An automatic control system for atomizing gas source gas production

Abstract

The present invention discloses an automated control system for atomized gas source gas production, which relates to the field of high-pressure gas preparation. The system comprises: a gas production instruction sending module for sending gas production instructions; a variable frequency gasification signal generating module for executing valve opening and closing control to perform downstream liquid gas pre-cooling with a cryogenic pump; an initialization variable frequency diversion parameter determination module for determining initialization variable frequency diversion parameters to introduce pre-cooled liquid gas into an air-temperature gasifier; a fluid dynamic model construction module for determining a gasification space and constructing a fluid dynamic model; a space gasification state analysis module for performing space gasification state analysis on pre-cooled liquid gas and determining a variable frequency diversion strategy; and a reverse guidance control module for performing reverse guidance control of the variable frequency diversion. The system solves the technical problems of cumbersome operation, low work efficiency, and poor safety and reliability in existing atomized gas source production, realizes fully automated control of atomized gas source production, and improves work efficiency and safety and reliability.

Description

Technical Field

[0001] The present application relates to the field of high-pressure gas preparation, and in particular to an automated control system for gas preparation using an atomized gas source. Background Art

[0002] In industrial production, compressed liquid air fractionation technology is widely used to produce cryogenic liquid gases such as liquid nitrogen, liquid oxygen, and liquid argon. After being transported to the point of use, these cryogenic liquid gases are typically stored in pressure tanks and then converted to high-pressure gas using an atomizing gas source. Traditional atomizing gas source systems are cumbersome and inefficient in producing high-pressure gas. Specifically, operators manually operate a cryogenic liquid pump to pump liquid gas from a storage tank into an air-cooled vaporizer. During this process, the liquid gas exchanges heat with the ambient air in the air-cooled vaporizer, transforming it from liquid to gas and forming a high-pressure gas. This high-pressure gas is then stored in a gas tank and adjusted to the desired operating pressure using a pressure regulating valve assembly. However, this gas production method requires multiple operators to work together due to the complex valve opening sequence. This not only increases labor costs but also can lead to operational errors and compromise gas production efficiency. Operators need to monitor key parameters such as pressure and temperature in real time to ensure the safe operation of the equipment. This real-time monitoring increases the workload, and the low temperature working environment also poses a potential threat to the health of operators.

[0003] In the current related technologies, atomized gas source gas production has technical problems such as complicated operation, low work efficiency, and poor safety and reliability. Summary of the Invention

[0004] This application provides an atomized gas source gas production automation control system, which realizes fully automated control of the atomized gas source preparation of high-pressure gas by integrating key components such as the gas production instruction sending module, the variable frequency gasification signal generation module, the initialization variable frequency diversion parameter determination module, the fluid dynamics model construction module, the spatial gasification state analysis module and the reverse guidance control module, thereby improving work efficiency and safety and reliability.

[0005] The present application provides an automated control system for atomizing gas source gas production, comprising:

[0006] A gas production instruction sending module is used to send gas production instructions through the main console and transmit them to the automatic control system based on the serial bus; a variable frequency gasification signal generating module is used to execute valve opening and closing control through the automatic control system, perform downstream liquid gas pre-cooling with a cryogenic pump, and generate a variable frequency gasification signal, wherein a preset temperature threshold is used as the pre-cooling condition; an initialization variable frequency diversion parameter determination module is used to determine the initialization variable frequency diversion parameter based on the variable frequency gasification signal, and introduce the pre-cooled liquid gas into the air-temperature vaporizer; a fluid dynamic model is constructed. Module, the fluid dynamic model construction module is used to determine the vaporization space based on the air-temperature vaporizer, perform finite difference processing, and construct a fluid dynamic model, wherein the vaporization space includes a first space for convective heat exchange and a second space for heat conduction, and the first space is connected to the second space; a space vaporization state analysis module, the space vaporization state analysis module is used to perform space vaporization state analysis on the pre-cooled liquid gas in the air-temperature vaporizer based on the fluid dynamic model, and determine the variable frequency diversion strategy; a reverse guidance control module, the reverse guidance control module is used to perform reverse guidance control of the variable frequency diversion based on the variable frequency diversion strategy.

