A steam safety emission control method

Abstract

The present invention discloses a steam safety emission control method, which specifically relates to the field of steam emission technology, and specifically includes collecting operation monitoring data during the steam safety emission process, determining potential risks during the steam safety emission process, fitting steam flow data by using a GARCH model, determining the fluctuation change of steam flow data during the steam safety emission process, determining the stability of the steam safety emission process, simulating different pressure relief scenarios by using numerical simulation tools, collecting theoretical pressure relief data during the steam safety emission process, and comparing the data with the performance of actual data in the frequency domain, collecting theoretical pressure relief data during the steam safety emission process, and comprehensively analyzing the operation monitoring data and theoretical pressure relief data during the steam safety emission process. The present invention helps to effectively monitor the instability of 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.

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, high-pressure steam pipelines and equipment, for pressure regulation and safety relief. When the pressure in the steam system exceeds the set safety range, the pressure relief device will activate and discharge the steam into the external environment, preventing 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-mentioned 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 operational monitoring data during the steam safety discharge process and determine potential risks during 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 release, and compare it with actual data in the frequency domain;

[0011] S4: Comprehensively analyze the operational 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 of the steam pressure relief process;

[0012] The operation monitoring data is expressed by the operation hidden accumulation coefficient and steam fluctuation variation coefficient;

[0013] The logic for obtaining the operation hidden accumulation coefficient is as follows: collect temperature, pressure, and vibration data during the steam pressure relief process, obtain the changes in temperature, pressure, and vibration over time during the monitoring period, and mark the changes in temperature, pressure, and vibration over time during the monitoring period as: 、 、 ;

[0014] Calculate the running hidden accumulation coefficient, the calculation formula is: ;in, is the running hidden accumulation coefficient, is the time period when the preset temperature threshold is exceeded. is the time period during which the preset pressure threshold is exceeded, is the time period during which the preset vibration threshold is exceeded;

[0015] The logic for obtaining the steam fluctuation variation coefficient is as follows: obtain the steam flow data within the monitoring period as input data, 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: , n=0, 1, 2, 3, ..., N, N is a positive integer, when n=0, is the conditional variance at the current time point in the GARCH model, and n is the order of the GARCH model;

[0016] Set a steam fluctuation threshold, compare the steam fluctuation threshold with the conditional variance 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;

[0017] Calculate the steam fluctuation coefficient of variation using the following formula: ;in, is the steam fluctuation coefficient.

[0018] In a preferred embodiment, the theoretical pressure relief data includes:

[0019] The theoretical pressure relief data is expressed by the data comparison deviation coefficient;

[0020] The logic for obtaining the data comparison deviation coefficient is as follows: the expected steam flow rate at different times within the monitoring period is determined through the physical model of pressure relief steam discharge, and the expected steam flow rate at different times within the monitoring period is marked as: , where i=1, 2, 3, ..., I, I is a positive integer, and i is a different time point in the monitoring period;

[0021] Determine the actual steam flow rate at different times during the monitoring period, and mark the actual steam flow rate at different times during the monitoring period as: , the time domain data of steam flow is converted into frequency domain data through Fourier transform, and the expression is: ,in, is the frequency domain signal of the actual steam flow, indicating the amplitude and phase at frequency k, where k is the frequency index;

[0022] Calculate the peak-to-valley difference coefficient using the following formula: ;in, is the peak-to-valley difference coefficient;

[0023] Calculate the data comparison deviation coefficient, the calculation formula is: ;in, is the coefficient of deviation for data comparison.

[0024] In a preferred embodiment, a comprehensive analysis of the operational monitoring data and theoretical pressure relief data during the steam safety discharge process is performed, including:

[0025] By performing weighted calculations 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: ;in, is the safety assessment factor, 、 、 They are the proportional coefficients of operation hidden accumulation coefficient, steam fluctuation variation coefficient, and data comparison deviation coefficient, respectively. 、 、 Both are greater than 0.

[0026] In a preferred embodiment, the process of steam pressure relief is analyzed for early warning, including;

[0027] 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 is greater than the safety assessment coefficient threshold, an early warning signal is generated, indicating that there is a safety problem in the current pressure relief steam discharge. If the safety assessment coefficient is less than the safety assessment coefficient threshold, no early warning signal is generated.

