Cooling water flow control method and device, storage medium and electronic device

Abstract

The invention discloses a cooling water flow control method and device, a storage medium and an electronic device, and the method comprises the steps: predicting the temperature change trend of a light-hydrogen coupling system according to historical environment data and the current system state of the light-hydrogen coupling system; and controlling the flow of first cooling water in the photo-hydrogen coupling system through the temperature change trend. The problems that the optical-hydrogen coupling system lacks an effective thermal management method, system efficiency fluctuation and premature aging of equipment are easily caused are solved, and the technical effect of effective thermal management on the optical-hydrogen coupling system is achieved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic power generation, and in particular to a method and device for controlling cooling water flow, a storage medium and an electronic device. Background Art

[0002] With the rapid growth of photovoltaic power generation capacity, photovoltaic hydrogen production has become an important part of building a new energy system. In the light-hydrogen coupling system, the power generation efficiency of photovoltaic panels decreases in high temperature environments, and the excessively high temperature of the electrolyzer may affect the electrolysis efficiency and system stability. Therefore, it can be seen that the existing system lacks effective thermal management methods, which can easily cause fluctuations in system efficiency and premature aging of equipment. Therefore, achieving effective control of system temperature is of great significance to improving system efficiency and extending system life.

[0003] Regarding the related technologies, the light-hydrogen coupling system lacks an effective thermal management method, which easily causes fluctuations in system efficiency and premature aging of equipment, and no effective solution has yet been found. Summary of the invention

[0004] The embodiments of the present application provide a cooling water flow control method and device, a storage medium and an electronic device to at least solve the problem in the prior art that the light-hydrogen coupling system lacks an effective thermal management method, which easily causes fluctuations in system efficiency and premature aging of equipment.

[0005] According to one embodiment of the embodiments of the present application, a method for controlling cooling water flow is provided, including: predicting a temperature change trend of the light-hydrogen coupling system based on historical environmental data and a current system state of the light-hydrogen coupling system; and controlling a first cooling water flow in the light-hydrogen coupling system according to the temperature change trend.

[0006] In an exemplary embodiment, predicting a temperature change trend of a light-hydrogen coupling system based on historical environmental data and a current system state of the light-hydrogen coupling system includes: acquiring the current system state, wherein the current system state includes: a temperature of the light-hydrogen coupling system at a current moment and a solar irradiance of the light-hydrogen coupling system at the current moment; and acquiring the historical environmental data, wherein the historical environmental data includes: a temperature of the light-hydrogen coupling system at a first moment and a solar irradiance of the light-hydrogen coupling system at the first moment, wherein the first moment is a historical corresponding moment of the current moment; and inputting the current system state and the historical environmental data into a pre-trained machine learning model to obtain the temperature change trend.

[0007] In an exemplary embodiment, controlling the first cooling water flow rate in the light-hydrogen coupling system by the temperature change trend includes: determining predicted temperatures corresponding to multiple second moments after the current moment by the temperature change trend, wherein the current moment is the moment of obtaining the current system state; and determining to increase the first cooling water flow rate at a third moment to a second cooling water flow rate when the predicted temperature is greater than or equal to a safety temperature threshold, wherein the third moment is a corresponding moment among the multiple second moments to a predicted temperature greater than or equal to the safety temperature threshold.

[0008] In an exemplary embodiment, after determining that the first cooling water flow rate at the third moment is increased to a second cooling water flow rate, the method further includes: determining a first heat recovered by a heat exchanger from the light-to-hydrogen coupling system, wherein the first heat is used to preheat an electrolyzer that needs a cold start in the light-to-hydrogen coupling system, and the heat exchanger is deployed in a cooling water circulation subsystem of the light-to-hydrogen coupling system; and calculating an increase in cooling water required to increase the first cooling water flow rate to a second cooling water flow rate using the first heat.

[0009] In an exemplary embodiment, determining the first heat recovered by the heat exchanger from the light-hydrogen coupling system includes: acquiring cooling water data corresponding to cooling water of the light-hydrogen coupling system, and environmental data corresponding to the cooling water, wherein the cooling water data includes: mass flow rate and specific heat capacity of the cooling water, and the environmental data includes: outlet temperature of the cooling water, and ambient temperature corresponding to the cooling water; determining the first heat through the cooling water data and the environmental data.