[0007] In a possible implementation, the fluid dynamics model is constructed by performing the following processing:

[0008] The spatial step and the time step are set, wherein the step settings of the first space and the second space are different; based on the spatial step and the time step, the vaporization space is gridded to define a four-dimensional grid distribution; the four-dimensional grid distribution is subjected to finite difference processing to construct the fluid dynamic model.

[0009] In a possible implementation, finite difference processing is performed on the four-dimensional grid distribution to construct the fluid dynamics model, and the following processing is performed:

[0010] For the four-dimensional grid distribution, central difference processing is performed on each grid to determine a discrete difference equation, wherein the difference equation is expressed as a division calculation of the difference between the thermal balance eigenvalues of the front and rear grid intersection nodes with any grid intersection node as the center and twice the spatial step length; boundary conditions are determined based on the external ambient temperature characteristics, and the boundary conditions include at least boundary temperature and heat flux density; with the four-dimensional grid distribution as a reference, the fluid dynamics model is constructed based on the discrete difference equation and the boundary conditions.

[0011] In a possible implementation, after performing the space gasification state analysis, the following processing is performed:

[0012] Identify the gasification state of the space and determine a first static feature and a first dynamic feature; traverse the frequency conversion decision library, match the first static feature with the first dynamic feature, and determine a first current limiting condition; based on the initialized frequency conversion diversion parameters, perform parameter control adjustment conversion on the first current limiting condition to determine a first frequency conversion strategy.

[0013] In a possible implementation, the frequency conversion decision library is constructed to perform the following processing:

[0014] Based on the static and dynamic gasification characteristics and combined with historical gasification records, feature mining is carried out under the four-dimensional grid to determine the flow limiting conditions. The flow limiting conditions are used for flow frequency conversion regulation and include the flow limiting direction and the flow limiting vector. A mapping between the static and dynamic gasification characteristics and the flow limiting conditions is established to construct a frequency conversion decision library.

[0015] In a possible implementation, the system further includes:

[0016] A surface image acquisition module is configured to acquire a surface image of the heat absorber of the air-temperature vaporizer by controlling a video monitoring device; an element recognition module is configured to perform element recognition on the surface image using frost characteristics as directional recognition factors to determine a real-time frost characteristic value; and a second frequency conversion strategy acquisition module is configured to determine the heat exchange loss based on the external environment based on the real-time frost characteristic value and acquire a second frequency conversion strategy.

[0017] In a possible implementation, the frequency conversion diversion strategy is determined by performing the following processing:

[0018] The first frequency conversion strategy and the second frequency conversion strategy are mutually verified to determine a verification result; if the verification result meets the standard, the first frequency conversion strategy and the second frequency conversion strategy are fitted to determine the frequency conversion diversion strategy.

[0019] In a possible implementation, after the pre-cooled liquid gas is introduced into the ambient temperature vaporizer, the following processes are performed:

[0020] Determine a high-pressure gas storage module, which is composed of N parallel gas tanks; the air-temperature gasifier outputs the prepared gas, which is transmitted to the high-pressure gas storage module through a pipeline for gas storage, wherein the high-pressure gas storage module is equipped with a temperature sensor and a pressure sensor; determine the sensor data of the high-pressure gas storage module, and generate a gas shutdown signal based on a preset temperature value and a preset pressure value, and transmit the signal to the main control console to perform gas shutdown control of the automatic control system.

[0021] The present application proposes an atomizing gas source gas production automation control system. The gas production instruction sending module sends the gas production instruction through the main console, which is transmitted to the automatic control system based on the serial bus. The variable frequency gasification signal generating module executes the valve opening and closing control through the automatic control system to perform the downstream of the liquid gas and pre-cooling with the cryogenic pump to generate a variable frequency gasification signal. The preset temperature threshold is used as the pre-cooling condition. The initialization variable frequency diversion parameter determination module determines the initialization variable frequency diversion parameter based on the variable frequency gasification signal, and the pre-cooled liquid gas is introduced into the air-temperature vaporizer. The fluid dynamic model construction module determines the initialization variable frequency diversion parameter based on the air-temperature gasification signal. The vaporization space of the vaporizer is subjected to finite difference processing to construct a fluid dynamic model, wherein the vaporization space includes a first space for convective heat exchange and a second space for heat conduction, and the first space is connected to the second space. The spatial vaporization state analysis module is used to analyze the spatial vaporization state of the pre-cooled liquid gas in the air-temperature vaporizer based on the fluid dynamic model, and the variable frequency diversion strategy is determined. The reverse guidance control of the variable frequency diversion is performed based on the variable frequency diversion strategy through the reverse guidance control module, thereby realizing fully automated control of the preparation of high-pressure gas from the atomization gas source, and improving work efficiency and safety and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 This is a structural diagram of an atomizing gas source gas production automation control system provided in an embodiment of the present application.