[0028] The technical effects and advantages of the present invention are as follows:

[0029] 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 assessment of the potential risks in the steam pressure relief process is carried out. By collecting real-time monitoring data during the safe discharge of steam, the steam flow data is fitted with a GARCH model, its volatility is analyzed and the stability of the pressure relief process is evaluated. Numerical simulation tools are used to simulate different pressure relief scenarios, and theoretical pressure relief data is collected. 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 of the steam pressure relief process and issue early warnings in a timely manner, thereby providing protection for the safe operation of the steam system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0031] Figure 1 The figure is a flow chart of a steam safety emission control method of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0033] Example 1

[0034] Figure 1 The following is a flow chart of a steam safety emission control method according to the present invention, which specifically includes the following steps:

[0035] S1: Collect operational monitoring data during the steam safety discharge process and determine potential risks during the steam safety discharge process by integrating the real-time collected data;

[0036] 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;

[0037] S3: Use numerical simulation tools to simulate different pressure relief scenarios, collect theoretical pressure relief data during steam safety release, and compare it with actual data in the frequency domain;

[0038] 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 thresholds, and conduct an early warning analysis of the steam pressure relief process.

[0039] Safe steam emission control involves ensuring that system pressure does not exceed safety thresholds, maintaining a stable steam supply, and responding to factors such as load fluctuations and demand changes. This is usually achieved by combining automatic control systems, pressure monitoring, and valve adjustment to ensure the safety and efficiency of the steam system. Real-time data collection is used to determine the safety of steam emissions during pressure relief. Data collection includes:

[0040] Pressure data acquisition, installed in key locations such as steam boilers, steam pipelines, and steam storage devices, is used to measure the system's steam pressure in real time. By monitoring the system pressure in real time, it automatically controls the on / off status of the pressure relief valve. If the system pressure exceeds the safety threshold, the control system will initiate the pressure relief process to avoid equipment damage or accidents.

[0041] Temperature data collection: Excessively high temperatures may damage equipment or exacerbate the pressure relief process. Changes in steam flow and temperature during the pressure relief process require real-time monitoring to prevent rapid temperature changes from affecting system stability.

[0042] Vibration data collection and analysis can be used to determine whether the steam system is operating normally. This is especially true during the pressure relief process, where vibration data can help determine whether the equipment is abnormal or faulty. Rapid changes in pressure, temperature, and flow during the pressure relief process can cause equipment vibration. For example, water hammer is a common phenomenon during the transportation of steam, liquids, and gases. To prevent damage from water hammer, engineers typically implement a series of measures, such as slowly opening and closing valves, installing soft-start pumps, and using water hammer absorbers, to mitigate water hammer impacts and improve system safety and stability.

[0043] Steam flow data collection: During the pressure relief process, the flow rate changes need to be monitored to ensure that the system can still operate stably under the load after pressure relief. By monitoring the flow rate, the opening of the pressure relief valve or other regulatory measures can be effectively adjusted.

[0044] 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:

[0045] During the steam pressure relief process, considering multiple operating data simultaneously 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;

[0046] For equipment and systems, short-term anomalies may not cause immediate serious consequences, but if they exceed the safety range for a long time, they may cause equipment damage or safety hazards.

[0047] 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 can be 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.

[0048] The logic for obtaining the operation hidden accumulation coefficient is as follows: collect temperature, pressure, and vibration data during the steam pressure relief process, obtain the changes in temperature, pressure, and vibration over time during the monitoring period, and mark the changes in temperature, pressure, and vibration over time during the monitoring period as: 、 、 ;

[0049] Calculate the running hidden accumulation coefficient, the calculation formula is: ;in, is the running hidden accumulation coefficient, is the time period when the preset temperature threshold is exceeded. is the time period during which the preset pressure threshold is exceeded, is the period of time during which the preset vibration threshold is exceeded.

[0050] 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, the flow rate and pressure changes of steam 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 occurrence of water hammer effect may cause the system to produce instantaneous high-pressure fluctuations, which are transmitted to the pipeline and its supporting structure, thereby generating vibration. 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.