[0010] In an exemplary embodiment, calculating the increase in cooling water required to increase the first cooling water flow rate to a second cooling water flow rate through the first heat includes: obtaining second heat generated by a photovoltaic panel of a light-to-hydrogen coupling system, and obtaining third heat generated by an electrolyzer of the light-to-hydrogen coupling system; determining a heat sum value of the second heat and the third heat; and calculating the increase in cooling water through the heat sum value and the first heat.

[0011] According to another embodiment of the embodiment of the present application, a cooling water flow control device is also provided, including: a prediction module, used to predict the temperature change trend of the light-hydrogen coupling system based on historical environmental data and the current system state of the light-hydrogen coupling system; a control module, used to control the first cooling water flow in the light-hydrogen coupling system according to the temperature change trend.

[0012] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0013] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0014] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0015] In an embodiment of the present application, the temperature change trend of the light-hydrogen coupling system is predicted based on historical environmental data and the current system state of the light-hydrogen coupling system; and the first cooling water flow rate in the light-hydrogen coupling system is controlled by the temperature change trend. That is to say, the temperature change trend of the light-hydrogen coupling system is first predicted, and then the first cooling water flow rate in the light-hydrogen coupling system can be adjusted according to the temperature change trend. Through the above embodiment, the problem that the light-hydrogen coupling system lacks an effective thermal management method in the prior art, which easily causes fluctuations in system efficiency and premature aging of equipment is solved, and the technical effect of effective thermal management of the light-hydrogen coupling system is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 It is a hardware structure block diagram of a computer terminal of a cooling water flow control method according to an embodiment of the present application;

[0018] Figure 2 is a flow chart of a method for controlling cooling water flow according to an embodiment of the present application;

[0019] Figure 3 It is a structural block diagram of a cooling water flow control device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device 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 units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0022] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a computer terminal of a cooling water flow control method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0023] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0024] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0025] In this embodiment, a method for controlling cooling water flow is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flow chart of a method for controlling cooling water flow according to an embodiment of the present application, the flow chart includes the following steps:

[0026] Step S202, predicting the temperature change trend of the light-hydrogen coupling system according to historical environmental data and the current system state of the light-hydrogen coupling system;

[0027] Step S204 , controlling a first cooling water flow rate in the light-to-hydrogen coupling system according to the temperature variation trend.

[0028] Through the above steps, the temperature change trend of the photohydrogen coupling system is predicted according to the historical environmental data and the current system state of the photohydrogen coupling system; and the first cooling water flow rate in the photohydrogen coupling system is controlled according to the temperature change trend. That is to say, the temperature change trend of the photohydrogen coupling system is first predicted, and then the first cooling water flow rate in the photohydrogen coupling system can be adjusted according to the temperature change trend. Through the above embodiments, the problem that the photohydrogen coupling system lacks an effective thermal management method in the prior art, which easily causes fluctuations in system efficiency and premature aging of equipment, is solved, and the technical effect of effective thermal management of the photohydrogen coupling system is achieved. It can warn of possible overheating of the photohydrogen coupling system in advance, thereby achieving more sophisticated temperature management in the photohydrogen coupling system, especially in high temperature environments, and can effectively prevent the system from shutting down due to overheating, thereby ensuring the continuous operation and efficiency of the photohydrogen coupling system.

[0029] There are multiple implementations for the above step S204. In an optional implementation, predicting the temperature change trend of the light-hydrogen coupling system based on historical environmental data and the current system state of the light-hydrogen coupling system includes: acquiring the current system state, wherein the current system state includes: the temperature of the light-hydrogen coupling system at the current moment and the solar irradiance of the light-hydrogen coupling system at the current moment; and acquiring the historical environmental data, wherein the historical environmental data includes: the temperature of the light-hydrogen coupling system at a first moment and the solar irradiance of the light-hydrogen coupling system at the first moment, wherein the first moment is the historical corresponding moment of the current moment; inputting the current system state and the historical environmental data into a pre-trained machine learning model to obtain the temperature change trend.