[0024] Figure 2 A schematic flow chart of determining a first frequency conversion strategy in an atomization gas source gas production automation control system provided in an embodiment of the present application.

[0025] Description of the reference numerals: gas production instruction sending module 10 , variable frequency gasification signal generating module 20 , initialization variable frequency flow guide parameter determining module 30 , fluid dynamic model building module 40 , space gasification state analyzing module 50 , reverse guidance control module 60 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0029] The embodiment of the present application provides an automatic control system for atomizing gas source gas production, such as Figure 1 As shown, the system includes:

[0030] The gas production instruction sending module 10 is used to send gas production instructions through the main console and transmit them to the automatic control system via a serial bus. Specifically, the user enters the gas production instruction through the main console (a user interface for inputting and displaying gas production instructions and related information). The instruction contains basic information such as the type and quantity of gas to be produced. After receiving the instruction, the gas production instruction sending module 10 transmits the instruction to the automatic control system via a serial bus (a communication protocol for transmitting data, such as RS-485, USB, etc., which allows data to be transmitted sequentially between multiple devices).

[0031] The variable frequency gasification signal generating module 20 is used to execute valve opening and closing control through the automatic control system, perform downstream liquid gas and cryogenic pump pre-cooling, and generate a variable frequency gasification signal, wherein the preset temperature threshold is used as the pre-cooling condition. Specifically, after the automatic control system receives the gas production instruction, it controls the downstream of the liquid gas by opening and closing the valve. At the same time, the cryogenic pump starts to pre-cool the liquid gas, that is, to prepare the liquid gas for gasification by lowering the temperature. After reaching the preset temperature threshold (the minimum temperature that the liquid gas needs to reach before it starts to gasify), a variable frequency gasification signal is generated. The variable frequency gasification signal represents a signal of the frequency change required during the liquid gas gasification process, and includes parameters such as the frequency and pressure required for the liquid gas gasification.

[0032] Initialization variable frequency flow diversion parameter determination module 30 is used to determine initial variable frequency flow diversion parameters based on the variable frequency vaporization signal to introduce the pre-cooled liquid gas into the air-temperature vaporizer. Specifically, based on the variable frequency vaporization signal, initialization variable frequency flow diversion parameter determination module 30 calculates the optimal parameters for introducing the liquid gas into the air-temperature vaporizer, including the introduction speed and frequency, to ensure that the liquid gas remains stable during the vaporization process. The air-temperature vaporizer is a device that vaporizes liquid gas at ambient temperature.

[0033] In one possible implementation, after the pre-cooled liquid gas is introduced into the air-temperature vaporizer, the process includes: determining a high-pressure gas storage module, which is composed of N parallel gas tanks; the air-temperature vaporizer outputs the prepared gas, which is transmitted to the high-pressure gas storage module through a pipeline for gas storage, wherein the high-pressure gas storage module is equipped with a temperature sensor and a pressure sensor; determining the sensor data of the high-pressure gas storage module, generating a gas shutdown signal based on a preset temperature value and a preset pressure value, and transmitting the signal to the main control console to perform gas shutdown control of the automatic control system.