[0051] The advantages of the steam fluctuation coefficient of variation are:

[0052] The steam fluctuation coefficient can capture the volatility changes in time series. For time series data such as steam flow, flow volatility is often 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 potential risks in the steam pressure relief process.

[0053] Steam flow time series are usually non-stationary and have autocorrelation. Traditional time series models (such as ARMA models) may not be able to effectively handle these problems. The GARCH model, through the combination of autoregression and conditional variance, can model the non-stationarity and autocorrelation in the data, thereby obtaining more accurate flow fluctuation predictions.

[0054] The steam fluctuation coefficient of variation quantifies 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.

[0055] The acquisition rate logic of the steam fluctuation variation coefficient is as follows: obtain the steam flow data within the monitoring period as input data, 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: , n=0, 1, 2, 3, ..., N, N is a positive integer, when n=0, is the conditional variance at the current time point in the GARCH model, and n is the order of the GARCH model;

[0056] It should be noted that if the variance of the conditions at the current moment and in 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.).

[0057] Set a steam fluctuation threshold, compare the steam fluctuation threshold with the conditional variance 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;

[0058] Calculate the steam fluctuation coefficient of variation using the following formula: ;in, is the steam fluctuation coefficient.

[0059] It can be seen from the formula that the larger the steam fluctuation coefficient of variation, 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.

[0060] Numerical simulations are performed based on physical and control models to determine the performance of steam pressure discharge under different pressure, temperature and flow conditions. A physical model of pressure relief steam discharge is established, including the use of fluid dynamics equations and thermodynamic models. The physical model of pressure relief steam discharge is used to simulate the automated control process of the pressure relief system. Numerical simulation tools are used to simulate different pressure relief scenarios, and theoretical pressure relief data during the steam safety discharge process is collected. The theoretical pressure relief data is represented by a data comparison deviation coefficient. The advantages of the data comparison deviation coefficient are:

[0061] By simulating different pressure relief scenarios, potential safety hazards that may arise in actual operations, such as over-pressure relief, water hammer effects, and pressure fluctuations, can be identified and analyzed in advance. This allows potential dangers to be discovered before the equipment is put into use, and appropriate measures can be taken to avoid accidents.

[0062] Numerical simulation can comprehensively evaluate the pressure relief system under a variety of different operating conditions. By simulating different pressure, temperature and flow conditions, we can understand the performance of the system under normal operation, emergencies and control failures, and thus fully understand the system's response speed, stability and safety.

[0063] 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 system operating efficiency and safety.

[0064] If the pressure relief system fails or experiences an abnormality during actual operation, the simulation results can be used as a reference for fault analysis. By reviewing the simulation results under different operating conditions, it can help quickly locate the problem, evaluate the cause of the failure, and avoid repeated debugging processes.

[0065] The logic for obtaining the data comparison deviation coefficient is as follows: the expected steam flow rate at different times within the monitoring period is determined through the physical model of pressure relief steam discharge, and the expected steam flow rate at different times within the monitoring period is marked as: , where i=1, 2, 3, ..., I, I is a positive integer, and i is a different time point in the monitoring period;

[0066] Determine the actual steam flow rate at different times during the monitoring period, and mark the actual steam flow rate at different times during the monitoring period as: , the time domain data of steam flow is converted into frequency domain data through Fourier transform, and the expression is: ,in, is the frequency domain signal of the actual steam flow, indicating the amplitude and phase at frequency k, where k is the frequency index;

[0067] It should be noted that when performing calculations in the time domain, the signal may be affected by noise, especially factors such as measurement errors and environmental noise. These noises will affect the fluctuation of the signal, so that 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.

[0068] Calculate the peak-to-valley difference coefficient using the following formula: ;in, is the peak-to-valley difference coefficient;

[0069] Calculate the data comparison deviation coefficient, the calculation formula is: ;in, is the coefficient of deviation for data comparison.

[0070] It can be seen from the formula that the larger the data comparison deviation coefficient, the greater the difference between the actual steam flow rate and the expected flow rate, indicating that the volatility in the steam discharge process increases and the hidden dangers in the pressure relief process also increase accordingly.

