressure relief scenarios, collect theoretical pressure relief data during steam safety discharge, and compare them with the actual data in the frequency domain to collect theoretical pressure relief data during steam safety discharge;

[0011] S4: Conduct a comprehensive analysis of the operational monitoring data and theoretical pressure relief data during the steam safety discharge process, compare them with the preset threshold value, and conduct an early warning analysis of the steam pressure relief process.

[0012] In a preferred embodiment, the operation monitoring data includes:

[0013] The operation monitoring data is expressed by the operation hidden accumulation coefficient and the steam fluctuation variation coefficient;

[0014] The logic for obtaining the operation hidden accumulation coefficient is as follows: collecting temperature, pressure, and vibration data during steam pressure relief, obtaining the changes of temperature, pressure, and vibration over time during the monitoring period, and marking the changes of temperature, pressure, and vibration over time during the monitoring period as: WD(t), YL(t), and ZD(t);

[0015] Calculate the running hidden accumulation coefficient, the calculation formula is: Among them, YX jl is the hidden accumulation coefficient of operation, t a ~t b is the time period that exceeds the preset temperature threshold, t c ~t d is the time period exceeding the preset pressure threshold, t e ~t f is the period of time during which the preset vibration threshold is exceeded.

[0016] In a preferred embodiment, the steam fluctuation variation coefficient includes:

[0017] The acquisition rate logic of the steam fluctuation coefficient of variation is as follows: obtain the steam flow data within the monitoring period as input, use the GARCH model to fit the steam flow data, obtain the conditional variance at the current time point and different lag periods, and uniformly mark the conditional variance at the current time point and different lag periods as: FC n , n=0, 1, 2, 3, ..., N, N is a positive integer. When n=0, FC0 is the conditional variance at the current time point in the GARCH model, and n is the order of the GARCH model;

[0018] Set a steam fluctuation threshold, compare the steam fluctuation threshold with the conditional variances at the current time point and different lag periods, obtain the number of conditional variances greater than the steam fluctuation threshold, and mark the number of conditional variances greater than the steam fluctuation threshold as: SL;

[0019] Calculate the steam fluctuation coefficient of variation using the following formula: Among them, BD bh is the steam fluctuation coefficient.

[0020] In a preferred embodiment, the theoretical pressure relief data includes:

[0021] The theoretical pressure relief data is expressed by the data comparison deviation coefficient;

[0022] The logic for obtaining the data comparison deviation coefficient is: determine the expected steam flow rate at different times within the monitoring time period through the physical model of pressure relief steam discharge, and mark the expected steam flow rate at different times within the monitoring time period as: QW i , where i = 1, 2, 3, ..., I, I is a positive integer, and i is a different time point in the monitoring period;

[0023] Determine the actual steam flow at different times during the monitoring period, and mark the actual steam flow at different times during the monitoring period as: SJ i , the time domain data of steam flow is converted into frequency domain data through Fourier transform, and the expression is: Wherein, P[k] is the frequency domain signal of the actual steam flow, which represents the amplitude and phase at frequency k, and k is the index of the frequency;

[0024] Calculate the peak-to-valley difference coefficient, the calculation formula is: CY fg =argmax(P[k])-argmin(P[k]); where CY fg is the peak-to-valley difference coefficient;

[0025] Calculate the data comparison deviation coefficient, the calculation formula is: Among them, PC sj is the coefficient of deviation for data comparison.

[0026] In a preferred embodiment, the operation monitoring data and theoretical pressure relief data during the steam safety discharge process are comprehensively analyzed, including:

[0027] By performing weighted calculation on the hidden accumulation coefficient, steam fluctuation variation coefficient and data comparison deviation coefficient, a safety assessment model is constructed to generate a safety assessment coefficient. The expression of the safety assessment coefficient is: Among them, PG aq is the safety assessment coefficient, α1, α2, and α3 are the proportional coefficients of the operation hidden accumulation coefficient, the steam fluctuation variation coefficient, and the data comparison deviation coefficient, respectively. α1, α2, and α3 are all greater than 0.