[0034] Specifically, the high-pressure gas storage module consists of N parallel gas tanks, which are used to store the prepared gas output from the air-temperature vaporizer. Each gas tank has the ability to store gas, and due to the parallel design, the system can selectively use some or all of the gas tanks as needed. After the air-temperature vaporizer completes the gasification process of the liquid gas, the output prepared gas is transmitted to the high-pressure gas storage module via a pipeline. After entering the high-pressure gas storage module, the prepared gas is distributed to each parallel gas tank for storage. The high-pressure gas storage module is equipped with temperature sensors and pressure sensors for real-time monitoring of the temperature and pressure within the gas tanks. The system obtains data from these sensors periodically or in real time, and compares the obtained temperature and pressure data with preset temperature and pressure values. If the actual temperature or pressure reaches or exceeds the preset value, the high-pressure gas storage module is considered full or there is a safety hazard, and the gas production process needs to be stopped. At this time, the system generates a gas production shutdown signal and transmits it to the main control console via the serial bus. Upon receiving the gas production shutdown signal, the main control console executes the corresponding shutdown control logic, including closing the liquid gas supply valve, stopping the cryogenic pump's pre-cooling operation, and shutting down other related equipment in the automatic control system. This implementation method, through the introduction of a high-pressure gas storage module equipped with temperature and pressure sensors, enables real-time monitoring of the storage status of the prepared gas. When the temperature or pressure within the high-pressure gas storage module reaches a preset value, a gas production shutdown signal is automatically generated, and shutdown control is executed by the main control console, improving safety.

[0035] The fluid dynamic model construction module 40 is used to determine the vaporization space based on the air-temperature vaporizer, perform finite difference processing, and construct a fluid dynamic model, wherein the vaporization space includes a first space for convective heat exchange and a second space for heat conduction, and the first space and the second space are connected. Specifically, the vaporization space of the air-temperature vaporizer is determined, and the space includes a first space for convective heat exchange and a second space for heat conduction. Convective heat exchange refers to the heat exchange between the fluid and the solid surface due to the temperature difference; heat conduction refers to the transfer of heat within the solid or between different solids due to the temperature gradient. Finite difference processing is performed on the vaporization space to construct a fluid dynamic model, which is used to simulate the flow and heat exchange of liquid gas during the vaporization process.

[0036] In one possible implementation, constructing the fluid dynamics model includes: setting a spatial step and a time step, wherein the step settings of the first space and the second space are different; based on the spatial step and the time step, gridding the vaporization space to define a four-dimensional grid distribution; performing finite difference processing on the four-dimensional grid distribution to construct the fluid dynamics model.

[0037] Specifically, when constructing a fluid dynamics model, the vaporization space is discretized, that is, the continuous space is divided into a series of small units (or grids). The spatial step size is the size of these small units in space, where the choice of step size is based on the balance between calculation accuracy and calculation efficiency. Too large a step size may lead to numerical instability, while too small a step size increases the amount of calculation. For the first space (convection heat transfer space) and the second space (heat conduction space), due to their different physical properties and heat exchange mechanisms, different spatial step sizes are set to more accurately simulate the fluid flow and heat transfer process inside them. When simulating fluid dynamic behavior, time also needs to be discretized, and the time step size is the length of time represented by each step in the simulation process.

[0038] The vaporization space is gridded based on the spatial and temporal steps, defining a four-dimensional grid distribution. Specifically, the vaporization space is divided into a series of three-dimensional grid cells, each representing a small volume within which the fluid flow and heat transfer processes can be simulated numerically. A time dimension is introduced to the three-dimensional grid, forming a four-dimensional grid distribution. Each grid cell thus corresponds to a specific temporal and spatial location, recording the fluid state (e.g., velocity, temperature, pressure, etc.) at that location at different points in time. Finite difference processing is performed on the four-dimensional grid distribution. Specifically, the continuous fluid dynamic equations are discretized on the four-dimensional grid to produce a series of algebraic equations that can be solved by a computer to determine the fluid state at different temporal and spatial locations. Through this finite difference processing, a fluid dynamic model describing the fluid flow and heat transfer processes within the vaporization space is constructed. This model is used to simulate and predict the performance of fluid systems. This implementation method, by setting different spatial and temporal steps, accurately simulates the fluid flow and heat transfer processes in different regions of the vaporization space, improving simulation accuracy.