[0071] The operation monitoring data and theoretical pressure relief data during the steam safety discharge process are comprehensively analyzed. A 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 a safety assessment coefficient. The expression of the safety assessment coefficient is: ;in, is the safety assessment factor, 、 、 They are the proportional coefficients of operation hidden accumulation coefficient, steam fluctuation variation coefficient, and data comparison deviation coefficient, respectively. 、 、 Both are greater than 0.

[0072] 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.

[0073] 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 is greater than the safety assessment coefficient threshold, an early warning signal is generated, indicating that there is a safety problem with the current pressure relief steam discharge and it needs to be inspected by professional staff. If the safety assessment coefficient is less than the safety assessment coefficient threshold, no early warning signal is generated.

[0074] 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 assessment of the potential risks in the steam pressure relief process is carried out. By collecting real-time monitoring data during the safe discharge of steam, the steam flow data is fitted with a GARCH model, its volatility is analyzed and the stability of the pressure relief process is evaluated. Numerical simulation tools are used to simulate different pressure relief scenarios, and theoretical pressure relief data is collected. 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 of the steam pressure relief process and issue early warnings in a timely manner, thereby providing protection for the safe operation of the steam system.

[0075] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0076] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using 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 program 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, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer 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 drive.

[0077] 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.

[0078] Those skilled 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 beyond the scope of this application.

[0079] Those skilled in the art will 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.

[0080] In the several embodiments provided in this 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 merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0081] 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0082] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A steam safety emission control method, characterized in that: The following steps are involved: S1: Collect operational monitoring data during the steam safety discharge process and determine potential risks during 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 release, and compare it with actual data in the frequency domain; S4: Comprehensively analyze the operational 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 of the steam pressure relief process; The operation monitoring data is expressed by the operation hidden accumulation coefficient and steam fluctuation variation coefficient; The logic for obtaining the operation hidden accumulation coefficient is as follows: collect temperature, pressure, and vibration data during the steam pressure relief process, obtain the changes in temperature, pressure, and vibration over time during the monitoring period, and mark the changes in temperature, pressure, and vibration over time during the monitoring period as: 、 、 ; Calculate the running hidden accumulation coefficient, the calculation formula is: ;in, is the running hidden accumulation coefficient, is the time period when the preset temperature threshold is exceeded. is the time period during which the preset pressure threshold is exceeded, is the time period during which the preset vibration threshold is exceeded; The logic for obtaining the steam fluctuation variation coefficient is as follows: obtain the steam flow data within the monitoring period as input data, 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: , n=0, 1, 2, 3, ..., N, N is a positive integer, when n=0, 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 variance 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: ;in, is the steam fluctuation coefficient.

2. A steam safety emission control method according to claim 1, 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 as follows: the expected steam flow rate at different times within the monitoring period is determined through the physical model of pressure relief steam discharge, and the expected steam flow rate at different times within the monitoring period is marked as: , 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 rate at different times during the monitoring period, and mark the actual steam flow rate at different times during the monitoring period as: , the time domain data of steam flow is converted into frequency domain data through Fourier transform, and the expression is: ,in, is the frequency domain signal of the actual steam flow, indicating the amplitude and phase at frequency k, where k is the frequency index; Calculate the peak-to-valley difference coefficient using the following formula: ;in, is the peak-to-valley difference coefficient; Calculate the data comparison deviation coefficient, the calculation formula is: ;in, is the coefficient of deviation for data comparison.

3. A steam safety emission control method according to claim 2, 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 calculations 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: ;in, is the safety assessment factor, 、 、 They are the proportional coefficients of operation hidden accumulation coefficient, steam fluctuation variation coefficient, and data comparison deviation coefficient, respectively. 、 、 Both are greater than 0.

4. A steam safety emission control method according to claim 3, characterized in that: Conduct early warning analysis of steam pressure relief processes, 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 is greater than the safety assessment coefficient threshold, an early warning signal is generated, indicating that there is a safety problem in the current pressure relief steam discharge. If the safety assessment coefficient is less than the safety assessment coefficient threshold, no early warning signal is generated.

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