[0028] In a preferred embodiment, early warning analysis of the steam pressure relief process includes:

[0029] A safety assessment coefficient threshold is set, and the safety assessment coefficient is compared with the safety assessment coefficient threshold. If the safety assessment coefficient threshold is greater than the safety assessment coefficient threshold, a warning signal is generated, indicating that there is a safety problem in the current pressure relief steam discharge. If the safety assessment coefficient threshold is less than the safety assessment coefficient threshold, no warning signal is generated.

[0030] Technical effects and advantages of the present invention:

[0031] The present invention provides a method for evaluating the safe discharge of steam. Through a variety of data analysis and numerical simulation methods, a comprehensive evaluation is performed on the potential risks in the steam pressure relief process. By collecting real-time monitoring data in the process of safe discharge of steam, the steam flow data is fitted using a GARCH model, the volatility is analyzed and the stability of the pressure relief process is evaluated. Numerical simulation tools are used to simulate different pressure relief scenarios, theoretical pressure relief data is collected, and the data is compared with actual data in the frequency domain to further analyze the performance of the pressure relief process. The present invention helps to effectively monitor the instability in the steam pressure relief process and issue early warnings in a timely manner, thereby providing a guarantee for the safe operation of the steam system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0033] Figure 1 The present invention is a schematic flow chart of a steam safety emission control method. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Example 1

[0036] Figure 1 The present invention is a flow chart of the steps of a steam safety emission control method, which specifically includes the following steps:

[0037] S1: Collect the operation monitoring data during the steam safety discharge process, and determine the potential risks in the steam safety discharge process by integrating the real-time collected data;

[0038] S2: By using the GARCH model to fit the steam flow data, the fluctuation of the steam flow data during the steam safety discharge process is determined, and the stability of the steam safety discharge process is determined;

[0039] S3: Use numerical simulation tools to simulate different pressure relief scenarios, collect theoretical pressure relief data during steam safety discharge, and compare them with the actual data in the frequency domain to collect theoretical pressure relief data during steam safety discharge;

[0040] S4: Comprehensively analyze the operation monitoring data and theoretical pressure relief data during the steam safety discharge process, compare them with the preset threshold value, and conduct early warning analysis on the steam pressure relief process.

[0041] The safe discharge control of steam involves ensuring that the system pressure does not exceed the safety threshold, maintaining a stable steam supply, and responding to factors such as load fluctuations and demand changes. It usually combines automatic control systems, pressure monitoring, valve adjustment and other means to ensure the safety and efficiency of the steam system. The safe discharge of steam during pressure relief is determined by real-time data collection. Data collection includes:

[0042] Pressure data acquisition, installed in key locations such as steam boilers, steam pipelines, and steam storage devices, is used to measure the steam pressure of the system in real time. By real-time monitoring of the system pressure, the switch status of the pressure relief valve is automatically controlled. If the system pressure exceeds the safety threshold, the control system will start the pressure relief process to avoid equipment damage or accidents;

[0043] Temperature data collection: Too high a temperature may cause equipment damage or aggravate the pressure relief process. The changes in steam flow and temperature during the pressure relief process need to be monitored in real time to prevent the system stability from being affected by rapid temperature changes.

[0044] Vibration data collection: Through vibration data analysis, it can be determined whether the steam system is operating normally. Especially during the pressure relief process, vibration data can help determine whether the equipment is abnormal or faulty. During the pressure relief process, the rapid changes in pressure, temperature and flow may cause vibration of the equipment. For example, the water hammer effect is prone to occur during the transportation of steam, liquid and gas. In order to avoid the damage caused by the water hammer effect, engineering designers usually take a series of measures, such as slowly opening and closing valves, installing soft-start pumps, using water hammer absorbers, etc. to slow down the water hammer impact and improve the safety and stability of the system.