[0039] In one possible implementation, finite difference processing is performed on the four-dimensional grid distribution to construct the fluid dynamics model, including: performing central difference processing on each grid in the four-dimensional grid distribution to determine a discrete difference equation, wherein the difference equation is expressed as a calculation of dividing the difference between the thermal balance eigenvalues of the front and rear grid intersection nodes by twice the spatial step length with any grid intersection node as the center; determining boundary conditions based on the ambient temperature characteristics, wherein the boundary conditions include at least boundary temperature and heat flux density; and constructing the fluid dynamics model based on the discrete difference equation and the boundary conditions with the four-dimensional grid distribution as a reference.

[0040] Specifically, the central difference method is applied to each grid intersection node in the four-dimensional grid distribution. Central difference is a numerical method used to solve partial differential equations. It approximates the derivatives of the continuous equation by calculating the difference between adjacent nodes on a discrete grid. By discretizing the continuous equation on the discrete grid, a series of algebraic equations is obtained. In this step, with any grid intersection node as the center, the surrounding grid intersection nodes are considered. For each node, the difference between its thermal equilibrium eigenvalues is calculated and divided by twice the spatial step size to obtain the discrete difference equation for that node. This equation describes the time-varying state of the fluid at the node (such as temperature and velocity). Through this central difference process, a series of discrete difference equations for the fluid state at the grid intersection nodes are obtained. The form of the discrete difference equations depends on the physical process being simulated and the numerical method selected. They can involve multiple variables such as temperature, velocity, and pressure, and describe the interactions and changing relationships between them.

[0041] Boundary conditions are crucial factors that must be considered during simulations. They describe the interaction between the simulation domain and the external environment, including boundary temperature, heat flux, and other parameters. For air-temperature vaporizers, the external ambient temperature influences the temperature distribution and fluid flow within the vaporizer. Therefore, when constructing the fluid dynamics model, the boundary conditions are determined based on the ambient temperature characteristics. After obtaining the discrete difference equations and boundary conditions, they are combined with the four-dimensional grid distribution to construct a complete fluid dynamics model. This model describes the detailed fluid flow and heat transfer within the vaporization volume and can be used to simulate and predict vaporizer performance under various operating conditions. This implementation method, through grid-by-grid central differencing, generates more precise discrete difference equations, thereby more accurately describing the fluid flow and heat transfer processes. Furthermore, by basing the boundary conditions on the ambient temperature characteristics, the model more accurately reflects the complex boundary conditions found in real-world scenarios, achieving the technical effect of improving the accuracy and reliability of simulation results.

[0042] The spatial vaporization state analysis module 50 is used to analyze the spatial vaporization state of the pre-cooled liquid gas within the air-temperature vaporizer based on the fluid dynamics model and determine a variable frequency flow diversion strategy. Specifically, based on the fluid dynamics model, the spatial vaporization state of the pre-cooled liquid gas within the air-temperature vaporizer is analyzed to determine the vaporization rate, temperature distribution, and other conditions of the liquid gas. Based on the analysis results, a variable frequency flow diversion strategy (a strategy for adjusting the frequency and speed of liquid gas introduction into the air-temperature vaporizer) is determined to optimize the vaporization process.

[0043] like Figure 2As shown, in a possible implementation method, after performing space gasification state analysis, it includes: identifying the space gasification state, determining the first static feature and the first dynamic feature; traversing the frequency conversion decision library, matching the first static feature with the first dynamic feature, and determining the first current limiting condition; based on the initialized frequency conversion diversion parameters, performing parameter control adjustment conversion on the first current limiting condition, and determining the first frequency conversion strategy.

[0044] Specifically, after performing a spatial vaporization state analysis, the vaporization state of the pre-cooled liquid gas within the air-temperature vaporizer is identified, including key parameters such as gas distribution, temperature, and pressure within the vaporizer. Key features of these vaporization states are extracted, namely, first static features and first dynamic features. Static features include relatively stable state parameters such as the temperature and pressure distribution of the gas within the vaporizer; dynamic features include time-varying parameters such as gas flow rate and temperature variation trends. A variable frequency decision library, pre-established based on historical data and experimental verification, stores variable frequency flow diversion strategies corresponding to various vaporization states to optimize gasification efficiency and energy utilization. After identifying the key vaporization state features, the variable frequency decision library is traversed to find the strategy that best matches the current first static and first dynamic features. This matching process identifies one or more possible variable frequency flow diversion strategies, and based on these strategies, determines the first flow limiting conditions. These first flow limiting conditions are the constraints on the flow diversion strategy required to maintain a stable gas state within the vaporizer, including upper and lower limits on the diversion rate and the allowable temperature range. Based on the initialized variable frequency flow diversion parameters, the first current limiting condition is adjusted and converted. Specifically, parameters such as the diversion rate and temperature control are adjusted according to the current gasification state and current limiting conditions to develop the optimal variable frequency flow diversion strategy, namely the first variable frequency strategy. This implementation method identifies the gasification state within the vaporizer and, based on historical data and experimentally verified variable frequency flow diversion strategies, develops the optimal variable frequency flow diversion solution. This ensures that the gas state within the vaporizer remains within a safe and stable range, achieving optimized and efficient operation of the atomized gas source gas production process.