[0030] Real-time monitoring of the temperature and solar irradiance of the light-hydrogen coupling system at the current moment. Temperature information involves the real-time temperature of the photovoltaic panels and electrolyzers, and solar irradiance reflects the current light intensity, which are important factors affecting temperature changes.

[0031] Get the stored temperature and solar irradiance data at different historical moments (especially the first moment corresponding to the current moment). These data are used to train the machine learning model to understand the relationship between environmental conditions and system temperature changes.

[0032] Using historical environmental data as input and the temperature change of the light-hydrogen coupling system as output, a machine learning model is trained. The model may use a variety of algorithms, such as regression analysis, neural networks, etc., in order to enable the model to learn and grasp how environmental conditions (such as temperature, solar irradiance) affect the temperature change of the light-hydrogen coupling system.

[0033] When it is necessary to predict future temperature changes, the current system state (including current temperature and solar irradiance) and the historical environmental data corresponding to the current moment are input into the trained machine learning model.

[0034] The model predicts the temperature change trend of the solar-hydrogen coupling system based on the current system status and historical environmental data. This step is crucial for adjusting the cooling water circulation system in advance to avoid excessively high or low temperatures.

[0035] The above embodiments of the present application use machine learning technology to predict future temperature changes of the system based on historical data and current conditions, thereby achieving more accurate temperature control. This predictive capability enables the light-hydrogen coupling system to actively adapt to environmental changes, maintain efficient and stable operation, and reduce energy waste. In this way, the system can not only maintain the optimal operating temperature, but also predict potential temperature problems and take measures in advance to prevent overheating or overcooling, thereby protecting the equipment and extending its service life.

[0036] In an exemplary embodiment, controlling the first cooling water flow rate in the light-hydrogen coupling system by the temperature change trend includes: determining predicted temperatures corresponding to multiple second moments after the current moment by the temperature change trend, wherein the current moment is the moment of obtaining the current system state; and determining to increase the first cooling water flow rate at a third moment to a second cooling water flow rate when the predicted temperature is greater than or equal to a safety temperature threshold, wherein the third moment is a corresponding moment among the multiple second moments to a predicted temperature greater than or equal to the safety temperature threshold.

[0037] First, based on the temperature change trend obtained from the machine learning model, the temperature at multiple time points after the current moment (called the second moment) is predicted. These predicted temperatures reflect the temperature fluctuations that the system may experience.

[0038] Secondly, make a safe temperature threshold judgment: check each predicted temperature to see if it reaches or exceeds the preset safe temperature threshold. The safe temperature threshold is a key parameter in system design. Exceeding this threshold may cause reduced efficiency of photovoltaic panels, damage to electrolyzers, or other system performance problems.

[0039] If it is predicted that the temperature at a certain point in the future (the third moment) will be equal to or exceed the safe temperature threshold, the cooling water flow rate will be automatically adjusted. Specifically, the system will increase the cooling water flow rate at the third moment (the first cooling water flow rate) to a new level (the second cooling water flow rate) to enhance the heat dissipation capacity of the system. This advance flow adjustment can prevent overheating and ensure that the system operates within a safe temperature range.

[0040] This prediction-based cooling water flow control strategy can better respond to temperature changes, rather than taking action when the temperature has risen to dangerous levels. By increasing the cooling water flow in advance, the system can effectively avoid overheating, protect the equipment, and improve overall energy efficiency. This dynamic, predictive control method is an innovation in the temperature management of the light-hydrogen coupling system, which can significantly improve the system stability and reliability, extend the system life, and reduce unnecessary energy consumption.

[0041] In an exemplary embodiment, after determining that the first cooling water flow rate at the third moment is increased to a second cooling water flow rate, the method further includes: determining a first heat recovered by a heat exchanger from the light-to-hydrogen coupling system, wherein the first heat is used to preheat an electrolyzer that needs a cold start in the light-to-hydrogen coupling system, and the heat exchanger is deployed in a cooling water circulation subsystem of the light-to-hydrogen coupling system; and calculating an increase in cooling water required to increase the first cooling water flow rate to a second cooling water flow rate using the first heat.