[0045] Steam flow data collection: During the pressure relief process, the flow change needs to be monitored to ensure that the system can still operate stably under the load after pressure relief. By monitoring the flow, the opening of the pressure relief valve or other adjustment measures can be effectively adjusted.

[0046] By collecting the operation monitoring data during the steam safety discharge process, the operation monitoring data is expressed by the operation hidden accumulation coefficient and the steam fluctuation variation coefficient. The advantages of the operation hidden accumulation coefficient are:

[0047] In the process of steam pressure relief, considering multiple operating data at the same time can more comprehensively reflect the operating status of the steam system and more effectively capture the dynamic changes of the system and possible safety risks;

[0048] For equipment and systems, short-term abnormalities may not cause serious consequences immediately, but if they exceed the safety range for a long time, they may cause equipment damage or safety hazards;

[0049] As the steam system ages, operating conditions change or the external environment affects it, a single threshold may not be able to adapt to the long-term operating status of the system. By operating the latent accumulation coefficient, the threshold is dynamically adjusted according to the actual operating conditions in different time periods. For example, temperature or pressure may have a certain fluctuation range in some cases.

[0050] The logic for obtaining the operation hidden accumulation coefficient is as follows: collecting temperature, pressure, and vibration data during steam pressure relief, obtaining the changes of temperature, pressure, and vibration over time during the monitoring period, and marking the changes of temperature, pressure, and vibration over time during the monitoring period as: WD(t), YL(t), and ZD(t);

[0051] Calculate the running hidden accumulation coefficient, the calculation formula is: Among them, YX jl is the hidden accumulation coefficient of operation, t a ~t b is the time period that exceeds the preset temperature threshold, t c ~t d is the time period exceeding the preset pressure threshold, t e ~t f is the period of time during which the preset vibration threshold is exceeded.

[0052] It can be seen from the formula that the larger the operation hidden accumulation coefficient is, the more likely it is that during the steam pressure relief process, the steam discharge flow rate may exceed the preset safety value, abnormal temperature and pressure changes may occur, and there may be a water hammer effect that causes the discharge pipe to vibrate greatly, thereby increasing the hidden dangers of steam discharge. For example: during the pressure relief process, changes in steam flow rate and pressure may cause temperature fluctuations, especially at the moment when steam comes into contact with condensate, ambient gas, etc., excessively high or low temperature may affect the equipment, pipelines and steam quality in the system. Abnormal changes in pressure (such as sudden increases or decreases) may cause unstable steam flow, increase the load on equipment and pipelines, and even cause dangerous situations such as pipeline rupture. The generation of water hammer effect may cause instantaneous high-pressure fluctuations in the system, which are transmitted to the pipeline and its supporting structure, thereby generating vibrations. This vibration not only affects the safety of the pipeline itself, but may also cause other safety problems, such as loose pipeline supports and damaged valves.

[0053] The advantages of the steam fluctuation coefficient of variation are:

[0054] The steam fluctuation coefficient can capture the volatility changes in the time series. For time series data such as steam flow, the volatility of flow is usually a key factor in the pressure relief process. By modeling the volatility of steam flow, the GARCH model can reflect the intensity and frequency changes of steam flow fluctuations, thereby helping to assess the potential risks in the steam pressure relief process.

[0055] Steam flow time series is usually non-stationary and has autocorrelation. Traditional time series models (such as ARMA models) may not be able to effectively handle these problems. The GARCH model can model the non-stationarity and autocorrelation in the data through the combination of autoregression and conditional variance, thereby obtaining a more accurate prediction of flow fluctuations.

[0056] The steam fluctuation coefficient of variation can quantify volatility, which can help understand the risk level of the pressure relief system in different time periods and help evaluate the impact of steam flow fluctuations on safety.