[0045] In one possible implementation, constructing the variable frequency decision library includes: performing feature mining on a four-dimensional grid based on static gasification characteristics and dynamic gasification characteristics in combination with historical gasification records to determine flow limiting conditions, wherein the flow limiting conditions are used for flow frequency regulation and control and include a flow limiting direction and a flow limiting vector; establishing a mapping between static gasification characteristics, dynamic gasification characteristics, and the flow limiting conditions to construct the variable frequency decision library.

[0046] Specifically, a large amount of static and dynamic gasification characteristic data was collected from historical gasification records, including parameters such as temperature distribution, pressure distribution, flow velocity, and flow rate within the gasifier. This data was preprocessed, including data cleaning (removing outliers and missing values) and data normalization (converting the data to a uniform magnitude) to ensure accuracy and comparability. Feature mining was performed on the preprocessed data on a four-dimensional grid (i.e., a grid partitioning in both spatial and temporal dimensions). This involved extracting key gasification characteristics, such as temperature gradients and flow velocity variations, and analyzing their relationships with target variables such as gasification efficiency and energy consumption. Through feature mining, the key factors influencing the gasification process were identified, as well as how these factors influenced the formulation of the variable frequency flow diversion strategy. Based on the results of feature mining, a series of flow limiting conditions were determined, including upper and lower flow limits and flow rate trends (increasing or decreasing). These conditions were used to guide subsequent flow frequency control. The flow limiting conditions included not only the flow limiting direction (i.e., flow increase or decrease) but also the flow limiting vector (i.e., the specific flow value or range). A machine learning algorithm establishes a mapping relationship between static and dynamic gasification characteristics and flow-limiting conditions. This mapping relationship describes the variable frequency flow diversion strategy to be adopted under different gasification states. Finally, the mapping relationship is stored in a variable frequency decision library, which contains the corresponding variable frequency flow diversion strategies for various gasification states and is used to guide the actual gas production process. This implementation method, by constructing a variable frequency decision library, enables the system to automatically select the optimal variable frequency flow diversion strategy based on the current gasification state, achieving intelligent control of the atomized gas source gas production process.

[0047] In one possible implementation, the system further includes: a surface image acquisition module, which is used to acquire a surface image of the air-temperature vaporizer heat absorber by controlling a video monitoring device; an element recognition module, which is used to perform element recognition on the surface image using frost characteristics as directional recognition factors, and determine a real-time frost characteristic value; and a second frequency conversion strategy acquisition module, which is used to determine the heat exchange loss based on the external environment based on the real-time frost characteristic value, and acquire a second frequency conversion strategy.

[0048] Specifically, the surface image acquisition module activates a video monitoring device (equipment used to capture and record real-time images of the ATU heat sink surface, including components such as a camera and image sensor). The video monitoring device collects surface images of the ATU heat sink at preset intervals or trigger conditions (such as changes in parameters like temperature and pressure). The feature recognition module preprocesses the collected surface images, including noise reduction and contrast enhancement, to improve image quality. Feature extraction is performed on the preprocessed images, focusing on features related to frost formation. Frost features refer to specific manifestations of frost condensed from water vapor on the ATU heat sink surface due to low temperatures, such as color changes and increased texture. The extracted features are matched and identified to determine a real-time frost feature value, a quantitative indicator that indicates the severity or extent of frost formation. The second frequency conversion strategy acquisition module uses the real-time frost feature value, combined with external parameters such as temperature and humidity, to assess heat exchange losses due to the external environment. These losses include decreased heat transfer efficiency and increased energy consumption caused by frost formation. The system searches the frequency conversion decision database for a second frequency conversion strategy that matches the current heat exchange losses. This strategy includes actions such as adjusting the diversion rate and changing the temperature setpoint. The found second frequency conversion strategy is sent to the automatic control system, which then executes the corresponding actions to optimize the gas production process and reduce heat exchange losses. This implementation method identifies frosting characteristics and formulates a frequency conversion strategy accordingly, enabling the system to adjust the gas production process in a timely manner, reducing heat exchange losses and increased energy consumption caused by frosting. This results in improved gas production efficiency, extended the life of the air-temperature vaporizer, reduced maintenance costs, and improved system operational stability.