[0042] Further, determining the first heat recovered by the heat exchanger from the light-hydrogen coupling system includes: acquiring cooling water data corresponding to the cooling water of the light-hydrogen coupling system, and environmental data corresponding to the cooling water, wherein the cooling water data includes: mass flow rate and specific heat capacity of the cooling water, and the environmental data includes: outlet temperature of the cooling water, and ambient temperature corresponding to the cooling water; determining the first heat through the cooling water data and the environmental data.

[0043] It should be noted that the heat exchanger is a key component in the cooling water cycle of the photohydrogen coupling system. It can recover a portion of the heat taken away by the cooling water (i.e., the first heat) for preheating the electrolyzer that needs to be cold-started. In order to calculate the first heat, the system needs to collect cooling water data and environmental data. The cooling water data includes the mass flow rate of cooling water (i.e., the mass of cooling water passing through the system per unit time) and the specific heat capacity (i.e., the heat required to increase the temperature of the unit mass of cooling water by 1 degree). The environmental data includes the outlet temperature of the cooling water when it leaves the system and the current ambient temperature. With these data, the system (i.e., the photohydrogen coupling system) can use the principles of thermodynamics to calculate the first heat actually recovered by the heat exchanger and used to preheat the electrolyzer. Optionally, the ambient temperature can be equal to the temperature of the photohydrogen coupling system at the current moment, or it can indicate the ambient temperature of the geographical location where the photohydrogen coupling system is located.

[0044] Specifically, the first heat amount is calculated in the following manner: determine the temperature difference between the outlet temperature and the ambient temperature; and determine the first heat amount as the product of the temperature difference, the mass flow rate, and the specific heat capacity.

[0045] Furthermore, calculating the increase in cooling water required to increase the first cooling water flow rate to a second cooling water flow rate through the first heat includes: obtaining a second heat generated by a photovoltaic panel of a light-to-hydrogen coupling system, and obtaining a third heat generated by an electrolyzer of the light-to-hydrogen coupling system; determining a heat sum value of the second heat and the third heat; and calculating the increase in cooling water through the heat sum value and the first heat.

[0046] After determining the first heat, it is also necessary to collect the heat generated by the photovoltaic panels (second heat) and the heat generated by the electrolyzer (third heat) of the light-hydrogen coupling system. The heat of the photovoltaic panels is usually related to the solar irradiance and the efficiency loss coefficient of the photovoltaic panels, while the heat of the electrolyzer is related to the operating voltage, operating current and the efficiency loss coefficient of the electrolyzer. These heat data are crucial for evaluating the overall thermal balance of the system.

[0047] Then, after determining the first heat (recovered heat), the second heat (heat generated by the photovoltaic panel) and the third heat (heat generated by the electrolyzer), the system can calculate the amount of cooling water required to increase the cooling water flow from the first cooling water flow to the second cooling water flow based on the principle of heat balance. This step ensures that the system can effectively utilize the recovered heat while increasing the cooling water flow, avoiding the use of excessive cooling water, thereby improving energy efficiency.

[0048] The calculation method of the increase in cooling water is as follows: calculating the heat sum and the heat difference of the first heat; and calculating the product of the temperature difference between the inlet temperature and the outlet temperature of the cooling water, the specific heat capacity of the cooling water, and the density of the cooling water; and determining the ratio of the heat difference to the product as the increase in cooling water.

[0049] Through the above implementation steps, the light-hydrogen coupling system can not only dynamically adjust the cooling water flow rate according to the predicted temperature change trend, effectively preventing the equipment from being damaged by overheating, but also make full use of the recovered first heat to preheat the cold-start electrolyzer while increasing the cooling water flow rate. This method not only improves the overall thermal management efficiency of the system, but also reduces energy waste, improves the economy and environmental friendliness of the system. At the same time, through the predictive ability of machine learning technology, the system can control the temperature more accurately and avoid unnecessary temperature fluctuations, thereby extending the life of the equipment and ensuring the stable operation and efficient output of the light-hydrogen coupling system under various environmental conditions.