[0057] The acquisition rate logic of the steam fluctuation coefficient of variation is as follows: obtain the steam flow data within the monitoring period as input, use the GARCH model to fit the steam flow data, obtain the conditional variance at the current time point and different lag periods, and uniformly mark the conditional variance at the current time point and different lag periods as: FC n , n=0, 1, 2, 3, ..., N, N is a positive integer. When n=0, FC0 is the conditional variance at the current time point in the GARCH model, and n is the order of the GARCH model;

[0058] It should be noted that if the condition variance between the current moment and the past few periods is high, it means that the steam flow fluctuates greatly, which may cause the pressure relief valve to over-respond, resulting in excessive steam discharge or water hammer effect. At this time, the system may need to take preventive measures (such as adjusting the pressure relief valve opening in advance, increasing pressure monitoring, etc.).

[0059] Set a steam fluctuation threshold, compare the steam fluctuation threshold with the conditional variances at the current time point and different lag periods, obtain the number of conditional variances greater than the steam fluctuation threshold, and mark the number of conditional variances greater than the steam fluctuation threshold as: SL;

[0060] Calculate the steam fluctuation coefficient of variation using the following formula: Among them, BD bh is the steam fluctuation coefficient.

[0061] It can be seen from the formula that the larger the steam fluctuation coefficient, the greater the volatility of the pressure relief process, which may lead to frequent adjustments or unstable discharge processes. If the conditional variance exceeds the threshold for a long time, the hidden dangers in the pressure relief process will also increase accordingly, which may cause the equipment to overload, or the thermal shock and pressure fluctuations in the pressure relief process will affect the long-term stability and safety of the entire steam system.

[0062] Numerical simulation is performed based on physical and control models to determine the performance of steam pressure discharge under different pressure, temperature and flow conditions. The physical model of pressure relief steam discharge is established, including the use of fluid dynamics equations, thermodynamic models, etc. The physical model of pressure relief steam discharge is used to simulate the automatic control process of the pressure relief system. Numerical simulation tools are used to simulate different pressure relief scenarios, collect theoretical pressure relief data during steam safety discharge, and represent the theoretical pressure relief data through data comparison deviation coefficients. The advantages of data comparison deviation coefficients are:

[0063] By simulating different pressure relief scenarios, potential safety hazards that may occur in actual operations, such as excessive pressure relief, water hammer effect, pressure fluctuations, etc., can be identified and analyzed in advance. This way, potential dangers can be discovered before the equipment is put into use, and appropriate measures can be taken to avoid accidents.

[0064] Numerical simulation can comprehensively evaluate the pressure relief system under a variety of different working conditions. By simulating different pressure, temperature and flow conditions, we can understand the performance of the system under normal operation, emergencies and control failures, so as to fully understand the response speed, stability and safety of the system;

[0065] Numerical simulation can help design and optimize control strategies (such as PID control, adaptive control or predictive control). By simulating the effects of different control schemes, it is possible to evaluate which control method is more effective and safer under different pressure relief conditions, thereby optimizing control parameters and improving the operating efficiency and safety of the system.

[0066] If the pressure relief system fails or fails during actual operation, the simulation results can be used as a reference for fault analysis. By reviewing the simulation results under different working conditions, it can help quickly locate the problem, evaluate the cause of the failure, and avoid repeated debugging processes.

[0067] The logic for obtaining the data comparison deviation coefficient is: determine the expected steam flow rate at different times within the monitoring time period through the physical model of pressure relief steam discharge, and mark the expected steam flow rate at different times within the monitoring time period as: QW i , where i = 1, 2, 3, ..., I, I is a positive integer, and i is a different time point in the monitoring period;

[0068] Determine the actual steam flow at different times during the monitoring period, and mark the actual steam flow at different times during the monitoring period as: SJ i , the time domain data of steam flow is converted into frequency domain data through Fourier transform, and the expression is: Wherein, P[k] is the frequency domain signal of the actual steam flow, which represents the amplitude and phase at frequency k, and k is the index of the frequency;

[0069] It should be noted that when performing calculations in the time domain, the signal may be affected by noise, especially measurement errors, environmental noise and other factors. These noises will affect the fluctuation of the signal, making the maximum value position in the time domain may not represent the actual dynamic changes or trends. Frequency domain analysis can effectively filter out high-frequency noise by decomposing the signal into different frequency components.