[0049] In one possible implementation, determining the frequency conversion diversion strategy includes: performing mutual verification on the first frequency conversion strategy and the second frequency conversion strategy to determine a verification result; if the verification result meets the standard, fitting the first frequency conversion strategy and the second frequency conversion strategy to determine the frequency conversion diversion strategy.

[0050] Specifically, key parameters of the two frequency conversion strategies are compared, including but not limited to flow diversion rate, temperature setpoint, and pressure control. Pre-set verification conditions are used to determine whether the two frequency conversion strategies are consistent or acceptable. These verification conditions include whether the parameter adjustment directions are consistent (e.g., both increase or decrease) and whether the parameter amplitude modulation is within a preset differential range (e.g., the increase or decrease in the flow diversion rate in the two strategies does not exceed a certain percentage). If both the first and second frequency conversion strategies meet the verification conditions for the adjustment direction and amplitude modulation of the key parameters, the verification result is determined to be satisfactory. For the two strategies that meet the verification results, a fitting method is used to determine the final frequency conversion flow diversion strategy. Fitting methods include averaging (i.e., taking the average of the parameters in the two frequency conversion strategies), weighted averaging (assigning different weights to the parameters of different strategies based on their reliability or priority, and then calculating the weighted average), or other optimization algorithms (e.g., least squares method, genetic algorithm, etc.). After fitting, a final frequency conversion flow diversion strategy is obtained that combines the advantages of both frequency conversion strategies. This implementation method improves the accuracy and reliability of the variable frequency diversion strategy by performing variable frequency decision analysis from both the internal side (based on fluid dynamics model and spatial gasification state analysis) and the external side (based on surface image acquisition and element recognition), and verifying each other. The internal analysis mainly relies on precise mathematical models and physical principles, which can capture the internal dynamic changes in the gasification process; while the external analysis can monitor the impact of the external environment (such as temperature, humidity, frost, etc.) on the gasification process in real time. Through mutual verification and fitting processing, the system can integrate information from both aspects to obtain a more comprehensive and optimized variable frequency diversion strategy. This not only improves gas production efficiency, but also reduces energy consumption and operating costs, while improving the stability and reliability of the system.

[0051] The reverse guidance control module 60 is used to perform reverse guidance control of the variable frequency diversion based on the variable frequency diversion strategy. Specifically, based on the variable frequency diversion strategy, the reverse guidance control module 60 controls the introduction process of the liquid gas in real time. According to the actual situation during the gasification process, the introduction parameters are dynamically adjusted to ensure the stability and efficiency of the gasification process. The embodiment of the present application realizes the fully automated control of the preparation of high-pressure gas from the atomized gas source by integrating key components such as the gas production instruction sending module, the variable frequency gasification signal generation module, the initialization variable frequency diversion parameter determination module, the fluid dynamics model construction module, the spatial gasification state analysis module and the reverse guidance control module, thereby improving the technical effect of improving work efficiency and safety and reliability.