[0050] In order to better understand the process of the above-mentioned cooling water flow control method, the above-mentioned cooling water flow control method is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0051] The optional embodiment of this application proposes a temperature control technology for the light-hydrogen coupling system based on machine learning (equivalent to the control method of the cooling water flow in the above embodiment), which adjusts the working temperature of the photovoltaic panel and the electrolyzer by combining the coolant circulation system with the intelligent temperature control device, maintains the efficient operation of the photovoltaic panel and the electrolyzer, extends the service life of the system, and uses the recovered heat to accelerate the cold start of the electrolyzer, while improving the stability and overall efficiency of the hydrogen production process. The specific implementation steps are as follows:

[0052] Step 1: Use machine learning models to predict temperature change trends in advance based on historical environmental data (such as temperature, solar irradiance) and current system status.

[0053] T pre =f(T current ,G current ,T history ,G history );

[0054] Among them, T pre Indicates the temperature change trend; T current represents the temperature of the light-hydrogen coupling system at the current moment, G current represents the solar irradiance of the light-hydrogen coupling system at the current moment; T history represents the temperature of the photohydrogen coupling system at the first moment; G history represents the solar irradiance of the light-hydrogen coupling system at the first moment.

[0055] Step 2: In the cooling water circulation, the heat taken away by the cooling water is recovered through the heat exchanger and used to preheat the electrolyzer that needs to be cold started.

[0056]

[0057] Where:

[0058] Q re ——Recovered heat (i.e. first heat);

[0059] ——Mass flow rate of cooling water;

[0060] c p ——Specific heat capacity of cooling water;

[0061] T out ——cooling water outlet temperature;

[0062] T ambient ——Ambient temperature.

[0063] When the system predicts that the temperature is about to exceed the preset safety temperature threshold, the cooling water flow rate is automatically increased to enhance the heat dissipation efficiency.

[0064]

[0065] Where:

[0066] ΔQ——increased cooling water flow rate (equivalent to the increased amount of cooling water in the above embodiment);

[0067] Q PV ——The heat generated by photovoltaic panels (i.e., the second heat);

[0068] Q el ——The heat generated by the electrolytic cell (i.e. the third heat);

[0069] ρ——density of cooling water.

[0070] Q PV =A PV ·G·η PV,loss ;

[0071] Q el =V el I el ·η el,loss ;

[0072] Where:

[0073] A PV ——Area of ​​photovoltaic panels;

[0074] G——solar irradiance;

[0075] η PV,loss ——The efficiency loss coefficient of photovoltaic panels due to temperature rise;

[0076] V el ——operating voltage of electrolyzer;

[0077] I el ——operating current of electrolytic cell;

[0078] η el,loss ——The efficiency loss coefficient of the electrolytic cell caused by the temperature rise.

[0079] Through the above steps, the optional embodiment of the present application predicts the temperature change trend of the light-hydrogen coupling system and adjusts the cooling water flow in advance, effectively avoiding system overheating and ensuring the stable operation of the light-hydrogen coupling system. At the same time, the heat recovered by the heat exchanger is used to preheat the electrolyzer, which realizes efficient use of energy and reduces energy consumption during cold start. Through the accurate prediction of the machine learning model, the accuracy and response speed of the cooling water flow control are further improved, which has a significant promoting effect on improving the overall performance and economic benefits of the light-hydrogen coupling system. This method is particularly suitable for large-scale light-hydrogen coupling projects, such as solar water electrolysis hydrogen production plants for green hydrogen production, which can significantly improve the operating efficiency of the plant and reduce maintenance costs. At the same time, it has important strategic significance for promoting the widespread application of renewable energy and reducing dependence on fossil energy. In practical applications, this method can also be combined with other intelligent control systems, such as power dispatching systems, meteorological forecasting systems, etc., to achieve more comprehensive and intelligent energy management, and provide technical support for building smart grids and realizing energy Internet.

[0080] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0081] The present application also provides a structural block diagram of a cooling water flow control device. Figure 3 is a structural block diagram of a cooling water flow control device according to an embodiment of the present application; Figure 3 As shown, including:

[0082] A prediction module 32, configured to predict a temperature change trend of the light-hydrogen coupling system according to historical environmental data and a current system state of the light-hydrogen coupling system;

[0083] The control module 34 is used to control the first cooling water flow rate in the light-hydrogen coupling system according to the temperature change trend.