[0070] Calculate the peak-to-valley difference coefficient, the calculation formula is: CY fg =argmax(P[k])-argmin(P[k]); where CY fg is the peak-to-valley difference coefficient;

[0071] Calculate the data comparison deviation coefficient, the calculation formula is: Among them, PC sj is the coefficient of deviation for data comparison.

[0072] It can be seen from the formula that the larger the data comparison deviation coefficient is, the greater the difference between the actual steam flow rate and the expected flow rate is, indicating that the volatility in the steam discharge process is enhanced and the hidden dangers in the pressure relief process are also correspondingly increased.

[0073] The operation monitoring data and theoretical pressure relief data in the steam safety discharge process are comprehensively analyzed, and the safety assessment model is constructed by weighted calculation of the operation hidden accumulation coefficient, steam fluctuation variation coefficient and data comparison deviation coefficient to generate the safety assessment coefficient. The expression of the safety assessment coefficient is: Among them, PG aq is the safety assessment coefficient, α1, α2, and α3 are the proportional coefficients of the operation hidden accumulation coefficient, the steam fluctuation variation coefficient, and the data comparison deviation coefficient, respectively. α1, α2, and α3 are all greater than 0.

[0074] It can be seen from the formula that the larger the safety assessment coefficient is, the larger the operation hidden accumulation coefficient, the steam fluctuation variation coefficient and the data comparison deviation coefficient are. Conversely, the smaller the operation hidden accumulation coefficient, the steam fluctuation variation coefficient and the data comparison deviation coefficient are, the smaller the safety assessment coefficient is.

[0075] A safety assessment coefficient threshold is set, and the safety assessment coefficient is compared with the safety assessment coefficient threshold. If the safety assessment coefficient threshold is greater than the safety assessment coefficient threshold, a warning signal is generated, indicating that there is a safety problem in the current pressure relief steam discharge and it needs to be inspected by professional staff. If the safety assessment coefficient threshold is less than the safety assessment coefficient threshold, no warning signal is generated.

[0076] The present invention provides a method for evaluating the safe discharge of steam. Through a variety of data analysis and numerical simulation methods, a comprehensive evaluation is performed on the potential risks in the steam pressure relief process. By collecting real-time monitoring data in the process of safe discharge of steam, the steam flow data is fitted using a GARCH model, the volatility is analyzed and the stability of the pressure relief process is evaluated. Numerical simulation tools are used to simulate different pressure relief scenarios, theoretical pressure relief data is collected, and the data is compared with actual data in the frequency domain to further analyze the performance of the pressure relief process. The present invention helps to effectively monitor the instability in the steam pressure relief process and issue early warnings in a timely manner, thereby providing a guarantee for the safe operation of the steam system.

[0077] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0078] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0079] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0080] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0081] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0082] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0083] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0084] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A steam safety emission control method, characterized in that: The following steps are involved: S1: Collect the operation monitoring data during the steam safety discharge process, and determine the potential risks in the steam safety discharge process by integrating the real-time collected data; S2: By using the GARCH model to fit the steam flow data, the fluctuation of the steam flow data during the steam safety discharge process is determined, and the stability of the steam safety discharge process is determined; S3: Use numerical simulation tools to simulate different pressure relief scenarios, collect theoretical pressure relief data during steam safety discharge, and compare them with the actual data in the frequency domain to collect theoretical pressure relief data during steam safety discharge; S4: Comprehensively analyze the operation monitoring data and theoretical pressure relief data during the steam safety discharge process, compare them with the preset threshold value, and conduct early warning analysis on the steam pressure relief process.