[0052] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0053] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An automatic control system for atomizing gas source, characterized in that: The system comprises: A gas control instruction sending module, which is used to send gas control instructions through the main console and transmit them to the automatic control system based on the serial bus; a variable frequency gasification signal generation module, the variable frequency gasification signal generation module being used to execute valve opening and closing control through the automatic control system, perform downstream liquid gas pre-cooling with a cryogenic pump, and generate a variable frequency gasification signal, wherein a preset temperature threshold is used as a pre-cooling condition; an initialization variable frequency flow diversion parameter determination module, the initialization variable frequency flow diversion parameter determination module being used to determine the initialization variable frequency flow diversion parameters based on the variable frequency gasification signal, and to introduce the pre-cooled liquid gas into the air-temperature gasifier; a fluid dynamic model construction module, the fluid dynamic model construction module being used to determine a vaporization space based on an air-temperature vaporizer, perform finite difference processing, and construct a fluid dynamic model, wherein the vaporization space includes a first space for convective heat exchange and a second space for heat conduction, the first space and the second space being connected; a space vaporization state analysis module, configured to perform a space vaporization state analysis on the pre-cooled liquid gas in the air-temperature vaporizer based on the fluid dynamics model and determine a variable frequency flow diversion strategy; A reverse guidance control module, configured to perform reverse guidance control of the variable frequency flow diversion based on the variable frequency flow diversion strategy; The constructing of the fluid dynamics model comprises: Setting a spatial step size and a time step size, wherein the step sizes of the first space and the second space are set differently; Based on the spatial step and the time step, the gasification space is gridded to define a four-dimensional grid distribution; Performing finite difference processing on the four-dimensional grid distribution to construct the fluid dynamics model; Performing finite difference processing on the four-dimensional grid distribution to construct the fluid dynamics model includes: For the four-dimensional grid distribution, performing central difference processing on each grid to determine a discrete difference equation, wherein the difference equation is expressed as the division of the difference between the thermal balance eigenvalues of the previous and next grid intersection nodes with any grid intersection node as the center and twice the spatial step size; Determining boundary conditions based on external ambient temperature characteristics, wherein the boundary conditions at least include boundary temperature and heat flux density; Constructing the fluid dynamics model based on the four-dimensional grid distribution and the discrete difference equations and the boundary conditions; After the space gasification state analysis is carried out, including: Identifying the gasification state of the space and determining a first static feature and a first dynamic feature; Traversing a frequency conversion decision library, matching the first static feature with the first dynamic feature, and determining a first current limiting condition; Based on the initialized frequency conversion flow diversion parameter, the first current limiting condition is subjected to parameter control adjustment conversion to determine a first frequency conversion strategy; Constructing the frequency conversion decision library includes: Based on the static and dynamic gasification characteristics and combined with historical gasification records, feature mining is performed on a four-dimensional grid to determine the flow limiting conditions. The flow limiting conditions are used for flow frequency control and include the flow limiting direction and flow limiting vector. A mapping between the static gasification characteristics, the dynamic gasification characteristics and the current limiting conditions is established, and a frequency conversion decision library is constructed.

2. The automatic control system for atomizing gas source according to claim 1, characterized in that: The system further comprises: a surface image acquisition module, the surface image acquisition module being used to acquire a surface image of the heat absorbing plate of the air-temperature vaporizer by controlling a video monitoring device; An element recognition module, the element recognition module is used to perform element recognition on the surface image using frost characteristics as directional recognition factors, and determine a real-time frost characteristic value; The second frequency conversion strategy acquisition module is used to determine the heat exchange loss based on the external environment based on the real-time frosting characteristic value and acquire the second frequency conversion strategy.

3. The automatic control system for atomizing gas source according to claim 2, characterized in that: Determining the frequency conversion diversion strategy includes: performing mutual verification on the first frequency conversion strategy and the second frequency conversion strategy to determine a verification result; If the verification result meets the requirements, the first frequency conversion strategy and the second frequency conversion strategy are fitted to determine the frequency conversion diversion strategy.

4. The automatic control system for atomizing gas source according to claim 1, characterized in that: After the pre-cooled liquid gas is introduced into the air-temperature vaporizer, it includes: Determine a high-pressure gas storage module, wherein the high-pressure gas storage module is composed of N parallel gas storage tanks; The prepared gas output by the air-temperature gasifier is transmitted to the high-pressure gas storage module through a pipeline for gas storage, wherein the high-pressure gas storage module is equipped with a temperature sensor and a pressure sensor; The sensor data of the high-pressure gas storage module is determined, and a gas shutdown signal is generated based on a preset temperature value and a preset pressure value, and is transmitted to the main control console to perform gas shutdown control of the automatic control system.

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