[0084] Through the above device, the temperature change trend of the light-hydrogen coupling system is predicted according to the historical environmental data and the current system state of the light-hydrogen coupling system; the first cooling water flow rate in the light-hydrogen coupling system is controlled according to the temperature change trend. That is to say, the temperature change trend of the light-hydrogen coupling system is first predicted, and then the first cooling water flow rate in the light-hydrogen coupling system can be adjusted according to the temperature change trend. Through the above embodiment, the problem that the light-hydrogen coupling system lacks an effective thermal management method in the prior art, which easily causes fluctuations in system efficiency and premature aging of equipment is solved, and the technical effect of effective thermal management of the light-hydrogen coupling system is achieved.

[0085] In an exemplary embodiment, the prediction module 32 is also used to: obtain the current system state, wherein the current system state includes: the temperature of the light-hydrogen coupling system at the current moment and the solar irradiance of the light-hydrogen coupling system at the current moment; and obtain the historical environmental data, wherein the historical environmental data includes: the temperature of the light-hydrogen coupling system at a first moment and the solar irradiance of the light-hydrogen coupling system at the first moment, wherein the first moment is the historical corresponding moment of the current moment; input the current system state and the historical environmental data into a pre-trained machine learning model to obtain the temperature change trend.

[0086] Real-time monitoring of the temperature and solar irradiance of the light-hydrogen coupling system at the current moment. Temperature information involves the real-time temperature of the photovoltaic panels and electrolyzers, and solar irradiance reflects the current light intensity, which are important factors affecting temperature changes.

[0087] Get the stored temperature and solar irradiance data at different historical moments (especially the first moment corresponding to the current moment). These data are used to train the machine learning model to understand the relationship between environmental conditions and system temperature changes.

[0088] Using historical environmental data as input and the temperature change of the light-hydrogen coupling system as output, a machine learning model is trained. The model may use a variety of algorithms, such as regression analysis, neural networks, etc., in order to enable the model to learn and grasp how environmental conditions (such as temperature, solar irradiance) affect the temperature change of the light-hydrogen coupling system.

[0089] When it is necessary to predict future temperature changes, the current system state (including current temperature and solar irradiance) and the historical environmental data corresponding to the current moment are input into the trained machine learning model.

[0090] The model predicts the temperature change trend of the solar-hydrogen coupling system based on the current system status and historical environmental data. This step is crucial for adjusting the cooling water circulation system in advance to avoid excessively high or low temperatures.

[0091] The above embodiments of the present application use machine learning technology to predict future temperature changes of the system based on historical data and current conditions, thereby achieving more accurate temperature control. This predictive capability enables the light-hydrogen coupling system to actively adapt to environmental changes, maintain efficient and stable operation, and reduce energy waste. In this way, the system can not only maintain the optimal operating temperature, but also predict potential temperature problems and take measures in advance to prevent overheating or overcooling, thereby protecting the equipment and extending its service life.

[0092] In an exemplary embodiment, the control module 34 is also used to: determine the predicted temperatures corresponding to multiple second moments after the current moment through the temperature change trend, wherein the current moment is the moment of obtaining the current system state; and when the predicted temperature is greater than or equal to a safety temperature threshold, determine to increase the first cooling water flow rate at the third moment to a second cooling water flow rate, wherein the third moment is the corresponding moment among the multiple second moments when the predicted temperature is greater than or equal to the safety temperature threshold.

[0093] First, based on the temperature change trend obtained from the machine learning model, the temperature at multiple time points after the current moment (called the second moment) is predicted. These predicted temperatures reflect the temperature fluctuations that the system may experience.

[0094] Secondly, make a safe temperature threshold judgment: check each predicted temperature to see if it reaches or exceeds the preset safe temperature threshold. The safe temperature threshold is a key parameter in system design. Exceeding this threshold may cause reduced efficiency of photovoltaic panels, damage to electrolyzers, or other system performance problems.