2. A steam safety emission control method according to claim 1, characterized in that: Operation monitoring data, including: The operation monitoring data is expressed by the operation hidden accumulation coefficient and the steam fluctuation variation coefficient; The logic for obtaining the operation hidden accumulation coefficient is as follows: collecting temperature, pressure, and vibration data during steam pressure relief, obtaining the changes of temperature, pressure, and vibration over time during the monitoring period, and marking the changes of temperature, pressure, and vibration over time during the monitoring period as: WD(t), YL(t), and ZD(t); Calculate the running hidden accumulation coefficient, the calculation formula is: Among them, YX jl is the hidden accumulation coefficient of operation, t a ~t b is the time period that exceeds the preset temperature threshold, t c ~t d is the time period exceeding the preset pressure threshold, t e ~t f is the period of time during which the preset vibration threshold is exceeded.

3. A steam safety emission control method according to claim 2, characterized in that: Steam fluctuation coefficient of variation, including: The acquisition rate logic of the steam fluctuation coefficient of variation is as follows: obtain the steam flow data within the monitoring period as input, use the GARCH model to fit the steam flow data, obtain the conditional variance at the current time point and different lag periods, and uniformly mark the conditional variance at the current time point and different lag periods as: FC n , n=0, 1, 2, 3, ..., N, N is a positive integer. When n=0, FC0 is the conditional variance at the current time point in the GARCH model, and n is the order of the GARCH model; Set a steam fluctuation threshold, compare the steam fluctuation threshold with the conditional variances at the current time point and different lag periods, obtain the number of conditional variances greater than the steam fluctuation threshold, and mark the number of conditional variances greater than the steam fluctuation threshold as: SL; Calculate the steam fluctuation coefficient of variation using the following formula: Among them, BD bh is the steam fluctuation coefficient.

4. A steam safety emission control method according to claim 3, characterized in that: Theoretical pressure relief data, including: The theoretical pressure relief data is expressed by the data comparison deviation coefficient; The logic for obtaining the data comparison deviation coefficient is: determine the expected steam flow rate at different times within the monitoring time period through the physical model of pressure relief steam discharge, and mark the expected steam flow rate at different times within the monitoring time period as: QW i , where i = 1, 2, 3, ..., I, I is a positive integer, and i is a different time point in the monitoring period; Determine the actual steam flow at different times during the monitoring period, and mark the actual steam flow at different times during the monitoring period as: SJ i , the time domain data of steam flow is converted into frequency domain data through Fourier transform, and the expression is: Wherein, P[k] is the frequency domain signal of the actual steam flow, which represents the amplitude and phase at frequency k, and k is the index of the frequency; Calculate the peak-to-valley difference coefficient, the calculation formula is: CY fg =argmax(P[k])-argmin(P[k]); where CY fg is the peak-to-valley difference coefficient; Calculate the data comparison deviation coefficient, the calculation formula is: Among them, PC sj is the coefficient of deviation for data comparison.

5. A steam safety emission control method according to claim 4, characterized in that: Comprehensive analysis of the operational monitoring data and theoretical pressure relief data during the steam safety discharge process, including: By performing weighted calculation on the hidden accumulation coefficient, steam fluctuation variation coefficient and data comparison deviation coefficient, a safety assessment model is constructed to generate a safety assessment coefficient. The expression of the safety assessment coefficient is: Among them, PG aq is the safety assessment coefficient, α1, α2, and α3 are the proportional coefficients of the operation hidden accumulation coefficient, the steam fluctuation variation coefficient, and the data comparison deviation coefficient, respectively. α1, α2, and α3 are all greater than 0.

6. A steam safety emission control method according to claim 5, characterized in that: Conduct early warning analysis of the steam pressure relief process, including; A safety assessment coefficient threshold is set, and the safety assessment coefficient is compared with the safety assessment coefficient threshold. If the safety assessment coefficient threshold is greater than the safety assessment coefficient threshold, a warning signal is generated, indicating that there is a safety problem in the current pressure relief steam discharge. If the safety assessment coefficient threshold is less than the safety assessment coefficient threshold, no warning signal is generated.

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