[0095] If it is predicted that the temperature at a certain point in the future (the third moment) will be equal to or exceed the safe temperature threshold, the cooling water flow rate will be automatically adjusted. Specifically, the system will increase the cooling water flow rate at the third moment (the first cooling water flow rate) to a new level (the second cooling water flow rate) to enhance the heat dissipation capacity of the system. This advance flow adjustment can prevent overheating and ensure that the system operates within a safe temperature range.

[0096] This prediction-based cooling water flow control strategy can better respond to temperature changes, rather than taking action when the temperature has risen to dangerous levels. By increasing the cooling water flow in advance, the system can effectively avoid overheating, protect the equipment, and improve overall energy efficiency. This dynamic, predictive control method is an innovation in the temperature management of the light-hydrogen coupling system, which can significantly improve the system stability and reliability, extend the system life, and reduce unnecessary energy consumption.

[0097] In an exemplary embodiment, the device also includes: a calculation module, used to determine after the first cooling water flow rate at a third moment is increased to a second cooling water flow rate: determine a first heat recovered by a heat exchanger from the light-to-hydrogen coupling system, wherein the first heat is used to preheat an electrolyzer that needs a cold start in the light-to-hydrogen coupling system, and the heat exchanger is deployed in a cooling water circulation subsystem of the light-to-hydrogen coupling system; and calculate the increase in cooling water required to increase the first cooling water flow rate to the second cooling water flow rate using the first heat.

[0098] In an exemplary embodiment, the calculation module is further used to: obtain cooling water data corresponding to the cooling water of the light-hydrogen coupling system, and environmental data corresponding to the cooling water, wherein the cooling water data includes: mass flow rate and specific heat capacity of the cooling water, and the environmental data includes: outlet temperature of the cooling water and ambient temperature corresponding to the cooling water; determine the first heat through the cooling water data and the environmental data.

[0099] The heat exchanger is a key component in the cooling water cycle of the light-hydrogen coupling system. It can recover part of the heat taken away by the cooling water (i.e., the first heat) for preheating the electrolyzer that needs to be cold-started. In order to calculate the first heat, the system needs to collect cooling water data and environmental data. The cooling water data includes the mass flow rate of cooling water (i.e., the mass of cooling water passing through the system per unit time) and the specific heat capacity (i.e., the heat required to increase the temperature of the unit mass of cooling water by 1 degree). The environmental data includes the outlet temperature of the cooling water when it leaves the system and the current ambient temperature. Through these data, the system (i.e., the light-hydrogen coupling system) can use the principles of thermodynamics to calculate the first heat actually recovered by the heat exchanger and used to preheat the electrolyzer. Optionally, the ambient temperature can be equal to the temperature of the light-hydrogen coupling system at the current moment, or it can indicate the ambient temperature of the geographical location where the light-hydrogen coupling system is located. Specifically, the first heat is calculated by: determining the temperature difference between the outlet temperature and the ambient temperature; and determining the product of the temperature difference, mass flow rate, and specific heat capacity as the first heat.

[0100] In an exemplary embodiment, the calculation module is also used to: obtain a second heat generated by a photovoltaic panel of a light-to-hydrogen coupling system, and obtain a third heat generated by an electrolyzer of the light-to-hydrogen coupling system; determine a heat sum value of the second heat and the third heat; and calculate the increase in cooling water using the heat sum value and the first heat.

[0101] After determining the first heat, it is also necessary to collect the heat generated by the photovoltaic panels (second heat) and the heat generated by the electrolyzer (third heat) of the light-hydrogen coupling system. The heat of the photovoltaic panels is usually related to the solar irradiance and the efficiency loss coefficient of the photovoltaic panels, while the heat of the electrolyzer is related to the operating voltage, operating current and the efficiency loss coefficient of the electrolyzer. These heat data are crucial for evaluating the overall thermal balance of the system.

[0102] Then, after determining the first heat (recovered heat), the second heat (heat generated by the photovoltaic panel) and the third heat (heat generated by the electrolyzer), the system can calculate the amount of cooling water required to increase the cooling water flow from the first cooling water flow to the second cooling water flow based on the principle of heat balance. This step ensures that the system can effectively utilize the recovered heat while increasing the cooling water flow, avoiding the use of excessive cooling water, thereby improving energy efficiency.

[0103] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0104] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0105] S1, predicting the temperature change trend of the light-hydrogen coupling system according to historical environmental data and the current system state of the light-hydrogen coupling system;

[0106] S2, controlling a first cooling water flow rate in the light-to-hydrogen coupling system according to the temperature change trend.

[0107] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0108] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0109] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0110] S1, predicting the temperature change trend of the light-hydrogen coupling system according to historical environmental data and the current system state of the light-hydrogen coupling system;

[0111] S2, controlling a first cooling water flow rate in the light-to-hydrogen coupling system according to the temperature change trend.

[0112] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0113] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0114] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0115] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0116] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0117] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0118] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling cooling water flow, characterized in that: include: Predicting a temperature change trend of the light-hydrogen coupling system based on historical environmental data and a current system state of the light-hydrogen coupling system; The first cooling water flow rate in the light-hydrogen coupling system is controlled according to the temperature change trend.

2. The method for controlling cooling water flow according to claim 1, characterized in that: Predicting the temperature change trend of the light-hydrogen coupling system according to historical environmental data and the current system state of the light-hydrogen coupling system, including: Acquiring the current system state, wherein the current system state includes: the temperature of the light-hydrogen coupling system at the current moment and the solar irradiance of the light-hydrogen coupling system at the current moment; and Acquiring the historical environmental data, wherein the historical environmental data includes: a temperature of the light-hydrogen coupling system at a first moment and a solar irradiance of the light-hydrogen coupling system at the first moment, wherein the first moment is a historical corresponding moment of the current moment; The current system state and the historical environmental data are input into a pre-trained machine learning model to obtain the temperature change trend.

3. The method for controlling cooling water flow according to claim 1, characterized in that: Controlling the first cooling water flow rate in the light-hydrogen coupling system according to the temperature change trend includes: Determining predicted temperatures corresponding to a plurality of second moments after a current moment according to the temperature change trend, wherein the current moment is a moment of acquiring the current system state; When the predicted temperature is greater than or equal to the safety temperature threshold, determine to increase the first cooling water flow rate at the third moment to the second cooling water flow rate, wherein the third moment is the corresponding moment among the multiple second moments when the predicted temperature is greater than or equal to the safety temperature threshold.

4. The method for controlling cooling water flow according to claim 3, characterized in that: After determining to increase the first cooling water flow rate at the third moment to a second cooling water flow rate, the method further includes: Determining a first amount of heat recovered by a heat exchanger from the light-hydrogen coupling system, wherein the first amount of heat is used to preheat an electrolyzer in the light-hydrogen coupling system that needs a cold start, and the heat exchanger is deployed in a cooling water circulation subsystem of the light-hydrogen coupling system; An increase in cooling water amount required to increase the first cooling water flow rate to a second cooling water flow rate is calculated based on the first heat amount.

5. The method for controlling the cooling water flow rate according to claim 4, characterized in that: Determining a first amount of heat recovered by a heat exchanger from the light-hydrogen coupling system comprises: Acquire cooling water data corresponding to the cooling water of the light-hydrogen coupling system and environmental data corresponding to the cooling water, wherein the cooling water data includes: mass flow rate and specific heat capacity of the cooling water, and the environmental data includes: outlet temperature of the cooling water and environmental temperature corresponding to the cooling water; The first heat amount is determined by the cooling water data and the environmental data.

6. The method for controlling cooling water flow rate according to claim 4, characterized in that: Calculating the additional amount of cooling water required to increase the first cooling water flow rate to a second cooling water flow rate through the first heat comprises: Acquiring a second heat generated by a photovoltaic panel of the light-hydrogen coupling system, and acquiring a third heat generated by an electrolyzer of the light-hydrogen coupling system; determining a calorific value of the second calorific value and the third calorific value; The cooling water increase amount is calculated using the heat sum value and the first heat amount.

7. A cooling water flow control device, characterized in that: include: A prediction module, used for predicting the temperature change trend of the light-hydrogen coupling system according to historical environmental data and the current system state of the light-hydrogen coupling system; A control module is used to control the first cooling water flow in the light-hydrogen coupling system according to the temperature change trend.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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